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Student paper Student paper

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4 Student paper 3 Student paper 5 Student paper

MGT530-14222

Forecasting (MGT530) – (Operation Management) Colorado State University – Saudi Electronic University

100 pts

Sarah Alsubaie

G200003625

Dr.Lind

October 1, 2020

Forecasting

These data trends do show cyclical variations as the data from the textbook is not on a downward or upward trend. Also, the quarterly demands of sales do not show doesn't show any linear trend of upward or downward pattern in a well-detailed manner. The demand can as well increase in the third quarter. The

barometer technique of demand forecasting was best suited for this situation because we had previous two-year demand estimates, and it was easy for me to predict the predicted demand for the four quarters in the third year. The trend from the previous two years could be deduced, making the third-year forecasts simple to predict. Because there is a simultaneous up and down movement based on the degree of changeable economic activity in this case scenario, the economic

indicator is a coincidental indicator. These economic indicators, as seen in the example above, assist a person in predicting the future of a business. PRODUCT A

Quarter Y1 Y2 Variation Y3=Y2

1 95 85 -10 81+Variation

2 85 75 -10 57

3 92 85 -7 124

3 4

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10/20/2021 Originality Report

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Source Matches (25)

4 65 50 -15 95

PRODUCT B Quarter Y1 Y2 Variation Variation

1 93 9 9 110

2 90 -15 -15 60

3 110 0 0 110

4 90 10 10 110

PRODUCT C Quarter Y1 Y2 Variation Y3=Y2

1 60 72 12 81+Variation

2 45 51 6 57

3 100 112 12 124

4 75 85 10 95

From the above data, what is evident is that B has the lowest rates. Service B happens to have its lowest demand in the excessive years. This forecast would help

me on how to increase or boost these sales and how to increase the cost of the sales. We can see service C being on a good peak in its third year. I should be concerned about the sales of product B as the sales have a risk of being lower if not taken into considerations (Stevenson, 2020).

Why wasn't each product forecasted using the same method? Because all of the products had historical data, I decided to use the Barometric method of demand forecasting for this example situation. Other variables that contributed to the shifts from one quarter to the next were also taken into account. We could have

used a statistical technique to data forecasting, specifically the trend projection method, because we had previously gathered demand data. Despite the fact that

there were no moving variables in our case, such as promotion, competition, or advertising, this method can still be used to estimate demand to some extent. Production will be planned based on demand rather than which things to create and the batch size, and will be based on orders and inventory levels. This will be a methodical approach. a) The problem of being overstocked and/or understocked will be solved. b) In the future, forecasting will take a more scientific approach

to drawing up production plans this will help to manage the problem of rising raw material costs in the future. Due to these estimates are for overall usage

within a company organization, it is preferable to use a formalized forecasting approach. A formalized approach can benefit an organization in a variety of ways. The first benefit of using a formalized forecasting method is that it helps with production and inventory management planning. When demand is accurately forecasted, an organization can determine how many products are needed in the market, allowing it to keep adequate inventory levels (Sumets, 2018). A formal demand

forecast assists a company or country in planning for the coordination of product A, B, and C exports and imports. It will be simple to determine which of the

three products can be introduced into global markets based on their performance. The use of a formalized forecasting approach can also aid in the development of advertising and pricing

strategies. As with product B, which has seen a drop in demand over the last four quarters, the service firm may decide to lower pricing in an effort to boost

demand. It would also engage in promotional activities to increase product awareness. An organization's decision-making on product choices is aided by a defined forecasting strategy. In this example, Highline Limited may opt to discontinue product B since it has consistently performed poorly, as seen by demand over the last four quarters. Decisions can also be taken on how to upgrade and improve the product so that it attracts more demand as a complementary product

(Stevenson, 2020). A standardized forecasting method can also help to mitigate some of the risks associated with business operations. Forecasting accuracy will

ensure that business operations operate smoothly, and that product enhancement efforts will not be scuttled. For goods A, B, and C, a systematic method to

forecasting improves performance assessment and evaluation. Looking at the amount of demand a product will draw, it will be easy to choose which product best satisfies the buyers. A methodical approach to budget development will ensure that the budget is properly prepared. This will determine how many resources

are needed for which products.

In the case of product B, for example, the company will be able to estimate how much money it will require to improve its performance (Sumets, 2018).

Furthermore, even for the best-performing products, such as C, the company can use this strategy to set a budget for product improvement in order to make it suitable for use in global markets. Finally, a formalized method enables a company to better oversee its sales and, as a result, set realistic yearly sales targets for goods A, B, and C.

Reference

Sumets, A. (2018). Key aspects of the modern paradigm of operational management. Agricultural and Resource Economics: International Scientific E-Journal, 4(3),

129-147. Devi, W. W. (2018). PENGENDALIAN PERSEDIAAN BAHAN BAKU UNTUK MENINGKATKAN KELANCARAN PROSES PRODUKSI PADA CV. SURYA INDAH

MULIA MADIUN (Doctoral dissertation, Universities Muhammadiyah Ponorogo). Stevenson, W. J. (2021). Operations Management. McGraw-Hill Education/Stony

Brook University.

10/20/2021 Originality Report

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Student paper 65%

Student paper 100%

Student paper 70%

Student paper 76%

Student paper 97%

Student paper 96%

Student paper 100%

Student paper

Forecasting (MGT530) – (Operation Management) Colorado State University – Saudi Electronic University

Original source

MGT530 – Operation Management Colorado State University – Global Campus

Student paper

October 1, 2020

Original source

October 1, 2020

Student paper

The demand can as well increase in the third quarter.

Original source

Demand in the third quarter may also increase

Student paper

The barometer technique of demand forecasting was best suited for this situation because we had previous two-year demand estimates, and it was easy for me to predict the predicted demand for the four quarters in the third year.

Original source

Because we already had previous two-year demand projections, the barometer method of demand forecasting was best suited for our circumstance, and I was able to predict predicted demand for the four quarters of the third year with ease

Student paper

Because there is a simultaneous up and down movement based on the degree of changeable economic activity in this case scenario, the economic indicator is a coincidental indicator. These economic indicators, as seen in the example above, assist a person in predicting the future of a business.

Original source

Because there is a simultaneous up and down movement based on the amount of variable economic activity, the economic indicator in this case scenario is a coincidental indicator These economic indicators, as in the example above, assist an individual in predicting the business's possible future developments (Ferbar Tratar, 2015)

Student paper

PRODUCT A Quarter Y1 Y2 Variation Y3=Y2 1 95 85 -10 81+Variation 2 85 75 -10 57 3 92 85 -7 124

Original source

Quarter Y1 Y2 Variation Y3=Y2 1 95 85 -10 81+Variation 2 85 75 -10 57 3 92 85 -7 124

Student paper

4 65 50 -15 95 PRODUCT B Quarter Y1 Y2 Variation Variation 1 93 9 9 110 2 90 -15 -15 60

Original source

4 65 50 -15 95 Quarter Y1 Y2 Variation Variation 1 93 9 9 110 2 90 -15 -15 60

Student paper

3 110 0 0 110 4 90 10 10 110 PRODUCT C Quarter Y1 Y2 Variation Y3=Y2 1 60 72 12 81+Variation

Original source

3 110 0 0 110 4 90 10 10 110 Quarter Y1 Y2 Variation Y3=Y2 1 60 72 12 81+Variation

Student paper

2 45 51 6 57 3 100 112 12 124 4 75 85 10 95

Original source

2 45 51 6 57 3 100 112 12 124 4 75 85 10 95

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Student paper 67%

Student paper 77%

Student paper 64%

Student paper 79%

Student paper 99%

Student paper 85%

Student paper 89%

Student paper

This forecast would help me on how to increase or boost these sales and how to increase the cost of the sales. We can see service C being on a good peak in its third year.

Original source

to increase or promote these sales and how to increase the cost of sales We can see that service C is at a very good peak in the third year

Student paper

Other variables that contributed to the shifts from one quarter to the next were also taken into account.

Original source

Other variable factors that were responsible for the changes from one quarter to the next were also taken into account

Student paper

Despite the fact that there were no moving variables in our case, such as promotion, competition, or advertising, this method can still be used to estimate demand to some extent.

Original source

However, in our example, there were no shifting variables like as promotion, competition, or advertising, but this method can still be utilized to estimate demand to some extent

Student paper

b) In the future, forecasting will take a more scientific approach to drawing up production plans this will help to manage the problem of rising raw material costs in the future.

Original source

This will help manage the problem of rising raw material costs in the future

Student paper

Due to these estimates are for overall usage within a company organization, it is preferable to use a formalized forecasting approach. A formalized approach can benefit an organization in a variety of ways. The first benefit of using a formalized forecasting method is that it helps with production and inventory management planning.

Original source

The adoption of a formalized forecasting approach should be preferred because these estimates are for overall usage within a company organization An organization can benefit from a formalized approach in a number of ways The first advantage of a formalized forecasting method is that it aids in production and inventory management planning

Student paper

A formal demand forecast assists a company or country in planning for the coordination of product A, B, and C exports and imports.

Original source

A formal demand forecast assists the company or country in planning for the coordination of product A, B, and C exports and imports

Student paper

It will be simple to determine which of the three products can be introduced into global markets based on their performance.

Original source

Based on their performance, it will be simple to evaluate which of the three products can be introduced into worldwide markets

Student paper

As with product B, which has seen a drop in demand over the last four quarters, the service firm may decide to lower pricing in an effort to boost demand. It would also engage in promotional activities to increase product awareness. An organization's decision-making on product choices is aided by a defined forecasting strategy. In this example, Highline Limited may opt to discontinue product B since it has consistently performed poorly, as seen by demand over the last four quarters.

Original source

As in the case of product B, which has seen a fall in demand over the last four quarters, the service firm may decide to lower pricing in an attempt to boost demand It would also carry out promotional activities in order to raise product awareness An organization's decision-making on product choices is aided by a defined forecasting strategy In this example, Highline Limited may opt to discontinue product B since it has consistently performed poorly, as seen by demand over the last four quarters

10/20/2021 Originality Report

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Student paper 70%

Student paper 100%

Student paper 92%

Student paper 78%

Student paper 83%

Student paper 91%

Student paper 69%

Student paper 63%

Student paper

Decisions can also be taken on how to upgrade and improve the product so that it attracts more demand as a complementary product (Stevenson, 2020).

Original source

Decisions can also be taken on how to develop and improve the product so that it attracts greater demand as a supplementary product (Barlas, & Gunduz, 2011)

Student paper

A standardized forecasting method can also help to mitigate some of the risks associated with business operations.

Original source

A standardized forecasting method can also help to mitigate some of the risks associated with business operations

Student paper

For goods A, B, and C, a systematic method to forecasting improves performance assessment and evaluation. Looking at the amount of demand a product will draw, it will be easy to choose which product best satisfies the buyers. A methodical approach to budget development will ensure that the budget is properly prepared.

Original source

For goods A, B, and C, a systematic method to forecasting improves performance assessment and evaluation Looking at the amount of demand a product will draw, it will be easy to choose which product best satisfies the buyers A systematic approach to budget formation will ensure that the budget is properly prepared

Student paper

This will determine how many resources are needed for which products.

Original source

This will determine which products require and how many resources

Student paper

In the case of product B, for example, the company will be able to estimate how much money it will require to improve its performance (Sumets, 2018). Furthermore, even for the best-performing products, such as C, the company can use this strategy to set a budget for product improvement in order to make it suitable for use in global markets. Finally, a formalized method enables a company to better oversee its sales and, as a result, set realistic yearly sales targets for goods A, B, and C.

Original source

For example, in the instance of product B, the company will be able to estimate how much money it will need to improve its performance Furthermore, even for the best- performing products, such as C, the business can utilize this strategy to set a budget for product improvement so that it is suitable for usage in global markets Finally, a formalized method allows a firm to better oversee its sales and, as a result, create realistic sales targets for goods A, B, and C on a yearly basis

Student paper

Key aspects of the modern paradigm of operational management. Agricultural and Resource Economics: International Scientific E-Journal, 4(3), 129-147.

Original source

Key aspects of the modern paradigm of operational Agricultural and Resource Economics International Scientific E-Journal, 4(3),

Student paper

PENGENDALIAN PERSEDIAAN BAHAN BAKU UNTUK MENINGKATKAN KELANCARAN PROSES PRODUKSI PADA CV. SURYA INDAH MULIA MADIUN (Doctoral dissertation, Universities Muhammadiyah Ponorogo).

Original source

MENINGKATKAN KELANCARAN PROSES PRODUKSI PADA CV MADIUN (Doctoral dissertation, Universitas Muhammadiyah Ponorogo)

Student paper

McGraw-Hill Education/Stony Brook University.

Original source

McGraw-Hill Education

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Operations Management

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Operations Management T H I R T E E N T H E D I T I O N

William J. Stevenson Saunders College of Business

Rochester Institute of Technology

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This book is dedicated to you.

OPERATIONS MANAGEMENT, THIRTEENTH EDITION

Published by McGraw-Hill Education, 2 Penn Plaza, New York, NY 10121. Copyright © 2018 by McGraw-Hill Education. All rights reserved. Printed in the United States of America. Previous editions © 2015, 2012, and 2009. No part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written consent of McGraw-Hill Education including, but not limited to, in any network or other electronic storage or transmission, or broadcast for distance learning.

Some ancillaries, including electronic and print components, may not be available to customers outside the United States.

This book is printed on acid-free paper.

1 2 3 4 5 6 7 8 9 0 LWI 21 20 19 18 17

ISBN 978-1-259-66747-3 MHID 1-259-66747-2

Chief Product Officer, SVP Products & Markets: G. Scott Virkler Vice President, General Manager, Products & Markets: Marty Lange Vice President, Content Design & Deliver: Betsy Whalen Managing Director: Tim Vertovec Senior Brand Manager: Charles Synovec Director, Product Development: Rose Koos Lead Product Developer: Michele Janicek Product Developer: Christina Holt / Ryan McAndrews Marketing Manager: Trina Maurer Senior Director of Digital Content: Douglas Ruby Digital Product Analyst: Kevin Shanahan Director, Content Design & Delivery: Linda Avenarius Program Manager: Mark Christianson Content Project Managers: Harvey Yep (Core) / Kristin Bradley (Assessment) Buyer: Sandy Ludovissy Design: Matt Diamond Content Licensing Specialists: Shawntel Schmitt (Image) / Beth Thole (Text) Typeface: 10/12 STIX Mathjax Main Compositor: SPi Global Printer: LSC Communications – Willard

Cover images: © Andrew Bret Wallis/Getty Images; © Peopleimages.com/Getty Images; © Echo/Getty Images; © Jorg Greuel/Getty Images; © Monty Rakusen/Getty Images

Library of Congress Cataloging-in-Publication Data

Names: Stevenson, William J., author. Title: Operations management / William J. Stevenson, Saunders College of Business, Rochester Institute of Technology. Description: Thirteenth edition. | New York, NY : McGraw-Hill Education, [2018] | Series: The McGraw-Hill series in operations and decision sciences Identifiers: LCCN 2016052871| ISBN 9781259667473 (alk. paper) | ISBN 1259667472 (alk. paper) Subjects: LCSH: Production management. Classification: LCC TS155 .S7824 2018 | DDC 658.5--dc23 LC record available at https://lccn.loc.gov/2016052871

All credits appearing on page or at the end of the book are considered to be an extension of the copyright page.

The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a website does not indicate an endorsement by the authors or McGraw-Hill Education, and McGraw-Hill Education does not guarantee the accuracy of the information presented at these sites.

mheducation.com/highered

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The McGraw-Hill Series in Operations and Decision Sciences

Operations Management

Beckman and Rosenfield, Operations, Strategy: Competing in the 21st Century, First Edition

Benton, Purchasing and Supply Chain Management, Second Edition Bowersox, Closs, Cooper, and Bowersox, Supply Chain Logistics Management, Fourth Edition

Brown and Hyer, Managing Projects: A Team-Based Approach, First Edition Burt, Petcavage, and Pinkerton, Supply Management, Eighth Edition Cachon and Terwiesch, Operations Management, First Edition Cachon and Terwiesch, Matching Supply with Demand: An Introduction to Operations Management, Third Edition Cooper and Schindler, Business Research Methods, Twelfth Edition Finch, Interactive Models for Operations and Supply Chain Management, First Edition

Fitzsimmons, Fitzsimmons, and Bordoloi, Service Management: Operations, Strategy, Information Technology, Eighth Edition

Gehrlein, Operations Management Cases, First Edition

Harrison and Samson, Technology Management, First Edition Hayen, SAP R/3 Enterprise Software: An Introduction, First Edition Hill, Manufacturing Strategy: Text & Cases, Third Edition Hopp, Supply Chain Science, First Edition Jacobs, Berry, Whybark, and Vollmann, Manufacturing Planning & Control for Supply Chain Management, Sixth Edition Jacobs and Chase, Operations and Supply Management: The Core, Fourth Edition Jacobs and Chase, Operations and Supply Management, Fifteenth Edition Jacobs and Whybark, Why ERP? First Edition

Larson and Gray, Project Management: The Managerial Process, Seventh Edition Leenders, Johnson, and Flynn, Purchasing and Supply Management, Fifteenth Edition

Olson, Introduction to Information Systems Project Management, Second Edition

Schroeder, Goldstein, Rungtusanatham, Operations Management: Contemporary Concepts and Cases, Seventh Edition Seppanen, Kumar, and Chandra, Process Analysis and Improvement, First Edition

Simchi-Levi, Kaminsky, and Simchi-Levi, Designing and Managing the Supply Chain: Concepts, Strategies, Case Studies, Third Edition Sterman, Business Dynamics: Systems Thinking and Modeling for Complex World, First Edition Stevenson, Operations Management, Thirteenth Edition

Swink, Melnyk, Cooper, and Hartley, Managing Operations Across the Supply Chain, Third Edition Thomke, Managing Product and Service Development: Text and Cases, First Edition

Ulrich and Eppinger, Product Design and Development, Fourth Edition Zipkin, Foundations of Inventory Management, First Edition

Quantitative Methods and Management Science

Hillier and Hillier, Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, Fifth Edition Stevenson and Ozgur, Introduction to Management Science with Spreadsheets, First Edition

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vii

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The material in this book is intended as an introduction to the field of operations management. The topics covered include both strategic issues and practical applications. Among the topics are forecasting, product and service design, capacity planning, management of quality and quality control, inven- tory management, scheduling, supply chain management, and project management.

My purpose in revising this book continues to be to provide a clear presentation of the concepts, tools, and applications of the field of operations management. Operations management is evolving and growing, and I have found updating and integrat- ing new material to be both rewarding and challenging, particu- larly due to the plethora of new developments in the field, while facing the practical limits on the length of the book.

This text offers a comprehensive and flexible amount of content that can be selected as appropriate for different courses and formats, including undergraduate, graduate, and executive education.

This allows instructors to select the chapters, or portions of chapters, that are most relevant for their purposes. That flex- ibility also extends to the choice of relative weighting of the qualitative or quantitative aspects of the material and the order in which chapters are covered because chapters do not depend on sequence. For example, some instructors cover project management early, others cover quality or lean early, etc.

As in previous editions, there are major pedagogical fea- tures designed to help students learn and understand the mate- rial. This section describes the key features of the book, the chapter elements, the supplements that are available for teach- ing the course, highlights of the eleventh edition, and sug- gested applications for classroom instruction. By providing this support, it is our hope that instructors and students will have the tools to make this learning experience a rewarding one.

What’s New in This Edition Class preparation exercises are now available for all chapters and chapter supplements. The purpose of these exercises is to introduce students to the subject matter before class in order to enhance classroom learning. These exercises are available in the Instructor’s Resource Manual. Special thanks to Linda Brooks for her help in developing the exercises.

Some content has been rewritten or added to improve clar- ity, shorten wording, or update information. New material has been added on supply chains (including a different, more realistic, way to conceptualize supply chains), as well as on product life-cycle management, 3-D printing, drones, loca- tions, and other topics. New critical thinking exercises have

been added. The explanation of learning curve time reduction has been simplified with a new diagram. Some older readings have been deleted, and new readings added on such topics as fracking, mass customization of fast foods, and self-driving vehicles.

Acknowledgments I want to thank the many contributors to this edition. Review- ers and adopters of the text have provided a “continuously improving” wealth of ideas and suggestions. It is encourag- ing to me as an author. I hope all reviewers and readers will know their suggestions were valuable, were carefully consid- ered, and are sincerely appreciated. The list includes post- publication reviewers.

Robert Aboolian, California State University—San Marcos Pamela Barnes, Kansas State University Greg Bier, University of Missouri Gary Black, University of Southern Indiana Jeff Brand, Marquette University Cenk Caliskan, Utah Valley University Cem Canel, University of North Carolina—Wilmington Jen-Yi Chen, Cleveland State University Robert Clark, Stony Brook University Dinesh Dave, Appalachian State University Abdelghani Elimam, San Francisco State Kurt Engemann, Iona College Michael Fathi, Georgia Southwestern State Warren Fisher, Stephen F. Austin State University Gene Fliedner, Oakland University Theodore Glickman, George Washington University Haresh Gurnani, University of Miami Johnny Ho, Columbus State University Ron Hoffman, Greenville Technical College Lisa Houts, California State University—Fresno Stella Hua, Western Washington University Neil Hunt, Suffolk University Faizul Huq, Ohio University Richard Jerz, St. Ambrose University George Kenyon, Lamar University Casey Kleindienst, California State University—Fullerton John Kros, East Carolina University

Preface

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viii Preface

Anita Lee-Post, University of Kentucky Nancy Levenburg, Grand Valley State University F. Edward Ziegler, Kent State University

Other contributors include accuracy checkers: Gary Black, University of Southern Indiana, Michael Godfrey, Univer- sity of Wisconsin at Oshkosh, and Richard White, Univer- sity of North Texas; Test Bank: Alan Cannon, University of Texas at Arlington; PowerPoints: David Cook, Old Dominion University; Data Sets: Mehdi Kaighobadi, Florida Atlantic University; Excel Templates and ScreenCam tutorials: Lee Tangedahl, University of Montana; Instructors Manual: Michael Godfrey.

Special thanks goes out to Larry White, Eastern Illinois University, who helped revise, design, and develop interactive content in Connect ® Operations Management for this edition.

Finally I would like to thank all the people at McGraw- Hill/Irwin for their efforts and support. It is always a pleasure to work with such a professional and competent group of peo- ple. Special thanks go to Dolly Womack, Senior Brand Man- ager; Michele Janicek, Lead Product Developer; Christina Holt and Ryan McAndrews, Product Developers; Harvey Yep and Kristin Bradley, Content Project Managers; Sandy Ludo- vissy, Buyer; Matt Diamond, Designer; Shawntel Schmitt and Beth Thole, Content Licensing Specialists; and many others who worked behind the scenes.

I would also like to thank the many reviewers of previous editions for their contributions. Vikas Agrawal, Fayetteville State University; Bahram Alidaee, University of Mississippi; Ardavan Asef-Faziri, California State University at North- ridge; Prabir Bagchi, George Washington State University; Gordon F. Bagot, California State University at Los Angeles; Ravi Behara, Florida Atlantic University; Michael Bendixen, Nova Southeastern; Ednilson Bernardes, Georgia Southern University; Prashanth N. Bharadwaj, Indiana University of Pennsylvania; Greg Bier, University of Missouri at Columbia; Joseph Biggs, Cal Poly State University; Kimball Bullington, Middle Tennessee State University; Alan Cannon, University of Texas at Arlington; Injazz Chen, Cleveland State Univer- sity; Alan Chow, University of Southern Alabama at Mobile; Chrwan-Jyh, Oklahoma State University; Chen Chung, Uni- versity of Kentucky; Robert Clark, Stony Brook University; Loretta Cochran, Arkansas Tech University; Lewis Cooper- smith, Rider University; Richard Crandall, Appalachian State University; Dinesh Dave, Appalachian State University; Scott Dellana, East Carolina University; Kathy Dhanda, DePaul University; Xin Ding, University of Utah; Ellen Dumond, California State University at Fullerton; Richard Ehrhardt, University of North Carolina at Greensboro; Kurt Engemann, Iona College; Diane Ervin, DeVry University; Farzaneh Fazel, Illinois State University; Wanda Fennell, University of Mississippi at Hattiesburg; Joy Field, Boston College; Warren Fisher, Stephen F. Austin State University; Lillian Fok, Uni- versity of New Orleans; Charles Foley, Columbus State

Community College; Matthew W. Ford, Northern Kentucky University; Phillip C. Fry, Boise State University; Charles A. Gates Jr., Aurora University; Tom Gattiker, Boise State University; Damodar Golhar, Western Michigan University; Robert Graham, Jacksonville State University; Angappa Gunasekaran, University of Massachusetts at Dartmouth; Haresh Gurnani, University of Miami; Terry Harrison, Penn State University; Vishwanath Hegde, California State Uni- versity at East Bay; Craig Hill, Georgia State University; Jim Ho, University of Illinois at Chicago; Seong Hyun Nam, University of North Dakota; Jonatan Jelen, Mercy College; Prafulla Joglekar, LaSalle University; Vijay Kannan, Utah State University; Sunder Kekre, Carnegie-Mellon Univer- sity; Jim Keyes, University of Wisconsin at Stout; Seung-Lae Kim, Drexel University; Beate Klingenberg, Marist College; John Kros, East Carolina University; Vinod Lall, Minnesota State University at Moorhead; Kenneth Lawrence, New Jersey Institute of Technology; Jooh Lee, Rowan University; Anita Lee-Post, University of Kentucky; Karen Lewis, Uni- versity of Mississippi; Bingguang Li, Albany State Univer- sity; Cheng Li, California State University at Los Angeles; Maureen P. Lojo, California State University at Sacramento; F. Victor Lu, St. John’s University; Janet Lyons, Utah State University; James Maddox, Friends University; Gita Mathur, San Jose State University; Mark McComb, Mississippi Col- lege; George Mechling, Western Carolina University; Scott Metlen, University of Idaho; Douglas Micklich, Illinois State University; Ajay Mishra, SUNY at Binghamton; Scott S. Morris, Southern Nazarene University; Philip F. Musa, University of Alabama at Birmingham; Roy Nersesian, Monmouth University; Jeffrey Ohlmann, University of Iowa at Iowa City; John Olson, University of St. Thomas; Ozgur Ozluk, San Francisco State University; Kenneth Paetsch, Cleveland State University; Taeho Park, San Jose State Uni- versity; Allison Pearson, Mississippi State University; Pat- rick Penfield, Syracuse University; Steve Peng, California State University at Hayward; Richard Peschke, Minnesota State University at Moorhead; Andru Peters, San Jose State University; Charles Phillips, Mississippi State University; Frank Pianki, Anderson University; Sharma Pillutla, Towson University; Zinovy Radovilsky, California State Univer- sity at Hayward; Stephen A. Raper, University of Missouri at Rolla; Pedro Reyes, Baylor University; Buddhadev Roy- choudhury, Minnesota State University at Mankato; Narendra Rustagi, Howard University; Herb Schiller, Stony Brook University; Dean T. Scott, DeVry University; Scott J. Seipel, Middle Tennessee State University; Raj Selladurai, Indiana University; Kaushic Sengupta, Hofstra University; Kenneth Shaw, Oregon State University; Dooyoung Shin, Minnesota State University at Mankato; Michael Shurden, Lander Uni- versity; Raymond E. Simko, Myers University; John Simon, Governors State University; Jake Simons, Georgia Southern University; Charles Smith, Virginia Commonwealth Uni- versity; Kenneth Solheim, DeVry University; Young Son,

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Preface ix

Bernard M. Baruch College; Victor Sower, Sam Houston State University; Jeremy Stafford, University of North Alabama; Donna Stewart, University of Wisconsin at Stout; Dothang Truong, Fayetteville State University; Mike Umble, Baylor University; Javad Varzandeh, California State Uni- versity at San Bernardino; Timothy Vaughan, University of Wisconsin at Eau Claire; Emre Veral, Baruch College; Mark Vroblefski, University of Arizona; Gustavo Vulcano, New York University; Walter Wallace, Georgia State University;

James Walters, Ball State University; John Wang, Montclair State University; Tekle Wanorie, Northwest Missouri State University; Jerry Wei, University of Notre Dame; Michael Whittenberg, University of Texas; Geoff Willis, University of Central Oklahoma; Pamela Zelbst, Sam Houston State University; Jiawei Zhang, NYU; Zhenying Zhao, University of Maryland; Yong-Pin Zhou, University of Washington.

William J. Stevenson

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Walkthrough

MAJOR STUDY AND LEARNING FEATURES

A number of key features in this text have been specifically

designed to help introductory students learn, understand, and

apply Operations concepts and problem-solving techniques.

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Determining a Regression Equation Sales of new houses and three-month lagged unemployment are shown in the following table. Determine if unemployment levels can be used to predict demand for new houses and, if so, derive a predictive equation.

Period . . . . . . . . . . . 1 2 3 4 5 6 7 8 9 10 11 Units sold . . . . . . . . 20 41 17 35 25 31 38 50 15 19 14 Unemployment % (three-month lag) 7.2 4.0 7.3 5.5 6.8 6.0 5.4 3.6 8.4 7.0 9.0

1. Plot the data to see if a linear model seems reasonable. In this case, a linear model seems appropriate for the range of the data.

0 2 4 6 8 10

Level of unemployment (%), x

U n

its s

o ld

, y

2. Check the correlation coefficient to confirm that it is not close to zero using the web- site template, and then obtain the regression equation: r = −.966 This is a fairly high negative correlation. The regression equation is y = 71.85 − 6.91x

Note that the equation pertains only to unemployment levels in the range 3.6 to 9.0, because sample observations covered only that range.

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E X A M P L E 1 0

S O L U T I O N

Examples with Solutions Throughout the text, wherever a quantitative or analytic technique is introduced, an example is included to illustrate the application of that tech- nique. These are designed to be easy to follow.

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Solved Problems At the end of chapters and chapter supplements, “Solved Problems” are pro- vided to illustrate problem solving and the core con- cepts in the chapter. These have been carefully prepared to help students understand the steps involved in solving different types of problems. The Excel logo indicates that a spreadsheet is available on the text’s website, to help solve the problem.

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Computing Productivity A company that processes fruits and vegetables is able to produce 400 cases of canned peaches in one-half hour with four workers. What is labor productivity?

Problem 1

SOLVED PROBLEMS

KEY POINTS 1. Competitive pressure often means that business organizations must frequently assess their com-

petitors’ strengths and weaknesses, as well as their own, to remain competitive. 2. Strategy formulation is critical because strategies provide direction for the organization, so they

can play a role in the success or failure of a business organization. 3. Functional strategies and supply chain strategies need to be aligned with the goals and strategies

of the overall organization. 4. The three primary business strategies are low cost, responsiveness, and differentiation. 5. Productivity is a key factor in the cost of goods and services. Increases in productivity can

become a competitive advantage. 6. High productivity is particularly important for organizations that have a strategy of low costs.

competitiveness, 42 core competencies, 46 environmental scanning, 48 goals, 44 mission, 44

KEY TERMS mission statement, 44 operations strategy, 51 order qualifiers, 48 order winners, 48 productivity, 56

quality-based strategies, 53 strategies, 44 SWOT, 47 tactics, 45 time-based strategies, 53

Labor productivity

Quantity produced ________________

Labor hours =

400 cases ________________________

4 workers × 1 / 2 hour / worker

= 200 cases per labor hour

Solution

Computing Multifactor Productivity A wrapping-paper company produced 2,000 rolls of paper one day. Labor cost was $160, material cost was $50, and overhead was $320. Determine the multifactor productivity.

Problem 2

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Multifactor productivity

Quantity produced ______________________________

Labor cost + Material cost + Overhead

= 2,000 rolls

_______________ $160 + $50 + $320

= 3.77 rolls per dollar input

A variation of the multifactor productivity calculation incorporates the standard price in the numerator by multiplying the units by the standard price.

Solution

Computing Multifactor Productivity Compute the multifactor productivity measure for an eight-hour day in which the usable output was 300 units, produced by three workers who used 600 pounds of materials. Workers have an hourly wage of $20, and material cost is $1 per pound. Overhead is 1.5 times labor cost.

Problem 3

Multifactor productivity

= Usable output

__________________________________ Labor cost + Material cost + Overhead cost

= 300 units

_____________________________________________________ ( 3 workers × 8 hours × $20 / hour ) + ( 600 pounds × $1 / pound ) +

( 3 workers × 8 hours × $20 / hour × 1.50 )

= 300 units

________________ $480 + $600 + $720

= .167units of output per dollar of input

Solution

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c. Using earliest due date as the selection criterion, the job sequence is C-A-E-B-D-F. The measures of effectiveness are as follows (see table):

(1) Average flow time: 110/6 = 18.33 days. (2) Average tardiness: 38/6 = 6.33 days. (3) Average number of jobs at the work center: 110/41 = 2.68.

Job Sequence

(1) Processing

Time

(2) Flow Time

(3) Due Date

(2) – (3) Days Tardy

[0 if negative]

C 4 4 4 0 A 2 6 7 0 E 5 11 15 0 B 8 19 16 3 D 10 29 17 12 F 12 41 18 23

41 110 38

TABLE 16.5 Excel solution for Example 2a

Excel Spreadsheet Solutions Where applicable, the exam- ples and solved problems include screen shots of a spreadsheet solution. Many of these were taken from the Excel templates, which are on the text’s website. Templates are programmed to be fully functional in Excel 2013 and earlier.

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CHAPTER ELEMENTS

Within each chapter, you will find the following elements

that are designed to facilitate study and learning. All of

these have been carefully developed over many editions and

have proven to be successful.

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4 Product and Service Design

4.1 Introduction, 138 What Does Product and Service Design Do?, 138 Key Questions, 138 Reasons for Product or Service Design or Redesign, 139

4.2 Idea Generation, 140

4.3 Legal and Ethical Considerations, 143

4.4 Human Factors, 144

4.5 Cultural Factors, 145

4.6 Global Product and Service Design, 145

4.7 Environmental Factors: Sustainability, 146 Cradle-to-Grave Assessment, 146 End-of-Life Programs, 146 The Three Rs: Reduce, Reuse, and Recycle, 146 Reduce: Value Analysis, 146 Reuse: Remanufacturing, 148 Recycle, 149

4.8 Other Design Considerations, 151 Strategies for Product or Service Life Stages, 151 Product Life Cycle Management, 152 Degree of Standardization, 153

Designing for Mass Customization, 154 Reliability, 155 Robust Design, 156 Degree of Newness, 157 Quality Function Deployment, 157 The Kano Model, 160

4.9 Phases in Product Design and Development, 161

4.10 Designing for Production, 162 Concurrent Engineering, 162 Computer-Aided Design, 163 Production Requirements, 164 Component Commonality, 164

C H A P T E R O U T L I N E

L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO4.1 Explain the strategic importance of product and service design.

LO4.2 Describe what product and service design does.

LO4.3 Name the key questions of product and service design.

LO4.4 Identify some reasons for design or redesign.

LO4.5 List some of the main sources of design ideas.

LO4.6 Discuss the importance of legal, ethical, and sustainability considerations in product and service design.

LO4.7 Explain the purpose and goal of life cycle assessment.

LO4.8 Explain the phrase “the 3 Rs.”

LO4.9 Briefly describe the phases in product design and development.

LO4.10 Discuss several key issues in product or service design.

LO4.11 Discuss the two key issues in service design.

LO4.12 List the characteristics of well-designed service systems.

LO4.13 List some guidelines for successful service design.

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The essence of a business organization is the products and services it offers, and every aspect of the organization and its supply chain are structured around those products and services. Organizations that have well-designed products or services are more likely to realize their goals than those with poorly designed products or services. Hence, orga- nizations have a strategic interest in product and service design. Product or service design should be closely tied to an organization’s strategy. It is a major factor in cost, quality, time-to-market, customer satisfaction, and competitive advan- tage. Consequently, marketing, finance, operations, accounting, IT, and HR need to be involved. Demand forecasts and projected costs are important, as is the expected impact on the supply chain. It is significant to note that an important cause of operations failures can be traced to faulty design. Designs that have not been well thought out, or incorrectly implemented, or instructions for assembly or usage that are wrong or unclear, can be the cause of product and service failures, leading to lawsuits, injuries and deaths, product recalls, and damaged reputations.

The introduction of new products or services, or changes to product or service designs, can have impacts throughout the organization and the entire supply chain. Some processes may change very little, while others may have to change considerably in terms of what they do or how and when they do it. New processes may have to be added, and some cur- rent ones may be eliminated. New suppliers and distributors may need to be found and integrated into the system, and some current suppliers and distributors may no longer be an appropriate fit. Moreover, it is necessary to take into account projected impact on demand as well as financial, marketing, and distribution implications. Because of the potential for widespread effects, taking a “big picture” systems approach early and throughout the design or redesign process is imperative to reduce the chance of missing some implications and costs, and to understand the time it will take. Likewise, input from engineering, operations, marketing, finance, accounting, and supply chains is crucial.

In this chapter you will discover insights into the design process that apply to both product and service design.

LO4.1 Explain the strate- gic importance of product and service design.

4.11 Service Design, 165 Overview of Service Design, 165 Differences between Service Design and Product Design, 165 Phases in the Service Design Process, 166

Service Blueprinting, 167 Characteristics of Well-Designed Service Systems, 168 Challenges of Service Design, 168 Guidelines for Successful Service Design, 168

4.12 Operations Strategy, 169

Operations Tour: High Acres Landfill, 173

Chapter Supplement: Reliability, 174

Learning Objectives Every chapter and supplement lists the learning objectives to achieve when studying the chap- ter material. The learning objectives are also included next to the specific material in the mar- gins of the text.

Chapter Outlines Every chapter and supplement includes an outline of the topics covered.

Opening Vignettes Each chapter opens with an introduction to the important operations topics covered in the chapter. This enables students to see the relevance of opera- tions management in order to actively engage in learning the material.

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of where process selection and capacity planning fit into system design. Forecasts, product and service design, and technological considerations all influence capacity planning and pro- cess selection. Moreover, capacity and process selection are interrelated, and are often done in concert. They, in turn, affect facility and equipment choices, layout, and work design.

How an organization approaches process selection is determined by the organization’s pro- cess strategy. Key aspects include:

• Capital intensity: The mix of equipment and labor that will be used by the organization. • Process flexibility: The degree to which the system can be adjusted to changes in

processing requirements due to such factors as changes in product or service design, changes in volume processed, and changes in technology.

6.2 PROCESS SELECTION

Process choice is demand driven. The two key questions in process selection are:

1. How much variety will the process need to be able to handle? 2. How much volume will the process need to be able to handle?

Answers to these questions will serve as a guide to selecting an appropriate process. Usu- ally, volume and variety are inversely related; a higher level of one means a lower level of the other. However, the need for flexibility of personnel and equipment is directly related to the level of variety the process will need to handle: the lower the variety, the less the need for flexibility, while the higher the variety, the greater the need for flexibility.

There is another aspect of variety that is important. Variety means either having separate operations for each product or service, with a steady demand for each, or being willing to live with some idle time, or to get equipment ready every time there is the need to change the product being produced or the service being provided.

Process Types There are five basic process types: job shop, batch, repetitive, continuous, and project.

Job Shop. A job shop usually operates on a relatively small scale. It is used when a low volume of high-variety goods or services will be needed. Processing is intermittent; work includes small jobs, each with somewhat different processing requirements. High flexibility using general-purpose equipment and skilled workers are important characteristics of a job shop. A manufacturing example of a job shop is a tool and die shop that is able to produce

LO6.2 Name the two main factors that influence process selection.

FIGURE 6.1 Process selection and capacity planning influence system design

Forecasting

Product and service design

Technological change

Facilities and equipment

Layout

Work design

Capacity Planning

Process Selection

Inputs Outputs

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A major key to Apple’s continued success is its ability to keep pushing the boundaries of innovation. Apple has demonstrated how to create growth by dreaming up products so new and ingenious that they have upended one industry after another.

Pieter Beens/Shutterstock

and business organizations need to be aware of the impact they are having in these areas and respond accordingly. Otherwise, organizations may be subject to attack by pressure groups and risk damage to their reputation.

2.7 PRODUCTIVITY

One of the primary responsibilities of a manager is to achieve productive use of an organiza- tion’s resources. The term productivity is used to describe this. Productivity is an index that measures output (goods and services) relative to the input (labor, materials, energy, and other resources) used to produce it. It is usually expressed as the ratio of output to input:

Productivity = Output

______ Input

(2–1)

Although productivity is important for all business organizations, it is particularly impor- tant for organizations that use a strategy of low cost, because the higher the productivity, the lower the cost of the output.

A productivity ratio can be computed for a single operation, a department, an organiza- tion, or an entire country. In business organizations, productivity ratios are used for plan- ning workforce requirements, scheduling equipment, financial analysis, and other important tasks.

Productivity has important implications for business organizations and for entire nations. For nonprofit organizations, higher productivity means lower costs; for profit-based organiza- tions, productivity is an important factor in determining how competitive a company is. For a nation, the rate of productivity growth is of great importance. Productivity growth is the increase in productivity from one period to the next relative to the productivity in the preced- ing period. Thus,

Productivity growth = Current productivity − Previous productivity

_____________________________________ Previous productivity

× 100 (2–2)

For example, if productivity increased from 80 to 84, the growth rate would be

84 − 80

______ 80

× 100 = 5%

LO2.6 Define the term productivity and explain why it is important to com- panies and to countries.

Productivity A measure of the effective use of resources, usually expressed as the ratio of output to input.

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Figures and Photos The text includes photographs and graphic illustrations to support student learning and provide interest and motivation. Approximately 100 carefully selected photos highlight the 13th edition. The photos illustrate applications of operations and supply chain concepts in many successful companies. More than 400 graphic illustrations, more than any other text in the field, are included and all are color coded with pedagogical consistency to assist students in understanding concepts.

Icons Icons are included in the text, to point out relevant applications in a discussion or concept. These include: Excel icons to point out Excel applications; and ScreenCam Tutorial icons to link to the tutorials on the text’s website.

mhhe.com/stevenson13e screenCam tutorial

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5.12 OPERATIONS STRATEGY

The strategic implications of capacity decisions can be enormous, impacting all areas of the organization. From an operations management standpoint, capacity decisions establish a set of conditions within which operations will be required to function. Hence, it is extremely important to include input from operations management people in making capacity decisions.

Flexibility can be a key issue in capacity decisions, although flexibility is not always an option, particularly in capital-intensive industries. However, where possible, flexibility allows an organi- zation to be agile—that is, responsive to changes in the marketplace. Also, it reduces to a certain extent the dependence on long-range forecasts to accurately predict demand. And flexibility makes it easier for organizations to take advantage of technological and other innovations. Maintaining excess capacity (a capacity cushion) may provide a degree of flexibility, albeit at added cost.

Some organizations use a strategy of maintaining a capacity cushion for the purpose of blocking entry into the market by new competitors. The excess capacity enables them to pro- duce at costs lower than what new competitors can. However, such a strategy means higher- than-necessary unit costs, and it makes it more difficult to cut back if demand slows, or to shift to new product or service offerings.

Efficiency improvements and utilization improvements can provide capacity increases. Such improvements can be achieved by streamlining operations and reducing waste. The chapter on lean operations describes ways for achieving those improvements.

Bottleneck management can be a way to increase effective capacity, by scheduling non- bottleneck operations to achieve maximum utilization of bottleneck operations.

In cases where capacity expansion will be undertaken, there are two strategies for determin- ing the timing and degree of capacity expansion. One is the expand-early strategy (i.e., before demand materializes). The intent might be to achieve economies of scale, to expand market share, or to preempt competitors from expanding. The risks of this strategy include an oversupply that would drive prices down, and underutilized equipment that would result in higher unit costs.

The other approach is the wait-and-see strategy (i.e., to expand capacity only after demand materializes, perhaps incrementally). Its advantages include a lower chance of oversupply due to more accurate matching of supply and demand, and higher capacity utilization. The key risks are loss of market share and the inability to meet demand if expansion requires a long lead time.

In cases where capacity contraction will be undertaken, capacity disposal strategies become important. This can be the result of the need to replace aging equipment with newer equipment. It can also be the result of outsourcing and downsizing operations. The cost or benefit of asset disposal should be taken into account when contemplating these actions.

Capacity refers to a system’s potential for producing goods or delivering services over a specified time interval. Capacity decisions are important because capacity is a ceiling on output and a major determi- nant of operating costs.

Three key inputs to capacity planning are the kind of capacity that will be needed, how much will be needed, and when it will be needed. Accurate forecasts are critical to the planning process.

The capacity planning decision is one of the most important decisions that managers make. The capacity decision is strategic and long-term in nature, often involving a significant initial investment of capital. Capacity planning is particularly difficult in cases where returns will accrue over a lengthy period and risk is a major consideration.

A variety of factors can interfere with effective capacity, so effective capacity is usually somewhat less than design capacity. These factors include facilities design and layout, human factors, product/ service design, equipment failures, scheduling problems, and quality considerations.

Capacity planning involves long-term and short-term considerations. Long-term considerations relate to the overall level of capacity; short-term considerations relate to variations in capacity requirements due to seasonal, random, and irregular fluctuations in demand. Ideally, capacity will match demand. Thus, there is a close link between forecasting and capacity planning, particularly in the long term. In the short term, emphasis shifts to describing and coping with variations in demand.

SUMMARY

Operations Strategies An Operations Strategy section is included at the ends of most chapters. These sections discuss how the chapters’ concepts can be applied and how they impact the operations of a company.

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smaller microprocessor that spawns a new generation of personal digital assistants or cell phones). Technology also can indirectly affect product and service design: Advances in pro- cessing technology may require altering an existing design to make it compatible with the new processing technology. Still another way that technology can impact product design is illustrated by new digital recording technology that allows television viewers to skip com- mercials when they view a recorded program. This means that advertisers (who support a television program) can’t get their message to viewers. To overcome this, some advertisers have adopted a strategy of making their products an integral part of a television program, say by having their products prominently displayed and/or mentioned by the actors as a way to call viewers’ attention to their products without the need for commercials.

The following reading suggests another potential benefit of product redesign.

4.2 IDEA GENERATION

Ideas for new or redesigned products or services can come from a variety of sources, includ- ing customers, the supply chain, competitors, employees, and research. Customer input can come from surveys, focus groups, complaints, and unsolicited suggestions for improvement.

Sherwin-Williams’ Dutch Boy Group put a revolutionary spin on paintcans with its innovative square-shaped Twist & Pour™ paint-delivery container for the Dirt Fighter interior latex paint line. The four-piece square container could be the first major change in how house paint is packaged in decades. Lightweight but sturdy, the Twist & Pour “bucket” is packed with so many conveniences, it’s next to impossible to mess up a painting project.

Winning Best of Show in an AmeriStar packaging competition sponsored by the Institute of Packaging Professionals, the exclu- sive, all-plastic paint container stands almost 7½ in. tall and holds 126 oz., a bit less than 1 gal. Rust-resistant and moisture-resistant, the plastic bucket gives users a new way to mix, brush, and store paint.

A hollow handle on one side makes it comfortable to pour and [carry]. A convenient, snap-in pour spout neatly pours paint into a tray with no dripping but can be removed if desired, to allow a wide brush to be dipped into the 5¾-in.-dia. mouth. Capping the container is a large, twist-off lid that requires no tools to open or close. Molded with two lugs for a snug-finger-tight closing, the threaded cap provides a tight seal to extend the shelf life of unused paint.

While the lid requires no tools to access, the snap-off carry bail is assembled on the container in a “locked-down position” and can be pulled up after purchase for toting or hanging on a ladder. Large, nearly 4½-inch-tall label panels allow glossy front and back labels printed and UV-coated to wrap around the can’s rounded corners, for an impressive display.

Jim MacDonald, co-designer of the Twist & Pour and a packag- ing engineer at Cleveland-based Sherwin-Williams, tells Packag- ing Digest that the space-efficient, square shape is easier to ship and for retailers to stack in stores. It can also be nested, courtesy

READING DUTCH BOY BRUSHES UP ITS PAINTS

of a recess in the bottom that mates with the lid’s top ring. “The new design allows for one additional shelf facing on an eight-foot rack or shelf area.”

The labels are applied automatically, quite a feat, considering their complexity, size, and the hollow handle they likely encounter during application. MacDonald admits, “Label application was a challenge. We had to modify the bottle several times to accom- modate the labeling machinery available.”

Courtesy of Dutch Boy

LO4.5 List some of the main sources of design ideas.

Readings Readings highlight important real-world applications, provide examples of production/opera- tions issues, and offer further elaboration of the text material. They also provide a basis for classroom discussion and gener- ate interest in the subject mat- ter. Many of the end-of-chapter readings include assignment questions.

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END-OF-CHAPTER RESOURCES

For student study and review, the following items are

provided at the end of each chapter or chapter supplement.

Summaries Chapters contain summaries that provide an overview of the material covered.

Key Points The key points of the chapter are emphasized.

Key Terms Key terms are highlighted in the text and then repeated in the margin with brief definitions for emphasis. They are listed at the end of each chapter (along with page references) to aid in reviewing.

Taking Stock and Critical Thinking Exercises These activities encourage analytical thinking and help broaden conceptual understanding. A question related to ethics is included in the Critical Thinking Exercises.

Discussion and Review Questions Each chapter and each supplement have a list of discussion and review questions. These precede the problem sets and are intended to serve as a student self-review or as class dis- cussion starters.

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7. What are models and why are they important? 8. Why is the degree of customization an important consideration in process planning? 9. List the trade-offs you would consider for each of these decisions:

a. Driving your own car versus public transportation. b. Buying a computer now versus waiting for an improved model. c. Buying a new car versus buying a used car. d. Speaking up in class versus waiting to get called on by the instructor. e. A small business owner having a website versus newspaper advertising.

10. Describe each of these systems: craft production, mass production, and lean production. 11. Why might some workers prefer not to work in a lean production environment? 12. Discuss the importance of each of the following:

a. Matching supply and demand b. Managing a supply chain

13. List and briefly explain the four basic sources of variation, and explain why it is important for managers to be able to effectively deal with variation.

14. Why do people do things that are unethical? 15. Explain the term value-added. 16. Discuss the various impacts of outsourcing. 17. Discuss the term sustainability, and its relevance for business organizations.

TAKING STOCK This item appears at the end of each chapter. It is intended to focus your attention on three key issues for business organizations in general, and operations management in particular. Those issues are trade-off decisions, collaboration among various functional areas of the organization, and the impact of technology. You will see three or more questions relating to these issues. Here is the first set of questions:

1. What are trade-offs? Why is careful consideration of trade-offs important in decision making? 2. Why is it important for the various functional areas of a business organization to collaborate? 3. In what general ways does technology have an impact on operations management decision making?

CRITICAL THINKING EXERCISES

This item also will appear in every chapter. It allows you to critically apply information you learned in the chapter to a practical situation. Here is the first set of exercises:

1. Many organizations offer a combination of goods and services to their customers. As you learned in this chapter, there are some key differences between production of goods and delivery of services. What are the implications of these differences relative to managing operations?

2. Why is it important to match supply and demand? If a manager believes that supply and demand will not be equal, what actions could the manager take to increase the probability of achieving a match?

3. One way that organizations compete is through technological innovation. However, there can be downsides for both the organization and the consumer. Explain.

4. a. What would cause a business person to make an unethical decision? b. What are the risks of doing so?

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1. Determine the utilization and the efficiency for each of these situations: a. A loan processing operation that processes an average of 7 loans per day. The operation has a

design capacity of 10 loans per day and an effective capacity of 8 loans per day. b. A furnace repair team that services an average of four furnaces a day if the design capacity is

six furnaces a day and the effective capacity is five furnaces a day. c. Would you say that systems that have higher efficiency ratios than other systems will always

have higher utilization ratios than those other systems? Explain. 2. In a job shop, effective capacity is only 50 percent of design capacity, and actual output is 80

percent of effective output. What design capacity would be needed to achieve an actual output of eight jobs per week?

3. A producer of pottery is considering the addition of a new plant to absorb the backlog of demand that now exists. The primary location being considered will have fixed costs of $9,200 per month and vari- able costs of 70 cents per unit produced. Each item is sold to retailers at a price that averages 90 cents. a. What volume per month is required in order to break even? b. What profit would be realized on a monthly volume of 61,000 units? 87,000 units? c. What volume is needed to obtain a profit of $16,000 per month? d. What volume is needed to provide a revenue of $23,000 per month? e. Plot the total cost and total revenue lines.

4. A small firm intends to increase the capacity of a bottleneck operation by adding a new machine. Two alternatives, A and B, have been identified, and the associated costs and revenues have been estimated. Annual fixed costs would be $40,000 for A and $30,000 for B; variable costs per unit would be $10 for A and $11 for B; and revenue per unit would be $15. a. Determine each alternative’s break-even point in units. b. At what volume of output would the two alternatives yield the same profit? c. If expected annual demand is 12,000 units, which alternative would yield the higher profit?

5. A producer of felt-tip pens has received a forecast of demand of 30,000 pens for the coming month from its marketing department. Fixed costs of $25,000 per month are allocated to the felt- tip operation, and variable costs are 37 cents per pen. a. Find the break-even quantity if pens sell for $1 each. b. At what price must pens be sold to obtain a monthly profit of $15,000, assuming that esti-

mated demand materializes? 6. A real estate agent is considering changing her cell phone plan. There are three plans to choose

from, all of which involve a monthly service charge of $20. Plan A has a cost of $.45 a minute for daytime calls and $.20 a minute for evening calls. Plan B has a charge of $.55 a minute for day- time calls and $.15 a minute for evening calls. Plan C has a flat rate of $80 with 200 minutes of calls allowed per month and a charge of $.40 per minute beyond that, day or evening. a. Determine the total charge under each plan for this case: 120 minutes of day calls and 40 min-

utes of evening calls in a month. b. Prepare a graph that shows total monthly cost for each plan versus daytime call minutes. c. If the agent will use the service for daytime calls, over what range of call minutes will each

plan be optimal? d. Suppose that the agent expects both daytime and evening calls. At what point (i.e., percentage

of call minutes for daytime calls) would she be indifferent between plans A and B? 7. A firm plans to begin production of a new small appliance. The manager must decide whether

to purchase the motors for the appliance from a vendor at $7 each or to produce them in-house. Either of two processes could be used for in-house production; one would have an annual fixed cost of $160,000 and a variable cost of $5 per unit, and the other would have an annual fixed cost of $190,000 and a variable cost of $4 per unit. Determine the range of annual volume for which each of the alternatives would be best.

8. A manager is trying to decide whether to purchase a certain part or to have it produced internally. Internal production could use either of two processes. One would entail a variable cost of $17 per unit and an annual fixed cost of $200,000; the other would entail a variable cost of $14 per unit and an annual fixed cost of $240,000. Three vendors are willing to provide the part. Vendor A has a price of $20 per unit for any volume up to 30,000 units. Vendor B has a price of $22 per unit for

PROBLEMS

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Technique Formula Definitions

Exponential smoothing forecast

Ft = Ft – 1 + α(At – 1 − Ft – 1) α = Smoothing factor

Linear trend forecast

F t = a + b t where

b = n∑ ty − ∑ t∑ y

______________ n∑ t 2 − ( ∑ t 2 )

a = ∑ y − b∑ t

______ n

or ̄ y − b ̄ t

a = y intercept b = Slope

Trend-adjusted forecast

TAF t+1 = S t + T t where S t = TAF t + α ( A t − TAF t ) T t = T t−1 + β ( TAF t − TAF t−1 − T t−1 )

t = Current period TAF t+1 = Trend-adjusted forecast for

next period S = Previous forecast plus

smoothed error T = Trend component

Linear regression forecast

Y c = a + bx where

b = n (∑ xy) − (∑ x) (∑ y)

___________________ n (∑ x 2 ) − ( ∑ x 2 )

a = ∑ y − b∑ x

______ n

or ̄ y − b ̄ x

y c = Computed value of dependent variable

x = Predictor (independent) variable b = Slope of the line a = Value of y c when x = 0

Standard error of estimate S e = √

________

∑ ( y − y c ) 2

_______ n − 2

S e = Standard error of estimate

y = y value of each data point n = Number of data points

Tracking signal TS t =

∑ n

e _____

MAD

Control limits UCL = 0 + z √ _____

MSE LCL = 0 − z √

_____ MSE

√

_____ MSE = standard deviation

z = Number of standard deviations;

2 and 3 are typical values

KEY POINTS 1. Demand forecasts are essential inputs for many business decisions; they help managers decide

how much supply or capacity will be needed to match expected demand, both within the organiza- tion and in the supply chain.

2. Because of random variations in demand, it is likely that the forecast will not be perfect, so man- agers need to be prepared to deal with forecast errors.

3. Other, nonrandom factors might also be present, so it is necessary to monitor forecast errors to check for nonrandom patterns in forecast errors.

4. It is important to choose a forecasting technique that is cost-effective and one that minimizes fore- cast error.

Problem Sets Each chapter includes a set of problems for assignment. The problems have been refined over many editions and are intended to be challenging but doable for students. Short answers to most of the problems are included in Appendix A so that students can check their understanding and see immedi- ately how they are progressing.

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First Pages

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Bruegger’s Bagel Bakery makes and sells a variety of bagels, including plain, onion, poppyseed, and cinnamon raisin, as well as assorted flavors of cream cheese. Bagels are the major source of revenue for the company.

The bagel business is a $3 billion industry. Bagels are very popular with consumers. Not only are they relatively low in fat, they are filling, and they taste good! Investors like the bagel industry because it can be highly profitable: it only costs about $.10 to make a bagel, and they can be sold for $.50 each or more. Although some bagel companies have done poorly in recent years, due mainly to poor management, Bruegger’s business is booming; it is number one nationally, with over 450 shops that sell bagels, coffee, and bagel sandwiches for takeout or onpremise consump- tion. Many stores in the Bruegger’s chain generate an average of $800,000 in sales annually.

Production of bagels is done in batches, according to flavor, with each flavor being produced on a daily basis. Production of bagels at Bruegger’s begins at a processing plant, where the basic ingredients of flour, water, yeast, and flavorings are combined in a special mixing machine. After the dough has been thoroughly mixed, it is transferred to another machine that shapes the dough into individual bagels. Once the bagels have been formed, they are loaded onto refrigerated trucks for shipping to individual stores. When the bagels reach a store, they are unloaded from the trucks and temporarily stored while they rise. The final two steps of processing involve boiling the bagels in a kettle of water and malt for one minute, and then baking the bagels in an oven for approximately 15 minutes.

The process is depicted in the figure. Quality is an important feature of a successful business. Custom-

ers judge the quality of bagels by their appearance (size, shape, and shine), taste, and consistency. Customers are also sensitive to the service they receive when they make their purchases. Bruegger’s devotes careful attention to quality at every stage of operation, from choosing suppliers of ingredients, careful monitoring of ingredients, and keeping equipment in good operating condition to monitoring

output at each step in the process. At the stores, employees are instructed to watch for deformed bagels and to remove them when they find them. (Deformed bagels are returned to a processing plant where they are sliced into bagel chips, packaged, and then taken back to the stores for sale, thereby reducing the scrap rate.) Employ- ees who work in the stores are carefully chosen and then trained so that they are competent to operate the necessary equipment in the stores and to provide the desired level of service to customers.

The company operates with minimal inventories of raw materi- als and inventories of partially completed bagels at the plant and very little inventory of bagels at the stores. One reason for this is to maintain a high degree of freshness in the final product by continually supplying fresh product to the stores. A second rea- son is to keep costs down; minimal inventories mean less space is needed for storage.

Questions 1. Bruegger’s maintains relatively little inventory at either its

plants or its retail stores. List the benefits and risks of this policy.

2. Quality is very important to Bruegger’s. a. What features of bagels do customers look at to judge their

quality? b. At what points in the production process do workers check

bagel quality? c. List the steps in the production process, beginning with

purchasing ingredients, and ending with the sale, and state how quality can be positively affected at each step.

3. Which inventory models could be used for ordering the ingre- dients for bagels? Which model do you think would be most appropriate for deciding how many bagels to make in a given batch?

4. Bruegger’s has bagel-making machines at its plants. Another possibility would be to have a bagel-making machine at each store. What advantages does each alternative have?

BRUEGGER’S BAGEL BAKERYOPERATIONS TOUR

Processing plant A retail store

Mixer

Shaper Kettle Oven

Trays of bagels

FLO UR

Operations Tours These provide a simple “walkthrough” of an opera- tion for students, describing the company, its product or service, and its process of managing operations. Companies featured include Wegmans Food Markets, Morton Salt, Stickley Furniture, and Boeing.

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Background Harvey Industries, a Wisconsin company, specializes in the assem- bly of highpressure washer systems and in the sale of repair parts for these systems. The products range from small portable high- pressure washers to large industrial installations for snow removal from vehicles stored outdoors during the winter months. Typical uses for high-pressure water cleaning include:

Automobiles Airplanes

Building maintenance Barns

Engines Ice cream plants

Lift trucks Machinery

Swimming pools

Industrial customers include General Motors, Ford, Chrysler, Delta Airlines, United Parcel Service, and Shell Oil Company.

Although the industrial applications are a significant part of its sales, Harvey Industries is primarily an assembler of equipment for coin operated self-service car wash systems. The typical car wash is of concrete block construction with an equipment room in the center, flanked on either side by a number of bays. The cars are driven into the bays where the owner can wash and wax the car, utilizing high-pressure hot water and liquid wax. A dollar bill changer is available to provide change for the use of the equip- ment and the purchase of various products from dispensers. The products include towels, tire cleaner, and upholstery cleaner.

CASE HARVEY INDUSTRIES

Current Inventory Control System The current inventory control “system” consists of orders for stock replenishment being made by the stockroom foreman, the pur- chasing manager, or the manufacturing manager whenever one of them notices that the inventory is low. An order for replenishment of inventory is also placed whenever someone (either a customer or an employee in the assembly area) wants an item and it is not in stock.

Some inventory is needed for the assembly of the high-pressure equipment for the car wash and industrial applications. There are current and accurate bills of material for these assemblies. The material needs to support the assembly schedule are generally known well in advance of the build schedule.

The majority of inventory transactions are for repair parts and for supplies used by the car washes, such as paper towels, deter- gent, and wax concentrate. Because of the constant and rugged use of the car wash equipment, there is a steady demand for the various repair parts.

The stockroom is well organized, with parts stored in locations according to each vendor. The number of vendors is relatively lim- ited, with each vendor generally supplying many different parts. For example, the repair parts from Allen Bradley, a manufacturer of electrical motors, are stocked in the same location. These repair parts will be used to provide service for the many electrical motors that are part of the high-pressure pump and motor assembly used by all of the car washes.

Manufacturing manager

Sales manager

Purchasing manager

Controller

President

Stockroom foreman

Assembly foreman

Quality engineer

In recent years Harvey Industries has been in financial difficulty. The company has lost money for three of the last four years, with the last year’s loss being $17,174 on sales of $1,238,674. Inventory levels have been steadily increasing to their present levels of $124,324.

The company employs 23 people with the management team consisting of the following key employees: president, sales man- ager, manufacturing manager, controller, and purchasing man- ager. The abbreviated organization chart reflects the reporting relationship of the key employees and the three individuals who report directly to the manufacturing manager.

Because of the heavy sales volume of repair parts, there are generally two employees working in the stockroom—a stockroom foreman who reports to the manufacturing manager and an assis- tant to the foreman. One of these two employees will handle cus- tomer orders. Many customers stop by and order the parts and supplies they need. Telephone orders are also received and are shipped by United Parcel Service the same day.

The assembly area has some inventory stored on the shop floor. This inventory consists of low-value items that are used every day, such as nuts, bolts, screws, and washers. These purchased items do not amount to very much dollar volume throughout the year.

(continued)

Cases The text includes short cases. The cases were selected to provide a broader, more integrated thinking opportunity for students without taking a full case approach.

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Automobiles Airplanes

Building maintenance Barns

Engines Ice cream plants

Lift trucks Machinery

Swimming pools

Industrial customers include General Motors, Ford, Chrysler, Delta Airlines, United Parcel Service, and Shell Oil Company.

CASE HARVEY INDUSTRIES

Manufacturing manager

Sales manager

Purchasing manager

Controller

President

Stockroom foreman

Assembly foreman

Quality engineer

(continued)

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INSTRUCTOR RESOURCES

Available within Connect, instructors have access to teaching supports such as electronic files of the ancillary materials: Solutions Manual, Instructor’s Manual, Test Bank, PowerPoint Lecture Slides, Digital Image Library, and Excel Lecture scripts.

Instructor’s Manual. This manual includes teaching notes, chapter overview, an outline for each chapter, and solutions to the problems in the text.

Test Bank. Prepared by Larry R. White, Eastern Illinois University, the Test Bank includes over 2,000 true/false, multiple-choice, and discussion questions/problems at varying levels of difficulty.

TestGen. TestGen is a complete, state-of-the-art test generator and editing application soft- ware that allows instructors to quickly and easily select test items from McGraw Hill’s testbank content. The instructors can then organize, edit and customize questions and answers to rapidly generate tests for paper or online administration. Questions can include stylized text, symbols, graphics, and equations that are inserted directly into questions using built-in mathematical tem- plates. TestGen’s random generator provides the option to display different text or calculated number values each time questions are used. With both quick-and-simple test creation and flex- ible and robust editing tools, TestGen is a complete test generator system for today’s educators.

PowerPoint Lecture Slides. Prepared by James Anthony Swaim, Kennesaw State Uni- versity, the PowerPoint slides draw on the highlights of each chapter and provide an opportu- nity for the instructor to emphasize the key concepts in class discussions.

Digital Image Library. All the figures in the book are included for insertion in PowerPoint slides or for class discussion.

Operations Management Video Series The operations management video series, free to text adopters, includes professionally devel- oped videos showing students applications of key manufacturing and service topics in real companies. Each segment includes on-site or plant footage, interviews with company manag- ers, and focused presentations of OM applications in use to help the companies gain competi- tive advantage. Companies such as Zappos, FedEx, Subaru, Disney, BP, Chase Bank, DHL, Louisville Slugger, McDonald’s, Noodles & Company, and Honda are featured.

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Required=Results

McGraw-Hill Connect® Learn Without Limits Connect is a teaching and learning platform that is proven to deliver better results for students and instructors.

Connect empowers students by continually adapting to deliver precisely what they need, when they need it, and how they need it, so your class time is more engaging and effective.

Mobile

Connect Insight® Connect Insight is Connect’s new one-of-a- kind visual analytics dashboard—now available for both instructors and students—that provides at-a-glance information regarding student performance, which is immediately actionable. By presenting assignment, assessment, and topical performance results together with a time metric that is easily visible for aggregate or individual results, Connect Insight gives the user the ability to take a just-in-time approach to teaching and learning, which was never before available. Connect Insight presents data that empowers students and helps instructors improve class performance in a way that is efficient and effective.

73% of instructors who use Connect require it; instructor satisfaction increases by 28%

when Connect is required.

Students can view their results for any

Connect course.

Analytics

Connect’s new, intuitive mobile interface gives students and instructors flexible and convenient, anytime–anywhere access to all components of the Connect platform.

©Getty Images/iStockphoto

Using Connect improves retention rates by 19.8%, passing rates by 12.7%, and exam scores by 9.1%.

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SmartBook® Proven to help students improve grades and study more efficiently, SmartBook contains the same content within the print book, but actively tailors that content to the needs of the individual. SmartBook’s adaptive technology provides precise, personalized instruction on what the student should do next, guiding the student to master and remember key concepts, targeting gaps in knowledge and offering customized feedback, and driving the student toward comprehension and retention of the subject matter. Available on tablets, SmartBook puts learning at the student’s fingertips—anywhere, anytime.

Adaptive

Over 8 billion questions have been answered, making McGraw-Hill

Education products more intelligent, reliable, and precise.

THE ADAPTIVE READING EXPERIENCE DESIGNED TO TRANSFORM THE WAY STUDENTS READ

More students earn A’s and B’s when they use McGraw-Hill Education Adaptive products.

www.mheducation.com

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SCREENCAM TUTORIALS

These screen “movies” and voiceover tutorials explain key chapter content, using Excel and other software platforms. Confirming Pages

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Trend-Adjusted Exponential Smoothing A variation of simple exponential smoothing can be used when a time series exhibits a linear trend. It is called trend-adjusted exponential smoothing or, sometimes, double smoothing, to differentiate it from simple exponential smoothing, which is appropriate only when data vary around an average or have step or gradual changes. If a series exhibits trend, and simple smoothing is used on it, the forecasts will all lag the trend: If the data are increasing, each forecast will be too low; if decreasing, each forecast will be too high.

The trend-adjusted forecast (TAF) is composed of two elements—a smoothed error and a trend factor.

TAF t+1 = S t + T t (3–11) where

St = Previous forecast plus smoothed error Tt = Current trend estimate

and S t = TAF t + α( A t − TAF t ) T t = T t−1 + β (TAF t − TAF t−1 − T t−1 ) (3–12)

where

α = Smoothing constant for average β = Smoothing constant for trend

In order to use this method, one must select values of α and β (usually through trial and error) and make a starting forecast and an estimate of trend. Using the cell phone data from the previous example (where it was concluded that the data exhibited a linear trend), use trend-adjusted exponential smoothing to obtain forecasts for periods 6 through 11, with α = .40 and β = .30.

The initial estimate of trend is based on the net change of 28 for the three changes from period 1 to period 4, for an average of 9.33. The Excel spreadsheet is shown in Table 3.2. Notice that an initial estimate of trend is estimated from the first four values and that the start- ing forecast (period 5) is developed using the previous (period 4) value of 728 plus the initial trend estimate:

Starting forecast = 728 + 9.33 = 737.33

Unlike a linear trend line, trend-adjusted smoothing has the ability to adjust to changes in trend. Of course, trend projections are much simpler with a trend line than with trend- adjusted forecasts, so a manager must decide which benefits are most important when choos- ing between these two techniques for trend.

Techniques for Seasonality Seasonal variations in time-series data are regularly repeating upward or downward move- ments in series values that can be tied to recurring events. Seasonality may refer to regu- lar annual variations. Familiar examples of seasonality are weather variations (e.g., sales of winter and summer sports equipment) and vacations or holidays (e.g., airline travel, greeting card sales, visitors at tourist and resort centers). The term seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data. For example, rush hour traffic occurs twice a day—incoming in the morning and outgoing in the late afternoon. Theaters and restaurants often experience weekly demand patterns, with demand higher later in the week. Banks may experience daily seasonal variations (heavier traffic during the noon hour and just before closing), weekly variations (heavier toward the end of the week), and monthly variations (heaviest around the beginning of the month because of Social Security, payroll, and welfare checks being cashed or deposited). Mail volume; sales of toys, beer, auto- mobiles, and turkeys; highway usage; hotel registrations; and gardening also exhibit seasonal variations.

Trend-adjusted exponential smoothing Variation of expo- nential smoothing used when a time series exhibits a linear trend.

Seasonal variations Regu- larly repeating movements in series values that can be tied to recurring events.

LO3.12 Prepare a trend- adjusted exponential smoothing forecast.

screenCam tutorial

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TABLE 3.1 Excel solution for Example 5

Week

2 4 6 8 10 121 3 5 7 9 11

S a

le s

Trend lineData

Forecasts

780

800

760

740

720

700

c. Substituting values of t into this equation, the forecasts for the next two periods (i.e., t = 11 and t = 12) are: F11 = 699.40 + 7.51(11) = 782.01 F12 = 699.40 + 7.51(12) = 789.52

d. For purposes of illustration, the original data, the trend line, and the two projections (forecasts) are shown on the following graph:

Excel Templates Templates created by Lee Tangedahl, University of Montana, are included on the OLC. The templates, over 70 total, include dynamically linked graphics and variable controls. They allow you to solve a number of problems in the text or additional problems. All templates have been revised to allow formatting of all cells, hiding rows or columns, and entering data or calculations in blank cells. Many of the templates have been expanded to accommodate solving larger problems and cases.

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.Note to Students

The material in this text is part of the core knowledge in your edu- cation. Consequently, you will derive considerable benefit from your study of operations management, regardless of your major. Practically speaking, operations is a course in management.

This book describes principles and concepts of operations management. You should be aware that many of these prin- ciples and concepts are applicable to other aspects of your professional and personal life. You can expect the benefits of your study of operations management to serve you in those other areas as well.

Some students approach this course with apprehension, and perhaps even some negative feelings. It may be that they have heard that the course contains a certain amount of quantitative material that they feel uncomfortable with, or that the subject mat- ter is dreary, or that the course is about “factory management.” This is unfortunate, because the subject matter of this book is interesting and vital for all business students. While it is true that some of the material is quantitative, numerous examples, solved problems, and answers at the back of the book will help you with the quantitative material. As for “factory management,” there is material on manufacturing as well as on services. Manufac- turing is important, and something that you should know about for a number of reasons. Look around you. Most of the “things” you see were manufactured: cars, trucks, planes, clothing, shoes, computers, books, pens and pencils, desks, and cell phones. And these are just the tip of the iceberg. So it makes sense to know something about how these things are produced. Beyond all that is the fact that manufacturing is largely responsible for the high standard of living people have in industrialized countries.

After reading each chapter or supplement in the text, attending related classroom lectures, and completing assigned questions and problems, you should be able to do each of the following:

1. Identify the key features of that material. 2. Define and use terminology. 3. Solve typical problems. 4. Recognize applications of the concepts and techniques

covered.

5. Discuss the subject matter in some depth, including its relevance, managerial considerations, and advantages and limitations.

You will encounter a number of chapter supplements. Check with your instructor to determine whether to study them.

This book places an emphasis on problem solving. There are many examples throughout the text illustrat- ing solutions. In addition, at the end of most chapters and supplements you will find a group of solved problems. The examples within the chapter itself serve to illustrate con- cepts and techniques. Too much detail at those points would be counterproductive. Yet, later on, when you begin to solve the end-of-chapter problems, you will find the solved prob- lems quite helpful. Moreover, those solved problems usu- ally illustrate more and different details than the problems within the chapter.

I suggest the following approach to increase your chances of getting a good grade in the course:

1. Look over the chapter outline and learning objectives. 2. Read the chapter summary, and then skim the chapter. 3. Read the chapter and take notes. 4. Look over and try to answer the discussion and review

questions. 5. Solve the problems, referring to the solved problems and

chapter examples as needed.

Note that the answers to many problems are given at the end of the book. Try to solve each problem before turning to the answer. Remember—tests don’t come with answers.

And here is one final thought: Homework is on the High- way to Happiness! Enjoy the journey!

W.J.S.

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Brief Contents

Preface vii

1 Introduction to Operations Management 2

2 Competitiveness, Strategy, and Productivity 40

3 Forecasting 74

4 Product and Service Design 136

SUPPLEMENT TO CHAPTER 4: Reliability 174

5 Strategic Capacity Planning for Products and Services 188

SUPPLEMENT TO CHAPTER 5: Decision Theory 220

6 Process Selection and Facility Layout 242

7 Work Design and Measurement 296

SUPPLEMENT TO CHAPTER 7: Learning Curves 330

8 Location Planning and Analysis 342

9 Management of Quality 372

10 Quality Control 416

11 Aggregate Planning and Master Scheduling 462

12 MRP and ERP 500

13 Inventory Management 550

14 JIT and Lean Operations 608

SUPPLEMENT TO CHAPTER 14: Maintenance 644

15 Supply Chain Management 652

16 Scheduling 690

17 Project Management 730

18 Management of Waiting Lines 782

19 Linear Programming 822

Appendix A: Answers to Selected Problems 854 Appendix B: Tables 866 Appendic C: Working with the Normal Distribution 872 Company Index 877 Subject Index 879

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Preface vii

1 Introduction to Operations Management 2 Introduction 4

Production of Goods Versus Providing Services 8

Why Learn About Operations Management? 10

Career Opportunities and Professional Societies 12

Process Management 13

The Scope of Operations Management 14

Reading: Why Manufacturing Matters 17

Operations Management and Decision Making 18

Reading: Analytics 20

The Historical Evolution of Operations Management 21

Operations Today 24

Reading: Agility Creates a Competitive Edge 26

Key issues for Today’s Business Operations 27

Readings: Universities Embrace Sustainability 28

Diet and the Environment: Vegetarian vs. Nonvegetarian 29

Operations Tour Wegmans Food Markets 33

Summary 36 Key Points 36 Key Terms 36 Discussion and Review Questions 36 Taking Stock 37 Critical Thinking Exercises 37

Case Hazel 38

Selected Bibliography & Further Readings 38 Problem-Solving Guide 39

2 Competitiveness, Strategy, and Productivity 40 Introduction 41

Competitiveness 42

Mission and Strategies 44

Reading: Amazon Tops in Customer Service 45

Operations Strategy 51

Implications of Organization Strategy for Operations Management 54

Transforming Strategy into Action: The Balanced Scorecard 54

Productivity 56

Readings: Why Productivity Matters 59

Dutch Tomato Growers’ Productivity Advantage 60

Productivity Improvement 62 Summary 62 Key Points 63 Key Terms 63 Solved Problems 63 Discussion and Review Questions 64 Taking Stock 64 Critical Thinking Exercises 65 Problems 65

Case An American Tragedy: How a Good Company Died 67

Home-Style Cookies 68

Hazel Revisited 69

“Your Garden Gloves” 70

Operations Tour The U.S. Postal Service 70

Selected Bibliography and Further Readings 73

3 Forecasting 74 Introduction 76

Features Common to All Forecasts 77

Elements of a Good Forecast 78

Contents

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Forecasting and the Supply Chain 78

Steps in the Forecasting Process 79

Forecast Accuracy 79

Reading: High Forecasts Can Be Bad News 80

Approaches to Forecasting 82

Qualitative Forecasts 82

Forecasts Based on Time-Series Data 84

Associative Forecasting Techniques 101

Monitoring Forecast Error 106

Choosing a Forecasting Technique 110

Using Forecast Information 111

Computer Software in Forecasting 112 Operations Strategy 112

Reading: Gazing at the Crystal Ball 113

Summary 114 Key Points 116 Key Terms 117 Solved Problems 117 Discussion and Review Questions 123 Taking Stock 124 Critical Thinking Exercises 124 Problems 124

Case M&L Manufacturing 134

Highline Financial Services, Ltd. 135 Selected Bibliography and Further Readings 135

4 Product and Service Design 136 Reading: Design as a Business Strategy 138

Introduction 138

Readings: Product Redesign, Not Offshoring, Holds Cost Advantage for U.S. Manufacturers 139

Dutch Boy Brushes up Its Paints 140

Idea Generation 140

Reading: Vlasic on a Roll with Huge Pickle Slices 142

Legal and Ethical Considerations 143

Human Factors 144

Reading: Do You Want Pickled Beets with That? 145

Cultural Factors 145

Global Product and Service Design 145

Environmental Factors: Sustainability 146

Readings: Best Buy Wants Your Junk 147

Kraft Foods’ Recipe for Sustainability 148

Xerox Diverts 2 Billion Pounds of Waste From Landfills Through Green Initiatives 149

Recycle City: Maria’s Market 149

Other Design Considerations 151

Readings: Lego A/S in the Pink 152

Fast-Food Chains Adopt Mass Customization 155

Phases in Product Design and Development 161

Designing for Production 162

Service Design 165

Operations Strategy 169

Reading: The Challenges of Managing Services 169

Summary 170 Key Points 170 Key Terms 170 Discussion and Review Questions 170 Taking Stock 171 Critical Thinking Exercises 171 Problems 172

Operations Tour High Acres Landfill 173

Selected Bibliography and Further Readings 173

SUPPLEMENT TO CHAPTER 4: Reliability 174

5 Strategic Capacity Planning for Products and Services 188 Introduction 189

Reading: Excess Capacity Can Be Bad News! 190

Capacity Decisions are Strategic 191

Defining and Measuring Capacity 192

Determinants of Effective Capacity 193

Strategy Formulation 195

Forecasting Capacity Requirements 196

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Additional Challenges of Planning Service Capacity 198

Do it in-House or Outsource it? 199

Reading: My Compliments to the Chef, er, Buyer 200

Developing Capacity Strategies 200

Constraint Management 205

Evaluating Alternatives 205

Operations Strategy 211 Summary 211 Key Points 212 Key Terms 212 Solved Problems 212 Discussion and Review Questions 215 Taking Stock 215 Critical Thinking Exercises 215 Problems 216

Case Outsourcing of Hospital Services 219

Selected Bibliography and Further Readings 219

SUPPLEMENT TO CHAPTER 5: Decision Theory 220

6 Process Selection and Facility Layout 242 Introduction 243

Process Selection 244

Operations Tour Morton Salt 248

Technology 250

Readings: Foxconn Shifts its Focus to Automation 252

Self-Driving Vehicles are on the Horizon 257

Process Strategy 257

Strategic Resource Organization: Facilities Layout 257

Reading: A Safe Hospital Room of the Future 267

Designing Product Layouts: Line Balancing 269

Reading: BMW’s Strategy: Flexibility 278

Designing Process Layouts 278 Summary 283 Key Points 283 Key Terms 283 Solved Problems 283 Discussion and Review Questions 287 Taking Stock 288

Critical Thinking Exercises 288 Problems 288 Selected Bibliography and Further Readings 295

7 Work Design and Measurement 296 Introduction 297

Job Design 297

Quality of Work Life 301

Methods Analysis 306

Motion Study 310

Work Measurement 311

Operations Strategy 322 Summary 323 Key Points 323 Key Terms 324 Solved Problems 324 Discussion and Review Questions 325 Taking Stock 326 Critical Thinking Exercises 326 Problems 326 Selected Bibliography and Further Readings 329

SUPPLEMENT TO CHAPTER 7: Learning Curves 330

8 Location Planning and Analysis 342 The Need for Location Decisions 343

The Nature of Location Decisions 344

Global Locations 346

General Procedure for Making Location Decisions 348

Identifying a Country, Region, Community, and Site 349

Service and Retail Locations 356

Reading: Site Selection Grows Up: Improved Tech Tools Make the Process Faster, Better 357

Evaluating Location Alternatives 358 Summary 364 Key Points 364 Key Terms 364 Solved Problems 364 Discussion and Review Questions 366 Taking Stock 366 Critical Thinking Exercises 366 Problems 367

Case Hello, Walmart? 370

Selected Bibliography and Further Readings 370

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9 Management of Quality 372 Introduction 373

Reading: Whatever Happened to Quality? 374

The Evolution of Quality Management 374

The Foundations of Modern Quality Management: The Gurus 375

Insights on Quality Management 378

Readings: The Sounds of Quality 380

Hyundai: Kissing Clunkers Goodbye 384

Rework and Morale 386

Quality Awards 386

Quality Certification 387

Quality and the Supply Chain 389

Reading: Improving Quality and Reducing Risk in Offshoring 390

Total Quality Management 390

Problem Solving and Process Improvement 394

Reading: What Keeps Six Sigma Practitioners up at Night? 397

Quality Tools 398

Reading: Benchmarking Corporate Websites of Fortune 500 Companies 406

Operations Strategy 406 Summary 406 Key Points 407 Key Terms 407 Solved Problem 407 Discussion and Review Questions 408 Taking Stock 409 Critical Thinking Exercises 409 Problems 409

Case Chick-N-Gravy Dinner Line 411

Tip Top Markets 412 Selected Bibliography and Further Readings 414

10 Quality Control 416 Introduction 417

Inspection 418

Reading: Making Potato Chips 422

Statistical Process Control 423

Process Capability 441

Operations Strategy 446

Reading: Bar Codes Might Cut Drug Errors in Hospitals 447

Summary 447 Key Points 447 Key Terms 449 Solved Problems 449 Discussion and Review Questions 453 Taking Stock 454 Critical Thinking Exercises 454 Problems 454

Case Toys, Inc. 460

Tiger Tools 460 Selected Bibliography and Further Readings 461

11 Aggregate Planning and Master Scheduling 462 Introduction 464

Reading: Duplicate Orders Can Lead to Excess Capacity 468

Basic Strategies for Meeting Uneven Demand 471

Techniques for Aggregate Planning 474

Aggregate Planning in Services 481

Disaggregating the Aggregate Plan 483

Master Scheduling 483

The Master Scheduling Process 484 Summary 489 Key Points 489 Key Terms 490 Solved Problems 490 Discussion and Review Questions 493 Taking Stock 493 Critical Thinking Exercises 493 Problems 493

Case Eight Glasses a Day (EGAD) 498

Selected Bibliography and Further Readings 498

12 MRP and ERP 500 Introduction 501

An Overview of MRP 501

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MRP Inputs 502

MRP Processing 506

MRP Outputs 513

Other Considerations 514

MRP in Services 516

Benefits and Requirements of MRP 516

MRP II 517

Capacity Requirements Planning 519

ERP 521

Readings: The ABCs of ERP 523

The Top 10 ERP Mistakes 527

Operations Strategy 529 Summary 529 Key Points 529 Key Terms 530 Solved Problems 530 Discussion and Review Questions 539 Taking Stock 539 Critical Thinking Exercises 540 Problems 540

Case Promotional Novelties 545

Dmd Enterprises 546

Operations Tour Stickley Furniture 546

Selected Bibliography and Further Readings 549

13 Inventory Management 550 Introduction 551

Reading: $$$ 552

The Nature and Importance of Inventories 552

Requirements for Effective Inventory Management 555

Reading: Radio Frequency Identification (RFID) Tags 557

Inventory Ordering Policies 561

How Much to Order: Economic Order Quantity Models 561

Reorder Point Ordering 573

How Much to Order: Fixed-Order-interval Model 577

The Single-Period Model 580

Operations Strategy 585

Summary 585 Key Points 586 Key Terms 587 Solved Problem 587 Discussion and Review Questions 592 Taking Stock 592 Critical Thinking Exercises 592 Problems 593

Case UPD Manufacturing 600

Harvey Industries 601

Grill Rite 602

Farmers Restaurant 603

Operations Tour Bruegger’s Bagel Bakery 604

PSC, Inc. 605 Selected Bibliography and Further Readings 607

14 JIT and Lean Operations 608 Introduction 610

Reading: Toyota Recalls 612

Supporting Goals 613

Building Blocks 614

Readings: General Mills Turns to Nascar to Reduce Changeover Time 617

“People” Firms Boost Profits, Study Shows 621

Lean Tools 631

Reading: Nearby Suppliers Match Ford’s Mix 633

Transitioning to a Lean System 633

Lean Services 634

Reading: To Build a Better Hospital, Virginia Mason Takes Lessons from Toyota Plants 636

JIT II 637

Operations Strategy 637 Summary 638 Key Points 639 Key Terms 639 Solved Problems 639 Discussion and Review Questions 640 Taking Stock 640 Critical Thinking Exercises 640 Problems 640

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Case Level Operations 641

Operations Tour Boeing 642

Selected Bibliography and Further Readings 643

SUPPLEMENT TO CHAPTER 14: Maintenance 644

15 Supply Chain Management 652 Introduction 654

Trends in Supply Chain Management 655

Reading: At 3M, a Long Road Became a Shorter Road 658

Global Supply Chains 659

ERP and Supply Chain Management 659

Ethics and the Supply Chain 660

Small Businesses 660

Management Responsibilities 661

Procurement 662

Reading: IBMs Supply Chain Social Responsibility 665

E-Business 666

Reading: E-Procurement at IBM 668

Supplier Management 669

Reading: NestléUSA and Ocean Spray Form Strategic Operations Alliance 671

Inventory Management 672

Order Fulfillment 673

Logistics 674

Operations Tour Wegmans’ Shipping System 675

Readings: Springdale Farm 677

RFID Tags: Keeping the Shelves Stocked 678

Active RFID vs. Passive RFID 678

Creating An Effective Supply Chain 680

Reading: Clicks or Bricks, or Both? 681

Strategy 684 Summary 685 Key Points 685

Key Terms 685 Discussion and Review Questions 685 Taking Stock 686 Critical Thinking Exercises 686 Problems 686

Case Master Tag 687

Selected Bibliography and Further Readings 688

16 Scheduling 690 Scheduling Operations 692

Scheduling in Low-Volume Systems 695

Scheduling Services 713

Operations Strategy 717 Summary 717 Key Points 717 Key Terms 718 Solved Problems 718 Discussion and Review Questions 722 Taking Stock 722 Critical Thinking Exercises 722 Problems 723

Case Hi–Ho, Yo–Yo, Inc. 729

Selected Bibliography and Further Readings 729

17 Project Management 730 Introduction 732

Project Life Cycle 732

Behavioral Aspects of Project Management 733

Reading: Project Managers Have Never Been More Critical 738

Work Breakdown Structure 739

Planning and Scheduling with Gantt Charts 739

PERT and CPM 740

Deterministic Time Estimates 743

A Computing Algorithm 744

Probabilistic Time Estimates 751

Determining Path Probabilities 754

Simulation 756

Budget Control 757

Time–Cost Trade-offs: Crashing 757

Advantages of Using PERT and Potential Sources of Error 760

Critical Chain Project Management 761

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Other Topics in Project Management 761

Project Management Software 762

Operations Strategy 763

Risk Management 763 Summary 764 Key Points 765 Key Terms 765 Solved Problems 765 Discussion and Review Questions 772 Taking Stock 772 Critical Thinking Exercises 772 Problems 772

Case The Case of the Mexican Crazy Quilt 779

Time, Please 781 Selected Bibliography and Further Readings 781

18 Management of Waiting Lines 782 Why is There Waiting? 784

Managerial Implications of Waiting Lines 785

Reading: New Yorkers Do Not Like Waiting in Line 785

Goal of Waiting-Line Management 785

Characteristics of Waiting Lines 786

Measures of Waiting-Line Performance 790

Queuing Models: Infinite-Source 790

Queuing Model: Finite-Source 805

Constraint Management 811

The Psychology of Waiting 811

Reading: David H. Maister on the Psychology of Waiting 812

Operations Strategy 812

Reading: Managing Waiting Lines at Disney World 813

Summary 813 Key Points 814 Key Terms 814 Solved Problems 814 Discussion and Review Questions 816 Taking Stock 816 Critical Thinking Exercises 816 Problems 816

Case Big Bank 820

Selected Bibliography and Further Readings 820

19 Linear Programming 822 Introduction 823

Linear Programming Models 824

Graphical Linear Programming 826

The Simplex Method 838

Computer Solutions 838

Sensitivity Analysis 841 Summary 844 Key Points 844 Key Terms 844 Solved Problems 844 Discussion and Review Questions 847 Problems 847

Case Son, Ltd. 851

Custom Cabinets, Inc.© 852 Selected Bibliography and Further Readings 853

APPENDIX A Answers to Selected Problems 854 APPENDIX B Tables 866 APPENDIX C Working with the Normal

Distribution 872

Company Index 877 Subject Index 879

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TEN THINGS TO REMEMBER BEYOND THE FINAL EXAM

1 The way work is organized (i.e., project, job shop, batch, assembly, or continuous) has significant implications for the entire organization, including the type of work that is done, forecasting, layout, equipment selection, equipment maintenance, accounting, marketing, purchasing, inventory control, material handling, schedul- ing, and more.

2 Pay attention to variability, and reduce it whenever you can. Variability causes problems for management, whether it is variability in demand (capacity planning, forecasting, and inventory management), variability in deliveries from suppliers (inventory management, operations, order fulfillment), or variability in production or service rates (operations planning and control). Any of these can adversely affect customer satisfaction and costs. Recognize this, and build an appropriate amount of flexibility into systems.

3 “Homework is on the Highway to Happiness.” This relates not only to course- work, but also to your career: Be prepared for interviews, meetings, conferences, presentations (yours and others’), and other events. You can achieve a great deal of success by simply “doing your homework.”

4 How managers relate to subordinates can have a tremendous influence on the success or lack of success of an organization. Selection, training, motivation, and support are all important. One philosophy is: “Choose the right people, give them the tools they need, and then stay out of their way.”

5 Quality and price will always be prominent factors in consumers’ buying decisions. Strive to integrate quality in every aspect of what you do, and to reduce costs.

6 Pay careful attention to technology; consider both the opportunities and the risks. Opportunities: improvements in quality, service, and response time. Risks: technology can be costly, difficult to integrate, needs to be periodically updated (for additional cost), requires training, and quality and service may temporarily suffer when new technology is introduced.

7 Pay attention to capacity; the roads to success and failure both run through capacity.

8 Never underestimate your competitors. Assume they will always make the best decisions.

9 Most decisions involve tradeoffs. Understand the tradeoffs.

10 Make ethics a part of everything you do.

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1.1 Introduction 4

1.2 Production of Goods versus Providing Services 8

1.3 Why Learn about Operations Management? 10

1.4 Career Opportunities and Professional Societies 12

1.5 Process Management 13 Managing a Process to Meet Demand 13 Process Variation 14

1.6 The Scope of Operations Management 14 Managing the Supply Chain to Achieve Schedule, Cost, and Quality Goals 15

1.7 Operations Management and Decision Making 18 Models 18 Quantitative Approaches 19 Performance Metrics 19 Analysis of Trade-Offs 19 Degree of Customization 20 A Systems Approach 20 Establishing Priorities 20

1.8 The Historical Evolution of Operations Management 21 The Industrial Revolution 21 Scientific Management 21 The Human Relations Movement 24 Decision Models and Management Science 24

The Influence of Japanese Manufacturers 24

1.9 Operations Today 24

1.10 Key Issues for Today’s Business Operations 27 Environmental Concerns 27 Ethical Conduct 29 The Need to Manage the Supply Chain 30 Elements of Supply Chain Management 32 Operations Tour: Wegmans Food Markets 33 Case: Hazel 38 Problem Solving Guide 39

Introduction to Operations Management

L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO1.1 Define the terms operations management and supply chain.

LO1.2 Identify similarities and differences between production and service operations.

LO1.3 Explain the importance of learning about operations management.

LO1.4 Identify the three major functional areas of organizations and describe how they interrelate.

LO1.5 Summarize the two major aspects of process management.

LO1.6 Describe the operations function and the nature of the operations manager’s job.

LO1.7 Explain the key aspects of operations management decision making.

LO1.8 Briefly describe the historical evolution of operations management.

LO1.9 Describe current issues in business that impact operations management.

LO1.10 Explain the need to manage the supply chain.

C H A P T E R O U T L I N E

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FP O

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Operations is what businesses do. Operations are processes that either provide services or create goods. Operations take place in businesses such as restaurants, retail stores, supermarkets, factories, hospitals, and colleges and universi- ties. In fact, they take place in every business organization. Moreover, operations are the core of what a business orga- nization does.

As you read this book, you will learn about managing those operations. The subject matter is relevant for you regard- less of your major. Productivity, quality, e-business, competition, and customer satisfaction are important for every aspect of a business organization. This first chapter presents an introduction and overview of operations management. Among the issues it addresses are: What is operations management? Why is it important? What do operations management pro- fessionals do?

The chapter also provides a description of the historical evolution of operations management and a discussion of the trends and issues that impact operations management.

You will learn about (1) the economic balance that every business organization seeks to achieve; (2) the condition that generally exists that makes achieving the economic balance challenging; (3) the line function that is the core of every business organization; (4) key steps in the history and evolution of operations management; (5) the differences and simi- larities between producing products and delivering services; (6) what a supply chain is, and why it is essential to manage it; and (7) the key issues for today’s business operations.

Recalls of automobiles, foods, toys, and other products; major oil spills; and even dysfunctional state and federal legislatures are all examples of operations failures. They underscore the need for effective operations management. Examples of operations successes include the many electronic devices we all use, medical breakthroughs in diagnosing and treating ailments, and high-quality goods and services that are widely available.

Michie Turpin/Piedmont Healthcare

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1.1 INTRODUCTION

Operations is that part of a business organization that is responsible for producing goods and/or services. Goods are physical items that include raw materials, parts, subassemblies such as motherboards that go into computers, and final products such as cell phones and automobiles. Services are activities that provide some combination of time, location, form, or psychological value. Examples of goods and services are found all around you. Every book you read, every video you watch, every e-mail or text message you send, every tele- phone conversation you have, and every medical treatment you receive involves the opera- tions function of one or more organizations. So does everything you wear, eat, travel in, sit on, and access the Internet with. The operations function in business can also be viewed from a more far-reaching perspective: The collective success or failure of companies’ opera- tions functions has an impact on the ability of a nation to compete with other nations, and on the nation’s economy.

The ideal situation for a business organization is to achieve an economic match of supply and demand. Having excess supply or excess capacity is wasteful and costly; having too little means lost opportunity and possible customer dissatisfaction. The key functions on the supply side are operations and supply chains, and sales and marketing on the demand side.

While the operations function is responsible for producing products and/or delivering ser- vices, it needs the support and input from other areas of the organization. Business organi- zations have three basic functional areas, as depicted in Figure 1.1: finance, marketing, and operations. It doesn’t matter whether the business is a retail store, a hospital, a manufacturing firm, a car wash, or some other type of business; all business organizations have these three basic functions.

Finance is responsible for securing financial resources at favorable prices and allocating those resources throughout the organization, as well as budgeting, analyzing investment pro- posals, and providing funds for operations. Marketing is responsible for assessing consumer wants and needs, and selling and promoting the organization’s goods or services. Operations is responsible for producing the goods or providing the services offered by the organiza- tion. To put this into perspective, if a business organization were a car, operations would be its engine. And just as the engine is the core of what a car does, in a business organization, operations is the core of what the organization does. Operations management is responsible for managing that core. Hence operations management is the management of systems or processes that create goods and/or provide services.

Operations and supply chains are intrinsically linked, and no business organization could exist without both. A supply chain is the sequence of organizations—their facilities, func- tions, and activities—that are involved in producing and delivering a product or service. The sequence begins with basic suppliers of raw materials and extends all the way to the final customer. See Figure 1.2. Facilities might include warehouses, factories, processing centers, offices, distribution centers, and retail outlets. Functions and activities include fore- casting, purchasing, inventory management, information management, quality assurance, scheduling, production, distribution, delivery, and customer service.

One way to think of a supply chain is that it is like a chain, as its name implies. That is shown in Figure 1.2. The links of the chain would represent various production and/or service

LO1.1 Define the terms operations management and supply chain.

Goods Physical items produced by business organizations.

Services Activities that provide some combination of time, location, form, and psychological value.

Operations management The management of sys- tems or processes that cre- ate goods and/or provide services.

Supply chain A sequence of organizations—their facilities, functions, and activities—that are involved in producing and delivering a product or service

Organization

Operations MarketingFinance

FIGURE 1.1 The three basic functions of business organizations

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FIGURE 1.3B

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FIGURE 1.2 A simple product supply chain

operations such as factories, storage facilities, activities, and modes of transportation (trains, railroads, ships, planes, cars, and people). The chain illustrates both the sequential nature of a supply chain and the interconnectedness of the elements of the supply chain. Each link is a customer of the previous link and a supplier to the following link. It also helps to understand that if any one of the links fails for any reason (quality or delivery issues, weather problems, or some other problem [there are numerous possibilities]), that can interrupt the flow in the supply chain for the following portion of the chain.

Figure 1.3a provides another illustration of a supply chain: a chain that extends from wheat growing on a farm and ends with a customer buying a loaf of bread in a supermarket. The value of the product increases as it moves through the supply chain.

Another way to think of a supply chain is as a tree with many branches, as shown in Figure 1.3b. The main branches of the tree represent key suppliers and transporters (e.g., trucking companies). That view is helpful in grasping the size and complexity that often exists in supply chains. Notice that the main branches of the tree have side branches (their own key

Final customers

DistributorProducer Direct suppliers

Suppliers’ suppliers

Supermarket

Bakery

Mill

Farm

Bread $1.29

Suppliers:

Trucking

Equipment suppliers Equipment repair Feed, seed, fertilizers, pesticides Energy/fuel

Suppliers:

Equipment suppliers Equipment repair Other ingredients Energy

Suppliers:

Equipment suppliers Equipment repair Energy

Suppliers:

Fuel Repairs Tires Drivers Trucks

FIGURE 1.3A A supply chain for bread

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suppliers), and those side branches also have their own side branches (their own key suppli- ers). In fact, an extension of the tree view of a supply chain is that each supplier (branch) has its own supply tree. Referring to Figure 1.3a, the farm, mill, and bakery of the trucking com- panies would have their own “tree” of suppliers.

Supply chains are both external and internal to the organization. The external parts of a supply chain provide raw materials, parts, equipment, supplies, and/or other inputs to the organization, and they deliver outputs that are goods to the organization’s customers. The internal parts of a supply chain are part of the operations function itself, supplying operations with parts and materials, performing work on products, and/or performing services.

The creation of goods or services involves transforming or converting inputs into outputs. Various inputs such as capital, labor, and information are used to create goods or services using one or more transformation processes (e.g., storing, transporting, repairing). To ensure that the desired outputs are obtained, an organization takes measurements at various points in the transformation process (feedback) and then compares them with previously established standards to determine whether corrective action is needed (control). Figure 1.4 depicts the conversion system.

Table 1.1 provides some examples of inputs, transformation processes, and outputs. Although goods and services are listed separately in Table 1.1, it is important to note that goods and services often occur jointly. For example, having the oil changed in your car is a service, but the oil that is delivered is a good. Similarly, house painting is a service, but the paint is a good. The goods–service combination is a continuum. It can range from primar- ily goods, with little service, to primarily service, with few goods. Figure 1.5 illustrates this continuum. Because there are relatively few pure goods or pure services, companies usually sell product packages, which are a combination of goods and services. There are elements of both goods production and service delivery in these product packages. This makes managing operations more interesting, and also more challenging.

Table 1.2 provides some specific illustrations of the transformation process. The essence of the operations function is to add value during the transformation process:

Value-added is the term used to describe the difference between the cost of inputs and the value or price of outputs. In nonprofit organizations, the value of outputs (e.g., highway con- struction, police and fire protection) is their value to society; the greater the value-added, the greater the effectiveness of these operations. In for-profit organizations, the value of outputs is measured by the prices that customers are willing to pay for those goods or services. Firms use the money generated by value-added for research and development, investment in new facilities and equipment, worker salaries, and profits. Consequently, the greater the value- added, the greater the amount of funds available for these purposes. Value can also be psycho- logical, as in branding.

Many factors affect the design and management of operations systems. Among them are the degree of involvement of customers in the process and the degree to which technology

Value-added The difference between the cost of inputs and the value or price of outputs.

Value -added

Control

Inputs Land Labor Capital Information

Outputs Goods Services

Transformation/ conversion process

Measurement and Feedback

FIGURE 1.4 The operations function involves the conversion of inputs into outputs

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TABLE 1.1 Examples of inputs, transformation, and outputs

Goods Service

Songwriting, software development

Computer repair, restaurant meal

Automobile repair, fast food

Home remodeling, retail sales

Automobile assembly, steelmaking

Surgery, teaching

FIGURE 1.5 The goods–service continuum

Inputs Transformation Outputs

Land Processes High goods percentage

Human Cutting, drilling Houses

Physical labor Transporting Automobiles

Intellectual labor Teaching Clothing

Capital Farming Computers

Raw materials Mixing Machines

Water Packing Televisions

Metals Copying Food products

Wood Analyzing Textbooks

Equipment Developing DVD players

Machines Searching High service percentage

Computers Researching Health care

Trucks Repairing Entertainment

Tools Innovating Car repair

Facilities Debugging Legal

Hospitals Selling Banking

Factories Emailing Communication

Retail stores

Energy

Other

Information

Time

Legal constraints

Government regulations

is used to produce and/or deliver a product or service. The greater the degree of customer involvement, the more challenging it can be to design and manage the operation. Technology choices can have a major impact on productivity, costs, flexibility, and quality and customer satisfaction.

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TABLE 1.2 Illustrations of the transformation process

1.2 PRODUCTION OF GOODS VERSUS PROVIDING SERVICES

Although goods and services often go hand in hand, there are some very basic differences between the two, differences that impact the management of the goods portion versus man- agement of the service portion. There are also many similarities between the two.

Production of goods results in a tangible output, such as an automobile, eyeglasses, a golf ball, a refrigerator—anything that we can see or touch. It may take place in a factory, but it can occur elsewhere. For example, farming and restaurants produce nonmanufactured goods. Delivery of service, on the other hand, generally implies an act. A physician’s examina- tion, TV and auto repair, lawn care, and the projection of a film in a theater are examples of services. The majority of service jobs fall into these categories:

Professional services (e.g., financial, health care, legal) Mass services (e.g., utilities, Internet, communications) Service shops (e.g., tailoring, appliance repair, car wash, auto repair/maintenance) Personal care (e.g., beauty salon, spa, barbershop) Government (e.g., Medicare, mail, social services, police, fire) Education (e.g., schools, universities) Food service (e.g., catering) Services within organizations (e.g., payroll, accounting, maintenance, IT, HR, janitorial) Retailing and wholesaling Shipping and delivery (e.g., truck, railroad, boat, air) Residential services (e.g., lawn care, painting, general repair, remodeling, interior design) Transportation (e.g., mass transit, taxi, airlines, ambulance) Travel and hospitality (e.g., travel bureaus, hotels, resorts) Miscellaneous services (e.g., copy service, temporary help)

Manufacturing and service are often different in terms of what is done but quite similar in terms of how it is done.

LO1.2 Identify the similarities and differences between production and service operations.

Inputs Processing Output

Food Processor Raw vegetables Cleaning Canned vegetables

Metal sheets Making cans

Water Cutting

Energy Cooking

Labor Packing

Building Labeling

Equipment

Hospital Doctors, nurses Examination Treated patients

Hospital Surgery

Medical supplies Monitoring

Equipment Medication

Laboratories Therapy

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Consider these points of comparison:

Degree of customer contact. Many services involve a high degree of customer contact, although services such as Internet providers, utilities, and mail service do not. When there is a high degree of contact, the interaction between server and customer becomes a “moment of truth” that will be judged by the customer every time the service occurs. Labor content of jobs. Services often have a higher degree of labor content than manu- facturing jobs do, although automated services are an exception. Uniformity of inputs. Service operations are often subject to a higher degree of vari- ability of inputs. Each client, patient, customer, repair job, and so on presents a some- what unique situation that requires assessment and flexibility. Conversely, manufacturing operations often have a greater ability to control the variability of inputs, which leads to more-uniform job requirements. Measurement of productivity. Measurement of productivity can be more difficult for service jobs due largely to the high variations of inputs. Thus, one doctor might have a higher level of routine cases to deal with, while another might have more difficult cases. Unless a careful analysis is conducted, it may appear that the doctor with the difficult cases has a much lower productivity than the one with the routine cases. Quality assurance. Quality assurance is usually more challenging for services due to the higher variation in input, and because delivery and consumption occur at the same time. Unlike manufacturing, which typically occurs away from the customer and allows mistakes that are iden- tified to be corrected, services have less opportunity to avoid exposing the customer to mistakes. Inventory. Many services tend to involve less use of inventory than manufacturing operations, so the costs of having inventory on hand are lower than they are for manufacturing. However, unlike manufactured goods, services cannot be stored. Instead, they must be provided “on demand.” Wages. Manufacturing jobs are often well paid, and have less wage variation than service jobs, which can range from highly paid professional services to minimum-wage workers. Ability to patent. Product designs are often easier to patent than service designs, and some services cannot be patented, making them easier for competitors to copy.

There are also many similarities between managing the production of products and manag- ing services. In fact, most of the topics in this book pertain to both. When there are important service considerations, these are highlighted in separate sections. Here are some of the pri- mary factors for both:

a. Forecasting and capacity planning to match supply and demand b. Process management c. Managing variations d. Monitoring and controlling costs and productivity e. Supply chain management f. Location planning, inventory management, quality control, and scheduling

Note that many service activities are essential in goods-producing companies. These include training, human resource management, customer service, equipment repair, procure- ment, and administrative services.

Table 1.3 provides an overview of the differences between production of goods and service operations. Remember, though, that most systems involve a blend of goods and services.

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1.3 WHY LEARN ABOUT OPERATIONS MANAGEMENT?

Whether operations management is your major or not, the skill set you gain studying opera- tions management will serve you well in your career.

There are many career-related reasons for wanting to learn about operations management, whether you plan to work in the field of operations or not. This is because every aspect of business affects or is affected by operations. Operations and sales are the two line functions in a business organization. All other functions—accounting, finance, marketing, IT, and so on—support the two line functions. Among the service jobs that are closely related to operations are financial services (e.g., stock market analyst, broker, investment banker, and loan officer), marketing services (e.g., market analyst, marketing researcher, advertising manager, and product manager), accounting ser- vices (e.g., corporate accountant, public accountant, and budget analyst), and information services (e.g., corporate intelligence, library services, management information systems design services).

A common complaint from employers is that college graduates come to them very focused, when employers would prefer them to have more of a general knowledge of how business organizations operate. This book provides some of the breadth that employers are looking for in their new hires. Apart from the career-related reasons is a not so obvious one: Through learning about operations and supply chains, you will have a much better understanding of the world you live in, the global dependencies of companies and nations, some of the reasons that companies succeed or fail, and the importance of working with others.

Working together successfully means that all members of the organization understand not only their own role, but they also understand the roles of others. In practice, there is signifi- cant interfacing and collaboration among the various functional areas, involving exchange of information and cooperative decision making. For example, although the three primary func- tions in business organizations perform different activities, many of their decisions impact the other areas of the organization. Consequently, these functions have numerous interactions, as depicted by the overlapping circles shown in Figure 1.6.

Finance and operations management personnel cooperate by exchanging information and expertise in such activities as the following:

1. Budgeting. Budgets must be periodically prepared to plan financial requirements. Bud- gets must sometimes be adjusted, and performance relative to a budget must be evaluated.

2. Economic analysis of investment proposals. Evaluation of alternative investments in plant and equipment requires inputs from both operations and finance people.

3. Provision of funds. The necessary funding of operations and the amount and timing of funding can be important and even critical when funds are tight. Careful planning can help avoid cash-flow problems.

Marketing’s focus is on selling and/or promoting the goods or services of an organization. Marketing is also responsible for assessing customer wants and needs, and for communicating

TABLE 1.3 Typical differences between production of goods and provision of services

LO1.3 Explain the impor- tance of learning about operations management.

Characteristic Goods Services

Output Tangible Intangible

Customer contact Low High

Labor content Low High

Uniformity of input High Low

Measurement of productivity Easy Difficult

Opportunity to correct problems before delivery High Low

Inventory Much Little

Wages Narrow range Wide range

Patentable Usually Not usually

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those to operations people (short term) and to design people (long term). That is, operations needs information about demand over the short to intermediate term so that it can plan accordingly (e.g., purchase materials or schedule work), while design people need information that relates to improving current products and services and designing new ones. Marketing, design, and produc- tion must work closely together to successfully implement design changes and to develop and produce new products. Marketing can provide valuable insight on what competitors are doing. Marketing also can supply information on consumer preferences so that design will know the kinds of products and features needed; operations can supply information about capacities and judge the manufacturability of designs. Operations will also have advance warning if new equip- ment or skills will be needed for new products or services. Finance people should be included in these exchanges in order to provide information on what funds might be available (short term) and to learn what funds might be needed for new products or services (intermediate to long term). One important piece of information marketing needs from operations is the manufacturing or service lead time in order to give customers realistic estimates of how long it will take to fill their orders.

Thus, marketing, operations, and finance must interface on product and process design, forecasting, setting realistic schedules, quality and quantity decisions, and keeping each other informed on the other’s strengths and weaknesses.

People in every area of business need to appreciate the importance of managing and coor- dinating operations decisions that affect the supply chain and the matching of supply and demand, and how those decisions impact other functions in an organization.

Operations also interacts with other functional areas of the organization, including legal, management information systems (MIS), accounting, personnel/human resources, and public relations, as depicted in Figure 1.7.

The legal department must be consulted on contracts with employees, customers, suppli- ers, and transporters, as well as on liability and environmental issues.

Accounting supplies information to management on costs of labor, materials, and over- head, and may provide reports on items such as scrap, downtime, and inventories.

LO1.4 Identify the three major functional areas of organizations and describe how they interrelate.

Lead time The time between ordering a good or service and receiving it.

FIGURE 1.6 The three major functions of business organizations overlap

Marketing and Sales

Operations

Finance

Legal Public relations

Personnel/ Human

resources Accounting

Operations

MIS

FIGURE 1.7 Operations interfaces with a number of supporting functions

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Management information systems (MIS) is concerned with providing management with the information it needs to effectively manage. This occurs mainly through designing systems to capture relevant information and designing reports. MIS is also important for managing the control and decision-making tools used in operations management.

The personnel or human resources department is concerned with recruitment and training of personnel, labor relations, contract negotiations, wage and salary administration, assisting in manpower projections, and ensuring the health and safety of employees.

Public relations is responsible for building and maintaining a positive public image of the organization. Good public relations provides many potential benefits. An obvious one is in the marketplace. Other potential benefits include public awareness of the organization as a good place to work (labor supply), improved chances of approval of zoning change requests, com- munity acceptance of expansion plans, and instilling a positive attitude among employees.

1.4 CAREER OPPORTUNITIES AND PROFESSIONAL SOCIETIES

There are many career opportunities in the operations management and supply chain fields. Among the numerous job titles are operations manager, production analyst, production man- ager, inventory manager, purchasing manager, schedule coordinator, distribution manager, supply chain manager, quality analyst, and quality manager. Other titles include office man- ager, store manager, and service manager.

People who work in the operations field should have a skill set that includes both people skills and knowledge skills. People skills include political awareness; mentoring ability; and collaboration, negotiation, and communication skills. Knowledge skills, necessary for cred- ibility and good decision making, include product and/or service knowledge, process knowl- edge, industry and global knowledge, financial and accounting skills, and project management skills. See Table 1.4.

If you are thinking of a career in operations management, you can benefit by joining one or more of the professional societies.

APICS, the Association for Operations Management 8430 West Bryn Mawr Avenue, Suite 1000, Chicago, Illinois 60631 www.apics.org American Society for Quality (ASQ) 230 West Wells Street, Milwaukee, Wisconsin 53203 www.asq.org Institute for Supply Management (ISM) 2055 East Centennial Circle, Tempe, Arizona 85284 www.ism.ws

TABLE 1.4 Sample operations management job descriptions

Production Supervisor Supply Chain Manager Social Media Product Manager

• Manage a production staff of 10–20.

• Ensure the department meets daily goals through the man- agement of productivity.

• Enforce safety policies. • Coordinate work between

departments. • Have strong problem-solving

skills, and strong written and oral communication skills.

• Have a general knowledge of materials management, information systems, and basic statistics.

• Direct, monitor, evaluate, and motivate employee performance.

• Be knowledgeable about shipping regulations.

• Manage budgetary accounts • Manage projects.

• Identify ways to increase consumer engagement.

• Analyze the key performance indicators and recommend improvements.

• Lead cross-functional teams to define product specifications.

• Collaborate with design and technical to create key product improvements.

• Develop requirements for new website enhancements.

• Monitor the competition to identify need for changes.

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Institute for Operations Research and the Management Sciences (INFORMS) 901 Elkridge Landing Road, Linthicum, Maryland 21090-2909 www.informs.org The Production and Operations Management Society (POMS) College of Engineering, Florida International University, EAS 2460, 10555 West Flagler Street, Miami, Florida 33174 www.poms.org The Project Management Institute (PMI) 4 Campus Boulevard, Newtown Square, Penn- sylvania 19073-3299 www.pmi.org Council of Supply Chain Management Professionals (CSCMP) 333 East Butterfield Road, Suite 140, Lombard, Illinois 60148 https//cscmp.org

APICS, ASQ, ISM, and other professional societies offer a practitioner certification examination that can enhance your qualifications. Information about job opportunities can be obtained from all of these societies as well as from other sources, such as the Decision Sciences Institute (University Plaza, Atlanta, Georgia 30303) and the Institute of Industrial Engineers (25 Technology Park, Norcross, Georgia 30092).

1.5 PROCESS MANAGEMENT

A key aspect of operations management is process management. A process consists of one or more actions that transform inputs into outputs. In essence, the central role of all management is process management.

Businesses are composed of many interrelated processes. Generally speaking, there are three categories of business processes:

1. Upper-management processes. These govern the operation of the entire organization. Examples include organizational governance and organizational strategy.

2. Operational processes. These are the core processes that make up the value stream. Examples include purchasing, production and/or service, marketing, and sales.

3. Supporting processes. These support the core processes. Examples include accounting, human resources, and IT (information technology).

Business processes, large and small, are composed of a series of supplier–customer rela- tionships, where every business organization, every department, and every individual opera- tion is both a customer of the previous step in the process and a supplier to the next step in the process. Figure 1.8 illustrates this concept.

A major process can consist of many subprocesses, each having its own goals that contrib- ute to the goals of the overall process. Business organizations and supply chains have many such processes and subprocesses, and they benefit greatly when management is using a process perspective. Business process management (BPM) activities include process design, process execution, and process monitoring. Two basic aspects of this for operations and supply chain management are managing processes to meet demand and dealing with process variability.

Managing a Process to Meet Demand Ideally, the capacity of a process will be such that its output just matches demand. Excess capacity is wasteful and costly; too little capacity means dissatisfied customers and lost rev- enue. Having the right capacity requires having accurate forecasts of demand, the ability to translate forecasts into capacity requirements, and a process in place capable of meeting

Process One or more actions that transform inputs into outputs.

LO1.5 Summarize the two major aspects of process management.

FIGURE 1.8 Business processes form a sequence of suppliers and customers

Supplier(s) Customer(s)

A business organization, a

department, or an individual operation

Input(s) from one or more suppliers

Output(s) to one or more customers

Transformation

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expected demand. Even so, process variation and demand variability can make the achieve- ment of a match between process output and demand difficult. Therefore, to be effective, it is also necessary for managers to be able to deal with variation.

Process Variation Variation occurs in all business processes. It can be due to variety or variability. For example, random variability is inherent in every process; it is always present. In addition, variation can occur as the result of deliberate management choices to offer customers variety.

There are four basic sources of variation:

1. The variety of goods or services being offered. The greater the variety of goods and services, the greater the variation in production or service requirements.

2. Structural variation in demand. These variations, which include trends and sea- sonal variations, are generally predictable. They are particularly important for capacity planning.

3. Random variation. This natural variability is present to some extent in all processes, as well as in demand for services and products, and it cannot generally be influenced by managers.

4. Assignable variation. These variations are caused by defective inputs, incorrect work methods, out-of-adjustment equipment, and so on. This type of variation can be reduced or eliminated by analysis and corrective action.

Variations can be disruptive to operations and supply chain processes, interfering with optimal functioning. Variations result in additional cost, delays and shortages, poor quality, and inefficient work systems. Poor quality and product shortages or service delays can lead to dissatisfied customers and can damage an organization’s reputation and image. It is not surprising, then, that the ability to deal with variability is absolutely necessary for managers.

Throughout this book, you will learn about some of the tools managers use to deal with vari- ation. An important aspect of being able to deal with variation is to use metrics to describe it. Two widely used metrics are the mean (average) and the standard deviation. The standard devi- ation quantifies variation around the mean. The mean and standard deviation are used through- out this book in conjunction with variation. So, too, is the normal distribution. Because you will come across many examples of how the normal distribution is used, you may find the overview on working with the normal distribution in the appendix at the end of the book helpful.

1.6 THE SCOPE OF OPERATIONS MANAGEMENT

The scope of operations management ranges across the organization. Operations management people are involved in product and service design, process selection, selection and manage- ment of technology, design of work systems, location planning, facilities planning, and qual- ity improvement of the organization’s products or services.

The operations function includes many interrelated activities, such as forecasting, capacity planning, scheduling, managing inventories, assuring quality, motivating employees, deciding where to locate facilities, and more.

We can use an airline company to illustrate a service organization’s operations system. The system consists of the airplanes, airport facilities, and maintenance facilities, sometimes spread out over a wide territory. The activities include:

Forecasting such things as weather and landing conditions, seat demand for flights, and the growth in air travel. Capacity planning, essential for the airline to maintain cash flow and make a reasonable profit. (Too few or too many planes, or even the right number of planes but in the wrong places, will hurt profits.)

LO1.6 Describe the operations function and the nature of the opera- tions manager’s job.

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Locating facilities according to managers’ decisions on which cities to provide service for, where to locate maintenance facilities, and where to locate major and minor hubs. Facilities and layout, important in achieving effective use of workers and equipment. Scheduling of planes for flights and for routine maintenance; scheduling of pilots and flight attendants; and scheduling of ground crews, counter staff, and baggage handlers. Managing inventories of such items as foods and beverages, first-aid equipment, in- flight magazines, pillows and blankets, and life preservers. Assuring quality, essential in flying and maintenance operations, where the emphasis is on safety, and important in dealing with customers at ticket counters, check-in, telephone and electronic reservations, and curb service, where the emphasis is on efficiency and courtesy. Motivating and training employees in all phases of operations.

Managing the Supply Chain to Achieve Schedule, Cost, and Quality Goals Consider a bicycle factory. This might be primarily an assembly operation: buying compo- nents such as frames, tires, wheels, gears, and other items from suppliers, and then assembling bicycles. The factory also might do some of the fabrication work itself, forming frames, mak- ing the gears and chains, and it might buy mainly raw materials and a few parts and materials such as paint, nuts and bolts, and tires. Among the key management tasks in either case are scheduling production, deciding which components to make and which to buy, ordering parts and materials, deciding on the style of bicycle to produce and how many, purchasing new equipment to replace old or worn-out equipment, maintaining equipment, motivating work- ers, and ensuring that quality standards are met.

Obviously, an airline company and a bicycle factory are completely different types of oper- ations. One is primarily a service operation, the other a producer of goods. Nonetheless, these two operations have much in common. Both involve scheduling activities, motivating employ- ees, ordering and managing supplies, selecting and maintaining equipment, satisfying quality

Scheduling planes, cargo, and flight and ground crews is an operations function for an airline.

Doug Letterman/Creative Commons

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A worker is making the bottom bracket lug for a Trek OCLV carbon road bike at Trek Bicycle Company in Waterloo, Wisconsin, world headquarters for Trek. Trek is a world leader in bicycle products and accessories, with 1,500 employees worldwide. Designers and engineers incorporate the most advanced technology into Trek products, resulting in award-winning bikes and components.

standards, and—above all—satisfying customers. And in both businesses, the success of the business depends on short- and long-term planning.

A primary function of an operations manager is to guide the system by decision making. Certain decisions affect the design of the system, and others affect the operation of the system.

System design involves decisions that relate to system capacity, the geographic location of facilities, arrangement of departments and placement of equipment within physical structures, product and service planning, and acquisition of equipment. These decisions usually, but not always, require long-term commitments. Moreover, they are typically strategic decisions. Sys- tem operation involves management of personnel, inventory planning and control, scheduling, project management, and quality assurance. These are generally tactical and operational deci- sions. Feedback on these decisions involves measurement and control. In many instances, the operations manager is more involved in day-to-day operating decisions than with decisions relating to system design. However, the operations manager has a vital stake in system design because system design essentially determines many of the parameters of system operation. For example, costs, space, capacities, and quality are directly affected by design decisions. Even though the operations manager is not responsible for making all design decisions, he or she can provide those decision makers with a wide range of information that will have a bearing on their decisions.

A number of other areas are part of, or support, the operations function. They include pur- chasing, industrial engineering, distribution, and maintenance.

Purchasing has responsibility for procurement of materials, supplies, and equipment. Close contact with operations is necessary to ensure correct quantities and timing of purchases. The purchasing department is often called on to evaluate vendors for quality, reliability, service, price, and ability to adjust to changing demand. Purchasing is also involved in receiving and inspecting the purchased goods. Industrial engineering is often concerned with scheduling, performance standards, work methods, quality control, and material handling. Distribution involves the shipping of goods to warehouses, retail outlets, or final customers. Maintenance is responsible for general upkeep and repair of equipment, buildings and grounds, heating and air-conditioning; removing toxic wastes; parking; and perhaps security.

Andy Manis/AP Images

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The operations manager is the key figure in the system: He or she has the ultimate respon- sibility for the creation of goods or provision of services.

The kinds of jobs that operations managers oversee vary tremendously from organization to organization largely because of the different products or services involved. Thus, manag- ing a banking operation obviously requires a different kind of expertise than managing a steelmaking operation. However, in a very important respect, the jobs are the same: They are both essentially managerial. The same thing can be said for the job of any operations manager regardless of the kinds of goods or services being created.

The service sector and the manufacturing sector are both important to the economy. The service sector now accounts for more than 70 percent of jobs in the United States, and it is growing in other countries as well. Moreover, the number of people working in services is increasing, while the number of people working in manufacturing is not. The reason for the decline in manufacturing jobs is twofold: As the operations function in manufacturing com- panies finds more productive ways of producing goods, the companies are able to maintain or even increase their output using fewer workers. Furthermore, some manufacturing work has been outsourced to more productive companies, many in other countries, that are able to produce goods at lower costs. Outsourcing and productivity will be discussed in more detail in this and other chapters.

Many of the concepts presented in this book apply equally to manufacturing and service. Consequently, whether your interest at this time is on manufacturing or on service, these con- cepts will be important, regardless of whether a manufacturing example or service example is used to illustrate the concept.

The Why Manufacturing Matters reading gives another reason for the importance of manu- facturing jobs.

The U.S. economy is becoming more and more service-based. The percentage of employment in manufacturing continues to decrease while the percentage employed in services continues to increase. However, it would be unwise to assume that manu- facturing isn’t important to the economy, or that service is more important. Let’s see why.

Not only is the percentage of manufacturing jobs decreasing, but the actual number of manufacturing jobs is also decreasing. There are two main reasons for the decline: increases in pro- ductivity, which means fewer workers are needed to maintain manufacturing output; and outsourcing, especially to countries that have much lower wages, an attractive option for companies seek- ing to maintain their competitiveness and boost their bottom lines.

However, when companies outsource part (or in some cases, all) of their manufacturing to lower-cost countries, the loss of jobs results in the loss of service jobs as well. Some are lost in the com- munity in retail businesses patronized by the manufacturing work- ers. Also included in that figure are factory service workers (e.g., workers who do machine repairs, maintenance, material handling, packaging, and so on). General estimates are that four service jobs are lost for each manufacturing job lost.

As the manufacturing base shrinks, workers who lose their manufacturing jobs are finding it tougher to find another opening in manufacturing. Instead they join the ranks of the unemployed, or take a service job, usually at a lower wage rate than what man- ufacturing paid.

READING WHY MANUFACTURING MATTERS

From a national perspective, not only is work transferred to a foreign country, intellectual knowledge is transferred. Moreover, as time passes, the domestic base of manufacturing skills and know-how is lost.

There are important consequences for taxes as well. Unem- ployment benefits are costly, and the erosion of federal, state, and local tax bases results in lower tax revenues collected from indi- viduals and from corporations.

Lastly, manufacturing is an important source of innovation. It is responsible for 70 percent of private-sector R&D and 90 percent of U.S. patents (Rana Foroohar, “Go Glocal,” Time, August 20, 2012, p. 30). Much of the work in getting a product ready for volume pro- duction is high-value-added knowledge work that supports future innovation. And innovation generates jobs. “Intel has invested tens of billions of dollars in its factories in Oregon, Arizona, and New Mexico so that they are able to produce the most advanced semiconductors” (Willy Shih and Gary Pisano,“Why Manufacturing Matters for America,” Special to CNN, Sept. 21, 2012).

Questions

1. How important is the loss of manufacturing jobs to the nation? 2. Can you suggest some actions the government (federal, state,

or local) can take to stem the job loss? 3. What evidence is there of the importance of manufacturing

innovation?

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1.7 OPERATIONS MANAGEMENT AND DECISION MAKING

The chief role of an operations manager is that of planner and decision maker. In this capacity, the operations manager exerts considerable influence over the degree to which the goals and objectives of the organization are realized. Most decisions involve many possible alternatives that can have quite different impacts on costs or profits. Consequently, it is important to make informed decisions.

Operations management professionals make a number of key decisions that affect the entire organization. These include the following:

What: What resources will be needed, and in what amounts? When: When will each resource be needed? When should the work be scheduled? When should materials and other supplies be ordered? When is corrective action needed? Where: Where will the work be done? How: How will the product or service be designed? How will the work be done (organi- zation, methods, equipment)? How will resources be allocated? Who: Who will do the work?

An operations manager’s daily concerns include costs (budget), quality, and schedules (time). Throughout this book, you will encounter the broad range of decisions that operations

managers must make, and you will be introduced to the tools necessary to handle those deci- sions. This section describes general approaches to decision making, including the use of models, quantitative methods, analysis of trade-offs, establishing priorities, ethics, and the systems approach. Models are often a key tool used by all decision makers.

Models A model is an abstraction of reality, a simplified representation of something. For example, a child’s toy car is a model of a real automobile. It has many of the same visual features (shape, relative proportions, wheels) that make it suitable for the child’s learning and playing. But the toy does not have a real engine, it cannot transport people, and it does not weigh 2,000 pounds.

Other examples of models include automobile test tracks and crash tests; formulas, graphs and charts; balance sheets and income statements; and financial ratios. Common statistical models include descriptive statistics such as the mean, median, mode, range, and standard deviation, as well as random sampling, the normal distribution, and regression equations.

Models are sometimes classified as physical, schematic, or mathematical.

Physical models look like their real-life counterparts. Examples include miniature cars, trucks, airplanes, toy animals and trains, and scale-model buildings. The advantage of these models is their visual correspondence with reality. Schematic models are more abstract than their physical counterparts; that is, they have less resemblance to the physical reality. Examples include graphs and charts, blueprints, pictures, and drawings. The advantage of schematic models is that they are often relatively simple to construct and change. Moreover, they have some degree of visual correspondence. Mathematical models are the most abstract: They do not look at all like their real-life counterparts. Examples include numbers, formulas, and symbols. These models are usually the easiest to manipulate, and they are important forms of inputs for computers and calculators.

The variety of models in use is enormous. Nonetheless, all have certain common features: They are all decision-making aids and simplifications of more complex real-life phenomena. Real life involves an overwhelming amount of detail, much of which is irrelevant for any par- ticular problem. Models omit unimportant details so that attention can be concentrated on the most important aspects of a situation.

Because models play a significant role in operations management decision making, they are heavily integrated into the material of this text. For each model, try to learn (1) its purpose,

LO1.7 Explain the key aspects of operations management decision making.

Model An abstraction of real- ity; a simplified representation of something.

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(2) how it is used to generate results, (3) how these results are interpreted and used, and (4) what assumptions and limitations apply.

The last point is particularly important because virtually every model has an associated set of assumptions or conditions under which the model is valid. Failure to satisfy all of the assumptions will make the results suspect. Attempts to apply the results to a problem under such circumstances can lead to disastrous consequences.

Managers use models in a variety of ways and for a variety of reasons. Models are benefi- cial because they

1. Are generally easy to use and less expensive than dealing directly with the actual situation. 2. Require users to organize and sometimes quantify information and, in the process, often

indicate areas where additional information is needed. 3. Increase understanding of the problem. 4. Enable managers to analyze what-if questions. 5. Serve as a consistent tool for evaluation and provide a standardized format for analyzing

a problem. 6. Enable users to bring the power of mathematics to bear on a problem.

This impressive list of benefits notwithstanding, models have certain limitations of which you should be aware. The following are three of the more important limitations.

1. Quantitative information may be emphasized at the expense of qualitative information. 2. Models may be incorrectly applied and the results misinterpreted. The widespread use

of computerized models adds to this risk because highly sophisticated models may be placed in the hands of users who are not sufficiently knowledgeable to appreciate the subtleties of a particular model; thus, they are unable to fully comprehend the circum- stances under which the model can be successfully employed.

3. The use of models does not guarantee good decisions.

Quantitative Approaches Quantitative approaches to problem solving often embody an attempt to obtain mathematically optimal solutions to managerial problems. Quantitative approaches to decision making in operations management (and in other functional business areas) have been accepted because of calculators and computers capable of handling the required calculations. Computers have had a major impact on operations management. Moreover, the growing availability of software pack- ages for quantitative techniques has greatly increased management’s use of those techniques.

Although quantitative approaches are widely used in operations management decision making, it is important to note that managers typically use a combination of qualitative and quantitative approaches, and many important decisions are based on qualitative approaches.

Performance Metrics Managers use metrics to manage and control operations. There are many metrics in use, including those related to profits, costs, quality, productivity, flexibility, assets, inventories, schedules, and forecast accuracy. As you read each chapter, note the metrics being used and how they are applied to manage operations.

Analysis of Trade-Offs Operations personnel frequently encounter decisions that can be described as trade-off deci- sions. For example, in deciding on the amount of inventory to stock, the decision maker must take into account the trade-off between the increased level of customer service that the addi- tional inventory would yield and the increased costs required to stock that inventory.

Decision makers sometimes deal with these decisions by listing the advantages and disadvantages—the pros and cons—of a course of action to better understand the conse- quences of the decisions they must make. In some instances, decision makers add weights

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to the items on their list that reflect the relative importance of various factors. This can help them “net out” the potential impacts of the trade-offs on their decision.

Degree of Customization A major influence on the entire organization is the degree of customization of products or ser- vices being offered to its customers. Providing highly customized products or services such as home remodeling, plastic surgery, and legal counseling tends to be more labor intensive than providing standardized products such as those you would buy “off the shelf” at a mall store or a supermarket or standardized services such as public utilities and Internet services. Fur- thermore, production of customized products or provision of customized services is generally more time consuming, requires more highly skilled people, and involves more flexible equip- ment than what is needed for standardized products or services. Customized processes tend to have a much lower volume of output than standardized processes, and customized output carries a higher price tag. The degree of customization has important implications for pro- cess selection and job requirements. The impact goes beyond operations and supply chains. It affects marketing, sales, accounting, finance, and information systems.

A Systems Approach A systems viewpoint is almost always beneficial in decision making. Think of it as a “big picture” view. A system can be defined as a set of interrelated parts that must work together. In a business organization, the organization can be thought of as a system composed of subsystems (e.g., marketing subsystem, operations subsystem, finance subsystem), which in turn are composed of lower subsystems. The systems approach emphasizes interrelationships among subsystems, but its main theme is that the whole is greater than the sum of its indi- vidual parts. Hence, from a systems viewpoint, the output and objectives of the organization as a whole take precedence over those of any one subsystem.

A systems approach is essential whenever something is being designed, redesigned, imple- mented, improved, or otherwise changed. It is important to take into account the impact on all parts of the system. For example, if the upcoming model of an automobile will add antilock brakes, a designer must take into account how customers will view the change, instructions for using the brakes, chances for misuse, the cost of producing the new brakes, installation procedures, recycling worn-out brakes, and repair procedures. In addition, workers will need training to make and/or assemble the brakes, production scheduling may change, inventory procedures may have to change, quality standards will have to be established, advertising must be informed of the new features, and parts suppliers must be selected.

Establishing Priorities In virtually every situation, managers discover that certain issues or items are more important than others. Recognizing this enables the managers to direct their efforts to where they will do the most good.

Typically, a relatively few issues or items are very important, so that dealing with those factors will generally have a disproportionately large impact on the results achieved. This well-known effect is referred to as the Pareto phenomenon. This is one of the most important and pervasive concepts in operations management. In fact, this concept can be applied at all levels of management and to every aspect of decision making, both professional and personal.

System A set of interrelated parts that must work together.

Pareto phenomenon A few factors account for a high per- centage of the occurrence of some event(s).

Analytics uses descriptive and predictive models to obtain insight from data and then uses that insight to recommend action or to guide decision making.

Commercial analytics software is available for the challenges of analyzing very large, dynamic data sets, referred to as big data. Analyzing big data presents opportunities for businesses such

READING ANALYTICS

as those that operate transactional online systems that generate massive volumes of data. For example, the McKinsey Global Insti- tute estimates that the U.S. health care system could save $300 billion from analyzing big data.1

1“Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data” The Economist, May 26, 2011.

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1.8 THE HISTORICAL EVOLUTION OF OPERATIONS MANAGEMENT

Systems for production have existed since ancient times. For example, the construction of pyramids and Roman aquaducts involved operations management skills. The production of goods for sale, at least in the modern sense, and the modern factory system had their roots in the Industrial Revolution.

The Industrial Revolution The Industrial Revolution began in the 1770s in England and spread to the rest of Europe and to the United States during the 19th century. Prior to that time, goods were produced in small shops by craftsmen and their apprentices. Under that system, it was common for one person to be responsible for making a product, such as a horse-drawn wagon or a piece of furniture, from start to finish. Only simple tools were available; the machines in use today had not been invented.

Then, a number of innovations in the 18th century changed the face of production forever by substituting machine power for human power. Perhaps the most significant of these was the steam engine, because it provided a source of power to operate machines in factories. Ample supplies of coal and iron ore provided materials for generating power and making machin- ery. The new machines, made of iron, were much stronger and more durable than the simple wooden machines they replaced.

In the earliest days of manufacturing, goods were produced using craft production: highly skilled workers using simple, flexible tools produced goods according to customer specifications.

Craft production had major shortcomings. Because products were made by skilled crafts- men who custom-fitted parts, production was slow and costly. And when parts failed, the replacements also had to be custom made, which was also slow and costly. Another shortcom- ing was that production costs did not decrease as volume increased; there were no economies of scale, which would have provided a major incentive for companies to expand. Instead, many small companies emerged, each with its own set of standards.

A major change occurred that gave the Industrial Revolution a boost: the development of standard gauging systems. This greatly reduced the need for custom-made goods. Factories began to spring up and grow rapidly, providing jobs for countless people who were attracted in large numbers from rural areas.

Despite the major changes that were taking place, management theory and practice had not progressed much from early days. What was needed was an enlightened and more systematic approach to management.

Scientific Management The scientific management era brought widespread changes to the management of factories. The movement was spearheaded by the efficiency engineer and inventor Frederick Winslow Taylor, who is often referred to as the father of scientific management. Taylor believed in a “science of management” based on observation, measurement, analysis and improvement of work methods, and economic incentives. He studied work methods in great detail to identify the best method for doing each job. Taylor also believed that management should be respon- sible for planning, carefully selecting and training workers, finding the best way to perform each job, achieving cooperation between management and workers, and separating manage- ment activities from work activities.

Taylor’s methods emphasized maximizing output. They were not always popular with work- ers, who sometimes thought the methods were used to unfairly increase output without a corre- sponding increase in compensation. Certainly some companies did abuse workers in their quest for efficiency. Eventually, the public outcry reached the halls of Congress, and hearings were held on the matter. Taylor himself was called to testify in 1911, the same year in which his classic book, The Principles of Scientific Management, was published. The publicity from those hear- ings actually helped scientific management principles to achieve wide acceptance in industry.

LO1.8 Briefly describe the historical evolution of operations management.

Craft production System in which highly skilled workers use simple, flexible tools to produce small quantities of customized goods.

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A number of other pioneers also contributed heavily to this movement, including the following.

Frank Gilbreth was an industrial engineer who is often referred to as the father of motion study. He developed principles of motion economy that could be applied to incredibly small portions of a task. Henry Gantt recognized the value of nonmonetary rewards to motivate workers, and developed a widely used system for scheduling, called Gantt charts. Harrington Emerson applied Taylor’s ideas to organization structure and encouraged the use of experts to improve organizational efficiency. He testified in a congressional hearing that railroads could save a million dollars a day by applying principles of scien- tific management. Henry Ford, the great industrialist, employed scientific management techniques in his factories.

During the early part of the 20th century, automobiles were just coming into vogue in the United States. Ford’s Model T was such a success that the company had trouble keeping up with orders for the cars. In an effort to improve the efficiency of operations, Ford adopted the scientific management principles espoused by Frederick Winslow Taylor. He also introduced the moving assembly line, which had a tremendous impact on production methods in many industries.

Among Ford’s many contributions was the introduction of mass production to the auto- motive industry, a system of production in which large volumes of standardized goods are produced by low-skilled or semiskilled workers using highly specialized, and often costly, equipment. Ford was able to do this by taking advantage of a number of important concepts. Perhaps the key concept that launched mass production was interchangeable parts, some- times attributed to Eli Whitney, an American inventor who applied the concept to assembling muskets in the late 1700s. The basis for interchangeable parts was to standardize parts so that any part in a batch of parts would fit any automobile coming down the assembly line. This meant that parts did not have to be custom fitted, as they were in craft production. The stan- dardized parts could also be used for replacement parts. The result was a tremendous decrease in assembly time and cost. Ford accomplished this by standardizing the gauges used to measure parts during production and by using newly developed processes to produce uniform parts.

Mass production System in which low-skilled workers use specialized machinery to produce high volumes of stan- dardized goods.

Interchangeable parts Parts of a product made to such precision that they do not have to be custom fitted.

Row of “Tin Lizzies,” or Model T’s, being manufactured on an early Ford assembly line.

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A second concept used by Ford was the division of labor, which Adam Smith wrote about in The Wealth of Nations (1776). Division of labor means that an operation, such as assem- bling an automobile, is divided up into a series of many small tasks, and individual workers are assigned to one of those tasks. Unlike craft production, where each worker was respon- sible for doing many tasks, and thus required skill, with division of labor the tasks were so narrow that virtually no skill was required.

Together, these concepts enabled Ford to tremendously increase the production rate at his factories using readily available inexpensive labor. Both Taylor and Ford were despised by many workers, because they held workers in such low regard, expecting them to perform like robots. This paved the way for the human relations movement.

The Human Relations Movement Whereas the scientific management movement heavily emphasized the technical aspects of work design, the human relations movement emphasized the importance of the human ele- ment in job design. Lillian Gilbreth, a psychologist and the wife of Frank Gilbreth, worked with her husband, focusing on the human factor in work. (The Gilbreths were the subject of a classic film, Cheaper by the Dozen.) Many of her studies dealt with worker fatigue. In the following decades, there was much emphasis on motivation. Elton Mayo conducted studies at the Hawthorne division of Western Electric. His studies revealed that in addition to the physical and technical aspects of work, worker motivation is critical for improving productiv- ity. Abraham Maslow developed motivational theories, which Frederick Hertzberg refined. Douglas McGregor added Theory X and Theory Y. These theories represented the two ends of the spectrum of how employees view work. Theory X, on the negative end, assumed that workers do not like to work, and have to be controlled—rewarded and punished—to get them to do good work. This attitude was quite common in the automobile industry and in some other industries, until the threat of global competition forced them to rethink that approach. Theory Y, on the other end of the spectrum, assumed that workers enjoy the physical and mental aspects of work and become committed to work. The Theory X approach resulted in an adversarial environment, whereas the Theory Y approach resulted in empowered workers and a more cooperative spirit. William Ouchi added Theory Z, which combined the Japanese approach with such features as lifetime employment, employee problem solving, and con- sensus building, and the traditional Western approach that features short-term employment, specialists, and individual decision making and responsibility.

Decision Models and Management Science The factory movement was accompanied by the development of several quantitative tech- niques. F. W. Harris developed one of the first models in 1915: a mathematical model for inventory order size. In the 1930s, three coworkers at Bell Telephone Labs—H. F. Dodge, H. G. Romig, and W. Shewhart—developed statistical procedures for sampling and quality control. In 1935, L.H.C. Tippett conducted studies that provided the groundwork for statisti- cal sampling theory.

At first, these quantitative models were not widely used in industry. However, the onset of World War II changed that. The war generated tremendous pressures on manufacturing output, and specialists from many disciplines combined efforts to achieve advancements in the military and in manufacturing. After the war, efforts to develop and refine quantitative tools for decision making continued, resulting in decision models for forecasting, inventory management, project management, and other areas of operations management.

During the 1960s and 1970s, management science techniques were highly regarded; in the 1980s, they lost some favor. However, the widespread use of personal computers and user-friendly software in the workplace contributed to a resurgence in the popularity of these techniques.

The Influence of Japanese Manufacturers A number of Japanese manufacturers developed or refined management practices that increased the productivity of their operations and the quality of their products, due in part to the influence of Americans W. Edwards Deming and Joseph Juran. This made them

Division of labor The break- ing up of a production process into small tasks, so that each worker performs a small por- tion of the overall job.

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very competitive, sparking interest in their approaches by companies outside Japan. Their approaches emphasized quality and continual improvement, worker teams and empowerment, and achieving customer satisfaction. The Japanese can be credited with spawning the “quality revolution” that occurred in industrialized countries, and with generating widespread interest in lean production.

The influence of the Japanese on U.S. manufacturing and service companies has been enormous and promises to continue for the foreseeable future. Because of that influence, this book will provide considerable information about Japanese methods and successes.

Table 1.5 provides a chronological summary of some of the key developments in the evolu- tion of operations management.

1.9 OPERATIONS TODAY

Advances in information technology and global competition have had a major influence on operations management. While the Internet offers great potential for business organizations, the potential as well as the risks must be clearly understood in order to determine if and how to exploit this potential. In many cases, the Internet has altered the way companies compete in the marketplace.

Electronic business, or e-business, involves the use of the Internet to transact business. E-business is changing the way business organizations interact with their customers and their suppliers. Most familiar to the general public is e-commerce, consumer–business transactions such as buying online or requesting information. However, business-to-business transactions such as e-procurement represent an increasing share of e-business. E-business is receiving

LO1.9 Describe current issues in business that impact operations management.

E-business The use of elec- tronic technology to facilitate business transactions.

E-commerce Consumer-to- business transactions.

TABLE 1.5 Historical summary of operations management

Approximate Date Contribution/Concept Originator

1776 Division of labor Adam Smith

1790 Interchangeable parts Eli Whitney

1911 Principles of scientific management Frederick W. Taylor

1911 Motion study, use of industrial psychology Frank and Lillian Gilbreth

1912 Chart for scheduling activities Henry Gantt

1913 Moving assembly line Henry Ford

1915 Mathematical model for inventory ordering F. W. Harris

1930 Hawthorne studies on worker motivation Elton Mayo

1935 Statistical procedures for sampling and quality control

H. F. Dodge, H. G. Romig, W. Shewhart, L.H.C. Tippett

1940 Operations research applications in warfare Operations research groups

1947 Linear programming George Dantzig

1951 Commercial digital computers Sperry Univac, IBM

1950s Automation Numerous

1960s Extensive development of quantitative tools Numerous

1960s Industrial dynamics Jay Forrester

1975 Emphasis an manufacturing strategy W. Skinner

1980s Emphasis on flexibility, time-based competi- tion, lean production

T. Ohno, S. Shingo, Toyota

1980s Emphasis on quality W. Edwards Deming, J. Juran, K. Ishikawa

1990s Internet, supply chain management Numerous

2000s Applications service providers and outsourcing Social media, YouTube, and others.

Numerous Numerous

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increased attention from business owners and managers in developing strategies, planning, and decision making.

The word technology has several definitions, depending on the context. Generally, tech- nology refers to the application of scientific knowledge to the development and improve- ment of goods and services. It can involve knowledge, materials, methods, and equipment. The term high technology refers to the most advanced and developed machines and methods. Operations management is primarily concerned with three kinds of technology: product and service technology, process technology, and information technology (IT). All three can have a major impact on costs, productivity, and competitiveness.

Product and service technology refers to the discovery and development of new products and services. This is done mainly by researchers and engineers, who use the scientific approach to develop new knowledge and translate that into commercial applications. Process technology refers to methods, procedures, and equipment used to produce goods and provide services. They include not only processes within an organization but also supply chain processes. Information technology (IT) refers to the science and use of computers and other elec- tronic equipment to store, process, and send information. Information technology is heav- ily ingrained in today’s business operations. This includes electronic data processing, the use of bar codes to identify and track goods, obtaining point-of-sale information, data transmission, the Internet, e-commerce, e-mail, and more.

Management of technology is high on the list of major trends, and it promises to be high well into the future. For example, computers have had a tremendous impact on businesses in many ways, including new product and service features, process management, medical diag- nosis, production planning and scheduling, data processing, and communication. Advances in materials, methods, and equipment also have had an impact on competition and productivity. Advances in information technology also have had a major impact on businesses. Obviously there have been—and will continue to be—many benefits from technological advances. How- ever, technological advance also places a burden on management. For example, management must keep abreast of changes and quickly assess both their benefits and risks. Predicting advances can be tricky at best, and new technologies often carry a high price tag and usually a high cost to operate or repair. And in the case of computer operating systems, as new systems are introduced, support for older versions is discontinued, making periodic upgrades neces- sary. Conflicting technologies can exist that make technological choices even more difficult. Technological innovations in both products and processes will continue to change the way businesses operate, and hence require continuing attention.

The North American Free Trade Agreement (NAFTA) opened borders for trade between the United States and Canada and Mexico. The General Agreement on Tariffs and Trade (GATT) of 1994 reduced tariffs and subsidies in many countries, expanding world trade. The resulting global competition and global markets have had an impact on the strategies and operations of businesses large and small around the world. One effect is the importance busi- ness organizations are giving to management of their supply chains.

Globalization and the need for global supply chains have broadened the scope of supply chain management. However, tightened border security in certain instances has slowed some movement of goods and people. Moreover, in some cases, organizations are reassessing their use of offshore outsourcing.

Competitive pressures and changing economic conditions have caused business organiza- tions to put more emphasis on operations strategy, working with fewer resources, revenue man- agement, process analysis and improvement, quality improvement, agility, and lean production.

During the latter part of the 1900s, many companies neglected to include operations strat- egy in their corporate strategy. Some of them paid dearly for that neglect. Now more and more companies are recognizing the importance of operations strategy on the overall success of their business as well as the necessity for relating it to their overall business strategy.

Working with fewer resources due to layoffs, corporate downsizing, and general cost cut- ting is forcing managers to make trade-off decisions on resource allocation, and to place increased emphasis on cost control and productivity improvement.

Technology The application of scientific knowledge to the development and improve- ment of products and services and operations processes.

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Revenue management is a method used by some companies to maximize the revenue they receive from fixed operating capacity by influencing demand through price manipulation. Also known as yield management, it has been successfully used in the travel and tourism industries by airlines, cruise lines, hotels, amusement parks, and rental car companies, and in other industries such as trucking and public utilities.

Process analysis and improvement includes cost and time reduction, productivity improve- ment, process yield improvement, and quality improvement and increasing customer satisfac- tion. This is sometimes referred to as a Six Sigma process.

Given a boost by the “quality revolution” of the 1980s and 1990s, quality is now ingrained in business. Some businesses use the term total quality management (TQM) to describe their quality efforts. A quality focus emphasizes customer satisfaction and often involves team- work. Process improvement can result in improved quality, cost reduction, and time reduc- tion. Time relates to costs and to competitive advantage, and businesses seek ways to reduce the time to bring new products and services to the marketplace to gain a competitive edge. If two companies can provide the same product at the same price and quality, but one can deliver it four weeks earlier than the other, the quicker company will invariably get the sale. Time reductions are being achieved in many companies now. Union Carbide was able to cut $400 million of fixed expenses, and Bell Atlantic was able to cut the time needed to hook up long-distance carriers from 15 days to less than 1, at a savings of $82 million.

Agility refers to the ability of an organization to respond quickly to demands or oppor- tunities. It is a strategy that involves maintaining a flexible system that can quickly respond to changes in either the volume of demand or changes in product/service offerings. This is particularly important as organizations scramble to remain competitive and cope with increas- ingly shorter product life cycles and strive to achieve shorter development times for new or improved products and services.

Lean production, a new approach to production, emerged in the 1990s. It incorporates a number of the recent trends listed here, with an emphasis on quality, flexibility, time reduc- tion, and teamwork. This has led to a flattening of the organizational structure, with fewer levels of management.

Lean systems are so named because they use much less of certain resources than typi- cal mass production systems use—space, inventory, and workers—to produce a comparable amount of output. Lean systems use a highly skilled workforce and flexible equipment. In effect, they incorporate advantages of both mass production (high volume, low unit cost) and craft production (variety and flexibility). And quality is higher than in mass production. This approach has now spread to services, including health care, offices, and shipping and delivery.

The skilled workers in lean production systems are more involved in maintaining and improving the system than their mass production counterparts. They are taught to stop an operation if they discover a defect, and to work with other employees to find and correct the cause of the defect so that it won’t recur. This results in an increasing level of quality over time and eliminates the need to inspect and rework at the end of the line.

Because lean production systems operate with lower amounts of inventory, additional emphasis is placed on anticipating when problems might occur before they arise and avoiding

Six Sigma A process for reducing costs, improving quality, and increasing cus- tomer satisfaction.

Agility The ability of an orga- nization to respond quickly to demands or opportunities.

Lean system System that uses minimal amounts of resources to produce a high volume of high-quality goods with some variety.

There is a huge demand in the United States and elsewhere for affordable women’s clothing. Low-cost clothing retailers such as Spain’s Zara and Sweden’s H&M are benefiting from their ability to quickly get mass-produced, trendy new fashions to store shelves while some less-agile competitors like Macy’s and Gap struggle to achieve the same results. A key factor for the agile retailers is their nearness to low-cost producers in Romania and Turkey, which greatly shortens transportation time. American retailers often source from China, but increasing wages there and the longer

READING AGILITY CREATES A COMPETITIVE EDGE

distance lessen their ability to take advantage of quickly introduc- ing new low-cost fashions.

Questions What possible solutions do you see for competitors such as Macy’s and Gap?

Source: Based on Roya Wolverson, “Need for Speed: Glamorizing Cheap Fashion Costs More than You Think,” Time, August 6, 2012, p. 18.

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those problems through planning. Even so, problems can still occur at times, and quick reso- lution is important. Workers participate in both the planning and correction stages.

Compared to workers in traditional systems, much more is expected of workers in lean production systems. They must be able to function in teams, playing active roles in operating and improving the system. Individual creativity is much less important than team success. Responsibilities also are much greater, which can lead to pressure and anxiety not present in traditional systems. Moreover, a flatter organizational structure means career paths are not as steep in lean production organizations. Workers tend to become generalists rather than specialists, another contrast to more traditional organizations.

1.10 KEY ISSUES FOR TODAY’S BUSINESS OPERATIONS

There are a number of issues that are high priorities of many business organizations. Although not every business is faced with these issues, many are. Chief among the issues are the following.

Economic conditions. The lingering recession and slow recovery in various sectors of the economy has made managers cautious about investment and rehiring workers who had been laid off during the recession. Innovating. Finding new or improved products or services are only two of the many possibilities that can provide value to an organization. Innovations can be made in pro- cesses, the use of the Internet, or the supply chain that reduce costs, increase productivity, expand markets, or improve customer service. Quality problems. The numerous operations failures mentioned at the beginning of the chapter underscore the need to improve the way operations are managed. That relates to product design and testing, oversight of suppliers, risk assessment, and timely response to potential problems. Risk management. The need for managing risk is underscored by recent events that include financial crises, product recalls, accidents, natural and man-made disasters, and economic ups and downs. Managing risks starts with identifying risks, assessing vulner- ability and potential damage (liability costs, reputation, demand), and taking steps to reduce or share risks. Cyber-security. The need to guard against intrusions from hackers whose goal is to steal personal information of employees and customers is becoming increasingly neces- sary. Moreover, interconnected systems increase intrusion risks in the form of industrial espionage. Competing in a global economy. Low labor costs in third-world countries have increased pressure to reduce labor costs. Companies must carefully weigh their options, which include outsourcing some or all of their operations to low-wage areas, reducing costs internally, changing designs, and working to improve productivity.

Three other key areas require more in-depth discussion: environmental concerns, ethical con- duct, and managing the supply chain.

Environmental Concerns Concern about global warming and pollution has had an increasing effect on how businesses operate.

Stricter environmental regulations, particularly in developed nations, are being imposed. Furthermore, business organizations are coming under increasing pressure to reduce their carbon footprint (the amount of carbon dioxide generated by their operations and their sup- ply chains) and to generally operate sustainable processes. Sustainability refers to service

Sustainability Using resources in ways that do not harm ecological systems that support human existence.

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Puma’s “Clever Little Bag” changes the idea of the shoebox by wrapping footwear in a cardboard structure with 65 percent less cardboard. It uses a bag made of recycled plastic as the outer layer that holds the inner cardboard structure together. Puma expects to cut carbon dioxide emissions by 10,000 tons per year and water, energy, and diesel use by 60 percent by using fewer materials—8,500 fewer tons of paper to be specific—and the new packaging’s lighter weight.

Universities and colleges are increasingly embracing sustain- ability, linking it to global warming, biodiversity, and global com- merce. Some are building sustainability into existing courses, while others are offering new courses, certificate programs, or degree programs. And some, such as Arizona State University and the Rochester Institute of Technology, are offering advanced degree programs.

Some universities are also “practicing what they preach,” by applying sustainable practices in their operations. Among them

READING UNIVERSITIES EMBRACE SUSTAINABILITY

are Dartmouth College, Harvard University, Stanford, Williams Col- lege, and the University of British Columbia, which was named by the environmental magazine Grist as one of the top 15 universi- ties in the world in reducing greenhouse gas emissions and being energy efficient.

Source: Based on “The Sustainable University: Saving the Planet by Degrees,” Chronicle of Higher Education, Special Report, October 20, 2006, Stanford News Service, January 2007, and “B.C.’s School of Greener Learning,” Toronto Globe and Mail, August 25, 2007, p. A6.

and production processes that use resources in ways that do not harm ecological systems that support both current and future human existence. Sustainability measures often go beyond traditional environmental and economic measures to include measures that incorporate social criteria in decision making.

All areas of business will be affected by this. Areas that will be most affected include product and service design, consumer education programs, disaster preparation and response, supply chain waste management, and outsourcing decisions. Note that outsourcing of goods production increases not only transportation costs, but also fuel consumption and carbon released into the atmosphere. Consequently, sustainability thinking may have implications for outsourcing decisions.

Because they all fall within the realm of operations, operations management is central to dealing with these issues. Sometimes referred to as “green initiatives,” the possibilities include reducing packaging, materials, water and energy use, and the environmental impact of the supply chain, including buying locally. Other possibilities include reconditioning used equipment (e.g., printers and copiers) for resale, and recycling.

The following reading suggests that even our choice of diet can affect the environment.

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The Fair Trade Certified™ label guarantees to consumers that strict economic, social, and environmental criteria were met in the production and trade of an agricultural product.

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Ethical Conduct The need for ethical conduct in business is becoming increasingly obvious, given numerous examples of questionable actions in recent history. In making decisions, managers must con- sider how their decisions will affect shareholders, management, employees, customers, the community at large, and the environment. Finding solutions that will be in the best interests of all of these stakeholders is not always easy, but it is a goal that all managers should strive to achieve. Furthermore, even managers with the best intentions will sometimes make mistakes. If mistakes do occur, managers should act responsibly to correct those mistakes as quickly as possible, and to address any negative consequences.

Many organizations have developed codes of ethics to guide employees’ or members’ con- duct. Ethics is a standard of behavior that guides how one should act in various situations. The Markula Center for Applied Ethics at Santa Clara University identifies five principles for thinking ethically:

• The Utilitarian Principle: The good done by an action or inaction should outweigh any harm it causes or might cause. An example is not allowing a person who has had too much to drink to drive.

• The Rights Principle: Actions should respect and protect the moral rights of others. An example is not taking advantage of a vulnerable person.

• The Fairness Principle: Equals should be held to, or evaluated by, the same standards. An example is equal pay for equal work.

• The Common Good Prin- ciple: Actions should contrib- ute to the common good of the community. An example is an ordinance on noise abatement.

• The Virtue Principle: Actions should be consistent with cer- tain ideal virtues. Examples include honesty, compassion, generosity, tolerance, fidelity, integrity, and self-control.

The center expands these princi- ples to create a framework for ethical

Ethics A standard of behavior that guides how one should act in various situations.

It is interesting to examine the environmental impact of dietary choices. There’s ample evidence that agricultural practices pol- lute the soil, air, and water. Factors range from the distance food travels to get to the consumer, to the amount of water and fertil- izer used. Of particular concern is the environmental impact of a diet high in animal protein. The Food and Agricultural Organization (FAO) of the United Nations recently reported that livestock pro- duction is one of the major causes of global warming and air and water pollution. Using a methodology that considers the entire supply chain, the FAO estimated that livestock accounts for 18 per- cent of greenhouse gas emissions.

A Vegetarian versus Nonvegetarian Diet and the Environ- ment The eco-friendliness of a meat eater’s diet was the subject

READING DIET AND THE ENVIRONMENT: VEGETARIAN VS. NONVEGETARIAN

of a study conducted by researchers from the Departments of Environmental Health and Nutrition of Loma Linda University in California. They compared the environmental effects of a vege- tarian vs. nonvegetarian diet in California in terms of agricultural production inputs, including pesticides and fertilizers, water and energy.

The study indicated that in the combined production of 11 food items the nonvegetarian diet required 2.9 times more water, 2.5 times more primary energy, 13 times more fertilizer, and 1.4 times more pesticides than the vegetarian diet. The greatest differences stemmed from including beef in the diet. Source: Based on “Finding a Scientific Connection Between Food Choices and the Environment,” Environmental Nutrition Newsletter, October 2009, p. 3.

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conduct. An ethical framework is a sequence of steps intended to guide thinking and subse- quent decisions or actions. Here is the one developed by the Markula Center for Applied Ethics:

1. Recognize an ethical issue by asking if an action could be damaging to a group or an individual. Is there more to it than just what is legal?

2. Make sure the pertinent facts are known, such as who will be impacted, and what options are available.

3. Evaluate the options by referring to the appropriate preceding ethical principle. 4. Identify the “best” option and then further examine it by asking how someone you

respect would view it. 5. In retrospect, consider the effect your decision had and what you can learn from it.

More detail is available at the Center’s website: http://www.scu.edu/ethics/practicing/deci- sion/framework.html.

Operations managers, like all managers, have the responsibility to make ethical decisions. Ethical issues arise in many aspects of operations management, including:

• Financial statements: accurately representing the organization’s financial condition. • Worker safety: providing adequate training, maintaining equipment in good working

condition, maintaining a safe working environment. • Product safety: providing products that minimize the risk of injury to users or damage to

property or the environment. • Quality: honoring warranties, avoiding hidden defects. • The environment: not doing things that will harm the environment. • The community: being a good neighbor. • Hiring and firing workers: avoiding false pretenses (e.g., promising a long-term job

when that is not what is intended). • Closing facilities: taking into account the impact on a community, and honoring

commitments that have been made. • Workers’ rights: respecting workers’ rights, dealing with workers’ problems quickly and fairly.

The Ethisphere Institute recognizes companies worldwide for their ethical leadership. Here are some samples from their list:

Apparel: Gap Automotive: Ford Motor Company Business services: Paychex Café: Starbucks Computer hardware: Intel Computer software: Adobe Systems, Microsoft Consumer electronics: Texas Instruments, Xerox E-commerce: eBay General retail: Costco, Target Groceries: Safeway, Wegmans, Whole Foods Health and beauty: L’Oreal Logistics: UPS

You can see a complete list of recent recipients and the selection criteria at Ethisphere.com.

The Need to Manage the Supply Chain Supply chain management is being given increasing attention as business organizations face mounting pressure to improve management of their supply chains. In the past, most organiza- tions did little to manage their supply chains. Instead, they tended to concentrate on their own

Ethical framework A sequence of steps intended to guide thinking and subse- quent decision or action.

LO1.10 Explain the need to manage the supply chain.

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operations and on their immediate suppliers. Moreover, the planning, marketing, production and inventory management functions in organizations in supply chains have often operated independently of each other. As a result, supply chains experienced a range of problems that were seemingly beyond the control of individual organizations. The problems included large oscillations of inventories, inventory stockouts, late deliveries, and quality problems. These and other issues now make it clear that management of supply chains is essential to business success. The other issues include the following:

1. The need to improve operations. Efforts on cost and time reduction, and productivity and quality improvement, have expanded in recent years to include the supply chain. Opportu- nity now lies largely with procurement, distribution, and logistics—the supply chain.

2. Increasing levels of outsourcing. Organizations are increasing their levels of outsourcing, buying goods or services instead of producing or providing them them- selves. As outsourcing increases, organizations are spending increasing amounts on supply-related activities (wrapping, packaging, moving, loading and unloading, and sorting). A significant amount of the cost and time spent on these and other related activities may be unnecessary. Issues with imported products, including tainted food products, toothpaste, and pet foods, as well as unsafe tires and toys, have led to questions of liability and the need for companies to take responsibility for monitoring the safety of outsourced goods.

3. Increasing transportation costs. Transportation costs are increasing, and they need to be more carefully managed.

4. Competitive pressures. Competitive pressures have led to an increasing number of new products, shorter product development cycles, and increased demand for customization. And in some industries, most notably consumer electronics, product life cycles are rela- tively short. Added to this are adoption of quick-response strategies and efforts to reduce lead times.

5. Increasing globalization. Increasing globalization has expanded the physical length of supply chains. A global supply chain increases the challenges of managing a supply chain. Having far-flung customers and/or suppliers means longer lead times and greater opportunities for disruption of deliveries. Often currency differences and monetary fluc- tuations are factors, as well as language and cultural differences. Also, tightened border security in some instances has slowed shipments of goods.

6. Increasing importance of e-business. The increasing importance of e-business has added new dimensions to business buying and selling and has presented new challenges.

7. The complexity of supply chains. Sup- ply chains are complex; they are dynamic, and they have many inherent uncertainties that can adversely affect them, such as inaccurate forecasts, late deliveries, sub- standard quality, equipment breakdowns, and canceled or changed orders.

8. The need to manage inventories. Inven- tories play a major role in the success or failure of a supply chain, so it is important to coordinate inventory levels throughout a supply chain. Shortages can severely disrupt the timely flow of work and have far-reaching impacts, while excess invento- ries add unnecessary costs. It would not be unusual to find inventory shortages in some parts of a supply chain and excess invento- ries in other parts of the same supply chain.

outsourcing Buying goods or services instead of producing or providing them in-house.

In Kachchh, India, Fairtrade allows cotton farmers to have the assurance of a minimum price, which gives them more security to plan their business and invest in their communities.

Courtesy of Marks & Spencer

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Elements of Supply Chain Management Supply chain management involves coordinating activities across the supply chain. Central to this is taking customer demand and translating it into corresponding activities at each level of the supply chain.

The key elements of supply chain management are listed in Table 1.6. The first element, customers, is the driving element. Typically, marketing is responsible for determining what customers want as well as forecasting the quantities and timing of customer demand. Product and service design must match customer wants with operations capabilities.

Processing occurs in each component of the supply chain: it is the core of each organization. The major portion of processing occurs in the organization that produces the product or service for the final customer (the organization that assembles the computer, services the car, etc.). A major aspect of this for both the internal and external portions of a supply chain is scheduling.

Inventory is a staple in most supply chains. Balance is the main objective; too little causes delays and disrupts schedules, but too much adds unnecessary costs and limits flexibility.

Purchasing is the link between an organization and its suppliers. It is responsible for obtaining goods and/or services that will be used to produce products or provide services for the organization’s customers. Purchasing selects suppliers, negotiates contracts, establishes alliances, and acts as liaison between suppliers and various internal departments.

The supply portion of a value chain is made up of one or more suppliers, all links in the chain, and each one capable of having an impact on the effectiveness—or the ineffectiveness— of the supply chain. Moreover, it is essential that the planning and execution be carefully coor- dinated between suppliers and all members of the demand portion of their chains.

Location can be a factor in a number of ways. Where suppliers are located can be important, as can location of processing facilities. Nearness to market, nearness to sources of supply, or nearness to both may be critical. Also, delivery time and cost are usually affected by location.

Two types of decisions are relevant to supply chain management—strategic and opera- tional. The strategic decisions are the design and policy decisions. The operational decisions relate to day-to-day activities: managing the flow of material and product and other aspects of the supply chain in accordance with strategic decisions.

The major decision areas in supply chain management are location, production, distribu- tion, and inventory. The location decision relates to the choice of locations for both produc- tion and distribution facilities. Production and transportation costs and delivery lead times are important. Production and distribution decisions focus on what customers want, when they want it, and how much is needed. Outsourcing can be a consideration. Distribution decisions are strongly influenced by transportation cost and delivery times, because transportation costs

TABLE 1.6 Elements of supply chain management

Element Typical Issues Chapter(s)

Customers Determining what products and/or services customers want 3, 4

Forecasting Predicting the quantity and timing of customer demand 3

Design Incorporating customers, wants, manufacturability, and time to market 4

Capacity planning Matching supply and demand 5, 11

Processing Controlling quality, scheduling work 10, 16

Inventory Meeting demand requirements while managing the costs of holding inventory

12, 13, 14

Purchasing Evaluating potential suppliers, supporting the needs of operations on purchased goods and services

Suppliers Monitoring supplier quality, on-time delivery, and flexibility; maintaining supplier relations

Location Determining the location of facilities 8

Logistics Deciding how to best move information and materials 15

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often represent a significant portion of total cost. Moreover, shipping alternatives are closely tied to production and inventory decisions. For example, using air transport means higher costs but faster deliveries and less inventory in transit than sea, rail, or trucking options. Dis- tribution decisions must also take into account capacity and quality issues. Operational deci- sions focus on scheduling, maintaining equipment, and meeting customer demand. Quality control and workload balancing are also important considerations. Inventory decisions relate to determining inventory needs and coordinating production and stocking decisions through- out the supply chain. Logistics management plays the key role in inventory decisions.

Enterprise Resource Planning (ERP) is being increasingly used to provide information sharing in real time among organizations and their major supply chain partners. This impor- tant topic is discussed in more detail in Chapter 12.

Operations Tours Throughout the book you will discover operations tours that describe operations in all sorts of companies. The tour you are about to read is Wegmans Food Markets, a major regional super- market chain. Wegmans has been consistently ranked high on Fortune magazine’s list of the 100 Best Companies to Work For since the inception of the survey a decade ago.

Wegmans Food Markets, Inc., is one of the premier grocery chains in the United States. Headquartered in Rochester, New York, Wegmans operates about 100 stores, mainly in Rochester, Buffalo, and Syracuse. There are also a handful of stores else- where in New York State as well as in New Jersey, Massachusetts, North Carolina, Pennsylvania, and Virginia. The company employs over 45,000 people, and has annual sales of over $3 billion.

Wegmans has a strong reputation for offering its customers high product quality and excellent service. Through a combination of market research, trial and error, and listening to its customers, Wegmans has evolved into a very successful organization. Its sales per square foot are 50 percent higher than the industry average.

Superstores Many of the company’s stores are giant 100,000-square-foot superstores, double or triple the size of average supermarkets. You can get an idea about the size of these stores from this: they usually have between 25 and 35 checkout lanes, and during busy periods, all of the checkouts are in operation. A superstore typi- cally employs from 500 to 600 people.

Individual stores differ somewhat in terms of actual size and some special features. Aside from the features normally found in supermarkets, they generally have a full-service deli (typically a 40-foot display case), a 500-square-foot fisherman’s wharf that has perhaps 10 different fresh fish offerings most days, a large bakery section (each store bakes its own bread, rolls, cakes, pies, and pastries), and extra-large produce sections. They also offer

WEGMANS FOOD MARKETSOPERATIONS TOUR

film processing, a complete pharmacy, a card shop, video rentals, and an Olde World Cheese section. In-store floral shops range in size up to 800 square feet of floor space and offer a wide variety of fresh-cut flowers, flower arrangements, vases, and plants. In- store card shops cover over 1,000 square feet of floor space. The bulk foods department provides customers with the opportunity to select the quantities they desire from a vast array of foodstuffs and some nonfood items such as birdseed and pet food.

Each store is a little different. Among the special features in some stores are a dry cleaning department, a wokery, and a salad bar. Some stores feature a Market Café that has different food sta- tions, each devoted to preparing and serving a certain type of food. For example, one station will have pizza and other Italian special- ties, and another oriental food, and still another chicken or fish. There also will be a sandwich bar, a salad bar, and a dessert sta- tion. Customers often wander among stations as they decide what to order. In some Market Cafés, diners can have wine with their meals and have brunch on Sundays. In several affluent locations, customers can stop in on their way home from work and choose from a selection of freshly prepared dinner entrees such as medal- lions of beef with herb butter, chicken Marsala, stuffed flank steak with mushrooms, Cajun tuna, crab cakes, and accompaniments such as roasted red potatoes, grilled vegetables, and Caesar salad. Many Wegmans stores offer ready-made sandwiches as well as made-to-order sandwiches. Some stores have a coffee-shop section with tables and chairs where shoppers can enjoy regular or specialty coffees and a variety of tempting pastries.

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Produce Department The company prides itself on fresh produce. Produce is replen- ished as often as 12 times a day. The larger stores have produce sections that are four to five times the size of a produce sec- tion in an average supermarket. Wegmans offers locally grown produce in season. Wegmans uses a “farm to market” system whereby some local growers deliver their produce directly to indi- vidual stores, bypassing the main warehouse. That reduces the company’s inventory holding costs and gets the produce into the stores as quickly as possible. Growers may use specially designed containers that go right onto the store floor instead of large bins. This avoids the bruising that often occurs when fruits and vegeta- bles are transferred from bins to display shelves and the need to devote labor to transfer the produce to shelves.

Meat Department In addition to large display cases of both fresh and frozen meat products, many stores have a full-service butcher shop that offers a variety of fresh meat products and where butchers are available to provide customized cuts of meat for customers.

Meat department employees attend Wegmans’ “Meat Uni- versity,” where they learn about different cuts of meat and how to best prepare them. They also learn about other items to pair with various meats, and suggest side dishes, breads, and wine. This helps instill a “selling culture” among employees, who often spend 75 percent of their time talking with customers.

Wegmans continually analyzes store operations to improve processes. In the meat department, a change from in-store cutting and traditional packaging to using a centralized meat processing facility and vacuum packaging extended the shelf life of meats

and reduced staffing requirements in meat departments, reducing costs and providing customers with an improved product.

Ordering Each department handles its own ordering. Although sales records are available from records of items scanned at the checkouts, they are not used directly for replenishing stock. Other factors—such as pricing, special promotions, and local circumstances (e.g., festivals, weather conditions)—must all be taken into account. However, for seasonal periods, such as holidays, managers often check scanner records to learn what past demand was during a comparable period.

The superstores typically receive one truckload of goods per day from the main warehouse. During peak periods, a store may receive two truckloads from the main warehouse. The short lead time greatly reduces the length of time an item might be out of stock, unless the main warehouse is also out of stock.

The company exercises strict control over suppliers, insisting on product quality and on-time deliveries.

Inventory Management Some stores carry as many as 70,000 individual units. Wegmans uses a companywide system to keep track of inventory. Depart- ments take a monthly inventory count to verify the amount shown in the companywide system. Departments receive a periodic report indicating how many days of inventory the department has on hand. Having an appropriate amount on hand is important to department managers: If they have too much inventory on hand, that will add to their department’s costs, whereas having too little inventory will result in shortages and thus lost sales and dissatis- fied customers.

Benjamin C. Tankersley/For The Washington Post via Getty Images

Suzanne Kreiter/The Boston Globe via Getty Images

Wegmans’ Patisserie is an authentic French pastry shop.

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Employees The company recognizes the value of good employees. It typically invests an average of $7,000 to train each new employee. In addi- tion to learning about store operations, new employees learn the importance of good customer service and how to provide it. The employees are helpful, cheerfully answering customer questions or handling complaints. Employees are motivated through a com- bination of compensation, profit sharing, and benefits. Employee turnover for full-time workers is about 6 percent, compared to the industry average of about 20 percent.

Quality Quality and customer satisfaction are utmost in the minds of Weg- mans’ management and its employees. Private-label food items as well as name brands are regularly evaluated in test kitchens, along with potential new products. Managers are responsible for checking and maintaining product and service quality in their departments. Moreover, employees are encouraged to report problems to their managers.

If a customer is dissatisfied with an item, and returns it, or even a portion of the item, the customer is offered a choice of a replacement or a refund. If the item is a Wegmans brand food item, it is then sent to the test kitchen to determine the cause of the problem. If the cause can be determined, corrective action is taken.

Technology Wegmans continues to adopt new technologies to maintain its competitive edge, including new approaches to tracking inventory and managing its supply chain, and new ways to maintain fresh- ness in the meat and produce departments.

Sustainability Wegmans began replacing incandescent light bulbs with compact fluorescent bulbs back in 2007, generating 3,000 fewer tons of car- bon dioxide each year. Also the company installed sensors in its dairy cases that reduced the time the cooling systems run by 50 percent.

Questions 1. How do customers judge the quality of a supermarket? 2. Indicate how and why each of these factors is important to the

successful operation of a supermarket: a. Customer satisfaction b. Forecasting c. Capacity planning d. Location e. Inventory management f. Layout of the store g. Scheduling 3. What are some of the ways Wegmans uses technology to gain

an edge over its competition?

Robert A. Reeder/The Washington Post/Getty Images

Fresh seafood is delivered daily, often direct from boat to store the same day it was caught.

Mark Gail/The Washington Post via Getty Images

Wegmans’ chefs prepare ready-to-eat entrees, side dishes, salads, sandwiches, and ready-to-heat entrees.

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The operations function in business organizations is responsible for producing goods and providing services. It is a core function of every business. Supply chains are the sequential system of suppliers and customers that begins with basic sources of inputs and ends with final customers of the system. Operations and supply chains are interdependent—one couldn’t exist without the other, and no business organization could exist without both.

Operations management involves system design and operating decisions related to product and ser- vice design, capacity planning, process selection, location selection, work management, inventory and supply management, production planning, quality assurance, scheduling, and project management.

The historical evolution of operations management provides interesting background information on the continuing evolution of this core business function.

The Operations Tours and Readings included in this and subsequent chapters provide insights into actual business operations.

SUMMARY

KEY POINTS 1. The operations function is that part of every business organization that produces products and/or

delivers services. 2. Operations consists of processes that convert inputs into outputs. Failure to manage those pro-

cesses effectively will have a negative impact on the organization. 3. A key goal of business organizations is to achieve an economic matching of supply and

demand. The operations function is responsible for providing the supply or service capacity for expected demand.

4. All processes exhibit variation that must be managed. 5. Although there are some basic differences between services and products that must be taken into

account from a managerial standpoint, there are also many similarities between the two. 6. Environmental issues will increasingly impact operations decision making. 7. Ethical behavior is an integral part of good management practice. 8. All business organizations have, and are part of, a supply chain that must be managed.

KEY TERMS agility 26 craft production 21 division of labor 23 e-business 24 e-commerce 24 ethical framework 30 ethics 29 goods 4

interchangeable parts 22 lead time 11 lean system 26 mass production 22 model 18 operations management 4 outsourcing 31 pareto phenomenon 20

process 13 services 4 six sigma 26 supply chain 4 sustainability 27 system 20 technology 25 value-added 6

DISCUSSION AND REVIEW QUESTIONS

1. Briefly describe the terms operations management and supply chain. 2. Identify the three major functional areas of business organizations and briefly describe how they

interrelate. 3. Describe the operations function and the nature of the operations manager’s job. 4. List five important differences between goods production and service operations; then list five

important similarities. 5. Briefly discuss each of these terms related to the historical evolution of operations management:

a. Industrial Revolution b. Scientific management c. Interchangeable parts d. Division of labor

6. Why are services important? Why is manufacturing important? What are nonmanufactured goods?

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a. Matching supply and demand b. Managing a supply chain

13. List and briefly explain the four basic sources of variation, and explain why it is important for managers to be able to effectively deal with variation.

CRITICAL THINKING EXERCISES

This item also will appear in every chapter. It allows you to critically apply information you learned in the chapter to a practical situation. Here is the first set of exercises:

3. One way that organizations compete is through technological innovation. However, there can be downsides for both the organization and the consumer. Explain.

4. a. What would cause a business person to make an unethical decision? b. What are the risks of doing so?

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SELECTED BIBLIOGRAPHY & FURTHER READINGS

Bloomberg Businessweek

Bowie, Norman E., ed. The Blackwell Guide to Busi- ness Ethics. Malden, MA: Blackwell, 2002.

Fitzsimmons, James, and Mona Fitzsimmons. Service Management, 4th ed. New York: McGraw-Hill/ Irwin, 2011.

Fortune magazine.

Womack, James P., Daniel Jones, and Daniel Roos. The Machine That Changed the World. New York: Harper Perennial, 1991, 2007.

Wisner, Joel D., and Linda L. Stanley. Process Man- agement: Creating Value Along the Supply Chain. Mason, OH: Thomson South-Western, 2008.

Hazel had worked for the same Fortune 500 company for almost 15 years. Although the company had gone through some tough times, things were starting to turn around. Customer orders were up, and quality and productivity had improved dramatically from what they had been only a few years earlier due to a company- wide quality improvement program. So it came as a real shock to Hazel and about 400 of her coworkers when they were sud- denly terminated following the new CEO’s decision to downsize the company.

After recovering from the initial shock, Hazel tried to find employment elsewhere. Despite her efforts, after eight months of searching she was no closer to finding a job than the day she started. Her funds were being depleted and she was getting more discouraged. There was one bright spot, though: She was able to bring in a little money by mowing lawns for her neighbors. She got involved quite by chance when she heard one neighbor remark that now that his children were on their own, nobody was around to cut the grass. Almost jokingly, Hazel asked him how much he’d be willing to pay. Soon Hazel was mowing the lawns of five neigh- bors. Other neighbors wanted her to work on their lawns, but she didn’t feel that she could spare any more time from her job search.

However, as the rejection letters began to pile up, Hazel knew she had to make a decision. On a sunny Tuesday morning, she decided, like many others in a similar situation, to go into business for herself—taking care of neighborhood lawns. She was relieved to give up the stress of job hunting, and she was excited about the prospect of being her own boss. But she was also fearful of being completely on her own. Nevertheless, Hazel was determined to make a go of it.

At first, business was a little slow, but once people realized Hazel was available, many asked her to take care of their lawns. Some people were simply glad to turn the work over to her; others switched from professional lawn care services. By the end of her first year in business, Hazel knew she could earn a living this way. She also performed other services such as fertilizing lawns, weed- ing gardens, and trimming shrubbery. Business became so good that Hazel hired two part-time workers to assist her and, even then, she believed she could expand further if she wanted to.

Questions 1. Hazel is the operations manager of her business. Among

her responsibilities are forecasting, inventory management, scheduling, quality assurance, and maintenance.

a. What kinds of things would likely require forecasts? b. What inventory items does Hazel probably have? Name one

inventory decision she has to make periodically. c. What scheduling must she do? What things might occur to

disrupt schedules and cause Hazel to reschedule? d. How important is quality assurance to Hazel’s business? Explain. e. What kinds of maintenance must be performed? 2. In what ways are Hazel’s customers most likely to judge the

quality of her lawn care services? 3. What are some of the trade-offs that Hazel probably consid-

ered relative to: a. Working for a company instead of for herself? b. Expanding the business? c. Launching a website? 4. The town is considering an ordinance that would prohibit

putting grass clippings at the curb for pickup because local landfills cannot handle the volume. What options might Hazel consider if the ordinance is passed? Name two advantages and two drawbacks of each option.

5. Hazel decided to offer the students who worked for her a bonus of $25 for ideas on how to improve the business, and they provided several good ideas. One idea that she initially rejected now appears to hold great promise. The student who proposed the idea has left, and is currently working for a com- petitor. Should Hazel send that student a check for the idea? What are the possible trade-offs?

6. All managers have to cope with variation. a. What are the major sources of variation that Hazel has to

contend with? b. How might these sources of variation impact Hazel’s ability

to match supply and demand? c. What are some ways she can cope with variation? 7. Hazel is thinking of making some of her operations sustain-

able. What are some ideas she might consider?

CASE HAZEL

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Here is a procedure that will help you solve most of the end-of-chapter problems in this book and on exams:

1. Identify the question to be answered. This is critical. 2. Summarize the information given in the problem statement using the appropriate symbols. 3. Determine what type of problem it is so you can select the appropriate problem-solving tools

such as a formula or table. Check your notes from class, chapter examples, and the Solved Prob- lems section of the chapter, and any preceding chapter problems you have already solved for guidance.

4. Solve the problem and indicate your answer.

Example 1

PROBLEM-SOLVING GUIDE

Department A can produce parts at a rate of 50/day. Department B uses those parts are the rate of 10/day. Each day unused parts are added to inventory. At what rate does the inventory of unused parts build up?

Solution 1. The question to be answered: At what rate does inventory of unused parts build up (i.e., increase) per day?

The given information: Production rate = 50 parts/day Usage rate = 10 parts/day

3. For this simple problem, no formula or table is needed. Inventory buildup is simply the difference between the production and usage rates.

Production rate

50 parts/day

Usage rate = 10 parts/day Inventory buildup

40 parts/day

Example 2Companies often use this formula to determine how much of a certain item to order: Q = √

____ 2DS ____

where

Q = order quantity D = annual demand S = ordering cost H = annual holding cost per unit

If annual demand is 400 units, ordering cost is $36, and annual holding cost is $2 per unit, what is the order quantity?

Solution 1. The question to be answered: What is the order quantity, Q? 2. The information given in the problem: D = 400 units/year, S = $36, H = $2 per year 3. To solve the problem, substitute the values given in the problem into the formula. 4. Solution:

Q = √ _______________

2 ( 400 units / yr . ) $36 ______________

$2 / unit / yr. = 120 units

Problem-Solving Template

Problem number:

The question to be answered:

Information given:

Solve using:

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2.1 Introduction, 41

2.2 Competitiveness, 42 Why Some Organizations Fail, 43

2.3 Mission and Strategies, 44 Strategies and Tactics, 45 Strategy Formulation, 46 Supply Chain Strategy, 50 Sustainability Strategy, 50 Global Strategy, 50

2.4 Operations Strategy, 51 Strategic Operations Management Decision Areas, 52

Quality and Time Strategies, 53

2.5 Implications of Organization Strategy for Operations Management, 54

2.6 Transforming Strategy into Action: The Balanced Scorecard, 54

2.7 Productivity, 56 Computing Productivity, 57 Productivity in the Service Sector, 59 Factors That Affect Productivity, 60

Improving Productivity, 61

Cases: An American Tragedy: How a Good Company Died, 67

Home-Style Cookies, 68 Hazel Revisited, 69 “Your Garden Gloves,” 70

Operations Tour: The U.S. Postal Service, 70

Competitiveness, Strategy, and Productivity

L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO2.1 List several ways that business organizations compete.

LO2.2 Name several reasons that business organizations fail.

LO2.3 Define the terms mission and strategy and explain why they are important.

LO2.4 Discuss and compare organization strategy and operations strategy and explain why it is important to link the two.

LO2.5 Describe and give examples of time-based strategies.

LO2.6 Define the term productivity and explain why it is important to organizations and to countries.

LO2.7 Describe several factors that affect productivity.

C H A P T E R O U T L I N E

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FP O

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2.1 INTRODUCTION

In this chapter you will learn about the different ways companies compete and why some firms do a very good job of competing. You will learn how effective strategies can lead to competitive organizations, and you will learn what productivity is, why it is important, and what organizations can do to improve it.

T H E C O L D H A R D F A C T S

The name of the game is competition. The playing field is global. Those who understand how to play the game will succeed; those who don’t are doomed to failure. And don’t think the game is just companies competing with each other. In companies that have multiple factories or divisions producing the same good or service, factories or divisions sometimes find themselves competing with each other. When a competitor—another company or a sister fac- tory or division in the same company—can turn out products better, cheaper, and faster, that spells real trouble for the factory or divi- sion that is performing at a lower level. The trouble can be layoffs or even a shutdown if the managers can’t turn things around. The bottom line? Better quality, higher productivity, lower costs, and the ability to quickly respond to customer needs are more important than ever, and the bar is getting higher. Business organizations need to develop solid strategies for dealing with these issues.

This chapter discusses competitiveness, strategy, and productivity, three separate but related topics that are vitally important to business organizations. Competitiveness relates to the effectiveness of an organization in the marketplace relative to other organizations that offer similar products or services. Operations and marketing have a major impact on competitiveness. Strategy relates to the plans that determine how an organization pursues its goals. Operations strategy is particularly important in this regard. Productivity relates to the effective use of resources, and it has a direct impact on competitiveness. Operations management is chiefly responsible for productivity.

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2.2 COMPETITIVENESS

Companies must be competitive to sell their goods and services in the marketplace. Competitiveness is an important factor in determining whether a company prospers, barely gets by, or fails. Business organizations compete through some combination of price, delivery time, and product or service differentiation.

Marketing influences competitiveness in several ways, including identifying consumer wants and needs, pricing, and advertising and promotion.

1. Identifying consumer wants and/or needs is a basic input in an organization’s decision-making process, and central to competitiveness. The ideal is to achieve a perfect match between those wants and needs and the organization’s goods and/or services.

2. Price and quality are key factors in consumer buying decisions. It is important to understand the trade-off decision consumers make between price and quality.

3. Advertising and promotion are ways organizations can inform potential customers about features of their products or services, and attract buyers.

Operations has a major influence on competitiveness through product and service design, cost, location, quality, response time, flexibility, inventory and supply chain management, and service. Many of these are interrelated.

1. Product and service design should reflect joint efforts of many areas of the firm to achieve a match between financial resources, operations capabilities, supply chain capa- bilities, and consumer wants and needs. Special characteristics or features of a product or service can be a key factor in consumer buying decisions. Other key factors include innovation and the time-to-market for new products and services.

2. Cost of an organization’s output is a key variable that affects pricing decisions and prof- its. Cost-reduction efforts are generally ongoing in business organizations. Productivity (discussed later in the chapter) is an important determinant of cost. Organizations with higher productivity rates than their competitors have a competitive cost advantage. A company may outsource a portion of its operation to achieve lower costs, higher produc- tivity, or better quality.

3. Location can be important in terms of cost and convenience for customers. Location near inputs can result in lower input costs. Location near markets can result in lower transportation costs and quicker delivery times. Convenient location is particularly important in the retail sector.

4. Quality refers to materials, workmanship, design, and service. Consumers judge quality in terms of how well they think a product or service will satisfy its intended purpose. Customers are generally willing to pay more for a product or service if they perceive the product or service has a higher quality than that of a competitor.

5. Quick response can be a competitive advantage. One way is quickly bringing new or improved products or services to the market. Another is being able to quickly deliver existing products and services to a customer after they are ordered, and still another is quickly handling customer complaints.

6. Flexibility is the ability to respond to changes. Changes might relate to alterations in design features of a product or service, or to the volume demanded by customers, or the mix of products or services offered by an organization. High flexibility can be a com- petitive advantage in a changeable environment.

7. Inventory management can be a competitive advantage by effectively matching sup- plies of goods with demand.

8. Supply chain management involves coordinating internal and external operations (buyers and suppliers) to achieve timely and cost-effective delivery of goods throughout the system.

9. Service might involve after-sale activities customers perceive as value-added, such as delivery, setup, warranty work, and technical support. Or it might involve extra atten- tion while work is in progress, such as courtesy, keeping the customer informed, and attention to details. Service quality can be a key differentiator; and it is one that is often

Competitiveness How effec- tively an organization meets the wants and needs of cus- tomers relative to others that offer similar goods or services.

LO2.1 List several ways that business organiza- tions compete.

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sustainable. Moreover, businesses rated highly by their customers for service quality tend to be more profitable, and grow faster, than businesses that are not rated highly.

10. Managers and workers are the people at the heart and soul of an organization, and if they are competent and motivated, they can provide a distinct competitive edge by their skills and the ideas they create. One often overlooked skill is answering the telephone. How complaint calls or requests for information are handled can be a positive or a negative. If a person answering is rude or not helpful, that can produce a negative image. Conversely, if calls are handled promptly and cheerfully, that can produce a positive image and, potentially, a competitive advantage.

Why Some Organizations Fail Organizations fail, or perform poorly, for a variety of reasons. Being aware of those reasons can help managers avoid making similar mistakes. Among the chief reasons are the following:

1. Neglecting operations strategy. 2. Failing to take advantage of strengths and opportunities, and/or failing to recognize

competitive threats. 3. Putting too much emphasis on short-term financial performance at the expense of

research and development. 4. Placing too much emphasis on product and service design and not enough on process

design and improvement. 5. Neglecting investments in capital and human resources. 6. Failing to establish good internal communications and cooperation among different

functional areas. 7. Failing to consider customer wants and needs.

The key to successfully competing is to determine what customers want and then directing efforts toward meeting (or even exceeding) customer expectations. Two basic issues must be addressed. First: What do the customers want? (Which items on the preceding list of the ways business organi- zations compete are important to customers?) Second: What is the best way to satisfy those wants?

Operations must work with marketing to obtain information on the relative importance of the various items to each major customer or target market.

Understanding competitive issues can help managers develop successful strategies.

LO2.2 Name several reasons that business organizations fail.

Indian operators take calls at Quatro call center in Gurgaon on the outskirts of New Delhi. Companies take advantage of communications and software support offshore to drive down costs. This industry in India already provides over one million jobs.

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2.3 MISSION AND STRATEGIES

An organization’s mission is the reason for its existence. It is expressed in its mission statement. For a business organization, the mission statement should answer the question “What business are we in?” Missions vary from organization to organization, depending on the nature of their business. Table 2.1 provides several examples of mission statements.

A mission statement serves as the basis for organizational goals, which provide more detail and describe the scope of the mission. The mission and goals often relate to how an organiza- tion wants to be perceived by the general public, and by its employees, suppliers, and custom- ers. Goals serve as a foundation for the development of organizational strategies. These, in turn, provide the basis for strategies and tactics of the functional units of the organization.

Organizational strategy is important because it guides the organization by providing direction for, and alignment of, the goals and strategies of the functional units. Moreover, strategies can be the main reason for the success or failure of an organization.

There are three basic business strategies:

• Low cost • Responsiveness • Differentiation from competitors

LO2.3 Define the terms mission and strategy and explain why they are important

Mission The reason for the existence of an organization.

Mission statement States the purpose of an organization.

Goals Provide detail and scope of the mission.

Strategies Plans for achiev- ing organizational goals.

TABLE 2.1 Selected portions of company mission statements

Microsoft To help people and businesses throughout the world to realize their full potential.

Verizon To help people and businesses communicate with each other.

Starbucks To inspire and nurture the human spirit—one cup and one neighborhood at a time.

U.S. Dept. of Education To promote student achievement and preparation for global competitiveness and fostering educational excellence and ensuring equal access.

Sometimes the same issue may apply to all three levels. However, a key difference is the time frame. From a strategic perspective, long-term implications are most relevant. From

I S I T A S T R AT E G I C , TAC T I C A L , O R O P E R AT I O N A L I S S U E ?

tactical and operational perspectives, the time frames are much shorter. In fact, the operational time frame is often measured in days.

Amazon’s service helped propel the company to a double-digit sales increase. Amazon started same-day shipping in major cities, launched a program to urge manufacturers to drop frustrating packaging, and extended its service reach by acquiring free-shipping pioneer Zappos.com.

Responsiveness relates to ability to respond to changing demands. Differentiation can relate to product or service features, quality, reputation, or customer service. Some organiza- tions focus on a single strategy while others employ a combination of strategies. One com- pany that has multiple strategies is Amazon.com. Not only does it offer low cost and quick, reliable deliveries, it also excels in customer service.

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Strategies and Tactics If you think of goals as destinations, then strategies are the roadmaps for reaching the destina- tions. Strategies provide focus for decision making. Generally speaking, organizations have overall strategies called organizational strategies, which relate to the entire organization. They also have functional strategies, which relate to each of the functional areas of the organization. The functional strategies should support the overall strategies of the organization, just as the organizational strategies should support the goals and mission of the organization.

Tactics are the methods and actions used to accomplish strategies. They are more specific than strategies, and they provide guidance and direction for carrying out actual operations, which need the most specific and detailed plans and decision making in an organization. You might think of tactics as the “how to” part of the process (e.g., how to reach the destination, following the strategy roadmap) and operations as the actual “doing” part of the process. Much of this book deals with tactical operations.

It should be apparent that the overall relationship that exists from the mission down to actual operations is hierarchical. This is illustrated in Figure 2.1.

A simple example may help to put this hierarchy into perspective.

Tactics The methods and actions taken to accomplish strategies.

Amazon received the top spot in customer service in a recent Business- Week ranking. Although most Amazon customers never talk with an employee, when something goes wrong, Amazon excels in dealing with the problem. In one case, when a New Jersey woman received a workbook she ordered that was described as “like new,” she was sur- prised to discover that it wasn’t even close to new—worksheets had already been filled in. She complained to the merchant but didn’t get a response. Then she complained to Amazon. She promptly received a refund, even though she had paid the merchant, not Amazon.

READING AMAZON TOPS IN CUSTOMER SERVICE

And she wasn’t asked to return the book. Amazon sees its customer service as a way to enhance cus-

tomer experience, and as a way to identify potential problems with merchants. In fact, if merchants have problems with more than 1 percent of their orders, that can get them removed from the site.

Source: Based on “How Amazon Aims to Keep You Clicking,” BusinessWeek, March 2009, p. 34.

FIGURE 2.1 Planning and decision making are hierarchical in organizations

Organizational goals

Mission

Organizational strategies

Functional goals

Marketing strategies

Finance strategies

Operations strategies

TacticsTactics Tactics

Operating procedures

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Here are some examples of different strategies an organization might choose from:

Low cost. Outsource operations to third-world countries that have low labor costs. Scale-based strategies. Use capital-intensive methods to achieve high output volume and low unit costs. Specialization. Focus on narrow product lines or limited service to achieve higher quality. Newness. Focus on innovation to create new products or services. Flexible operations. Focus on quick response and/or customization. High quality. Focus on achieving higher quality than competitors. Service. Focus on various aspects of service (e.g., helpful, courteous, reliable, etc.). Sustainability. Focus on environmental-friendly and energy-efficient operations.

A wide range of business organizations are beginning to recognize the strategic advantages of sustainability, not only in economic terms, but also in promotional benefit by publicizing their sustainability efforts and achievements.

Sometimes organizations will combine two or more of these or other approaches into their strategy. However, unless they are careful, they risk losing focus and not achieving advan- tage in any category. Generally speaking, strategy formulation takes into account the way organizations compete and a particular organization’s assessment of its own strengths and weaknesses in order to take advantage of its core competencies—those special attributes or abilities possessed by an organization that give it a competitive edge.

The most effective organizations use an approach that develops core competencies based on customer needs as well as on what the competition is doing. Marketing and operations work closely to match customer needs with operations capabilities. Competitor compe- tencies are important for several reasons. For example, if a competitor is able to supply high-quality products, it may be necessary to meet that high quality as a baseline. However, merely matching a competitor is usually not sufficient to gain market share. It may be neces- sary to exceed the quality level of the competitor or gain an edge by excelling in one or more other dimensions, such as rapid delivery or service after the sale. Walmart, for example, has been very successful in managing its supply chain, which has contributed to its competitive advantage.

To be effective, strategies and core competencies need to be aligned. Table 2.2 lists exam- ples of strategies and companies that have successfully employed those strategies.

Strategy Formulation Strategy formulation is almost always critical to the success of a strategy. Walmart discovered that when it opened stores in Japan. Although Walmart thrived in many countries on its reputation for low-cost items, Japanese consumers associated low cost with low quality, causing Walmart to rethink its strategy in the Japanese market. And many felt that Hewlett-Packard (HP) committed a strategic error when it acquired Compaq Computers at a cost of $19 billion. HP’s share of the

Core competencies The spe- cial attributes or abilities that give an organization a com- petitive edge.

Rita is a high school student in Southern California. She would like to have a career in busi- ness, have a good job, and earn enough income to live comfortably.

A possible scenario for achieving her goals might look something like this:

Mission: Live a good life. Goal: Successful career, good income. Strategy: Obtain a college education. Tactics: Select a college and a major; decide how to finance college. Operations: Register, buy books, take courses, study.

E X A M P L E 1

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computer market was less after the merger than the sum of the shares of the separate companies before the merger. In another example, U.S. automakers adopted a strategy in the early 2000s of offering discounts and rebates on a range of cars and SUVs, many of which were on low-margin vehicles. The strategy put a strain on profits, but customers began to expect those incentives, and the companies maintained them to keep from losing additional market share.

On the other hand, Coach, the maker of leather handbags and purses, successfully changed its longtime strategy to grow its market by creating new products. Long known for its highly durable leather goods in a market where women typically owned few handbags, Coach cre- ated a new market for itself by changing women’s view of handbags by promoting “different handbags for different occasions” such as party bags, totes, clutches, wristlets, overnight bags, purses, and day bags. And Coach introduced many fashion styles and colors.

To formulate an effective strategy, senior managers must take into account the core com- petencies of the organizations, and they must scan the environment. They must determine what competitors are doing, or planning to do, and take that into account. They must critically examine other factors that could have either positive or negative effects. This is sometimes referred to as the SWOT approach (strengths, weaknesses, opportunities, and threats). Strengths and weaknesses have an internal focus and are typically evaluated by operations people. Threats and opportunities have an external focus and are typically evaluated by mar- keting people. SWOT is often regarded as the link between organizational strategy and opera- tions strategy.

SWOT Analysis of strengths, weaknesses, opportunities, and threats.

TABLE 2.2 Examples of operations strategies

Organization Strategy

Operations Strategy

Examples of Companies or Services

Low price Low cost U.S. first-class postage Walmart Southwest Airlines

Responsiveness Short processing time

On-time delivery

McDonald’s restaurants Express Mail, UPS, FedEx One-hour photo Domino’s Pizza FedEx

Differentiation: High quality

High-performance design and/or high-quality processing

Consistent quality

TV: Sony, Samsung, LG Lexus Disneyland Five-star restaurants or hotels Coca-Cola, PepsiCo Wegmans Electrical power

Differentiation: Newness

Innovation 3M, Apple Google

Differentiation: Variety

Flexibility Volume

Burger King (“Have it your way”) Hospital emergency room McDonald’s (“Buses welcome”) Toyota Supermarkets (additional checkouts)

Differentiation: Service

Superior customer service Disneyland Amazon IBM Nordstrom

Differentiation: Location

Convenience Supermarkets, dry cleaners Mall stores Service stations Banks, ATMs

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An alternative to SWOT analysis is Michael Porter’s five forces model,1 which takes into account the threat of new competition, the threat of substitute products or services, the bargain- ing power of customers, the bargaining power of suppliers, and the intensity of competition.

In formulating a successful strategy, organizations must take into account both order quali- fiers and order winners. Order qualifiers are those characteristics that potential customers perceive as minimum standards of acceptability for a product to be considered for purchase. However, that may not be sufficient to get a potential customer to purchase from the organi- zation. Order winners are those characteristics of an organization’s goods or services that cause them to be perceived as better than the competition.

Characteristics such as price, delivery reliability, delivery speed, and quality can be order qualifiers or order winners. Thus, quality may be an order winner in some situations, but in others only an order qualifier. Over time, a characteristic that was once an order winner may become an order qualifier, and vice versa.

Obviously, it is important to determine the set of order qualifier characteristics and the set of order winner characteristics. It is also necessary to decide on the relative importance of each characteristic so that appropriate attention can be given to the various characteristics. Marketing must make that determination and communicate it to operations.

Environmental scanning is the monitoring of events and trends that present either threats or opportunities for the organization. Generally these include competitors’ activities; chang- ing consumer needs; legal, economic, political, and environmental issues; the potential for new markets; and the like.

Another key factor to consider when developing strategies is technological change, which can present real opportunities and threats to an organization. Technological changes occur in products (high-definition TV, improved computer chips, improved cellular telephone systems, and improved designs for earthquake-proof structures); in services (faster order processing, faster delivery); and in processes (robotics, automation, computer-assisted processing, point- of-sale scanners, and flexible manufacturing systems). The obvious benefit is a competitive edge; the risk is that incorrect choices, poor execution, and higher-than-expected operating costs will create competitive disadvantages.

Important factors may be internal or external. The following are key external factors:

1. Economic conditions. These include the general health and direction of the economy, inflation and deflation, interest rates, tax laws, and tariffs.

2. Political conditions. These include favorable or unfavorable attitudes toward business, political stability or instability, and wars.

3. Legal environment. This includes antitrust laws, government regulations, trade restric- tions, minimum wage laws, product liability laws and recent court experience, labor laws, and patents.

4. Technology. This can include the rate at which product innovations are occurring, current and future process technology (equipment, materials handling), and design technology.

5. Competition. This includes the number and strength of competitors, the basis of com- petition (price, quality, special features), and the ease of market entry.

6. Markets. This includes size, location, brand loyalties, ease of entry, potential for growth, long-term stability, and demographics.

The organization also must take into account various internal factors that relate to possible strengths or weaknesses. Among the key internal factors are the following:

1. Human resources. These include the skills and abilities of managers and workers, spe- cial talents (creativity, designing, problem solving), loyalty to the organization, exper- tise, dedication, and experience.

1Michael E. Porter, “The Five Competitive Forces That Shape Strategy,” Harvard Business Review 86, no. 1 (January 2008), pp. 78–93, 137.

Order qualifiers Characteris- tics that customers perceive as minimum standards of acceptability to be considered as a potential for purchase.

Order winners Characteris- tics of an organization’s goods or services that cause it to be perceived as better than the competition.

Environmental scanning The monitoring of events and trends that present threats or opportunities for a company.

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2. Facilities and equipment. Capacities, location, age, and cost to maintain or replace can have a significant impact on operations.

3. Financial resources. Cash flow, access to additional funding, existing debt burden, and cost of capital are important considerations.

4. Customers. Loyalty, existing relationships, and understanding of wants and needs are important.

5. Products and services. These include existing products and services, and the potential for new products and services.

6. Technology. This includes existing technology, the ability to integrate new technology, and the probable impact of technology on current and future operations.

7. Suppliers. Supplier relationships, dependability of suppliers, quality, flexibility, and service are typical considerations.

8. Other. Other factors include patents, labor relations, company or product image, dis- tribution channels, relationships with distributors, maintenance of facilities and equip- ment, access to resources, and access to markets.

After assessing internal and external factors and an organization’s distinctive competence, a strategy or strategies must be formulated that will give the organization the best chance of success. Among the types of questions that may need to be addressed are the following:

What role, if any, will the Internet play? Will the organization have a global presence? To what extent will outsourcing be used? What will the supply chain management strategy be? To what extent will new products or services be introduced? What rate of growth is desirable and sustainable? What emphasis, if any, should be placed on lean production? How will the organization differentiate its products and/or services from competitors’?

The organization may decide to have a single, dominant strategy (e.g., be the price leader) or to have multiple strategies. A single strategy would allow the organization to concentrate on one particular strength or market condition. On the other hand, multiple strategies may be needed to address a particular set of conditions.

Many companies are increasing their use of outsourcing to reduce overhead, gain flexibil- ity, and take advantage of suppliers’ expertise. Dell Computers provides a great example of some of the potential benefits of outsourcing as part of a business strategy.

Growth is often a component of strategy, especially for new companies. A key aspect of this strategy is the need to seek a growth rate that is sustainable. In the 1990s, fast-food com- pany Boston Market dazzled investors and fast-food consumers alike. Fueled by its success, it undertook rapid expansion. By the end of the decade, the company was nearly bankrupt; it had overexpanded. In 2000, it was absorbed by fast-food giant McDonald’s.

Companies increase their risk of failure not only by missing or incomplete strategies; they also fail due to poor execution of strategies. And sometimes they fail due to factors beyond their control, such as natural or man-made disasters, major political or economic changes, or competitors that have an overwhelming advantage (e.g., deep pockets, very low labor costs, less rigorous environmental requirements).

A useful resource on successful business strategies is the Profit Impact of Market Strategy (PIMS) database (www.pimsonline.com). The database contains profiles of over 3,000 busi- nesses located primarily in the United States, Canada, and western Europe. It is used by com- panies and academic institutions to guide strategic thinking. It allows subscribers to answer strategy questions about their business. Moreover, they can use it to generate benchmarks and develop successful strategies.

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According to the PIMS website,

The database is a collection of statistically documented experiences drawn from thousands of businesses, designed to help understand what kinds of strategies (e.g. quality, pricing, vertical integration, innovation, advertising) work best in what kinds of business environments. The data constitute a key resource for such critical management tasks as evaluating business performance, analyzing new business opportunities, evaluating and reality testing new strategies, and screening business portfolios. The primary role of the PIMS Program of the Strategic Planning Institute is to help managers understand and react to their business environment. PIMS does this by assisting managers as they develop and test strategies that will achieve an acceptable level of winning as defined by various strategies and financial measures.

Supply Chain Strategy A supply chain strategy specifies how the supply chain should function to achieve supply chain goals. The supply chain strategy should be aligned with the business strategy. If it is well executed, it can create value for the organization. It establishes how the organization should work with suppliers and policies relating to customer relationships and sustainability. Supply chain strategy is covered in more detail in a later chapter.

Sustainability Strategy Society is placing increasing emphasis on corporate sustainability practices in the form of governmental regulations and interest groups. For these and other reasons, business organiza- tions are or should be devoting attention to sustainability goals. To be successful, they will need a sustainability strategy. That requires elevating sustainability to the level of organi- zational governance; formulating goals for products and services, for processes, and for the entire supply chain; measuring achievements and striving for improvements; and possibly linking executive compensation to the achievement of sustainability goals.

Global Strategy As globalization increased, many companies realized that strategic decisions with respect to globalization must be made. One issue companies must face is that what works in one country

In 1984, Michael Dell, then a college student, started selling personal computers from his dorm room. He didn’t have the resources to make computer components, so he let others do that, choosing instead to concentrate on selling the comput- ers. And, unlike the major computer producers, he didn’t sell to dealers. Instead, he sold directly to PC buyers, eliminating some intermediaries, which allowed for lower cost and faster deliv- ery. Although direct selling of PCs is fairly commonplace now, in those days it was a major departure from the norm.

What did Dell do that was so different from the big guys? To start, he bought components from suppliers instead of making them. That gave him tremendous leverage. He had little inven- tory, no R&D expenditures, and relatively few employees. And the risks of this approach were spread among his suppliers.

Suppliers were willing to do this because Dell worked closely with them, and kept them informed. And because he was in direct contact with his customers, he gained tremendous insight into their expectations and needs, which he communicated to his suppliers.

Having little inventory gave Dell several advantages over his competitors. Aside from the lower costs of inventory, when new, faster computer chips became available, there was little inven- tory to work off, so he was able to offer the newer models much sooner than competitors with larger inventories. Also, when the prices of various components dropped, as they frequently did, he was able to take advantage of the lower prices, which kept his average costs lower than competitors’ costs.

Today the company is worth billions, and so is Michael Dell.

The key steps in strategy formulation are:

1. Link strategy directly to the organization’s mission or vision statement.

S T R AT E G Y F O R M U L AT I O N

2. Assess strengths, weaknesses, threats and opportunities, and identify core competencies.

3. Identify order winners and order qualifiers. 4. Select one or two strategies (e.g., low cost, speed, customer

service) to focus on.

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or region will not necessarily work in another, and strategies must be carefully crafted to take these variabilities into account. Another issue is the threat of political or social upheaval. Still another issue is the difficulty of coordinating and managing far-flung operations. Indeed, “In today’s global markets, you don’t have to go abroad to experience international competition. Sooner or later the world comes to you.”2

2Christopher A. Bartlett and Sumantra Ghoshal, “Going Global: Lessons from Late Movers,” Harvard Business Review, March–April 2000, p. 139.

2.4 OPERATIONS STRATEGY

The organization strategy provides the overall direction for the organization. It is broad in scope, covering the entire organization. Operations strategy is narrower in scope, deal- ing primarily with the operations aspect of the organization. Operations strategy relates to products, processes, methods, operating resources, quality, costs, lead times, and scheduling. Table 2.3 provides a comparison of an organization’s mission, its overall strategy, and its operations strategy, tactics, and operations.

In order for operations strategy to be truly effective, it is important to link it to organiza- tion strategy; that is, the two should not be formulated independently. Rather, formulation of organization strategy should take into account the realities of operations’ strengths and weak- nesses, capitalizing on strengths and dealing with weaknesses. Similarly, operations strategy must be consistent with the overall strategy of the organization, and with the other functional units of the organization. This requires that senior managers work with functional units to for- mulate strategies that will support, rather than conflict with, each other and the overall strat- egy of the organization. As obvious as this may seem, it doesn’t always happen in practice. Instead, we may find power struggles between various functional units. These struggles are detrimental to the organization because they pit functional units against each other rather than focusing their energy on making the organization more competitive and better able to serve the customer. Some of the latest approaches in organizations, involving teams of managers and workers, may reflect a growing awareness of the synergistic effects of working together rather than competing internally.

LO2.4 Discuss and com- pare organization strategy and operations strategy and explain why it is important to link the two.

Operations strategy The approach, consistent with the organization strategy, that is used to guide the operations function.

At this McDonald’s in Singapore, one variable is the use of rice as a staple of the Chinese diet. This ad highlights rice burgers.

McGraw-Hill Education/Christopher Kerrigan

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In the 1970s and early 1980s, operations strategy in the United States was often neglected in favor of marketing and financial strategies. That may have occurred because many chief executive officers did not come from operations backgrounds and perhaps did not fully appreciate the importance of the operations function. Mergers and acquisitions were com- mon; leveraged buyouts were used, and conglomerates were formed that joined dissimilar operations. These did little to add value to the organization; they were purely financial in nature. Decisions were often made by individuals who were unfamiliar with the business, frequently to the detriment of that business. Meanwhile, foreign competitors began to fill the resulting vacuum with a careful focus on operations strategy.

In the late 1980s and early 1990s, many companies began to realize this approach was not working. They recognized that they were less competitive than other companies. This caused them to focus attention on operations strategy. A key element of both organization strategy and operations strategy is strategy formulation.

Operations strategy can have a major influence on the competitiveness of an organization. If it is well designed and well executed, there is a good chance that the organization will be successful; if it is not well designed or executed, the chances are much less that the organiza- tion will be successful.

Strategic Operations Management Decision Areas Operations management people play a strategic role in many strategic decisions in a business organization. Table 2.4 highlights some key decision areas. Notice that most of the decision areas have cost implications.

TABLE 2.3 Comparison of mission, organization strategy, and operations strategy

Management Level

Time Horizon Scope

Level of Detail Relates to

The overall organization

Mission Strategy

Top Senior

Long Long

Broad Broad

Low Low

Survival, profitability Growth rate, market share

Operations Strategic

Tactical

Operational

Senior

Middle

Low

Moderate to long

Moderate

Short

Broad

Moderate

Narrow

Low

Moderate

High

Product design, choice of location, choice of technology, new facilities

Employment levels, output levels, equip- ment selection, facility layout

Scheduling personnel, adjusting out- put rates, inventory management, purchasing

Decision Area What the Decisions Affect

1. Product and service design 2. Capacity 3. Process selection and layout 4. Work design 5. Location 6. Quality 7. Inventory 8. Maintenance 9. Scheduling 10. Supply chains 11. Projects

Costs, quality, liability, and environmental issues Cost structure, flexibility Costs, flexibility, skill level needed, capacity Quality of work life, employee safety, productivity Costs, visibility Ability to meet or exceed customer expectations Costs, shortages Costs, equipment reliability, productivity Flexibility, efficiency Costs, quality, agility, shortages, vendor relations Costs, new products, services, or operating systems

TABLE 2.4 Strategic operations management decisions

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Two factors that tend to have universal strategic operations importance relate to quality and time. The following section discusses quality and time strategies.

Quality and Time Strategies Traditional strategies of business organizations have tended to emphasize cost minimization or product differentiation. While not abandoning those strategies, many organizations have embraced strategies based on quality and/or time.

Quality-based strategies focus on maintaining or improving the quality of an orga- nization’s products or services. Quality is generally a factor in both attracting and retain- ing customers. Quality-based strategies may be motivated by a variety of factors. They may reflect an effort to overcome an image of poor quality, a desire to catch up with the competition, a desire to maintain an existing image of high quality, or some combination of these and other factors. Interestingly enough, quality-based strategies can be part of another strategy such as cost reduction, increased productivity, or time, all of which benefit from higher quality.

Time-based strategies focus on reducing the time required to accomplish various activi- ties (e.g., develop new products or services and market them, respond to a change in customer demand, or deliver a product or perform a service). By doing so, organizations seek to improve service to the customer and to gain a competitive advantage over rivals who take more time to accomplish the same tasks.

Time-based strategies focus on reducing the time needed to conduct the various activities in a process. The rationale is that by reducing time, costs are generally less, productivity is higher, quality tends to be higher, product innovations appear on the market sooner, and cus- tomer service is improved.

Organizations have achieved time reduction in some of the following:

Planning time: The time needed to react to a competitive threat, to develop strategies and select tactics, to approve proposed changes to facilities, to adopt new technologies, and so on. Product/service design time: The time needed to develop and market new or redesigned products or services. Processing time: The time needed to produce goods or provide services. This can involve scheduling, repairing equipment, methods used, inventories, quality, training, and the like. Changeover time: The time needed to change from producing one type of product or service to another. This may involve new equipment settings and attachments, different methods, equipment, schedules, or materials. Delivery time: The time needed to fill orders. Response time for complaints: These might be customer complaints about quality, tim- ing of deliveries, and incorrect shipments. These might also be complaints from employ- ees about working conditions (e.g., safety, lighting, heat or cold), equipment problems, or quality problems.

It is essential for marketing and operations personnel to collaborate on strategy formulation in order to ensure that the buying criteria of the most important customers in each market seg- ment are addressed.

Agile operations is a strategic approach for competitive advantage that emphasizes the use of flexibility to adapt and prosper in an environment of change. Agility involves a blending of several distinct competencies such as cost, quality, and reliability along with flexibility. Processing aspects of flexibility include quick equipment changeovers, scheduling, and inno- vation. Product or service aspects include varying output volumes and product mix.

Successful agile operations requires careful planning to achieve a system that includes people, flexible equipment, and information technology. Reducing the time needed to per- form work is one of the ways an organization can improve a key metric: productivity.

LO2.5 Describe and give examples of time-based strategies.

Quality-based strategies Strategy that focuses on quality in all phases of an organization.

Time-based strategies Strategy that focuses on reduction of time needed to accomplish tasks.

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2.5 IMPLICATIONS OF ORGANIZATION STRATEGY FOR OPERATIONS MANAGEMENT

Organization strategy has a major impact on operations and supply chain management strate- gies. For example, organizations that use a low-cost, high-volume strategy limit the amount of variety offered to customers. As a result, variations for operations and the supply chain are minimal, so they are easier to deal with. Conversely, a strategy to offer a wide variety of products or services, or to perform customized work, creates substantial operational and sup- ply chain variations and, hence, more challenges in achieving a smooth flow of goods and services throughout the supply chain, thus making the matching of supply to demand more difficult. Similarly, increasing service reduces the ability to compete on price. Table 2.5 pro- vides a brief overview of variety and some other key implications.

2.6 TRANSFORMING STRATEGY INTO ACTION: THE BALANCED SCORECARD

The Balanced Scorecard (BSC) is a top-down management system that organizations can use to clarify their vision and strategy and transform them into action. It was introduced in the early 1990s by Robert Kaplan and David Norton,3 and it has been revised and improved since then. The idea was to move away from a purely financial perspective of the organization and integrate other perspectives such as customers, internal business processes, and learning and growth. Using this approach, managers develop objectives, metrics, and targets for each objective and initiatives to achieve objectives, and they identify links among the various perspectives. Results are monitored and used to improve strategic performance results. Figure 2.2 illustrates the con- ceptual framework of this approach. Many organizations employ this or a similar approach.

3Robert S. Kaplan and David P. Norton, Balanced Scorecard: Translating Strategy into Action (Cambridge, MA: Harvard Business School Press, 1996).

TABLE 2.5 Organization strategies and their implications for operations management

Organization Strategy Implications for Operations Management

Low price Requires low variation in products/services and a high-volume, steady flow of goods results in maximum use of resources through the system. Standardized work, material, and inventory requirements.

High quality Entails higher initial cost for product and service design, and process design, and more emphasis on assuring supplier quality.

Quick response Requires flexibility, extra capacity, and higher levels of some inventory items.

Newness/innovation Entails large investment in research and development for new or improved products and services plus the need to adapt operations and supply processes to suit new products or services.

Product or service variety Requires high variation in resource and more emphasis on product and service design; higher worker skills needed, cost estimation more difficult; scheduling more complex; quality assurance more involved; inventory management more complex; and matching supply to demand more difficult.

Sustainability Affects location planning, product and service design, process design, outsourcing decisions, returns policies, and waste management.

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As seen in Figure 2.2, the four perspectives are intended to balance not only financial and nonfinancial performance, but also internal and external performance as well as past and future performance. This approach can also help organizations focus on how they differ from the competition in each of the four areas if their vision is realized. Table 2.6 has some examples of factors for key focal points.

Although the Balanced Scorecard helps focus managers’ attention on strategic issues and the implementation of strategy, it is important to note that it has no role in strategy formulation.

Moreover, this approach pays little attention to suppliers and government regulations, and community, environmental, and sustainability issues are missing. These are closely linked,

FIGURE 2.2 The Balanced Scorecard

Source: Adapted from Robert S. Kaplan and David P. Norton, “Using the Balanced Score- card as a Strategic Management System,” Harvard Business Review (January-February 1996): 76.

Financial Performance

Customer/Stakeholder Satisfaction

Organizational Knowledge

and Innovation

Strategy Objectives Strategy Initiatives

Performance Measures & Targets

E�ciency of Internal Business Processes

Vision and

Strategy

TABLE 2.6 Balanced scorecard factors Examples

Focal Point Factors

Suppliers Delivery performance Quality performance Number of suppliers Supplier locations Duplicate activities

Internal Processes Bottlenecks Automation potential Turnover

Employees Job satisfaction Learning opportunities Delivery performance

Customers Quality performance Satisfaction Retention rate

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56 Chapter Two Competitiveness, Strategy, and Productivity

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Pieter Beens/Shutterstock

2.7 PRODUCTIVITY

Productivity = Output

______ Input

(2–1)

Productivity growth = Current productivity − Previous productivity

_____________________________________ Previous productivity

× 100 (2–2)

For example, if productivity increased from 80 to 84, the growth rate would be

84 − 80

______ 80

× 100 = 5%

LO2.6 Define the term productivity and explain why it is important to com- panies and to countries.

Productivity A measure of the effective use of resources, usually expressed as the ratio of output to input.

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Productivity growth is a key factor in a country’s rate of inflation and the standard of liv- ing of its people. Productivity increases add value to the economy while keeping inflation in check. Productivity growth was a major factor in the long period of sustained economic growth in the United States in the 1990s.

Computing Productivity Productivity measures can be based on a single input (partial productivity), on more than one input (multifactor productivity), or on all inputs (total productivity). Table 2.7 lists some examples of productivity measures. The choice of productivity measure depends primarily on the purpose of the measurement. If the purpose is to track improvements in labor productivity, then labor becomes the obvious input measure.

Partial measures are often of greatest use in operations management. Table 2.8 provides some examples of partial productivity measures.

The units of output used in productivity measures depend on the type of job performed. The following are examples of labor productivity:

Yards of carpet installed

____________________ Labor hours

= Yards of carpet installed per labor hour

Number of motel rooms cleaned

___________________________ Number of workers

= Number of motel rooms cleaned per worker

Similar examples can be listed for machine productivity (e.g., the number of pieces per hour turned out by a machine).

Productivity can be enhanced by the use of robotic equipment. Robots can operate for long periods with consistent precision and high speed. The Hyundai Motor Company manufacturing plant in Montgomery, Alabama, uses robots for assembly work. This $1.4 billion automotive plant is one of the most advanced assembly plants in North America.

Courtesy of Hyundai Motor Group

TABLE 2.7 Some examples of different types of productivity measures

Partial measures Output

_______ Labor

Output

________ Machine

Output

_______ Capital

Output

_______ Energy

Multifactor measures Output

_______________ Labor + Machine

Output

_______________________ Labor + Capital + Energy

Total measure Goods or services produced

_____________________________ All inputs used to produce them

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TABLE 2.8 Some examples of partial productivity measures

Labor productivity Units of output per labor hour Units of output per shift Value-added per labor hour Dollar value of output per labor hour

Machine productivity Units of output per machine hour Dollar value of output per machine hour

Capital productivity Units of output per dollar input Dollar value of output per dollar input

Energy productivity Units of output per kilowatt-hour Dollar value of output per kilowatt-hour

Calculations of multifactor productivity measure inputs and outputs using a common unit of mea- surement, such as cost. For instance, the measure might use cost of inputs and units of the output:

Quantity of production

_______________________________ Labor cost + Materials cost + Overhead

(2–3)

Note: The unit of measure must be the same for all factors in the denominator

Computing Multifactor Productivity Determine the multifactor productivity for the combined input of labor and machine time using the following data:

Output: 7,040 units Input Labor: $1,000 Materials: $520 Overhead: $2,000

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E X A M P L E 3

E X A M P L E 2 Computing Productivity Determine the productivity for these cases: a. Four workers installed 720 square yards of carpeting in eight hours. b. A machine produced 70 pieces in two hours. However, two pieces were unusable.

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S O L U T I O N

a. Productivity

= Yards of carpet installed

____________________ Labor hours worked

= 720 square yards

_______________________ 4 workers × 8 hours / worker

= 720 yards

_________ 32 hours

= 22.5 yards / hour

b. Productivity

= Usable pieces

______________ Production time

= 70 − 2 = 68 usable pieces

______________________ 2 hours

= 34 pieces / hour

Multifactor productivity

Output ________________________

Labor + Materials + Overhead

= 7,040 units

___________________ $1,000 + $520 + $2, 000

= 2 units per dollar input

S O L U T I O N

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Productivity measures are useful on a number of levels. For an individual department or organization, productivity measures can be used to track performance over time. This allows managers to judge performance and to decide where improvements are needed. For example, if productivity has slipped in a certain area, operations staff can examine the factors used to compute productivity to determine what has changed and then devise a means of improving productivity in subsequent periods.

Productivity measures also can be used to judge the performance of an entire industry or the productivity of a country as a whole. These productivity measures are aggregate measures.

In essence, productivity measurements serve as scorecards of the effective use of resources. Business leaders are concerned with productivity as it relates to competitiveness: If two firms both have the same level of output but one requires less input because of higher productivity, that one will be able to charge a lower price and consequently increase its share of the mar- ket. Or that firm might elect to charge the same price, thereby reaping a greater profit. Gov- ernment leaders are concerned with national productivity because of the close relationship between productivity and a nation’s standard of living. High levels of productivity are largely responsible for the relatively high standards of living enjoyed by people in industrial nations. Furthermore, wage and price increases not accompanied by productivity increases tend to cre- ate inflationary pressures on a nation’s economy.

Advantages of domestic-based operations for domestic markets often include higher worker productivity, better control of quality, avoidance of intellectual property losses, lower shipping costs, political stability, low inflation, and faster delivery.

Productivity in the Service Sector Service productivity is more problematic than manufacturing productivity. In many situa- tions, it is more difficult to measure, and thus to manage, because it involves intellectual activities and a high degree of variability. Think about medical diagnoses, surgery, con- sulting, legal services, customer service, and computer repair work. This makes productiv- ity improvements more difficult to achieve. Nonetheless, because service is becoming an increasingly large portion of our economy, the issues related to service productivity will have to be dealt with. It is interesting to note that government statistics normally do not include service firms.

It is sometimes easy to overlook the importance of productiv- ity. National figures are often reported in the media. They may seem to be ho-hum; there’s nothing glamorous about them to get our attention. But make no mistake; they are key eco- nomic indicators—barometers, if you will, that affect every- body. How? High productivity and high standard of living go hand-in-hand. If a country becomes more service-based, as the United States has become, some (but not all) high-produc- tivity manufacturing jobs are replaced by lower-productivity service jobs. That makes it more difficult to support a high standard of living.

Productivity gains can offset inflationary pressures related to wage increases. Productivity increases result in lower cost per unit. Those savings not only generate higher profits, they also help pay for wage increases.

READING WHY PRODUCTIVITY MATTERS

Productivity levels are also important for industries and companies. For companies, a higher productivity relative to their competitors gives them a competitive advantage in the marketplace. With a higher productivity, they can afford to undercut competitors’ prices to gain market share or charge the same prices but realize greater profits! For an industry, higher relative productivity means it is less likely to be sup- planted by foreign industry.

Questions 1. Why is high productivity important for a nation? 2. Why do you suppose that service jobs have lower productivity

than manufacturing jobs? 3. How can a company gain a competitive advantage by having

higher productivity than its competitors have?

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A useful measure closely related to productivity is process yield. Where products are involved, process yield is defined as the ratio of output of good product (i.e., defective product is not included) to the quantity of raw material input. Where services are involved, process yield measurement is often dependent on the particular process. For example, in a car rental agency, a measure of yield is the ratio of cars rented to cars available for a given day. In educa- tion, a measure for college and university admission yield is the ratio of student acceptances to the total number of students approved for admission. For subscription services, yield is the ratio of new subscriptions to the number of calls made or the number of letters mailed. How- ever, not all services lend themselves to a simple yield measurement. For example, services such as automotive, appliance, and computer repair don’t readily lend themselves to such measures.

Factors That Affect Productivity Numerous factors affect productivity. Generally, they are methods, capital, quality, technol- ogy, and management.

A commonly held misconception is that workers are the main determinant of productivity. According to that theory, the route to productivity gains involves getting employees to work harder. However, the fact is that many productivity gains in the past have come from techno- logical improvements. Familiar examples include:

Drones Automation GPS devices Copiers and scanners Calculators Smartphones The Internet, search engines Computers Apps Voice mail, cellular phones E-mail 3-D printing Radio frequency ID tags Software Medical imaging

However, technology alone won’t guarantee productivity gains; it must be used wisely and thoughtfully. Without careful planning, technology can actually reduce productivity, espe- cially if it leads to inflexibility, high costs, or mismatched operations. Another current pro- ductivity pitfall results from employees’ use of computers or smartphones for nonwork-related activities (playing games or checking stock prices or sports scores on the Internet or smart- phones, and texting friends and relatives). Beyond all of these is the dip in productivity that results while employees learn to use new equipment or procedures that will eventually lead to productivity gains after the learning phase ends.

LO2.7 Describe several factors that affect productivity.

Tomato growers in the Netherlands have a huge productivity advantage over their competitors in Italy and Greece. Although those countries are sun drenched while the Netherlands are any- thing but, computerized, climate-controlled greenhouses, and a “soil” spun from basalt and chalk that resembles cotton candy, allows for precise control of humidity and nutrition, and enables growers to produce their crops year around. Growers in Italy and Greece generally grow their crops outdoors or in unheated green- houses, and can only manage two crops a year. Dutch growers are able to achieve yields that are about ten times per square yard of those of Italian and Greek growers. And the Dutch have a sup- ply chain advantage: an integrated Dutch trading company works closely with supermarket chains in Europe and suppliers around the world, so farmers are able to sell their output in high volume,

READING DUTCH TOMATO GROWERS’ PRODUCTIVITY ADVANTAGE

rather than locally the way many farmers in other countries do. That enables Dutch growers to more closely match supply with supermarket demand. Finally, the Dutch tomato has been engi- neered to achieve a firmness that allows growers to harvest and ship tomatoes at their peak, while the “outdoor” farmers typically need to harvest their tomatoes before they are fully ripe to allow for firmness during shipping.

Questions 1. What factors enable Dutch tomato growers to achieve much

higher productivity than the Italian and Greek growers? 2. Discuss the importance of the Dutch growers’ supply chain.

Source: Based on “Tomato,” Time, March 25, 2013, pp. 9–14.

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Other factors that affect productivity include the following:

Standardizing processes and procedures wherever possible to reduce variability can have a significant benefit for both productivity and quality. Quality differences may distort productivity measurements. One way this can happen is when comparisons are made over time, such as comparing the productivity of a factory now with one 30 years ago. Quality is now much higher than it was then, but there is no simple way to incorporate quality improvements into productivity measurements. Use of the Internet can lower costs of a wide range of transactions, thereby increasing productivity. It is likely that this effect will continue to increase productivity in the fore- seeable future. Computer viruses can have an immense negative impact on productivity. Searching for lost or misplaced items wastes time, hence negatively affecting productivity. Scrap rates have an adverse effect on productivity, signaling inefficient use of resources. New workers tend to have lower productivity than seasoned workers. Thus, growing companies may experience a productivity lag. Safety should be addressed. Accidents can take a toll on productivity. A shortage of technology-savvy workers hampers the ability of companies to update com- puting resources, generate and sustain growth, and take advantage of new opportunities. Layoffs often affect productivity. The effect can be positive and negative. Initially, pro- ductivity may increase after a layoff, because the workload remains the same but fewer workers do the work—although they have to work harder and longer to do it. However, as time goes by, the remaining workers may experience an increased risk of burnout, and they may fear additional job cuts. The most capable workers may decide to leave. Labor turnover has a negative effect on productivity; replacements need time to get up to speed. Design of the workspace can impact productivity. For example, having tools and other work items within easy reach can positively impact productivity. Incentive plans that reward productivity increases can boost productivity.

And there are still other factors that affect productivity, such as equipment breakdowns and shortages of parts or materials. The education level and training of workers and their health can greatly affect productivity. The opportunity to obtain lower costs due to higher productiv- ity elsewhere is a key reason many organizations turn to outsourcing. Hence, an alternative to outsourcing can be improved productivity. Moreover, as a part of their strategy for quality, the best organizations strive for continuous improvement. Productivity improvements can be an important aspect of that approach.

Improving Productivity A company or a department can take a number of key steps toward improving productivity:

1. Develop productivity measures for all operations. Measurement is the first step in man- aging and controlling an operation.

2. Look at the system as a whole in deciding which operations are most critical. It is over- all productivity that is important. Managers need to reflect on the value of potential productivity improvements before okaying improvement efforts. The issue is effective- ness. There are several aspects of this. One is to make sure the result will be something customers want. For example, if a company is able to increase its output through pro- ductivity improvements, but then is unable to sell the increased output, the increase in productivity isn’t effective. Second, it is important to adopt a systems viewpoint: A pro- ductivity increase in one part of an operation that doesn’t increase the productivity of the system would not be effective. For example, suppose a system consists of a sequence of two operations, where the output of the first operation is the input to the second

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operation, and each operation can complete its part of the process at a rate of 20 units per hour. If the productivity of the first operation is increased, but the productivity of the second operation is not, the output of the system will still be 20 units per hour.

3. Develop methods for achieving productivity improvements, such as soliciting ideas from workers (perhaps organizing teams of workers, engineers, and managers), studying how other firms have increased productivity, and reexamining the way work is done.

4. Establish reasonable goals for improvement. 5. Make it clear that management supports and encourages productivity improvement.

Consider incentives to reward workers for contributions. 6. Measure improvements and publicize them.

Don’t confuse productivity with efficiency. Efficiency is a narrower concept that pertains to getting the most out of a fixed set of resources; productivity is a broader concept that per- tains to effective use of overall resources. For example, an efficiency perspective on mowing a lawn given a hand mower would focus on the best way to use the hand mower; a productivity perspective would include the possibility of using a power mower.

Fracking productivity improvement is another example. Drilling methods have become more effective. Drillers are now adopting a hydraulic fracturing method pioneered by com- panies such as Liberty Resources and EOG Resources that uses larger amounts of water and minerals. Although it’s a more costly process, it has increased production rates in the first year of a well’s life, after which output tends to drop off dramatically. Processes such as these have reduced the break-even cost of producing a barrel of oil and kept profitable some acreage that drillers might otherwise have left idle.

Competition is the driving force in many organizations. It may involve price, quality, special features or services, time, or other factors. To develop effective strategies for business, it is essential for organiza- tions to determine what combinations of factors are important to customers, which factors are order qualifiers, and which are order winners.

It is essential that goals and strategies be aligned with the organization’s mission. Strategies are plans for achieving organizational goals. They provide focus for decision making. Strategies must take into account present and future customer wants, as well as the organization’s strengths and weaknesses, threats and opportunities. These can run the gamut from what competitors are doing, or are likely to do, to technology, supply chain management, and e-business. Organizations generally have overall strategies that pertain to the entire organization and strategies that pertain to each of the functional areas. Func- tional strategies are narrower in scope and should be linked to overall strategies. Time-based strategies and quality-based strategies are among the most widely used strategies business organizations employ to serve their customers and to become more productive. The chapter includes a description of the Bal- anced Scorecard approach, which can be helpful for transforming strategies into actions, and the impli- cations of organization strategy for operations management.

Productivity is a measure of the use of resources. There is considerable interest in productivity both from an organizational standpoint and from a national standpoint. Business organizations want higher productivity because it yields lower costs and helps them to become more competitive. Nations want higher productivity because it makes their goods and services more attractive, offsets inflationary pres- sures associated with higher wages, and results in a higher standard of living for their people.

SUMMARY

Stryker Howmedica set up a team to improve the running of its packaging line. A strategy focus on productivity improvement was used. The team adopted an approach based on the production sys- tem of Toyota. The goal was to satisfy the customer expectations for delivery and quality, while achieving gains in productivity. After the team identified needs and set objectives, a number of improve- ments were implemented. A one-piece flow was established that

READING PRODUCTIVITY IMPROVEMENT

reduced bottlenecks in the flow of devices through a clean room and the total time spent blister sealing devices was lowered. Within a short time, productivity nearly doubled from 36 devices per hour to 60 devices per hour, work-in-progress inventory fell, and a 10 percent reduction in the standard cost of product was achieved.

Source: Based on Lauraine Howley, “A Strategy for Company Improvement,” Medi- cal Device Technology 11, no. 2 (March 2000), p. 33.

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Computing Productivity A company that processes fruits and vegetables is able to produce 400 cases of canned peaches in one-half hour with four workers. What is labor productivity?

Problem 1

SOLVED PROBLEMS

KEY POINTS 1. Competitive pressure often means that business organizations must frequently assess their com-

petitors’ strengths and weaknesses, as well as their own, to remain competitive. 2. Strategy formulation is critical because strategies provide direction for the organization, so they

can play a role in the success or failure of a business organization. 3. Functional strategies and supply chain strategies need to be aligned with the goals and strategies

become a competitive advantage. 6. High productivity is particularly important for organizations that have a strategy of low costs.

competitiveness, 42 core competencies, 46 environmental scanning, 48 goals, 44 mission, 44

KEY TERMS mission statement, 44 operations strategy, 51 order qualifiers, 48 order winners, 48 productivity, 56

quality-based strategies, 53 strategies, 44 SWOT, 47 tactics, 45 time-based strategies, 53

Labor productivity

Quantity produced ________________

Labor hours =

400 cases ________________________

4 workers × 1 / 2 hour / worker

= 200 cases per labor hour

Solution

Problem 2

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Multifactor productivity

Quantity produced ______________________________

Labor cost + Material cost + Overhead

= 2,000 rolls

_______________ $160 + $50 + $320

= 3.77 rolls per dollar input

A variation of the multifactor productivity calculation incorporates the standard price in the numerator by multiplying the units by the standard price.

Solution

Problem 3

Multifactor productivity

= Usable output

__________________________________ Labor cost + Material cost + Overhead cost

= 300 units

_____________________________________________________ ( 3 workers × 8 hours × $20 / hour ) + ( 600 pounds × $1 / pound ) +

( 3 workers × 8 hours × $20 / hour × 1.50 )

= 300 units

________________ $480 + $600 + $720

= .167units of output per dollar of input

Solution

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Problem 4 Computing Multifactor Productivity

A health club has two employees who work on lead generation. Each employee works 40 hours a week, and is paid $20 an hour. Each employee identifies an average of 400 possible leads a week from a list of 8,000 names. Approximately 10 percent of the leads become members and pay a onetime fee of $100. Material costs are $130 per week, and overhead costs are $1,000 per week. Calculate the multifactor productivity for this operation in fees generated per dollar of input.

Solution MFP

(Possible leads)(No. of workers)(Fee)(Conversion percentage) ___________________________________________________

Labor cost + Material cost + Overhead cost

= ( 400 ) ( 2 ) ( $100 ) ( .10 )

______________________ 2 ( 40 ) ( $20 ) + $130 + $1, 000

= $8, 000

______ $2, 730

= 2.93

1. From time to time, various groups clamor for import restrictions or tariffs on foreign-produced goods, particularly automobiles. How might these be helpful? Harmful?

2. List the key ways that organizations compete. 3. Explain the importance of identifying and differentiating order qualifiers and order winners. 4. Select two stores you shop at, and state how they compete. 5. What is the Balanced Scorecard and how is it useful? 6. Contrast the terms strategies and tactics. 7. Contrast organization strategy and operations strategy. 8. Explain the term time-based strategies and give three examples. 9. Productivity should be a concern of every business organization.

a. How is productivity defined? b. How are productivity measures used? c. Why is productivity important? d. What part of the organization has primary responsibility for productivity? e. How is efficiency different from productivity?

10. List some factors that can affect productivity and some ways that productivity can be improved. 11. It has been said that a typical Japanese automobile manufacturer produces more cars with fewer

workers than its U.S. counterpart. What are some possible explanations for this, assuming that U.S. workers are as hardworking as Japanese workers?

12. Boeing’s strategy appears to focus on its 777 midsize plane’s ability to fly into smaller, nonhub air- ports. Rival European Airbus’s strategy appears to focus on large planes. Compare the advantages and disadvantages of these two strategies.

13. Name 10 ways that banks compete for customers. 14. Explain the rationale of an operations strategy that seeks to increase the opportunity for use of

technology by reducing variability in processing requirements. 15. Identify two companies that have time-based strategies, and two that have quality-based strategies.

DISCUSSION AND REVIEW QUESTIONS

1. Who needs to be involved in formulating organizational strategy? 2. Name some of the competitive trade-offs that might arise in a fast-food restaurant. 3. How can technology improve

a. Competitiveness? b. Productivity?

TAKING STOCK

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1. In the past there was concern about a “productivity paradox” related to IT services. More recently, there have been few references to this phenomenon. Using the Internet, explain the term productiv- ity paradox. Why do you think that the discussion of that topic has faded?

2. A U.S. company has two manufacturing plants, one in the United States and one in another country. Both produce the same item, each for sale in their respective countries. However, their productivity figures are quite different. The analyst thinks this is because the U.S. plant uses more automated equipment for processing while the other plant uses a higher percentage of labor. Explain how that factor can cause productivity figures to be misleading. Is there another way to compare the two plants that would be more meaningful?

3. While it is true that increases in efficiency generate productivity increases, it is possible to get caught in an “efficiency improvement trap.” Explain what this means.

4. It is common knowledge that Sam’s boss Dom has been fudging the weekly productivity figures. Several employees, including Sam, have spoken to him about this, but he continues to do it. Sam has observed a drop in morale among his coworkers due to this. Sam is thinking about sending an anonymous note to Dom’s boss. Would that be ethical? What would you do if you were Sam?

5. Give two examples of what would be considered unethical involving competition and the ethical principles (see Chapter 1) that would be violated.

CRITICAL THINKING EXERCISES

1. A catering company prepared and served 300 meals at an anniversary celebration last week using eight workers. The week before, six workers prepared and served 240 meals at a wedding reception. a. For which event was the labor productivity higher? Explain. b. What are some possible reasons for the productivity differences?

2. The manager of a crew that installs carpeting has tracked the crew’s output over the past several weeks, obtaining these figures:

Week Crew Size Yards Installed

1 4 96

2 3 72

3 4 92

4 2 50

5 3 69

6 2 52 Compute the labor productivity for each of the weeks. On the basis of your calculations, what can

you conclude about crew size and productivity? 3. Compute the multifactor productivity measure for each of the weeks shown for production of choc-

olate bars. What do the productivity figures suggest? Assume 40-hour weeks and an hourly wage of $12. Overhead is 1.5 times weekly labor cost. Material cost is $6 per pound.

Week Output (units) Workers Material (lbs)

1 30,000 6 450

2 33,600 7 470

3 32,200 7 460

4 35,400 8 480 4. A company that makes shopping carts for supermarkets and other stores recently purchased some

new equipment that reduces the labor content of the jobs needed to produce the shopping carts. Prior to buying the new equipment, the company used five workers, who produced an average of 80 carts per hour. Workers receive $10 per hour, and machine cost was $40 per hour. With the new equipment, it was possible to transfer one of the workers to another department, and equipment cost increased by $10 per hour while output increased by four carts per hour. a. Compute labor productivity under each system. Use carts per worker per hour as the measure of

labor productivity. b. Compute the multifactor productivity under each system. Use carts per dollar cost (labor plus

equipment) as the measure. c. Comment on the changes in productivity according to the two measures, and on which one you

believe is the more pertinent for this situation.

PROBLEMS

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66 Chapter Two Competitiveness, Strategy, and Productivity

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5. An operation has a 10 percent scrap rate. As a result, 72 pieces per hour are produced. What is the potential increase in labor productivity that could be achieved by eliminating the scrap?

6. A manager checked production records and found that a worker produced 160 units while working 40 hours. In the previous week, the same worker produced 138 units while working 36 hours. Did the worker’s productivity increase, decrease, or remain the same? Explain.

7. The following table shows data on the average number of customers processed by several bank ser- vice units each day. The hourly wage rate is $25, the overhead rate is 1.0 times labor cost, and mate- rial cost is $5 per customer.

Unit Employees Customers

Processed/Day

A 4 36

B 5 40

C 8 60

D 3 20

a. Compute the labor productivity and the multifactor productivity for each unit. Use an eight- hour day for multifactor productivity.

b. Suppose a new, more standardized procedure is to be introduced that will enable each employee to process one additional customer per day. Compute the expected labor and multifactor produc- tivity rates for each unit.

8. A property title search firm is contemplating using online software to increase its search productiv- ity. Currently an average of 40 minutes is needed to do a title search. The researcher cost is $2 per minute. Clients are charged a fee of $400. Company A’s software would reduce the average search time by 10 minutes, at a cost of $3.50 per search. Company B’s software would reduce the average search time by 12 minutes at a cost of $3.60 per search. Which option would have the higher pro- ductivity in terms of revenue per dollar of input?

9. A company offers ID theft protection using leads obtained from client banks. Three employees work 40 hours a week on the leads, at a pay rate of $25 per hour per employee. Each employee identifies an average of 3,000 potential leads a week from a list of 5,000. An average of 4 percent actually sign up for the service, paying a one-time fee of $70. Material costs are $1,000 per week, and overhead costs are $9,000 per week. Calculate the multifactor productivity for this operation in fees generated per dollar of input.

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CASE AN AMERICAN TRAGEDY: HOW A GOOD COMPANY DIED

ZACHARY SCHILLER The Rust Belt is back. So say bullish observers as U.S. exports surge, long-moribund industries glow with newfound profits, and unemployment dips to lows not seen in a decade. But in the smokestack citadels, there’s disquiet. Too many machine-tool and auto parts factories are silent; too many U.S. industries still can’t hold their own.

What went wrong since the heyday of the 1960s? That’s the issue Max Holland, a contributing editor of The Nation, takes up in his nutsy-boltsy but fascinating study, When the Machine Stopped.*

The focus of the story is Burgmaster Corp., a Los Angeles–area machine-tool maker founded in 1944 by Czechoslovakian immi- grant Fred Burg. Holland’s father worked there for 29 years, and the author interviewed 22 former employees. His shop-floor view of this small company is a refreshing change from academic trea- tises on why America can’t compete.

The discussions of spindles and numerical control can be tough going. But Holland compensates by conveying the excitement and innovation of the company’s early days and the disgust and cyni- cism accompanying its decline. Moreover, the fate of Burgmaster and its brethren is crucial to the U.S. industrial economy: Any man- ufactured item is either made by a machine tool or by a machine made by a machine tool.

Producing innovative turret drills used in a wide variety of metal working tasks, Burgmaster was a thriving enterprise by 1965, when annual sales amounted to about $8 million. The company needed backing to expand, however, so it sold out to Buffalo-based conglomerate Houdaille Industries Inc. Hou- daille was in turn purchased in a 1979 leveraged buyout (LBO) led by Kohlberg Kravis Roberts & Co. By 1982, when debt, com- petition, and a sickly machine-tool market had battered Burg- master badly, Houdaille went to Washington with a petition to withhold the investment tax credit for certain Japanese-made machine tools.

Thanks to deft lobbying, the Senate passed a resolution sup- porting Houdaille’s position, but President Reagan refused to go along. Houdaille’s subsequent attempt to link Burgmaster up with a Japanese rival also failed, and Burgmaster was closed.

Holland uses Burgmaster’s demise to explore some key issues of economic and trade policy. Houdaille’s charge that a cartel led by the Japanese government had injured U.S. toolmakers, for example, became a rallying point for those who would blame a fearsome Japan Inc. for the problems of U.S. industry.

Holland describes the Washington wrangling over Houdaille in painful detail. But he does show that such government deci- sions are often made without much knowledge of what’s going on in industry. He shows, too, that Japanese producers succeeded less because of government help than because they made better, cheaper machines.

For those who see LBOs as a symptom of what ails the U.S. economy, Holland offers plenty of ammunition. He argues persua- sively that the LBO crippled Burgmaster by creating enormous pres- sure to generate cash. As Burgmaster pushed its products out as fast as possible, he writes, it routinely shipped defective machines. It promised customers features that engineers hadn’t yet designed. And although KKR disputes the claim, Holland concludes that the LBO choked off Burgmaster’s investment funds just when foreign competition made them most necessary. As for Houdaille, it was recapitalized and sold to Britain’s Tube Investments Group.

But Burgmaster’s problems had started even before the LBO. Holland’s history of the company under Houdaille is a veritable catalog of modern management techniques that flopped. One of the most disastrous was a system for computerizing production scheduling that was too crude for complex machine-tool manu- facturing. Holland gives a dramatic depiction of supply snafus that resulted in delays and cost increases.

As an independent company, “Burgmaster thrived because the Burgs knew their business,” Holland writes. Their departure under Houdaille was followed by an “endless and ultimately futile search for a better formula.” But, he concludes: “No formula was a substi- tute for management involvement on the shop floor.”

In the end, however, Holland puts most of the blame for the industry’s decline on government policy. He targets tax laws and macroeconomic policies that encourage LBOs and speculation instead of productive investment. He also criticizes Pentagon procurement policies for favoring exotic, custom machines over standard, low-cost models. This adds up to an industrial policy, Holland writes—a bad one.

The point is well taken, but Holland gives it excessive weight. Like their brethren in Detroit and Pittsburgh, domestic tool- makers in the 1970s were too complacent when imports seized the lower end of the product line. The conservatism that had for years served them in their cyclical industry left them ill-prepared for change. Even now some of the largest U.S. tool-makers are struggling to restructure. Blame the government, yes. But blame the industry, too.

Questions 1. Write a brief report that outlines the reasons (both internal and

external) for Burgmaster’s demise, and whether operations management played a significant role in the demise.

2. Do you think that inadequate strategic planning was a factor that resulted in the company’s asking for trade protection?

3. Can you think of a strategy that could have increased Burg- master’s chance of survival? Explain why you think that strat- egy would have been effective.

*Max Holland, When the Machine Stopped: A Contemporary Tale from Industrial America (Boston: Harvard Business School Press, 1988).

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CASE HOME-STYLE COOKIES

The Company The baking company is located in a small town in New York State. The bakery is run by two brothers. The company employs fewer than 200 people, mainly blue-collar workers, and the atmo- sphere is informal.

The Product The company’s only product is soft cookies, of which it makes over 50 varieties. Larger companies, such as Nabisco, Sunshine, and Keebler, have traditionally produced biscuit cookies, in which most of the water has been baked out, resulting in crisp cookies. The cookies have no additives or preservatives. The high quality of the cookies has enabled the company to develop a strong mar- ket niche for its product.

The Customers The cookies are sold in convenience stores and supermarkets throughout New York, Connecticut, and New Jersey. The company markets its cookies as “good food”—no additives or preservatives— and this appeals to a health-conscious segment of the market. Many customers are over 45 years of age, and prefer a cookie that is soft and not too sweet. Parents with young children also buy the cookies.

The Production Process The company has two continuous band ovens that it uses to bake the cookies. The production process is called a batch processing system. It begins as soon as management gets orders from distrib- utors. These orders are used to schedule production. At the start of each shift, a list of the cookies to be made that day is delivered to the person in charge of mixing. That person checks a master list, which indicates the ingredients needed for each type of cookie, and enters that information into the computer. The computer then determines the amount of each ingredient needed, according to the quantity of cookies ordered, and relays that information to storage silos located outside the plant where the main ingredi- ents (flour, sugar, and cake flour) are stored. The ingredients are automatically sent to giant mixing machines where the ingredients are combined with proper amounts of eggs, water, and flavor- ings. After the ingredients have been mixed, the batter is poured into a cutting machine where it is cut into individual cookies. The cookies are then dropped onto a conveyor belt and transported through one of two ovens. Filled cookies, such as apple, date, and raspberry, require an additional step for filling and folding.

The nonfilled cookies are cut on a diagonal rather than round. The diagonal-cut cookies require less space than straight-cut cookies, and the result is a higher level of productivity. In addi- tion, the company recently increased the length of each oven by 25 feet, which also increased the rate of production.

As the cookies emerge from the ovens, they are fed onto spiral cooling racks 20 feet high and 3 feet wide. As the cookies come off the cooling racks, workers place the cookies into boxes manu- ally, removing any broken or deformed cookies in the process. The boxes are then wrapped, sealed, and labeled automatically.

Inventory Most cookies are loaded immediately onto trucks and shipped to distributors. A small percentage are stored temporarily in the com- pany’s warehouse, but they must be shipped shortly because of their limited shelf life. Other inventory includes individual cookie boxes, shipping boxes, labels, and cellophane for wrapping. Labels are reordered frequently, in small batches, because FDA label requirements are subject to change, and the company does not want to get stuck with labels it can’t use. The bulk silos are refilled two or three times a week, depending on how quickly sup- plies are used.

Cookies are baked in a sequence that minimizes downtime for cleaning. For instance, light-colored cookies (e.g., chocolate chip) are baked before dark-colored cookies (e.g., fudge), and oatmeal cookies are baked before oatmeal raisin cookies. This permits the company to avoid having to clean the processing equipment every time a different type of cookie is produced.

Quality The bakery prides itself on the quality of its cookies. Cookies are sampled randomly by a quality control inspector as they come off the line to assure that their taste and consistency are satis- factory, and that they have been baked to the proper degree. Also, workers on the line are responsible for removing defective cookies when they spot them. The company has also installed an X-ray machine on the line that can detect small bits of metal filings that may have gotten into cookies during the production process. The use of automatic equipment for transporting raw materials and mixing batter has made it easier to maintain a ster- ile process.

Scrap The bakery is run very efficiently and has minimal amounts of scrap. For example, if a batch is mixed improperly, it is sold for dog food. Broken cookies are used in the oatmeal cook- ies. These practices reduce the cost of ingredients and save on waste disposal costs. The company also uses heat reclamation: The heat that escapes from the two ovens is captured and used to boil the water that supplies the heat to the building. Also, the use of automation in the mixing process has resulted in a reduction in waste compared with the manual methods used previously.

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New Products Ideas for new products come from customers, employees, and observations of competitors’ products. New ideas are first exam- ined to determine whether the cookies can be made with existing equipment. If so, a sample run is made to determine the cost and time requirements. If the results are satisfactory, marketing tests are conducted to see if there is a demand for the product.

Potential Improvements There are a number of areas of potential improvement at the bakery. One possibility would be to automate packing the cook- ies into boxes. Although labor costs are not high, automating the process might save some money and increase efficiency. So far, the owners have resisted making this change because they feel an obligation to the community to employ the 30 women who now do the boxing manually. Another possible improvement would be to use suppliers who are located closer to the plant. That would reduce delivery lead times and transportation costs, but the own- ers are not convinced that local suppliers could provide the same good quality. Other opportunities have been proposed in recent years, but the owners rejected them because they feared that the quality of the product might suffer.

Questions 1. Briefly describe the cookie production process. 2. What are two ways that the company has increased produc-

tivity? Why did increasing the length of the ovens result in a faster output rate?

3. Do you think that the company is making the right decision by not automating the packing of cookies? Explain your reason- ing. What obligation does a company have to its employees in a situation such as this? What obligation does it have to the community? Is the size of the town a factor? Would it make a difference if the company was located in a large city? Is the size of the company a factor? What if it were a much larger company?

4. What factors cause the company to carry minimal amounts of certain inventories? What benefits result from this policy?

5. As a consumer, what things do you consider in judging the quality of cookies you buy in a supermarket?

6. What advantages and what limitations stem from the com- pany’s not using preservatives in cookies?

7. Briefly describe the company’s strategy.

CASE HAZEL REVISITED

(Refer to the Hazel Case at the end of chapter 1.)

1. What competitive advantage does Hazel have over a profes- sional lawn care service?

2. Hazel would like to increase her profits, but she doesn’t believe that it would be wise to raise her prices considering the current state of the local economy. Instead, she has given some thought to increasing productivity.

a. Explain how increased productivity could be an alternative to increased prices.

b. What are some ways that Hazel could increase productivity?

3. Hazel is thinking about the purchase of new equipment. One would be power sidewalk edgers. She believes edgers will lead to an increase in productivity. Another would be a chain

saw, which would be used for tree pruning. What trade-offs should she consider in her analysis?

4. Hazel has been fairly successful in her neighborhood, and now wants to expand to other neighborhoods, including some that are five miles away. What would be the advantages and disadvantages of doing this?

5. Hazel does not have a mission statement or a set of objec- tives. Take one of the following positions and defend it:

a. Hazel doesn’t need a formal mission statement and objectives. Many small businesses don’t have them.

b. She definitely needs a mission statement and a set of objectives. They would be extremely beneficial.

c. There may be some benefit to Hazel’s business, and she should consider developing one.

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“Neither rain, nor snow . . .” The U.S. Postal Service (USPS) is the largest postal service in

the world, handling about 41 percent (630 million pieces a day) of the world’s mail volume. The second largest is Japan’s, which han- dles only about 6 percent of the world’s mail. The USPS is huge by any standard. It employs over 760,000 workers, making it the largest civilian employer in the United States. It has over 300,000 mail collection boxes, 38,000 post offices, 130 million mail deliv- ery points, more than 300 processing plants to sort and ship mail, and more than 75,000 pieces of mail processing equipment. It handles over 100 billion pieces of first-class mail a year, and ships about 3 billion pounds of mail on commercial airline flights, mak- ing it the airlines’ largest shipper.

Processing First-Class Mail The essence of processing the mail is sorting, which means organiz- ing the mail into smaller and smaller subgroups to facilitate its timely

THE U.S. POSTAL SERVICEOPERATIONS TOUR

delivery. Sorting involves a combination of manual and automatic operations. Much of the mail that is processed is first-class mail.

Most first-class mail is handled using automated equipment. A small portion that cannot be handled by automated equipment must be sorted by hand, just the way it was done in colonial times.

The majority of first-class mail begins at the advanced facer canceling system. This system positions each letter so that it is face up, with the stamp in the upper corner, checks to see if the address is handwritten, and pulls the hand-addressed letters off the line. It also rejects letters that have the stamp covered by tape, have no postage, are third-class mail, or have meter impres- sions that are too light to read. The rejects are handled manually. The remaining letters are cancelled and date stamped, and then sorted to one of seven stackers.

Next the letters go to the multiline optical character readers, which can handle both printed and pre–bar-coded mail, but not

CASE “YOUR GARDEN GLOVES”

JOSEPH MURRAY, GRAND VALLEY STATE UNIVERSITY “Your Garden Gloves” is a small gardening business located in Michigan. The company plants and maintains flower gardens for both commercial and residential clients. The company was founded about five years ago, and has since grown substantially, averaging about 10 new clients and one new employee a year. The company currently employs eight seasonal employees who are responsible for a certain number of clients.

Each morning crews are assigned to jobs by the owner. Crew sizes range from two to four workers. Crew size and composition are a function of the square footage of the garden and require- ments of the job. The owner feels that large jobs should be assigned to crews of four workers in order to complete the job in a reasonable amount of time.

From time to time, the owner noticed that some jobs, espe- cially the largest ones, took longer than she had estimated, based on the square footage of the garden space involved. The owner’s son, Joe, decided to investigate. He kept records of job times and crew sizes, and then used those records to compute labor produc- tivity. The results were:

Crew Size Average Productivity per Crew

2 4,234 square feet per day

3 5,352 square feet per day

4 7,860 square feet per day

The company operates on a small profit margin, so it is espe- cially important to take worker productivity into account.

Questions 1. Which crew size had the highest productivity per worker?

Which crew size had the lowest productivity per worker? What are some possible explanations for these results?

2. After a recent storm, a customer called in a panic, saying that she had planned a garden party for the upcoming weekend and her garden was in shambles. The owner decided to send a crew of four workers, even though a two-worker crew would have a higher productivity. Explain the rationale for this decision.

3. What is a possible qualitative issue that may very well influ- ence productivity levels that the productivity ratios fail to take into account?

(continued)

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hand-addressed mail. The optical reader sprays a bar code on the mail that hasn’t been pre–bar-coded, which represents up to an 11-digit zip code. For hand-addressed mail, a camera focuses on the front of the letter, and the image is displayed on a remote ter- minal, often in another city, where an operator views the image and provides the information that the optical readers could not determine so that a bar code can be added.

Bar-code readers then sort the mail into one of 96 stackers, doing this at a rate of more than 500 a minute. The mail goes through another sort using manually controlled mechanical equip- ment. At that point, the mail is separated according to whether it is local or out-of-town mail. The out-of-town mail is placed into appropriate sacks according to its destination, and moved to the outgoing send area where it will be loaded on trucks.

The local mail is moved to another machine that not only sorts the mail into local carrier delivery routes, it sorts it according to delivery walk sequence!

Small parcels, bundles of letters, and bundles of flats are sorted by a bundle-sorting machine.

Productivity Over the years, the USPS has experienced an ever-increasing volume of mail. Productivity has been an important factor for the USPS in keeping postal rates low and maintaining rapid delivery service. Two key factors in improved productivity have been the increased use of automation and the introduction of zip codes.

Mail processing underwent a major shift to mechanization dur- ing the 1950s and 1960s, which led to more rapid processing and higher productivity. In 1978, an expanded zip code was introduced. That was followed in 1983 by a four-digit expansion in zip codes. These changes required new, automated processing equipment, and the use of bar codes and optical readers. All of these changes added greatly to productivity. But even with these improvements, the USPS faced increasing competitive pressures.

Competition In the late 1980s, the USPS experienced a slowdown in the volume of mail. Some of this was due to a slowing of the economy, but most of it was the result of increasing competition. Delivery giants FedEx and UPS, as well as other companies that offer speedy delivery and package tracking, gave businesses and the general public convenient alternatives for some mail services. At the same time, there was a growing use of fax machines and electronic communications and increased use of alternate forms of advertis- ing such as cable TV, all of which cut into the volume of mail. Early in this century, e-mail and automated bill paying also cut into mail volume.

Strategies and Tactics Used to Make the Postal Service More Competitive To meet these challenges, the USPS developed several strate- gies to become more competitive. These included reorganizing, continuing to seek ways to keep costs down, increasing produc- tivity, and emphasizing quality and customer service. Here is an overview of the situation and the strategies and tactics used by the USPS.

The USPS began working more closely with customers to identify better ways to meet their needs and expanded customer conveniences such as stamps on consignment. With the help of business mailers, the USPS continued support for rates reflect- ing customer work-sharing features, many tied to automation, to give customers more flexibility. At the same time, the USPS began forming Customer Advisory Councils—groups of citizens who volunteered to work with local postal management on postal issues of interest to the community. In 1990, the USPS awarded two contracts to private firms to measure first-class mail service and customer satisfaction. In 1992, the USPS stepped up its quest to become more competitive by reducing bureaucracy and over- head in order to improve service and customer satisfaction, and to reduce the need to increase postage rates.

To help accomplish these goals, the USPS underwent a reor- ganization. Layers of management were eliminated and over- head positions were cut by about 30,000. Five regions and 73 field divisions were replaced by 10 areas, each with a manager for customer services and a manager for processing and distribu- tion. Ten customer service areas were established, with manag- ers for customer service and processing and distribution in each area, as well as a marketing and sales office. The new structure allowed postal managers to be focused, improved communica- tions, and empowered employees to meet customer needs. The USPS also took other steps to improve service. In 1993, it imple- mented improvements in processing and mail delivery at major postal facilities, expanded retail hours, and developed a more user-friendly Domestic Mail Manual. In cooperation with business customers, the USPS began to develop new services to meet specific mailer needs and to overhaul and simplify its complex rate structure. It also awarded contracts for two more external tracking systems, one to measure satisfaction levels of business mailers, and the other to measure service performance of third- class mail.

The reorganization eliminated some programs, cut costs, attracted new business, and reduced the USPS’s projected deficit.

The postal services’ sustainability scorecard for 2015 is shown as follows.

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United States Postal Service

January 2015 OMB Scorecard on Sustainability/Energy

Scope 1&2 GHG Emission Reduction Target

For Scope 1&2 GHG Reduction Target of 20% by 2020: 17% reduction in 2014 and on track

Score: GREEN

Scope 3GHG Emission Reduction Target

For Scope 3GHG Reduction Target of 20% by 2020: 24% reduction in 2014 and on track

Score: GREEN

Reduction in Energy Intensity

Reduction inenergy intensity in goal-subject facilities compared with 2003: 32% and on track for 30% by 2015

Score: GREEN

Use of Renewable Energy

Not applicable

Score: N/A

Reduction in Potable Water Intensity

Reduction inpotable water intensity compared with 2007: 30% and on track for 26% in 2020

Score: GREEN

Reduction in Fleet Petroleum Use

Reduction infleet petroleum use compared to 2005: 10.1% increase and not ontrack

Score: RED

Green Buildings

Not applicable

Score: N/A

(continued)

Questions

1. Why is it important for the USPS to have a high volume of mail to process?

2. What caused productivity to increase? 3. What impact did competitive pressures have on the USPS? 4. What measures did the USPS adopt to increase competitiveness?

5. What results were achieved by the USPS’s changes? 6. What effect does the increased use of e-mail have on postal

productivity? 7. How does the use of standard shipping containers and flat-

rate mailers help competitiveness?

Source: http://about.usps.com/what-we-are-doing/green/pdf/omb-scorecard-2015.pdf

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Standards for Success — Red Standard, Yellow Standard, Green Standard

Scope 1&2 GHG Emission Reduction Target

Scope 3 GHG Emission Reduction Target

Reduction in Energy Intensity

Use of Renewable Energy

Reduction in Potable Water Intensity

Reduction in Fleet Petroleum Use

Green Buildings

GREEN: On track to achieve agency’s proposed 2020 GHG Scopes 1&2 emissions reduction target.

YELLOW: Less than a year behind glide path to achieve agency’s 2020 target for GHG Scopes 1&2.

RED: More than a year behind glide path to achieve agency’s 2020 target for GHG Scopes 1&2.

GREEN: On track to achieve agency’s proposed 2020 GHG Scope 3 emissions reduction target.

YELLOW: Less than a year behind glide path to achieve agency’s 2020 target for GHG Scope 3.

RED: More than a year behind glide path to achieve agency’s 2020 target for GHG Scope 3.

GREEN: Reduced energy intensity (Btu/GSF*) in EISA goal-subject facilities by at least 27 percent compared with 2003 and is on track for 30 percent reduction by 2015.

YELLOW: Reduced energy intensity (Btu/GSF) in EISA goal-subject facilities by at least 24 percent compared with 2003.

RED: Did not reduce energy intensity (Btu/GSF) in EISA goal-subject facilities by at least 24 percent compared with 2003.

GREEN: Uses at least 7.5 percent electricity from renewable sources as a percentage of facility electricity use & at least 3.75 percent of facility electricity use comes from new sources (post-1999). (Thermal and mechanical renewable can be included in the 3.75 percent new requirement, but not the 7.5 percent goal; i.e., an agency meets all new sources requirement with thermal or mechanical energy (3.75 percent) but would still need an additional 7.5 percent from renewable electricity sources.)

YELLOW: Uses at least 7.5 percent renewable energy from electric, thermal or mechanical sources to power facilities and equipment; but less than half was obtained from new sources (post-1999) or part of the requirement was met with thermal and mechanical renewable energy.

RED: Did not use at least 7.5 percent renewable energy from electric, thermal or mechanical sources to power facilities and equipment.

GREEN: Reduced water intensity by at least 14 percent from final approved 2007 baseline and is on track for 26 percent reduction by 2020.

YELLOW: Reduced water intensity by at least 12 percent from final approved 2007 baseline.

RED: Did not reduce water intensity by at least 12 percent from final approved 2007 baseline.

GREEN: Achieved an 18 percent reduction in petroleum use in its entire vehicle fleet compared to 2005 and is on track for 20 percent reduction by 2015.

YELLOW: Achieved at least 16 percent reduction in petroleum use in the entire vehicle fleet compared to 2005.

RED: Did not achieve at least 16 percent reduction in petroleum use in its entire vehicle fleet since 2005.

GREEN: Demonstrates implementation of Guiding Principles for Federal Leadership in High Performance and Sustainable Buildings (GP) for new, existing and leased buildings; and is on track to meet 15% goal by 2015 by reporting that at least 13% of buildings >5,000 GSF meet GP as reported in the Federal Real Property Profi (FRPP).

YELLOW: Incorporates Guiding Principles into all new design contracts for construction, major renovations and leases and at least 13 percent of GSF of its building inventory over 5,000 GSF meets GP as reported in FRPP.

RED: Cannot demonstrate compliance with GP on new construction, major renovations, or leases; and/or less than 13 percent of building inventory, either by number of buildings or GSF, over 5,000 GSF meets GP as reported in FRPP.

*GSF = Gross Square Footage

Fortune.

Hammer, Michael, and Steven Stanton. “Ignore Opera- tions at Your Peril.” Harvard Business Review 6565 (April 2004).

Hill, Terry. Manufacturing Strategy: Text and Cases, 3rd ed. New York: McGraw-Hill, 2000.

Michael E. Porter, “The Five Competitive Forces That Shape Strategy,” Harvard Business Review 86, no. 1 (January 2008), pp. 78–93, 137.

SELECTED BIBLIOGRAPHY AND FURTHER READINGS

Slack, Nigel, and Michael Lewis. Operations Strat- egy, 4e. Upper Saddle River, NJ: Prentice-Hall, 2011. Global Competitiveness Report.

Werbach, Adam. Strategy for Sustainability: A Busi- ness Manifesto. Boston: Harvard Business Press, 2009.

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3 Forecasting L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO3.1 List features common to all forecasts.

LO3.2 Explain why forecasts are generally wrong.

LO3.3 List the elements of a good forecast.

LO3.4 Outline the steps in the forecasting process.

LO3.5 Summarize forecast errors and use summaries to make decisions.

LO3.6 Describe four qualitative forecasting techniques.

LO3.7 Use a naive method to make a forecast.

LO3.8 Prepare a moving average forecast.

LO3.9 Prepare a weighted-average forecast.

LO3.10 Prepare an exponential smoothing forecast.

LO3.11 Prepare a linear trend forecast.

LO3.12 Prepare a trend-adjusted exponential smoothing forecast.

LO3.13 Compute and use seasonal relatives.

LO3.14 Compute and use regression and correlation coefficients.

LO3.15 Construct control charts and use them to monitor forecast errors.

LO3.16 Describe the key factors and trade-offs to consider when choosing a forecasting technique.

3.1 Introduction 76

3.2 Features Common to All Forecasts 77

3.3 Elements of a Good Forecast 78

3.4 Forecasting and the Supply Chain 78

3.5 Steps in the Forecasting Process 79

3.6 Forecast Accuracy 79 Summarizing Forecast Accuracy 81

3.7 Approaches to Forecasting 82

3.8 Qualitative Forecasts 82

Executive Opinions 83 Salesforce Opinions 83 Consumer Surveys 83 Other Approaches 83

3.9 Forecasts Based on Time-Series Data 84 Naive Methods 84

C H A P T E R O U T L I N E

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Weather forecasts are one of the many types of forecasts used by some business organizations. Although some businesses simply rely on publicly available weather forecasts, others turn to firms that specialize in weather-related forecasts. For example, Home Depot, Gap, and JCPenney use such firms to help them take weather factors into account for estimating demand.

Many new car buyers have a thing or two in common. Once they make the decision to buy a new car, they want it as soon as possible. They usually don’t want to order it and then have to wait six weeks or more for delivery. If the car dealer they visit doesn’t have the car they want, they’ll look elsewhere. Hence, it is important for a dealer to anticipate buyer wants and to have those models, with the necessary options, in stock. The dealer who can correctly forecast buyer wants, and have those cars available, is going to be much more successful than a competitor who guesses instead of forecasting—and guesses wrong—and gets stuck with cars customers don’t want. So how does the dealer know how many cars of each type to stock? The answer is, the dealer doesn’t know for sure, but by analyzing previous buying patterns, and perhaps making allowances for current conditions, the dealer can come up with a reasonable approxima- tion of what buyers will want.

Planning is an integral part of a manager’s job. If uncertainties cloud the planning horizon, manag- ers will find it difficult to plan effectively. Forecasts help managers by reducing some of the uncertainty, thereby enabling them to develop more meaningful plans. A forecast is an estimate about the future value of a variable such as demand. The better the estimate, the more informed decisions can be. Some forecasts are long range, covering several years or more. Long-range forecasts are especially important for decisions that will have long-term consequences for an organization or for a town, city, country, state, or nation. One example is deciding on the right capacity for a planned power plant that will operate for the next 20 years. Other forecasts are used to determine if there is a profit potential for a new service or a new product: Will there be sufficient demand to make the innovation worthwhile? Many forecasts are short term, covering a day or week. They are especially helpful in planning and scheduling day-to-day opera- tions. This chapter provides a survey of business forecasting. It describes the elements of good forecasts, the necessary steps in preparing a forecast, basic forecasting techniques, and how to monitor a forecast.

Forecast A statement about the future value of a variable of interest.

Techniques for Averaging 86 Other Forecasting Methods 91 Techniques for Trend 91 Trend-Adjusted Exponential Smoothing 95 Techniques for Seasonality 95 Techniques for Cycles 100

3.10 Associative Forecasting Techniques 101

Simple Linear Regression 101 Comments on the Use of Linear Regression Analysis 104 Nonlinear and Multiple Regression Analysis 106

3.11 Monitoring Forecast Error 106

3.12 Choosing a Forecasting Technique 110

3.13 Using Forecast Information 112

3.14 Computer Software in Forecasting 112

3.15 Operations Strategy 112

Cases: M&L Manufacturing 134 Highline Financial Services, Ltd. 135

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3.1 INTRODUCTION

Forecasts are a basic input in the decision processes of operations management because they provide information on future demand. The importance of forecasting to operations manage- ment cannot be overstated. The primary goal of operations management is to match supply to demand. Having a forecast of demand is essential for determining how much capacity or supply will be needed to meet demand. For instance, operations needs to know what capacity will be needed to make staffing and equipment decisions, budgets must be prepared, purchasing needs information for ordering from suppliers, and supply chain partners need to make their plans.

Businesses make plans for future operations based on anticipated future demand. Antici- pated demand is derived from two possible sources, actual customer orders and forecasts. For businesses where customer orders make up most or all of anticipated demand, planning is straightforward, and little or no forecasting is needed. However, for many businesses, most or all of anticipated demand is derived from forecasts.

Two aspects of forecasts are important. One is the expected level of demand; the other is the degree of accuracy that can be assigned to a forecast (i.e., the potential size of forecast error). The expected level of demand can be a function of some structural variation, such as a trend or seasonal variation. Forecast accuracy is a function of the ability of forecasters to cor- rectly model demand, random variation, and sometimes unforeseen events.

Forecasts are made with reference to a specific time horizon. The time horizon may be fairly short (e.g., an hour, day, week, or month), or somewhat longer (e.g., the next six months, the next year, the next five years, or the life of a product or service). Short-term forecasts per- tain to ongoing operations. Long-range forecasts can be an important strategic planning tool. Long-term forecasts pertain to new products or services, new equipment, new facilities, or something else that will require a somewhat long lead time to develop, construct, or otherwise implement.

Forecasts are the basis for budgeting, planning capacity, sales, production and inventory, personnel, purchasing, and more. Forecasts play an important role in the planning process because they enable managers to anticipate the future so they can plan accordingly.

Forecasts affect decisions and activities throughout an organization, in accounting, finance, human resources, marketing, and management information systems (MIS), as well as

The Walt Disney World forecasting department has 20 employees who formulate forecasts on volume and revenue for the theme parks, water parks, resort hotels, as well as merchandise, food, and beverage revenue by location.

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in operations and other parts of an organization. Here are some examples of uses of forecasts in business organizations:

Accounting. New product/process cost estimates, profit projections, cash management. Finance. Equipment/equipment replacement needs, timing and amount of funding/ borrowing needs. Human resources. Hiring activities, including recruitment, interviewing, and training; layoff planning, including outplacement counseling. Marketing. Pricing and promotion, e-business strategies, global competition strategies. MIS. New/revised information systems, Internet services. Operations. Schedules, capacity planning, work assignments and workloads, inventory planning, make-or-buy decisions, outsourcing, project management. Product/service design. Revision of current features, design of new products or services.

In most of these uses of forecasts, decisions in one area have consequences in other areas. Therefore, it is very important for all affected areas to agree on a common forecast. How- ever, this may not be easy to accomplish. Different departments often have very different perspectives on a forecast, making a consensus forecast difficult to achieve. For example, salespeople, by their very nature, may be overly optimistic with their forecasts, and may want to “reserve” capacity for their customers. This can result in excess costs for operations and inventory storage. Conversely, if demand exceeds forecasts, operations and the supply chain may not be able to meet demand, which would mean lost business and dissatisfied customers.

Forecasting is also an important component of yield management, which relates to the per- centage of capacity being used. Accurate forecasts can help managers plan tactics (e.g., offer dis- counts, don’t offer discounts) to match capacity with demand, thereby achieving high yield levels.

There are two uses for forecasts. One is to help managers plan the system, and the other is to help them plan the use of the system. Planning the system generally involves long-range plans about the types of products and services to offer, what facilities and equipment to have, where to locate, and so on. Planning the use of the system refers to short-range and interme- diate-range planning, which involve tasks such as planning inventory and workforce levels, planning purchasing and production, budgeting, and scheduling.

Business forecasting pertains to more than predicting demand. Forecasts are also used to predict profits, revenues, costs, productivity changes, prices and availability of energy and raw materials, interest rates, movements of key economic indicators (e.g., gross domestic product, inflation, government borrowing), and prices of stocks and bonds. For the sake of simplicity, this chapter will focus on the forecasting of demand. Keep in mind, however, that the concepts and techniques apply equally well to the other variables.

In spite of its use of computers and sophisticated mathematical models, forecasting is not an exact science. Instead, successful forecasting often requires a skillful blending of science and intuition. Experience, judgment, and technical expertise all play a role in developing use- ful forecasts. Along with these, a certain amount of luck and a dash of humility can be helpful, because the worst forecasters occasionally produce a very good forecast, and even the best forecasters sometimes miss completely. Current forecasting techniques range from the mun- dane to the exotic. Some work better than others, but no single technique works all the time.

3.2 FEATURES COMMON TO ALL FORECASTS

A wide variety of forecasting techniques are in use. In many respects, they are quite different from each other, as you shall soon discover. Nonetheless, certain features are common to all, and it is important to recognize them.

1. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

Comment A manager cannot simply delegate forecasting to models or computers and then forget about it, because unplanned occurrences can wreak havoc with forecasts. For instance,

LO3.1 List features com- mon to all forecasts.

LO3.2 Explain why fore- casts are generally wrong.

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weather-related events, tax increases or decreases, and changes in features or prices of competing products or services can have a major impact on demand. Consequently, a manager must be alert to such occurrences and be ready to override forecasts, which assume a stable causal system. 2. Forecasts are not perfect; actual results usually differ from predicted values; the presence

of randomness precludes a perfect forecast. Allowances should be made for forecast errors. 3. Forecasts for groups of items tend to be more accurate than forecasts for individual

items because forecasting errors among items in a group usually have a canceling effect. Opportunities for grouping may arise if parts or raw materials are used for multiple products or if a product or service is demanded by a number of independent sources.

4. Forecast accuracy decreases as the time period covered by the forecast—the time horizon—increases. Generally speaking, short-range forecasts must contend with fewer uncertainties than longer-range forecasts, so they tend to be more accurate.

An important consequence of the last point is that flexible business organizations—those that can respond quickly to changes in demand—require a shorter forecasting horizon and, hence, benefit from more accurate short-range forecasts than competitors who are less flex- ible and who must therefore use longer forecast horizons.

3.3 ELEMENTS OF A GOOD FORECAST

A properly prepared forecast should fulfill certain requirements:

1. The forecast should be timely. Usually, a certain amount of time is needed to respond to the information contained in a forecast. For example, capacity cannot be expanded over- night, nor can inventory levels be changed immediately. Hence, the forecasting horizon must cover the time necessary to implement possible changes.

2. The forecast should be accurate, and the degree of accuracy should be stated. This will enable users to plan for possible errors and will provide a basis for comparing alterna- tive forecasts.

3. The forecast should be reliable; it should work consistently. A technique that sometimes provides a good forecast and sometimes a poor one will leave users with the uneasy feeling that they may get burned every time a new forecast is issued.

4. The forecast should be expressed in meaningful units. Financial planners need to know how many dollars will be needed, production planners need to know how many units will be needed, and schedulers need to know what machines and skills will be required. The choice of units depends on user needs.

5. The forecast should be in writing. Although this will not guarantee that all concerned are using the same information, it will at least increase the likelihood of it. In addition, a written forecast will permit an objective basis for evaluating the forecast once actual results are in.

6. The forecasting technique should be simple to understand and use. Users often lack confidence in forecasts based on sophisticated techniques; they do not understand either the circumstances in which the techniques are appropriate or the limitations of the tech- niques. Misuse of techniques is an obvious consequence. Not surprisingly, fairly simple forecasting techniques enjoy widespread popularity because users are more comfortable working with them.

7. The forecast should be cost-effective: The benefits should outweigh the costs.

3.4 FORECASTING AND THE SUPPLY CHAIN

Accurate forecasts are very important for the supply chain. Inaccurate forecasts can lead to shortages and excesses throughout the supply chain. Shortages of materials, parts, and ser- vices can lead to missed deliveries, work disruption, and poor customer service. Conversely,

LO3.3 List the elements of a good forecast.

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overly optimistic forecasts can lead to excesses of materials and/or capacity, which increase costs. Both shortages and excesses in the supply chain have a negative impact not only on customer service but also on profits. Furthermore, inaccurate forecasts can result in tempo- rary increases and decreases in orders to the supply chain, which can be misinterpreted by the supply chain.

Organizations can reduce the likelihood of such occurrences in a number of ways. One, obviously, is by striving to develop the best possible forecasts. Another is through collabora- tive planning and forecasting with major supply chain partners. Yet another way is through information sharing among partners and perhaps increasing supply chain visibility by allow- ing supply chain partners to have real-time access to sales and inventory information. Also important is rapid communication about poor forecasts as well as about unplanned events that disrupt operations (e.g., flooding, work stoppages), and changes in plans.

3.5 STEPS IN THE FORECASTING PROCESS

There are six basic steps in the forecasting process:

1. Determine the purpose of the forecast. How will it be used and when will it be needed? This step will provide an indication of the level of detail required in the fore- cast, the amount of resources (personnel, computer time, dollars) that can be justified, and the level of accuracy necessary.

2. Establish a time horizon. The forecast must indicate a time interval, keeping in mind that accuracy decreases as the time horizon increases.

3. Obtain, clean, and analyze appropriate data. Obtaining the data can involve signifi- cant effort. Once obtained, the data may need to be “cleaned” to get rid of outliers and obviously incorrect data before analysis.

4. Select a forecasting technique. 5. Make the forecast. 6. Monitor the forecast errors. The forecast errors should be monitored to determine if

the forecast is performing in a satisfactory manner. If it is not, reexamine the method, assumptions, validity of data, and so on; modify as needed; and prepare a revised forecast.

Note too that additional action may be necessary. For example, if demand was much less than the forecast, an action such as a price reduction or a promotion may be needed. Con- versely, if demand was much more than predicted, increased output may be advantageous. That may involve working overtime, outsourcing, or taking other measures.

3.6 FORECAST ACCURACY

Accuracy and control of forecasts is a vital aspect of forecasting, so forecasters want to mini- mize forecast errors. However, the complex nature of most real-world variables makes it almost impossible to correctly predict future values of those variables on a regular basis. Moreover, because random variation is always present, there will always be some residual error, even if all other factors have been accounted for. Consequently, it is important to include an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs. This will provide the forecast user with a better perspective on how far off a forecast might be.

Decision makers will want to include accuracy as a factor when choosing among different techniques, along with cost. Accurate forecasts are necessary for the success of daily activities of every business organization. Forecasts are the basis for an organization’s schedules, and unless the forecasts are accurate, schedules will be generated that may provide for too few or too many resources, too little or too much output, the wrong output, or the wrong timing of output, all of which can lead to additional costs, dissatisfied customers, and headaches for managers.

LO3.4 Outline the steps in the forecasting process.

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Some forecasting applications involve a series of forecasts (e.g., weekly revenues), whereas others involve a single forecast that will be used for a one-time decision (e.g., the size of a power plant). When making periodic forecasts, it is important to monitor forecast errors to determine if the errors are within reasonable bounds. If they are not, it will be necessary to take corrective action.

Forecast error is the difference between the value that occurs and the value that was pre- dicted for a given time period. Hence, Error = Actual − Forecast:

e t = A t − F t (3–1) where t = Any given time period

Positive errors result when the forecast is too low, negative errors when the forecast is too high. For example, if actual demand for a week is 100 units and forecast demand was 90 units, the forecast was too low; the error is 100 − 90 = +10.

Forecast errors influence decisions in two somewhat different ways. One is in making a choice between various forecasting alternatives, and the other is in evaluating the success or failure of a technique in use. We shall begin by examining ways to summarize forecast error over time, and see how that information can be applied to compare forecasting alternatives.

Error Difference between the actual value and the value that was predicted for a given period.

Overly optimistic forecasts by retail store buyers can easily lead retailers to overorder, resulting in bloated inventories. When that happens, there is pressure on stores to cut prices in order to move the excess merchandise. Although customers delight in these markdowns, retailer profits generally suffer. Furthermore, retailers will naturally cut back on new orders while they work off

READING HIGH FORECASTS CAN BE BAD NEWS

their inventories, creating a ripple effect that hits the entire supply chain, from shippers, to producers, to suppliers of raw materials. Moreover, the cutbacks to the supply chain could be misinter- preted. The message is clear: Overly optimistic forecasts can be bad news.

screenCam tutorial

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Summarizing Forecast Accuracy Forecast accuracy is a significant factor when deciding among forecasting alternatives. Accu- racy is based on the historical error performance of a forecast.

Three commonly used measures for summarizing historical errors are the mean absolute deviation (MAD), the mean squared error (MSE), and the mean absolute percent error (MAPE). MAD is the average absolute error, MSE is the average of squared errors, and MAPE is the average absolute percent error. The formulas used to compute MAD,1 MSE, and MAPE are as follows:

MAD = ∑ | Actual t − Forecast t | ___________________

n (3–2)

MSE = ∑ ( Actual t − Forecast t ) 2 ____________________

n − 1 (3–3)

MAPE = ∑

| Actual t − Forecast t | _________________ Actual t

× 100 ________________________

n (3–4)

Example 1 illustrates the computation of MAD, MSE, and MAPE.

1 The absolute value, represented by the two vertical lines in Formula 3–2, ignores minus signs; all data are treated as positive values. For example, −2 becomes +2.

Mean absolute deviation (MAD) The average absolute forecast error.

Mean squared error (MSE) The average of squared forecast errors.

Mean absolute percent error (MAPE) The average abso- lute percent error.

From a computational standpoint, the difference between these measures is that MAD weights all errors evenly, MSE weights errors according to their squared values, and MAPE weights according to relative error.

LO3.5 Summarize fore- cast errors and use sum- maries to make decisions.

S O L U T I O N

Computing MAD, MSE, and MAPE Compute MAD, MSE, and MAPE for the following data, showing actual and forecasted numbers of accounts serviced.

Period Actual Forecast (A-F) Error |Error| Error2 [|Error|÷ Actual] × 100 1 217 215 2 2 4 .92%

2 213 216 −3 3 9 1.41 3 216 215 1 1 1 .46

4 210 214 −4 4 16 1.90 5 213 211 2 2 4 .94

6 219 214 5 5 25 2.28

7 216 217 −1 1 1 .46 8 212 216 −4 4 16 1.89

−2 22 76 10.26%

Using the figures shown in the table,

MAD

∑ | e | ____

n =

22 ___

8 = 2.75

MSE

∑ e 2

_____ n − 1

= 76 _____

8 − 1 = 10.86

MAP

∑ [

| e | ______ Actual

× 100 ] _______________

n =

10.26% _______

8 = 1.28%

E X A M P L E 1

mhhe.com/stevenson13e

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One use for these measures is to compare the accuracy of alternative forecasting methods. For instance, a manager could compare the results to determine one which yields the lowest MAD, MSE, or MAPE for a given set of data. Another use is to track error performance over time to decide if attention is needed. Is error performance getting better or worse, or is it stay- ing about the same?

In some instances, historical error performance is secondary to the ability of a forecast to respond to changes in data patterns. Choice among alternative methods would then focus on the cost of not responding quickly to a change relative to the cost of responding to changes that are not really there (i.e., random fluctuations).

Overall, the operations manager must settle on the relative importance of historical perfor- mance versus responsiveness and whether to use MAD, MSE, or MAPE to measure historical performance. MAD is the easiest to compute, but weights errors linearly. MSE squares errors, thereby giving more weight to larger errors, which typically cause more problems. MAPE should be used when there is a need to put errors in perspective. For example, an error of 10 in a forecast of 15 is huge. Conversely, an error of 10 in a forecast of 10,000 is insignificant. Hence, to put large errors in perspective, MAPE would be used.

3.7 APPROACHES TO FORECASTING

There are two general approaches to forecasting: qualitative and quantitative. Qualitative methods consist mainly of subjective inputs, which often defy precise numerical description. Quantitative methods involve either the projection of historical data or the development of associative models that attempt to utilize causal (explanatory) variables to make a forecast.

Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinions, hunches) in the forecasting process. Those factors are often omitted or downplayed when quantitative techniques are used because they are difficult or impossible to quantify. Quantitative techniques consist mainly of analyzing objective, or hard, data. They usually avoid personal biases that sometimes contaminate qualitative methods. In practice, either approach or a combination of both approaches might be used to develop a forecast.

The following pages present a variety of forecasting techniques that are classified as judg- mental, time-series, or associative.

Judgmental forecasts rely on analysis of subjective inputs obtained from various sources, such as consumer surveys, the sales staff, managers and executives, and panels of experts. Quite frequently, these sources provide insights that are not otherwise available.

Time-series forecasts simply attempt to project past experience into the future. These techniques use historical data with the assumption that the future will be like the past. Some models merely attempt to smooth out random variations in historical data; others attempt to identify specific patterns in the data and project or extrapolate those patterns into the future, without trying to identify causes of the patterns.

Associative models use equations that consist of one or more explanatory variables that can be used to predict demand. For example, demand for paint might be related to variables such as the price per gallon and the amount spent on advertising, as well as to specific charac- teristics of the paint (e.g., drying time, ease of cleanup).

3.8 QUALITATIVE FORECASTS

In some situations, forecasters rely solely on judgment and opinion to make forecasts. If management must have a forecast quickly, there may not be enough time to gather and ana- lyze quantitative data. At other times, especially when political and economic conditions are changing, available data may be obsolete and more up-to-date information might not yet be available. Similarly, the introduction of new products and the redesign of existing products or

Judgmental forecasts Fore- casts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts.

Time-series forecasts Fore- casts that project patterns identified in recent time-series observations. Associative model Fore- casting technique that uses explanatory variables to pre- dict future demand.

LO3.6 Describe four qualitative forecasting techniques.

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packaging suffer from the absence of historical data that would be useful in forecasting. In such instances, forecasts are based on executive opinions, consumer surveys, opinions of the sales staff, and opinions of experts.

Executive Opinions A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range plan- ning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast.

Salesforce Opinions Members of the sales staff or the customer service staff are often good sources of informa- tion because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future. There are, however, several drawbacks to using salesforce opinions. One is that staff members may be unable to distinguish between what customers would like to do and what they actually will do. Another is that these people are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic. In addition, if forecasts are used to establish sales quotas, there will be a conflict of interest because it is to the salesperson’s advantage to provide low sales estimates.

Consumer Surveys Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers. Therefore, organizations seeking consumer input usually resort to consumer sur- veys, which enable them to sample consumer opinions. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. On the other hand, a considerable amount of knowledge and skill is required to construct a survey, admin- ister it, and correctly interpret the results for valid information. Surveys can be expensive and time-consuming. In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns. For example, much of the consumer’s thoughtful information gathering before purchasing a new car is often under- mined by the glitter of a new car showroom or a high-pressure sales pitch. Along the same lines, low response rates to a mail survey should—but often don’t—make the results suspect.

If these and similar pitfalls can be avoided, surveys can produce useful information.

Other Approaches A manager may solicit opinions from a number of other managers and staff people. Occasion- ally, outside experts are needed to help with a forecast. Advice may be needed on political or economic conditions in the United States or a foreign country, or some other aspect of impor- tance with which an organization lacks familiarity.

Another approach is the Delphi method, an iterative process intended to achieve a con- sensus forecast. This method involves circulating a series of questionnaires among individu- als who possess the knowledge and ability to contribute meaningfully. Responses are kept anonymous, which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail. Each new questionnaire is developed using the information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgments.

The Delphi method has been applied to a variety of situations, not all of which involve forecasting. The discussion here is limited to its use as a forecasting tool.

Delphi method An iterative process in which managers and staff complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecast.

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As a forecasting tool, the Delphi method is useful for technological forecasting, that is, for assessing changes in technology and their impact on an organization. Often the goal is to predict when a certain event will occur. For instance, the goal of a Delphi forecast might be to predict when video telephones might be installed in at least 50 percent of residential homes or when a vaccine for a disease might be developed and ready for mass distribution. For the most part, these are long-term, single-time forecasts, which usually have very little hard infor- mation to go by or data that are costly to obtain, so the problem does not lend itself to analyti- cal techniques. Rather, judgments of experts or others who possess sufficient knowledge to make predictions are used.

3.9 FORECASTS BASED ON TIME-SERIES DATA

A time series is a time-ordered sequence of observations taken at regular intervals (e.g., hourly, daily, weekly, monthly, quarterly, annually). The data may be measurements of demand, earn- ings, profits, shipments, accidents, output, precipitation, productivity, or the consumer price index. Forecasting techniques based on time-series data are made on the assumption that future values of the series can be estimated from past values. Although no attempt is made to identify variables that influence the series, these methods are widely used, often with quite satisfactory results.

Analysis of time-series data requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, or variations around an average. In addition, there will be random and perhaps irregular variations. These behaviors can be described as follows:

1. Trend refers to a long-term upward or downward movement in the data. Population shifts, changing incomes, and cultural changes often account for such movements.

2. Seasonality refers to short-term, fairly regular variations generally related to factors such as the calendar or time of day. Restaurants, supermarkets, and theaters experience weekly and even daily “seasonal” variations.

3. Cycles are wavelike variations of more than one year’s duration. These are often related to a variety of economic, political, and even agricultural conditions.

4. Irregular variations are due to unusual circumstances such as severe weather condi- tions, strikes, or a major change in a product or service. They do not reflect typical behavior, and their inclusion in the series can distort the overall picture. Whenever pos- sible, these should be identified and removed from the data.

5. Random variations are residual variations that remain after all other behaviors have been accounted for.

These behaviors are illustrated in Figure 3.1. The small “bumps” in the plots represent random variability.

The remainder of this section describes the various approaches to the analysis of time- series data. Before turning to those discussions, one point should be emphasized: A demand forecast should be based on a time series of past demand rather than unit sales. Sales would not truly reflect demand if one or more stockouts occurred.

Naive Methods A simple but widely used approach to forecasting is the naive approach. A naive forecast uses a single previous value of a time series as the basis of a forecast. The naive approach can be used with a stable series (variations around an average), with seasonal variations, or with trend. With a stable series, the last data point becomes the forecast for the next period. Thus, if demand for a product last week was 20 cases, the forecast for this week is 20 cases. With seasonal variations, the forecast for this “season” is equal to the value of the series last “sea- son.” For example, the forecast for demand for turkeys this Thanksgiving season is equal to demand for turkeys last Thanksgiving; the forecast of the number of checks cashed at a bank

Time series A time-ordered sequence of observations taken at regular intervals.

Trend A long-term upward or downward movement in data.

Seasonality Short-term regu- lar variations related to the calendar or time of day.

Cycle Wavelike variations lasting more than one year.

Irregular variation Caused by unusual circumstances, not reflective of typical behavior.

Random variations Residual variations after all other behaviors are accounted for.

Naive forecast A forecast for any period that equals the previous period’s actual value.

LO3.7 Use a naive method to make a forecast.

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on the first day of the month next month is equal to the number of checks cashed on the first day of this month; and the forecast for highway traffic volume this Friday is equal to the high- way traffic volume last Friday. For data with trend, the forecast is equal to the last value of the series plus or minus the difference between the last two values of the series. For example, suppose the last two values were 50 and 53. The next forecast would be 56:

Period Actual Change from

Previous Value Forecast

1 50 2 53 +3 3 53 + 3 = 56

Although at first glance the naive approach may appear too simplistic, it is nonetheless a legitimate forecasting tool. Consider the advantages: It has virtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and it is easily understandable. The main objection to this method is its inability to provide highly accurate forecasts. However, if resulting accuracy is acceptable, this approach deserves serious consideration. Moreover, even if other forecasting techniques offer better accuracy, they will almost always involve a greater cost. The accuracy of a naive forecast can serve as a standard of comparison against which to judge the cost and accuracy of other techniques. Thus, managers must answer the question: Is the increased accuracy of another method worth the additional resources required to achieve that accuracy?

FIGURE 3.1 Trend, cyclical, and seasonal data plots, with random and irregular variations

0 Time

Time

F M A M J J A S O N D

Year 4

Year 3

Year 2

Year 1

Seasonal variations

Month

Cycles

Irregular variation

Trend

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Techniques for Averaging Historical data typically contain a certain amount of random variation, or white noise, that tends to obscure systematic movements in the data. This randomness arises from the com- bined influence of many—perhaps a great many—relatively unimportant factors, and it can- not be reliably predicted. Averaging techniques smooth variations in the data. Ideally, it would be desirable to completely remove any randomness from the data and leave only “real” varia- tions, such as changes in the demand. As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are “real.”

Averaging techniques smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. A forecast based on an average thus tends to exhibit less variability than the original data (see Figure 3.2). This can be advantageous because many of these movements merely reflect random variability rather than a true change in the series. Moreover, because responding to changes in expected demand often entails considerable cost (e.g., changes in production rate, changes in the size of a workforce, inventory changes), it is desirable to avoid reacting to minor variations. Thus, minor variations are treated as random variations, whereas larger variations are viewed as more likely to reflect “real” changes, although these, too, are smoothed to a certain degree.

Averaging techniques generate forecasts that reflect recent values of a time series (e.g., the average value over the last several periods). These techniques work best when a series tends to vary around an average, although they also can handle step changes or gradual changes in the level of the series. Three techniques for averaging are described in this section:

1. Moving average 2. Weighted moving average 3. Exponential smoothing

Moving Average. One weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all. But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome. A moving average fore- cast uses a number of the most recent actual data values in generating a forecast. The moving average forecast can be computed using the following equation:

F t = MA n = ∑ i=1

A t−i ______

n =

A t−n + ··· + A t−2 + A t−1 _____________________ n (3–5)

where

Ft = Forecast for time period t MAn = n period moving average

At–i = Actual value in period t − i n = Number of periods (data points) in the moving average

For example, MA3 would refer to a three-period moving average forecast, and MA5 would refer to a five-period moving average forecast.

Moving average Technique that averages a number of recent actual values, updated as new values become available.

FIGURE 3.2 Averaging applied to three possible patterns

Data

Forecast Ideal Step change

(Forecast lags)

Gradual change

(Forecast lags)

LO3.8 Prepare a moving average forecast.

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Note that in a moving average, as each new actual value becomes available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the aver- age. Consequently, the forecast “moves” by reflecting only the most recent values.

In computing a moving average, including a moving total column—which gives the sum of the n most current values from which the average will be computed—aids computations. To update the moving total: Subtract the oldest value from the newest value and add that amount to the moving total for each update.

Figure 3.3 illustrates a three-period moving average forecast plotted against actual demand over 31 periods. Note how the moving average forecast lags the actual values and how smooth the forecasted values are compared with the actual values.

The moving average can incorporate as many data points as desired. In selecting the number of periods to include, the decision maker must take into account that the number of data points in the average determines its sensitivity to each new data point: The fewer the data points in an average, the more sensitive (responsive) the average tends to be. (See Figure 3.4A.)

If responsiveness is important, a moving average with relatively few data points should be used. This will permit quick adjustment to, say, a step change in the data, but it also will

FIGURE 3.3 A moving average forecast tends to smooth and lag changes in the data

Period

10 15 20 25 300 t

D e

m a

n d

3-period moving average (MA)

Demand

Computing a Moving Average Compute a three-period moving average forecast given demand for shopping carts for the last five periods.

Period Demand

1 42

2 40

3 43

the 3 most recent demands4 40

5 41

E X A M P L E 2

S O L U T I O N F 6 =

43 + 40 + 41 ________

3 = 41.33

If actual demand in period 6 turns out to be 38, the moving average forecast for period 7 would be

F 7 = 40 + 41 + 38

________ 3 = 39.67

}

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cause the forecast to be somewhat responsive even to random variations. Conversely, moving averages based on more data points will smooth more but be less responsive to “real” changes. Hence, the decision maker must weigh the cost of responding more slowly to changes in the data against the cost of responding to what might simply be random variations. A review of forecast errors can help in this decision.

The advantages of a moving average forecast are that it is easy to compute and easy to understand. A possible disadvantage is that all values in the average are weighted equally. For instance, in a 10-period moving average, each value has a weight of 1/10. Hence, the oldest value has the same weight as the most recent value. If a change occurs in the series, a moving average forecast can be slow to react, especially if there are a large number of values in the average. Decreasing the number of values in the average increases the weight of more recent values, but it does so at the expense of losing potential information from less recent values.

Weighted Moving Average. A weighted average is similar to a moving average, except that it typically assigns more weight to the most recent values in a time series. For instance, the most recent value might be assigned a weight of .40, the next most recent value a weight of .30, the next after that a weight of .20, and the next after that a weight of .10. Note that the weights must sum to 1.00, and that the heaviest weights are assigned to the most recent values.

F t = w t−n ( A t−n ) + ... + w t−2 ( A t−2 ) + w t−1 ( A t−1 ) + ... + w t−n ( A t−n ) (3–6)

where

wt–1 = Weight for period t − 1, etc. At–1 = Actual value for period t − 1, etc.

Weighted average More recent values in a series are given more weight in comput- ing a forecast.

LO3.9 Prepare a weighted-average forecast.

FIGURE 3.4A The more periods in a moving average, the greater the forecast will lag changes in the data

Period

64 5321 7 8 9 10 11 12 13 14 15

D e

m a

n d

Data

MA 3

MA 5

Computing a Weighted Moving Average Given the following demand data,

a. Compute a weighted average forecast using a weight of .40 for the most recent period, .30 for the next most recent, .20 for the next, and .10 for the next.

b. If the actual demand for period 6 is 39, forecast demand for period 7 using the same weights as in part a.

Period Demand 1 42 2 40 3 43 4 40 5 41

E X A M P L E 3

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Note that if four weights are used, only the four most recent demands are used to prepare the forecast.

The advantage of a weighted average over a simple moving average is that the weighted average is more reflective of the most recent occurrences. However, the choice of weights is somewhat arbitrary and generally involves the use of trial and error to find a suitable weighting scheme.

Exponential Smoothing. Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point. That is:

Next forecast = Previous forecast + α(Actual – Previous forecast)

where (Actual – Previous forecast) represents the forecast error and α is a percentage of the error. More concisely,

F t = F t−1 + α ( A t−1 − F t−1 ) (3–7a)

where

Ft = Forecast for period t Ft–1 = Forecast for the previous period (i.e., period t − 1)

α = Smoothing constant (percentage) At–1 = Actual demand or sales for the previous period

The smoothing constant α represents a percentage of the forecast error. Each new forecast is equal to the previous forecast plus a percentage of the previous error. For example, suppose the previous forecast was 42 units, actual demand was 40 units, and α = .10. The new forecast would be computed as follows:

F t = 42 + .10(40 − 42) = 41.8

Then, if the actual demand turns out to be 43, the next forecast would be

F t = 41.8 + .10(43 − 41.8) = 41.92

An alternate form of Formula 3–7a reveals the weighting of the previous forecast and the latest actual demand:

F t = (1 − α ) F t−1 + α A t−1 (3–7b)

For example, if α = .10, this would be

F t = .90 F t−1 + .10 A t−1

The quickness of forecast adjustment to error is determined by the smoothing constant, α. The closer its value is to zero, the slower the forecast will be to adjust to forecast errors (i.e., the greater the smoothing). Conversely, the closer the value of α is to 1.00, the greater the responsiveness and the less the smoothing. This is illustrated in Figure 3.4B.

Exponential smoothing A weighted averaging method based on previous forecast plus a percentage of the fore- cast error.

LO3.10 Prepare an exponential smoothing forecast.

screenCam tutorial

a. F 6 = .10 ( 40 ) + .20 ( 43 ) + .30 ( 40 ) + .40 ( 41 ) = 41.0 b. F 7 = .10 ( 43 ) + .20 ( 40 ) + .30 ( 41 ) + .40 ( 39 ) = 40.2

S O L U T I O N

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Selecting a smoothing constant is basically a matter of judgment or trial and error, using forecast errors to guide the decision. The goal is to select a smoothing constant that balances the benefits of smoothing random variations with the benefits of responding to real changes if and when they occur. Commonly used values of α range from .05 to .50. Low values of α are used when the underlying average tends to be stable; higher values are used when the underly- ing average is susceptible to change.

Some computer packages include a feature that permits automatic modification of the smoothing constant if the forecast errors become unacceptably large.

Exponential smoothing is one of the most widely used techniques in forecasting, partly because of its ease of calculation and partly because of the ease with which the weighting scheme can be altered—simply by changing the value of α.

Note Exponential smoothing should begin several periods back to enable forecasts to adjust to the data, instead of starting one period back. A number of different approaches can be used to obtain a starting forecast, such as the average of the first several periods, a subjective estimate, or the first actual value as the forecast for period 2 (i.e., the naive approach). For simplicity, the naive approach is used in this book. In practice, using an average of, say, the first three values as a forecast for period 4 would provide a better starting forecast because that would tend to be more representative.

FIGURE 3.4B The closer α is to zero, the greater the smoothing

Period

Actual α = .4

α = .1 45

D e

m a

n d

2 4 6 8 10 120

Period (t) Actual

Demand α = .10 Forecast

α = .40 Forecast

1 42 — — 2 40 42 42 3 43 41.8 41.2 4 40 41.92 41.92 5 41 41.73 41.15 6 39 41.66 41.09 7 46 41.39 40.25 8 44 41.85 42.55 9 45 42.07 43.13

10 38 42.35 43.88 11 40 41.92 41.53 12 41.73 40.92

starting forecast

Comparing Forecast Errors Compare the error performance of these three forecasting techniques using MAD, MSE, and MAPE: a naive forecast, a two-period moving average, and exponential smoothing with α = .10 for periods 3 through 11, using the data shown in Figure 3.4B.

E X A M P L E 4

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If lowest MAD is the criterion, the two-period moving average forecast has the greatest accu- racy; if lowest MSE is the criterion, exponential smoothing works best; and if lowest MAPE is the criterion, the two-period moving average method is again best. Of course, with other data, or with different values of α for exponential smoothing, and different moving averages, the best performers could be different.

Other Forecasting Methods You may find two other approaches to forecasting interesting. They are briefly described in this section.

Focus Forecasting. Some companies use forecasts based on a “best recent performance” basis. This approach, called focus forecasting, was developed by Bernard T. Smith, and is described in several of his books.2 It involves the use of several forecasting methods (e.g., moving average, weighted average, and exponential smoothing) all being applied to the last few months of historical data after any irregular variations have been removed. The method that has the highest accuracy is then used to make the forecast for the next month. This pro- cess is used for each product or service, and is repeated monthly. Example 4 illustrates this kind of comparison.

Diffusion Models. When new products or services are introduced, historical data are not generally available on which to base forecasts. Instead, predictions are based on rates of prod- uct adoption and usage spread from other established products, using mathematical diffusion models. These models take into account such factors as market potential, attention from mass media, and word of mouth. Although the details are beyond the scope of this text, it is impor- tant to point out that diffusion models are widely used in marketing and to assess the merits of investing in new technologies.

Techniques for Trend Analysis of trend involves developing an equation that will suitably describe trend (assuming that trend is present in the data). The trend component may be linear, or it may not. Some

2 See, for example, Bernard T. Smith and Virginia Brice, Focus Forecasting: Computer Techniques for Inventory Control Revised for the Twenty-First Century (Essex Junction, VT: Oliver Wight, 1984).

Focus forecasting Using the forecasting method that demonstrates the best recent success.

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Naive Two-period MA Exponential Smoothing

Period, t Demand Forecast Error Forecast Error Forecast Error

1 42 — —

2 40 42 −2 42 −2

3 43 40 3 41 2 41.8 1.2

4 40 43 −3 41.5 −1.5 41.92 −1.92

5 41 40 1 41.5 −0.5 41.73 −0.73

6 39 41 −2 40.5 −1.5 41.66 −2.66

7 46 39 7 40 6 41.39 4.61

8 44 46 −2 42.5 1.5 41.85 2.15

9 45 44 1 45 0 42.07 2.93

10 38 45 −7 44.5 −6.5 42.36 −4.36

11 40 38 2 41.5 −1.5 41.92 −1.92

MAD 3.11 2.33 2.50

MSE 16.25 11.44 8.73

MAPE 7.49% 5.64% 5.98%

S O L U T I O N

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commonly encountered nonlinear trend types are illustrated in Figure 3.5. A simple plot of the data often can reveal the existence and nature of a trend. The discussion here focuses exclu- sively on linear trends because these are fairly common.

There are two important techniques that can be used to develop forecasts when trend is present. One involves use of a trend equation; the other is an extension of exponential smoothing.

Trend Equation. A linear trend equation has the form

Ft = a + bt (3–8)

where

Ft = Forecast for period t a = Value of Ft at t = 0, which is the y intercept b = Slope of the line t = Specified number of time periods from t = 0

Linear trend equation Ft = a + bt, used to develop fore- casts when trend is present.

FIGURE 3.5 Graphs of some nonlinear trends

Time

Parabolic trend

Life cycle trend

Exponential trend

Growth curve

LO3.11 Prepare a linear trend forecast.

b =

Ft = a + bt

∆y

∆t ∆y ∆t

For example, consider the trend equation Ft = 45 + 5t. The value of Ft when t = 0 is 45, and the slope of the line is 5, which means that, on the average, the value of Ft will increase by five units for each time period. If t = 10, the forecast, Ft, is 45 + 5(10) = 95 units. The equation can be plotted by finding two points on the line. One can be found by substituting some value of t into the equation (e.g., t = 10) and then solving for Ft. The other point is a (i.e., Ft at t = 0). Plotting those two points and drawing a line through them yields a graph of the linear trend line.

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The coefficients of the line, a and b, are based on the following two equations:

b = n∑ ty − ∑ t∑ y

_____________ n∑ t 2 − ( ∑ t ) 2

(3–9)

a = ∑ y − b∑ t

_________ n or y ¯ − b t ¯ (3–10)

where

n = Number of periods y = Value of the time series

Note that these two equations are identical to those used for computing a linear regression line, except that t replaces x in the equations. Values for the trend equation can be obtained easily by using the Excel template for linear trend.

Obtaining and Using a Trend Equation Cell phone sales for a California-based firm over the last 10 weeks are shown in the follow- ing table. Plot the data, and visually check to see if a linear trend line would be appropriate. Then determine the equation of the trend line, and predict sales for weeks 11 and 12.

Week Unit Sales

1 700

2 724

3 720

4 728

5 740

6 742

7 758

8 750

9 770

10 775

E X A M P L E 5

Week

2 4 6 8 10 121 3 5 7 9 11

780

S a

le s

760

740

720

700

S O L U T I O N a. A plot suggests that a linear trend line would be appropriate:

b. The solution obtained by using the Excel template for linear trend is shown in Table 3.1. b = 7.51 and a = 699.40 The trend line is Ft = 699.40 + 7.51t, where t = 0 for period 0.

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TABLE 3.1 Excel solution for Example 5

Week

2 4 6 8 10 121 3 5 7 9 11

S a

le s

Trend lineData

Forecasts

780

800

760

740

720

700

c. Substituting values of t into this equation, the forecasts for the next two periods (i.e., t = 11 and t = 12) are: F11 = 699.40 + 7.51(11) = 782.01 F12 = 699.40 + 7.51(12) = 789.52

d. For purposes of illustration, the original data, the trend line, and the two projections (forecasts) are shown on the following graph:

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The trend-adjusted forecast (TAF) is composed of two elements—a smoothed error and a trend factor.

TAF t+1 = S t + T t (3–11) where

St = Previous forecast plus smoothed error Tt = Current trend estimate

and S t = TAF t + α( A t − TAF t ) T t = T t−1 + β (TAF t − TAF t−1 − T t−1 ) (3–12)

where

α = Smoothing constant for average β = Smoothing constant for trend

Starting forecast = 728 + 9.33 = 737.33

Trend-adjusted exponential smoothing Variation of expo- nential smoothing used when a time series exhibits a linear trend.

Seasonal variations Regu- larly repeating movements in series values that can be tied to recurring events.

LO3.12 Prepare a trend- adjusted exponential smoothing forecast.

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Seasonality in a time series is expressed in terms of the amount that actual values devi- ate from the average value of a series. If the series tends to vary around an average value, then seasonality is expressed in terms of that average (or a mov- ing average); if trend is pres- ent, seasonality is expressed in terms of the trend value.

There are two different mod- els of seasonality: additive and multiplicative. In the additive model, seasonality is expressed as a quantity (e.g., 20 units), which is added to or subtracted from the series average in order to incorporate seasonality. In the multiplicative model, sea- sonality is expressed as a per- centage of the average (or trend) amount (e.g., 1.10), which is then used to multiply the value

of a series to incorporate seasonality. Figure 3.6 illustrates the two models for a linear trend line. In practice, businesses use the multiplicative model much more widely than the additive model, because it tends to be more representative of actual experience, so we will focus exclu- sively on the multiplicative model.

The seasonal percentages in the multiplicative model are referred to as seasonal relatives or seasonal indexes. Suppose that the seasonal relative for the quantity of toys sold in May at a store is 1.20. This indicates that toy sales for that month are 20 percent above the monthly average. A seasonal relative of .90 for July indicates that July sales are 90 percent of the monthly average.

Seasonal relative Percentage of average or trend.

TABLE 3.2 Using the Excel template for trend-adjusted smoothing

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FIGURE 3.6 Seasonality: the additive and multiplicative models compared using a linear trend

Time

Demand

Multiplicative model Demand = Trend × Seasonality

Additive model Demand = Trend + Seasonality

Knowledge of seasonal variations is an important factor in retail planning and scheduling. Moreover, seasonality can be an important factor in capacity planning for systems that must be designed to handle peak loads (e.g., public transportation, electric power plants, highways, and bridges). Knowledge of the extent of seasonality in a time series can enable one to remove seasonality from the data (i.e., to seasonally adjust data) in order to discern other patterns or the lack of patterns in the series. Thus, one frequently reads or hears about “seasonally adjusted unemployment” and “seasonally adjusted personal income.”

The next section briefly describes how seasonal relatives are used.

Using Seasonal Relatives. Seasonal relatives are used in two different ways in forecast- ing. One way is to deseasonalize data; the other way is to incorporate seasonality in a forecast.

To deseasonalize data is to remove the seasonal component from the data in order to get a clearer picture of the nonseasonal (e.g., trend) components. Deseasonalizing data is accomplished by dividing each data point by its corresponding seasonal relative (e.g., divide November demand by the November relative, divide December demand by the December relative, and so on).

Incorporating seasonality in a forecast is useful when demand has both trend (or average) and seasonal components. Incorporating seasonality can be accomplished in this way:

1. Obtain trend estimates for desired periods using a trend equation. 2. Add seasonality to the trend estimates by multiplying (assuming a multiplicative model

is appropriate) these trend estimates by the corresponding seasonal relative (e.g., mul- tiply the November trend estimate by the November seasonal relative, multiply the December trend estimate by the December seasonal relative, and so on).

Example 7 illustrates these two techniques.

LO3.13 Compute and use seasonal relatives.

a. Deseasonalizing Data and b. Using Trend and Seasonal Relatives to make a Forecast

A coffee shop owner wants to estimate demand for the next two quarters for hot chocolate. Sales data consist of trend and seasonality.

a. Quarter relatives are 1.20 for the first quarter, 1.10 for the second quarter, 0.75 for the third quarter, and 0.95 for the fourth quarter. Use this information to deseasonalize sales for quarters 1 through 8.

b. Using the appropriate values of quarter relatives and the equation Ft = 124 + 7.5t for the trend component, estimate demand for periods 9 and 10.

E X A M P L E 7

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Computing Seasonal Relatives. A widely used method for computing seasonal relatives involves the use of a centered moving average. This approach effectively accounts for any trend (linear or curvilinear) that might be present in the data. For example, Figure 3.7 illustrates how a three-period centered moving average closely tracks the data originally shown in Figure 3.3.

Manual computation of seasonal relatives using the centered moving average method is a bit cumbersome, so the use of software is recommended. Manual computation is illustrated in Solved Problem 4 at the end of the chapter. The Excel template (on the website) is a simple and conve- nient way to obtain values of seasonal relatives (indexes). Example 8A illustrates this approach.

Centered moving average A moving average positioned at the center of the data that were used to compute it.

FIGURE 3.7 A centered moving average closely tracks the data

5 Period

10 15 20 25 300

D e

m a

n d

Centered MA

Data

Period Quarter Sales (gal.) ÷

Quarter Relative =

Deseasonalized Sales

1 1 158.4 ÷ 1.20 = 132.0 2 2 153.0 ÷ 1.10 = 139.1 3 3 110.0 ÷ 0.75 = 146.7 4 4 146.3 ÷ 0.95 = 154.0 5 1 192.0 ÷ 1.20 = 160.0 6 2 187.0 ÷ 1.10 = 170.0 7 3 132.0 ÷ 0.75 = 176.0 8 4 173.8 ÷ 0.95 = 182.9

b. The trend values are: Period 9: Ft = 124 + 7.5(9) = 191.5 Period 10: Ft= 124 + 7.5(10) = 199.0 Period 9 is a first quarter and period 10 is a second quarter. Multiplying each trend value by the appropriate quarter relative results in: Period 9: 191.5(1.20) = 229.8 Period 10: 199.0(1.10) = 218.9

S O L U T I O N

Computing Seasonal Relatives The manager of a call center recorded the volume of calls received between 9 and 10 a.m. for 21 days and wants to obtain a seasonal index for each day for that hour.

Day Volume Day Volume Day Volume Tues 67 Tues 60 Tues 64

Wed 75 Wed 73 Wed 76

Thurs 82 Thurs 85 Thurs 87

E X A M P L E 8 A

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For practical purposes, you can round the relatives to two decimal places. Thus, the sea- sonal (standard) index values are:

Day Index Tues 0.87

Wed 1.05

Thurs 1.20

Fri 1.37

Sat 1.24

Sun 0.53

Mon 0.75

Computing Seasonal Relatives Using the Simple Average Method. The simple average (SA) method is an alternative way to compute seasonal relatives. Each seasonal rela- tive is the average for that season divided by the average of all seasons. This method is illus- trated in Example 8B, where the seasons are days. Note that there is no need to standardize the relatives when using the SA method.

The obvious advantage of the SA method compared to the centered MA method is the simplicity of computations. When the data have a stationary mean (i.e., variation around an average), the SA method works quite well, providing values of relatives that are quite close

Day Volume Day Volume Day Volume Fri 98 Fri 99 Fri 96

Sat 90 Sat 86 Sat 88

Sun 36 Sun 40 Sun 44

Mon 55 Mon 52 Mon 50

Compute Seasonal Indexes <Back Number of "seasons" =

Tues Wed Thur Fri Sat Sun Mon

0.8688 1.0460 1.1980 1.3648 1.2383 0.5339 0.7484

0.8690 1.0463 1.1983 1.3652 1.2386 0.5341 0.7486

Season Average

Index Standard

Index

SeasonPeriod MAActual IndexCenter Tues1 67 Wed2 75 Thur3 82 Fri4 98 1.3638171 Sat5 90 1.2701613 Sun6 38 0.5101215 Mon7 55 0.7746479 Tues8 60 0.8433735 Wed9 73 1.034413 Thur10 85 1.1947791 Fri11 99 1.4 Sat12 86 1.2064128 Sun13 40 0.5577689 Mon14 62 0.7222222 Tues15 64 0.8942116 Wed16 76 1.0576541 Thur17 87 1.2011834 Fri18 96 1.3306931 Sat19 88 Sun20 44 Mon21

71.857143 70.857143 70.571429

71 71.142857 70.571429 71.142857 70.714286 71.285714 71.714286

72 71.571429 71.857143 72.428571 72.142857

71.857143 70.857143 70.571429

71 71.142857 70.571429 71.142857 70.714286 71.285714 71.714286

72 71.571429 71.857143 72.428571 72.142857

0.2

0.4

0.6

0.8

1.2

1.4

1.6

T u

e s

W e

T h

u r

F ri

S a

S u

M o

Clear

S O L U T I O N

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Computing Seasonal Relatives Using the Simple Averaging Method This example illustrates the steps needed to compute seasonal relatives using the SA method.

E X A M P L E 8 B

to those obtained using the centered MA method, which is generally accepted as accurate. Conventional wisdom is that the SA method should not be used when linear trend is present in the data. However, it can be used to obtain fairly good values of seasonal relatives as long as the ratio of the intercept to the slope is large, or when variations are large relative to the slope, shown as follows. Also, the larger the ratio, the smaller the error. The general relationship is illustrated in the following figure.

S O L U T I O N

Variations are large relative to the slope of the line, so it is okay to use the SA method.

Variations are small relative to the slope of the line, so it is not okay to use the SA method.

Step 1: Compute the season averages

Step 3: Compute the SA relatives

Step 2: Compute the overall average

Quarter Year

1 Year

2 Year

3 Quarter

Total Quarter Average

Quarter Relative

1 20 23 17 60 20 20/20 = 1.00 2 10 12 8 30 10 10/20 = 0.50 3 25 17 22 66 22 22/20 = 1.10 4 28 26 30 84 28 28/20 = 1.40

80 4.00

Average of quarter averages = 80/4 = 20

Techniques for Cycles Cycles are up-and-down movements similar to seasonal variations but of longer duration—say, two to six years between peaks. When cycles occur in time-series data, their frequent irregu- larity makes it difficult or impossible to project them from past data because turning points are difficult to identify. A short moving average or a naive approach may be of some value, although both will produce forecasts that lag cyclical movements by one or several periods.

The most commonly used approach is explanatory: Search for another variable that relates to, and leads, the variable of interest. For example, the number of housing starts (i.e., permits to build houses) in a given month often is an indicator of demand a few months later for products and services directly tied to construction of new homes (landscaping; sales of wash- ers and dryers, carpeting, and furniture; new demands for shopping, transportation, schools). Thus, if an organization is able to establish a high correlation with such a leading variable (i.e., changes in the variable precede changes in the variable of interest), it can develop an equation that describes the relationship, enabling forecasts to be made. It is important that a persistent relationship exists between the two variables. Moreover, the higher the correlation, the better the chances that the forecast will be on target.

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3.10 ASSOCIATIVE FORECASTING TECHNIQUES

Associative techniques rely on identification of related variables that can be used to predict values of the variable of interest. For example, sales of beef may be related to the price per pound charged for beef and the prices of substitutes such as chicken, pork, and lamb; real estate prices are usually related to property location and square footage; and crop yields are related to soil conditions and the amounts and timing of water and fertilizer applications.

The essence of associative techniques is the development of an equation that summarizes the effects of predictor variables. The primary method of analysis is known as regression. A brief overview of regression should suffice to place this approach into perspective relative to the other forecasting approaches described in this chapter.

Simple Linear Regression The simplest and most widely used form of regression involves a linear relationship between two variables. A plot of the values might appear like that in Figure 3.8. The object in linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations of data points from the line (i.e., the least squares criterion). This least squares line has the equation

yc = a + bx (3–13) where yc = Predicted (dependent) variable x = Predictor (independent) variable b = Slope of the line a = Value of yc when x = 0 (i.e., the height of the line at the y intercept)

(Note: It is conventional to represent values of the predicted variable on the y axis and values of the predictor variable on the x axis.) Figure 3.9 is a general graph of a linear regression line.

Predictor variables Variables that can be used to predict values of the variable of interest.

Regression Technique for fit- ting a line to a set of points.

Least squares line Minimizes the sum of the squared verti- cal deviations around the line.

LO3.14 Compute and use regression and correlation coefficients.

FIGURE 3.8 A straight line is fitted to a set of sample points

Predictor variable x0

P re

d ic

te d

v a

ri a

b le Computed

relationship

FIGURE 3.9 Equation of a straight line: The line represents the average (expected) values of variable y given values of variable x

a b =

yc = a + bx

The line intersects the y axis where y = a. The slope of the line = b.

∆x ∆x

∆y ∆y

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The coefficients a and b of the line are based on the following two equations:

b = n ( ∑ xy ) − ( ∑ x ) ( ∑ y )

_________________ n ( ∑ x 2 ) − ( ∑ x ) 2

(3–14)

a = ∑ y − b∑ x

__________ n or y ¯ − b x ¯ (3–15)

where n = Number of paired observations

E X A M P L E 9

S O L U T I O N

Determining a Regression Equation Healthy Hamburgers has a chain of 12 stores in northern Illinois. Sales figures and profits for the stores are given in the following table. Obtain a regression equation for the data, and predict profit for a store assuming sales of $10 million.

Unit Sales, x (in $ millions)

Profits, y (in $ millions)

$7 $0.15 2 0.10 6 0.13 4 0.15

14 0.25 15 0.27 16 0.24 12 0.20 14 0.27 20 0.44 15 0.34

7 0.17

First, plot the data and decide if a linear model is reasonable. (That is, do the points seem to scatter around a straight line? Figure 3.10 suggests they do.) Next, using the appropriate Excel template on the text website, obtain the regression equation yc = 0.0506 + 0.0159x (see Table 3.3). For sales of x = 10 (i.e., 10 million), estimated profit is yc = 0.0506 + 0.0159(10) = .2099, or $209,900. (Substituting x = 0 into the equation to produce a pre- dicted profit of $50,600 may appear strange because it seems to suggest that amount of profit will occur with no sales. However, the value of x = 0 is outside the range of observed values. The regression line should be used only for the range of values from which it was developed; the relationship may be nonlinear outside that range. The purpose of the a value is simply to establish the height of the line where it crosses the y axis.)

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FIGURE 3.10 A linear model seems reasonable

0 2 4 6 8 10 12 14 16 18 20

Sales ($ millions)

P ro

fit s

($ t

e n

t h

o u

sa n

d s)

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Simple Linear Regression <Back

Slope = 0. 0159 r = 0.9166657

Intercept = 0.0506008 r 2 = 0.840276

x y Forecast Error 7 0. 1 5 2 0. 1 6 0. 1 3 4 0. 1 5

1 4 0. 25 1 5 0. 27 1 6 0. 24 1 2 0. 2 1 4 0. 27 20 0. 44 1 5 0. 34 7 0. 1 7

x = 10 ∆x = 1 Forecast: 0.2099031

0.21

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25

y Forecast

Clear

0.1621124 0.0824612 0.1461822 0.1143217 0.273624

0.2895543 0.3054845 0.2417636 0.273624

0.3692054 0.2895543 0.1621124

–0.0121124 0.0175388

–0.0161822 0.0356783 –0.023624

–0.0195543 –0.0654845 –0.0417636

–0.003624 0.0707946 0.0504457 0.0078876

One indication of how accurate a prediction might be for a linear regression line is the amount of scatter of the data points around the line. If the data points tend to be relatively close to the line, predictions using the linear equation will tend to be more accurate than if the data points are widely scattered. The scatter can be summarized using the standard error of estimate. It can be computed by finding the vertical difference between each data point and the computed value of the regression equation for that value of x, squaring each differ- ence, adding the squared differences, dividing by n − 2, and then finding the square root of that value.

S e = √ __________

∑ ( y − y c ) 2 _________

n − 2 (3–16)

where Se = Standard error of estimate y = y value of each data point n = Number of data points

For the data given in Table 3.3, the error column shows the y − yc differences. Squaring each error and summing the squares yields .01659. Hence, the standard error of estimate is

S e = √ ______

.01659

______ 12 − 2

= .0407 million

One application of regression in forecasting relates to the use of indicators. These are uncontrollable variables that tend to lead or precede changes in a variable of interest. For example, changes in the Federal Reserve Board’s discount rate may influence certain business activities. Similarly, an increase in energy costs can lead to price increases for a wide range of products and services. Careful identification and analysis of indicators may yield insight into possible future demand in some situations. There are numerous published indexes and web- sites from which to choose.3 These include:

Net change in inventories on hand and on order Interest rates for commercial loans Industrial output Consumer price index (CPI)

Standard error of estimate A measure of the scatter of points around a regression line.

3 See, for example, The National Bureau of Economic Research, The Survey of Current Business, The Monthly Labor Review, and Business Conditions Digest.

TABLE 3.3 Using the Excel template for linear regression

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The wholesale price index Stock market prices

Other potential indicators are population shifts, local political climates, and activities of other firms (e.g., the opening of a shopping center may result in increased sales for nearby businesses). Three conditions are required for an indicator to be valid:

1. The relationship between movements of an indicator and movements of the variable should have a logical explanation.

2. Movements of the indicator must precede movements of the dependent variable by enough time so that the forecast isn’t outdated before it can be acted upon.

3. A fairly high correlation should exist between the two variables.

Correlation measures the strength and direction of relationship between two variables. Correlation can range from −1.00 to +1.00. A correlation of +1.00 indicates that changes in one variable are always matched by changes in the other; a correlation of −1.00 indicates that increases in one variable are matched by decreases in the other; and a correlation close to zero indicates little linear relationship between two variables. The correlation between two variables can be computed using the equation

r = n ( ∑ xy ) − ( ∑ x ) ( ∑ y )

________________________________ √

______________ n ( ∑ x 2 ) − ( ∑ x ) 2 √

______________ n ( ∑ y 2 ) − ( ∑ y ) 2 (3–17)

The square of the correlation coefficient, r2, provides a measure of the percentage of vari- ability in the values of y that is “explained” by the independent variable. The possible values of r2 range from 0 to 1.00. The closer r2 is to 1.00, the greater the percentage of explained variation. A high value of r2, say .80 or more, would indicate that the independent variable is a good predictor of values of the dependent variable. A low value, say .25 or less, would indi- cate a poor predictor, and a value between .25 and .80 would indicate a moderate predictor.

Comments on the Use of Linear Regression Analysis Use of simple regression analysis implies that certain assumptions have been satisfied. Basi- cally, these are as follows:

1. Variations around the line are random. If they are random, no patterns such as cycles or trends should be apparent when the line and data are plotted.

2. Deviations around the average value (i.e., the line) should be normally distributed. A concentration of values close to the line with a small proportion of larger deviations supports the assumption of normality.

3. Predictions are being made only within the range of observed values.

If the assumptions are satisfied, regression analysis can be a powerful tool. To obtain the best results, observe the following:

1. Always plot the data to verify that a linear relationship is appropriate. 2. The data may be time-dependent. Check this by plotting the dependent variable versus

time; if patterns appear, use analysis of time series instead of regression, or use time as an independent variable as part of a multiple regression analysis.

3. A small correlation may imply that other variables are important.

In addition, note these weaknesses of regression:

1. Simple linear regression applies only to linear relationships with one independent variable. 2. One needs a considerable amount of data to establish the relationship—in practice, 20

or more observations. 3. All observations are weighted equally.

Correlation A measure of the strength and direction of relationship between two variables.

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Period . . . . . . . . . . . 1 2 3 4 5 6 7 8 9 10 11 Units sold . . . . . . . . 20 41 17 35 25 31 38 50 15 19 14 Unemployment % (three-month lag) 7.2 4.0 7.3 5.5 6.8 6.0 5.4 3.6 8.4 7.0 9.0

1. Plot the data to see if a linear model seems reasonable. In this case, a linear model seems appropriate for the range of the data.

0 2 4 6 8 10

Level of unemployment (%), x

U n

its s

o ld

, y

Note that the equation pertains only to unemployment levels in the range 3.6 to 9.0, because sample observations covered only that range.

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E X A M P L E 1 0

S O L U T I O N

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Nonlinear and Multiple Regression Analysis Simple linear regression may prove inadequate to handle certain problems because a linear model is inappropriate or because more than one predictor variable is involved. When non- linear relationships are present, you should employ nonlinear regression; models that involve more than one predictor require the use of multiple regression analysis. While these analyses are beyond the scope of this text, you should be aware that they are often used. Multiple regression forecasting substantially increases data requirements.

3.11 MONITORING FORECAST ERROR

Many forecasts are made at regular intervals (e.g., weekly, monthly, quarterly). Because fore- cast errors are the rule rather than the exception, there will be a succession of forecast errors. Tracking the forecast errors and analyzing them can provide useful insight on whether fore- casts are performing satisfactorily.

There are a variety of possible sources of forecast errors, including the following:

1. The model may be inadequate due to (a) the omission of an important variable, (b) a change or shift in the variable that the model cannot deal with (e.g., sudden appearance of a trend or cycle), or (c) the appearance of a new variable (e.g., new competitor).

2. Irregular variations may occur due to severe weather or other natural phenomena, temporary shortages or breakdowns, catastrophes, or similar events.

3. Random variations. Randomness is the inherent variation that remains in the data after all causes of variation have been accounted for. There are always random variations.

A forecast is generally deemed to perform adequately when the errors exhibit only ran- dom variations. Hence, the key to judging when to reexamine the validity of a particular forecasting technique is whether forecast errors are random. If they are not random, it is necessary to investigate to determine which of the other sources is present and how to correct the problem.

A very useful tool for detecting nonrandomness in errors is a control chart.Errors are plotted on a control chart in the order that they occur, such as the one depicted in Figure 3.11. The center line of the chart represents an error of zero. Note the two other lines, one above and one below the center line. They are called the upper and lower control limits because they represent the upper and lower ends of the range of acceptable variation for the errors.

In order for the forecast errors to be judged “in control” (i.e., random), two things are necessary. One is that all errors are within the control limits. The other is that no patterns (e.g., trends, cycles, noncentered data) are present. Both can be accomplished by inspection. Figure 3.12 illustrates some examples of nonrandom errors.

Technically speaking, one could determine if any values exceeded either control limit without actually plotting the errors, but the visual detection of patterns generally requires plotting the errors, so it is best to construct a control chart and plot the errors on the chart.

Control chart A visual tool for monitoring forecast errors.

LO3.15 Construct control charts and use them to monitor forecast errors.

FIGURE 3.11 Conceptual representation of a control chart

Lower control limit

Range of random

variability

Error

Upper control limit Normal distribution of forecast errors

Forecast error for period 8

0 1 2 3 4 5 6 7 8 Time Period

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To construct a control chart, first compute the MSE. The square root of MSE is used in practice as an estimate of the standard deviation of the distribution of errors.4 That is,

s = √ _____

MSE (3–18)

Control charts are based on the assumption that when errors are random, they will be dis- tributed according to a normal distribution around a mean of zero. Recall that for a normal distribution, approximately 95.5 percent of the values (errors in this case) can be expected to fall within limits of 0 ± 2S (i.e., 0 ± 2 standard deviations), and approximately 99.7 percent of the values can be expected to fall within ±3s of zero. With that in mind, the following for- mulas can be used to obtain the upper control limit (UCL) and the lower control limit (LCL):

UCL: 0 + z √

_____ MSE

LCL: 0 − z √ _____

MSE

where

z = Number of standard deviations from the mean

Combining these two formulas, we obtain the following expression for the control limits:

Control limits: 0 ± z √ _____

MSE (3–19)

4The actual value could be computed as

FIGURE 3.12 Examples of nonrandomness Error above the upper control limit Trend

Cycling Bias (too many points on one side of the centerline)

Point beyond a control limit

Upper control limit

Lower control limit

Computing Control Limits for Errors Compute 2s control limits for forecast errors when the MSE is 9.0.

s = √

_____ MSE = 3.0

UCL = 0+2(3.0) = +6.0 LCL = 0−2(3.0) = − 6.0

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S O L U T I O N

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Another method is the tracking signal. It relates the cumulative forecast error to the aver- age absolute error (i.e., MAD). The intent is to detect any bias in errors over time (i.e., a tendency for a sequence of errors to be positive or negative). The tracking signal is computed period by period using the following formula:

Tracking signal t = ∑ ( Actual t − Forecast t ) ___________________

MAD t (3–20)

Values can be positive or negative. A value of zero would be ideal; limits of ± 4 or ± 5 are often used for a range of acceptable values of the tracking signal. If a value outside the acceptable range occurs, that would be taken as a signal that there is bias in the forecast, and that corrective action is needed.

After an initial value of MAD has been determined, MAD can be updated and smoothed (SMAD) using exponential smoothing:

SMAD t = MAD t−1 + α ( | Actual − Forecast | t − MAD t−1 ) (3–21)

Tracking signal The ratio of cumulative forecast error to the corresponding value of MAD, used to monitor a forecast.

Bias Persistent tendency for forecasts to be greater or less than the actual values of a time series.

a. Computing a Tracking Signal and b. Computing Control Limits Monthly attendance at financial planning seminars for the past 24 months, and forecasts and errors for those months, are shown in the following table. Determine if the forecast is working using these approaches:

1. A tracking signal, beginning with month 10, updating MAD with exponential smooth- ing. Use limits of ± 4 and α = .2.

2. A control chart with 2s limits. Use data from the first eight months to develop the con- trol chart, and then evaluate the remaining data with the control chart.

Month A

(Attendance) F

(Forecast) A – F

(Error) |e| Cumulative |e| 1 47 43 4 4 4 2 51 44 7 7 11 3 54 50 4 4 15 4 55 51 4 4 19 5 49 54 −5 5 24 6 46 48 −2 2 26 7 38 46 −8 8 34 8 32 44 −12 12 46 9 25 35 −10 10 56 10 24 26 −2 2 58 11 30 25 5 5 12 35 32 3 3 13 44 34 10 10 14 57 50 7 7 15 60 51 9 9 16 55 54 1 1 17 51 55 −4 4 18 48 51 −3 3 19 42 50 −8 8 20 30 43 −13 13 21 28 38 −10 10 22 25 27 −2 2 23 35 27 8 8 24 38 32 6 6

−11

E X A M P L E 1 2

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1. The sum of absolute errors through the 10th month is 58. Hence, the initial MAD is 58/10 = 5.8. The subsequent MADs are updated using the formula MADnew = MADold α(|e| − MADold). The results are shown in the following table.

The tracking signal for any month is

Cumulative error at that month

_________________________ Updated MAD at that month

t (Month) |e|

MADt = MADt –1 + .2(|e| − MADt –1)

Cumulative Error

Tracking Signal = Cumulative Errort ÷ MADt

10 −20 −20/5.800 = −3.45 11 5 5.640 = 5.8 + .2(5 − 5.8) −15 −15/5.640 = −2.66 12 3 5.112 = 5.640 + .2(3 − 5.64) −12 −12/5.112 = −2.35 13 10 6.090 = 5.112 + .2(10 − 5.112) −2 −2/6.090 = −0.33 14 7 6.272 = 6.090 + .2(7 − 6.090) 5 5/6.272 = 0.80 15 9 6.818 = 6.272 + .2(9 − 6.272) 14 14/6.818 = 2.05 16 1 5.654 = 6.818 + .2(1 − 6.818) 15 15/5.654 = 2.65 17 4 5.323 = 5.654 + .2(4 − 5.654) 11 11/5.323 = 2.07 18 3 4.858 = 5.323 + .2(3 - 5.323) 8 8/4.858 = 1.65 19 8 5.486 = 4.858 + .2(8 - 4.858) 0 0/5.486 = 0.00 20 13 6.989 = 5.486 + .2(13 − 5.486) −13 −13/6.989 = −1.86 21 10 7.591 = 6.989 + .2(10 − 6.989) −23 −23/7.591 = −3.03 22 2 6.473 = 7.591 + .2(2 25 7.591) −25 2525/6.473 = −3.86 23 8 6.778 = 6.473 + .2(8 − 6.473) −17 −17/6.778 = −2.51 24 6 6.622 = 6.778 + .2(6 − 6.778) −11 −11/6.622 = −1.66

Because the tracking signal is within ± 4 every month, there is no evidence of a problem.

2. a. Make sure that the average error is approximately zero, because a large average would suggest a biased forecast.

Average error = ∑ errors

_______ n =

− 11 ____

24 = − 46

b. Compute the standard deviation:

s = √

_____ MSE = √

_____

∑ e 2

_____ n − 1

= √ ____________________________________________

4 2 + 7 2 + 4 2 + 4 2 + (− 5) 2 + (− 2) 2 + (− 8) 2 + (− 12) 2

____________________________________________ 8 − 1

= 6.91

c. Determine 2s control limits:

0 ± 2s = 0 ± 2(6.91) = −13.82 to +13.82

d. (1) Check that all errors are within the limits. (They are.) (2) Plot the data (see the following graph), and check for nonrandom patterns. Note

the strings of positive and negative errors. This suggests nonrandomness (and that an improved forecast is possible). The tracking signal did not reveal this.

10 5 0

0 4 8 12 16 20 24

-5 -10 -15

Month

E rr

o r

S O L U T I O N

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A plot helps you to visualize the process and enables you to check for possible patterns (i.e., nonrandomness) within the limits that suggest an improved forecast is possible.5

Like the tracking signal, a control chart focuses attention on deviations that lie outside pre- determined limits. With either approach, however, it is desirable to check for possible patterns in the errors, even if all errors are within the limits.

If nonrandomness is found, corrective action is needed. That will result in less variability in forecast errors, and, thus, in narrower control limits. (Revised control limits must be com- puted using the resulting forecast errors.) Figure 3.13 illustrates the impact on control limits due to decreased error variability.

Comment The control chart approach is generally superior to the tracking signal approach. A major weakness of the tracking signal approach is its use of cumulative errors: Individual errors can be obscured so that large positive and negative values cancel each other. Con- versely, with control charts, every error is judged individually. Thus, it can be misleading to rely on a tracking signal approach to monitor errors. In fact, the historical roots of the tracking signal approach date from before the first use of computers in business. At that time, it was much more difficult to compute standard deviations than to compute average deviations; for that reason, the concept of a tracking signal was developed. Now computers and calculators can easily provide standard deviations. Nonetheless, the use of tracking signals has persisted, probably because users are unaware of the superiority of the control chart approach.

3.12 CHOOSING A FORECASTING TECHNIQUE

Many different kinds of forecasting techniques are available, and no single technique works best in every situation. When selecting a technique, the manager or analyst must take a num- ber of factors into consideration.

The two most important factors are cost and accuracy. How much money is budgeted for generating the forecast? What are the possible costs of errors, and what are the benefits that might accrue from an accurate forecast? Generally speaking, the higher the accuracy, the higher the cost, so it is important to weigh cost–accuracy trade-offs carefully. The best fore- cast is not necessarily the most accurate or the least costly; rather, it is some combination of accuracy and cost deemed best by management.

Other factors to consider in selecting a forecasting technique include the availability of his- torical data; the availability of computer software; and the time needed to gather and analyze data and to prepare the forecast. The forecast horizon is important because some techniques are more suited to long-range forecasts while others work best for the short range. For exam- ple, moving averages and exponential smoothing are essentially short-range techniques, since they produce forecasts for the next period. Trend equations can be used to project over much longer time periods. When using time-series data, plotting the data can be very helpful in choosing an appropriate method. Several of the qualitative techniques are well suited to long- range forecasts because they do not require historical data. The Delphi method and executive

LO3.16 Describe four qualitative forecasting techniques.

FIGURE 3.13

Removal of a pattern usually results in less variability, and, hence, narrower control limits

Before Time

+ 0 -

After Time

5 The theory and application of control charts and the various methods for detecting patterns in the data are cov- ered in more detail in Chapter 10, on quality control.

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opinion methods are often used for long-range planning. New products and services lack his- torical data, so forecasts for them must be based on subjective estimates. In many cases, expe- rience with similar items is relevant. Table 3.4 provides a guide for selecting a forecasting method. Table 3.5 provides additional perspectives on forecasts in terms of the time horizon.

In some instances, a manager might use more than one forecasting technique to obtain independent forecasts. If the different techniques produced approximately the same predic- tions, that would give increased confidence in the results; disagreement among the forecasts would indicate that additional analysis may be needed.

3.13 USING FORECAST INFORMATION

A manager can take a reactive or a proactive approach to a forecast. A reactive approach views forecasts as probable future demand, and a manager reacts to meet that demand (e.g., adjusts production rates, inventories, the workforce). Conversely, a proactive approach seeks to actively influence demand (e.g., by means of advertising, pricing, or product/service changes).

Source: Adapted from J. Holton Wilson and Deborah Allison-Koerber, “Combining Subjective and Objective Forecasts Improves Results,” Journal of Business Forecasting, Fall 1992, p. 4. Copyright © 1992 Institute of Business Forecasting. Used with permission.

TABLE 3.4 A guide to selecting an appropriate forecasting method

Forecasting Method

Amount of Historical Data

Data Pattern Forecast Horizon

Preparation Time

Personnel Background

Moving average 2 to 30 observations Variation around an average

Short Short Little sophistication

Simple exponential smoothing

5 to 10 observations Variation around an average

Short Short Little sophistication

Trend-adjusted exponential smoothing

10 to 15 observations Trend Short to medium Short Moderate sophistication

Trend models 10 to 20; for seasonality at least 5 per season

Trend Short to medium Short Moderate sophistication

Seasonal Enough to see 2 peaks and troughs

Handles cyclical and seasonal patterns

Short to medium Short to moderate Little sophistication

Causal regression models

10 observations per independent variable

Can handle complex patterns

Short, medium, or long

Long development time, short time for implementation

Considerable sophistication

Factor Short Range Intermediate Range Long Range

1. Frequency Often Occasional Infrequent

2. Level of aggregation Item Product family Total output Type of

product/service

3. Type of model Smoothing Projection Regression

Projection Seasonal Regression

Managerial judgment

4. Degree of management involvement

Low Moderate High

5. Cost per forecast Low Moderate High

TABLE 3.5 Forecast factors, by range of forecast

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Generally speaking, a proactive approach requires either an explanatory model (e.g., regression) or a subjective assessment of the influence on demand. A manager might make two forecasts—one to predict what will happen under the status quo and a second one based on a “what if” approach, if the results of the status quo forecast are unacceptable.

3.14 COMPUTER SOFTWARE IN FORECASTING

Computers play an important role in preparing forecasts based on quantitative data. Their use allows managers to develop and revise forecasts quickly, and without the burden of manual computations. There is a wide range of software packages available for forecasting. The Excel templates on the text website are an example of a spreadsheet approach. There are templates for moving averages, exponential smoothing, linear trend equation, trend-adjusted exponen- tial smoothing, and simple linear regression. Some templates are illustrated in the Solved Problems section at the end of the chapter.

3.15 OPERATIONS STRATEGY

Forecasts are the basis for many decisions and an essential input for matching supply and demand. Clearly, the more accurate an organization’s forecasts, the better prepared it will be to take advantage of future opportunities and reduce potential risks. A worthwhile strat- egy can be to work to improve short-term forecasts. Better short-term forecasts will not only enhance profits through lower inventory levels, fewer shortages, and improved customer ser- vice, they also will enhance forecasting credibility throughout the organization: If short-term forecasts are inaccurate, why should other areas of the organization put faith in long-term forecasts? Also, the sense of confidence accurate short-term forecasts would generate would allow allocating more resources to strategic and medium- to longer-term planning and less on short-term, tactical activities.

Maintaining accurate, up-to-date information on prices, demand, and other variables can have a significant impact on forecast accuracy. An organization also can do other things to improve forecasts. These do not involve searching for improved techniques but relate to the inverse relation of accuracy to the forecast horizon: Forecasts that cover shorter time frames tend to be more accurate than longer-term forecasts. Recognizing this, management might choose to devote efforts to shortening the time horizon that forecasts must cover. Essentially, this means shortening the lead time needed to respond to a forecast. This might involve build- ing flexibility into operations to permit rapid response to changing demands for products and services, or to changing volumes in quantities demanded; shortening the lead time required to obtain supplies, equipment, and raw materials or the time needed to train or retrain employ- ees; or shortening the time needed to develop new products and services.

Lean systems are demand driven; goods are produced to fulfill orders rather than to hold in inventory until demand arises. Consequently, they are far less dependent on short-term fore- casts than more traditional systems.

In certain situations forecasting can be very difficult when orders have to be placed far in advance. This is the case, for example, when demand is sensitive to weather conditions, such as the arrival of spring, and there is a narrow window for demand. Orders for products or services that relate to this (e.g., garden materials, advertising space) often have to be placed many months in advance—far beyond the ability of forecasters to accurately predict weather conditions and, hence, the timing of demand. In such cases, there may be pressures from salespeople who want low quotas and financial people who don’t want to have to deal with the cost of excess inventory to have conservative forecasts. Conversely, operations people may want more optimistic forecasts to reduce the risk of being blamed for possible shortages.

Sharing forecasts or demand data throughout the supply chain can improve forecast qual- ity in the supply chain, resulting in lower costs and shorter lead times. For example, both Hewlett-Packard and IBM require resellers to include such information in their contracts.

The following reading provides additional insights on forecasting and supply chains.

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RAM REDDY

Disregarding Demand Forecasting Technologies during Tough Economic Times Can Be a Costly Mistake It’s no secret that the IT sector has felt the brunt of the economic downturn. Caught up in the general disillusionment with IT has been demand forecasting (DF) technologies. Many companies blame DF technologies for supply chain problems such as excess inventory. Pinning the blame on and discontinuing DF technolo- gies is the equivalent of throwing out the baby with the bathwater. The DF misunderstanding stems from the fact that, despite sophis- ticated mathematical models and underlying technologies, the output from these systems is, at best, an educated guess about the future.

A forecast from these systems is only as good as the assump- tions and data used to build the forecast. Even the best forecast fails when an unexpected event—such as a recession—clobbers the underlying assumptions. However, this doesn’t imply that DF technologies aren’t delivering the goods. But, unfortunately, many DF and supply chain technology implementations have recently fallen victim to this mindset. DF is part science and part art (or intuition)—having the potential to significantly impact a company’s bottom line. In this column, you’ll find an overview of how DF is supposed to work and contrast that with how most companies actually practice it. I’ll conclude with suggestions on how to avoid common mistakes implementing and using this particular class of technologies.

The Need for DF Systems DF is crucial to minimizing working capital and associated expenses and extracting maximum value from a company’s capi- tal investments in property, plant, and equipment (PPE). It takes a manufacturing company a lot of lead time to assemble and stage the raw materials and components to manufacture a given number of products per day. The manufacturing company, in turn, generates its sales forecast numbers using data from a variety of sources such as distribution channels, factory outlets, value- added resellers, historical sales data, and general macroeconomic data. Manufacturing companies can’t operate without a demand forecast because they won’t know the quantities of finished goods to produce. The manufacturing company wants to make sure all or much of its finished product moves off the store shelves or dealer lots as quickly as possible. Unsold products represent millions of dollars tied up in inventory.

The flip side of this equation is the millions of dollars invested in PPE to manufacture the finished products. The company and its supporting supply chain must utilize as close to 100 percent of its PPE investments. Some manufacturing plants make prod- ucts in lots of 100 or 1,000. Generally, it’s cost prohibitive to have

READING GAZING AT THE CRYSTAL BALL

production runs of one unit. So how do you extract maximum value from your investments and avoid having money tied up in unsold inventory?

DF and supply chain management (SCM) technologies try to solve this problem by generating a production plan to meet forecasted demand and extract maximum value from PPE, while reducing the amount of capital tied up in inventory. Usually, the demand forecast is pretty close to the actual outcomes, but there are times when demand forecasts don’t match the outcomes. In addition to unforeseen economic events, a new product introduc- tion may be a stellar success or an abysmal failure. In the case of a phenomenal success, the manufacturing plant may not be able to meet demand for its product.

Consider the case of the Chrysler PT Cruiser. It succeeded way beyond the demand forecast’s projections. Should it have started with manufacturing capacity to fulfill the runaway demand? Abso- lutely not. Given the additional millions of dollars of investment in PPE necessary to add that capacity, it would’ve backfired if the PT Cruiser had been a flop. The value provided by DF and sup- porting SCM technologies in this instance was the ability to add capacity to meet the amended forecast based on actual events. Demand forecasts can and do frequently miss their targets. The point to underscore here is that the underlying DF and support- ing SCM technologies are critical to a company’s ability to react and respond in a coordinated manner when market conditions change.

The manufacturing company and its supply chain are able to benefit from sharing information about the changed market condi- tions and responding to them in a coordinated manner. Despite best practices embedded in DF and SCM technologies to support this manner of collaboration, it plays out differently in the real world.

How It Works in Real Life—Worst Practices A company prepares its forecast by taking into account data about past sales, feedback from distribution channels, quali- tative assessments from field sales managers, and macroeco- nomic data. DF and SCM technologies take these inputs and add existing capacities within the company and across the sup- ply chain to generate a production plan for optimum financial performance.

There’s been incredible pressure on executives of publicly traded companies to keep up stock prices. This pressure, among other reasons, may cause manufacturing company executives to make bold projections to external financial analysts (or Wall Street) about future sales without using the demand forecast generated from the bottom up. When the company realizes this disparity between the initial projection and the forecast, the fore- cast is changed to reflect the projections made by the company’s officers, negating its accuracy.

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Forecasts are vital inputs for the design and the operation of the productive systems because they help managers to anticipate the future.

Forecasting techniques can be classified as qualitative or quantitative. Qualitative techniques rely on judgment, experience, and expertise to formulate forecasts; quantitative techniques rely on the use of historical data or associations among variables to develop forecasts. Some of the techniques are simple, and others are complex. Some work better than others, but no technique works all the time. Moreover, all forecasts include a certain degree of inaccuracy, and allowance should be made for this. The techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

The qualitative techniques described in this chapter include consumer surveys, salesforce estimates, executive opinions, and manager and staff opinions. Two major quantitative approaches are described: analysis of time-series data and associative techniques. The time-series techniques rely strictly on the examination of historical data; predictions are made by projecting past movements of a variable into the future without considering specific factors that might influence the variable. Associative techniques

SUMMARY

The company arbitrarily sets sales targets for various regions to meet Wall Street numbers that are totally out of sync with input provided by the regional sales managers for the DF process. Even though the regional sales managers’ input may have a qualitative element (art), they tend to be more accurate, given their proximity to the customers in the region. Unfortunately, the arbitrary sales targets make their way back to the supply chain, and the result is often excessive inventory build-up starting at the distribution channels to the upstream suppliers.

Seeing the inventory pile up, the manufacturing company may decide to shut down a production line. This action affects upstream suppliers who had procured raw materials and com- ponents to meet the executive-mandated production numbers, which may cause them to treat any future forecasted numbers with suspicion. Most cost efficiencies that could be obtained through planned procurement of raw materials and components go out the window. It’s very likely that the companies try to blame DF and SCM technologies for failing to provide a responsive and efficient supply chain, even though the fault may lie in the company’s misuse of the technologies and not the technologies themselves.

Guarding against the Extremes Earlier in this column, I said that DF is part art or intuition and part science. The art/intuition part comes in when subject- matter experts (SMEs) make educated estimates about future sales. These SMEs could range from distribution outlet owners to sales and marketing gurus and economists. Their intuition is typically combined with data (such as historical sales fig- ures) to generate the forecast for the next quarter or year. Dur- ing a recession, the SMEs tend to get overly pessimistic. The demand forecasts generated from this mindset lead to inven- tory shortages when the economy recovers. Similarly, during

an economic expansion, the SMEs tend to have an overly rosy picture of the future. This optimism leads to inventory gluts when the economy starts to slow down. In both instances, blaming and invalidating DF and SCM technologies is counter- productive in the long run.

It’s very rare that a demand forecast and the actual outcome match 100 percent. If it’s close enough to avoid lost sales or cre- ate an excess inventory situation, it’s deemed a success. DF and supporting SCM technologies are supposed to form a closed loop with actual sales at the cash register providing a feedback mech- anism. This feedback is especially essential during economic upturns or downturns. It provides the necessary information to a company and its supply chain to react in a coordinated and effi- cient manner.

Don’t let the current disillusionment with DF and SCM technol- ogies impede the decision-making process within your company. The intelligent enterprise needs these technologies to effectively utilize its capital resources and efficiently produce to meet its sales forecasts.

Ram Reddy is the author of Supply Chains to Virtual Integra- tion (McGraw-Hill, 2001). He is the president of Tactica Consulting Group, a technology and business strategy consulting company.

Questions 1. What is DF and why is it important? 2. Why might a company executive make bold predictions about

future demand to Wall Street analysts? 3. How might an executive’s comments to Wall Street analysts

affect demand forecasts, and what are the consequences of doing so?

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attempt to explicitly identify influencing factors and to incorporate that information into equations that can be used for predictive purposes.

All forecasts tend to be inaccurate; therefore, it is important to provide a measure of accuracy. It is possible to compute several measures of forecast accuracy that help managers to evaluate the perfor- mance of a given technique and to choose among alternative forecasting techniques. Control of forecasts involves deciding whether a forecast is performing adequately, typically using a control chart.

When selecting a forecasting technique, a manager must choose a technique that will serve the intended purpose at an acceptable level of cost and accuracy.

The various forecasting techniques are summarized in Table 3.6. Table 3.7 lists the formulas used in the forecasting techniques and in the methods of measuring their accuracy. Note that the Excel templates on the text website that accompanies this book are especially useful for tedious calculations.

TABLE 3.6 Forecasting approaches

Approaches Brief Description

Judgment/opinion: Consumer surveys Questioning consumers on future plans

Direct-contact composites Joint estimates obtained from salespeople or customer service people

Executive opinion Finance, marketing, and manufacturing managers join to prepare forecast

Delphi technique Series of questionnaires answered anonymously by knowledgeable people; successive questionnaires are based on information obtained from previous surveys

Outside opinion Consultants or other outside experts prepare the forecast

Statistical: Time series:

Naive Next value in a series will equal the previous value in a comparable period

Moving averages Forecast is based on an average of recent values

Exponential smoothing Sophisticated form of weighted moving average

Associative models:

Simple regression Values of one variable are used to predict values of a dependent variable

Multiple regression Two or more variables are used to predict values of a dependent variable

TABLE 3.7 Summary of formulas

Technique Formula Definitions

MAD MAD = ∑ n

| e | _____

MAD = Mean absolute deviation e = Error, A − F n = Number of errors

MSE MSE = ∑ n

e 2 _____

n − 1

MSE = Mean square error n = Number of errors

MAPE MAPE =

∑ n

[ | e t |

______ Actual t

× 100 ] _______________ n

MAPE = Mean absolute percent error n = Number of errors

Moving average forecast

F t = ∑ i = 1

n A t − i

_______ n

A = Demand in period t − i n = Number of periods

Weighted average F t = w t−n ( A t−n ) + ⋅ ⋅ ⋅ + w t−2 ( A t−2 ) + w t−1 ( A t−1 ) + w t−1 ( A t−1 ) + ⋅ ⋅ ⋅ + w t−n ( A t−n )

wt = Weight for the period t At = Actual value in period t

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Technique Formula Definitions

Exponential smoothing forecast

Ft = Ft – 1 + α(At – 1 − Ft – 1) α = Smoothing factor

Linear trend forecast

F t = a + b t where

b = n∑ ty − ∑ t∑ y

______________ n∑ t 2 − ( ∑ t 2 )

a = ∑ y − b∑ t

______ n

or ̄ y − b ̄ t

a = y intercept b = Slope

Trend-adjusted forecast

TAF t+1 = S t + T t where S t = TAF t + α ( A t − TAF t ) T t = T t−1 + β ( TAF t − TAF t−1 − T t−1 )

t = Current period TAF t+1 = Trend-adjusted forecast for

next period S = Previous forecast plus

smoothed error T = Trend component

Linear regression forecast

Y c = a + bx where

b = n (∑ xy) − (∑ x) (∑ y)

___________________ n (∑ x 2 ) − ( ∑ x 2 )

a = ∑ y − b∑ x

______ n

or ̄ y − b ̄ x

y c = Computed value of dependent variable

x = Predictor (independent) variable b = Slope of the line a = Value of y c when x = 0

Standard error of estimate S e = √

________

∑ ( y − y c ) 2

_______ n − 2

S e = Standard error of estimate

y = y value of each data point n = Number of data points

Tracking signal TS t =

∑ n

e _____

MAD

Control limits UCL = 0 + z √ _____

MSE LCL = 0 − z √

_____ MSE

√

_____ MSE = standard deviation

z = Number of standard deviations;

2 and 3 are typical values

KEY POINTS 1. Demand forecasts are essential inputs for many business decisions; they help managers decide

how much supply or capacity will be needed to match expected demand, both within the organiza- tion and in the supply chain.

2. Because of random variations in demand, it is likely that the forecast will not be perfect, so man- agers need to be prepared to deal with forecast errors.

3. Other, nonrandom factors might also be present, so it is necessary to monitor forecast errors to check for nonrandom patterns in forecast errors.

4. It is important to choose a forecasting technique that is cost-effective and one that minimizes fore- cast error.

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associative models, 82 bias, 108 centered moving average, 98 control chart, 106 correlation, 104 cycles, 84 Delphi method, 83 error, 80 exponential smoothing, 89 focus forecasting, 91 forecast, 75 forecasts, 76 irregular variations, 84

KEY TERMS judgmental forecasts, 82 least squares line, 101 linear trend equation, 92 mean absolute deviation

(MAD), 81 mean absolute percent error

(MAPE), 81 mean squared error (MSE), 81 moving average, 86 naive forecast, 84 predictor variables, 101 random variations, 84 regression, 101

seasonality, 84 seasonal relatives, 96 seasonal variations, 95 standard error of estimate, 103 time series, 84 time-series forecasts, 82 tracking signal, 108 trend, 84 trend-adjusted exponential

smoothing, 95 weighted average, 88

Forecasts based on averages. Given the following data:

Period Number of Complaints

1 60

2 65

3 55

4 58

5 64

Prepare a forecast for period 6 using each of these approaches:

a. The appropriate naive approach. b. A three-period moving average. c. A weighted average using weights of .50 (most recent), .30, and .20. d. Exponential smoothing with a smoothing constant of .40.

Problem 1

SOLVED PROBLEMS

Solution Step by step

a. Plot the data to see if there is a pattern. Here we have only variations around an average (i.e., no trend or cycles). Therefore, the most recent value of the series becomes the next forecast: 64.

b. Use the latest values. MA 3 = 55 + 58 + 64

___________ 3 = 59

c. F = .20(55) + .30(58) + .50(64) = 60.4 d. Start with period 2. Use the data in period 1 as the forecast for period 2, and then use exponential

smoothing for successive forecasts.

Period Number of Complaints Forecast Calculations

1 60 [The previous value of the series is used 2 65 60 as the starting forecast.]

3 55 62 60 + .40(65 – 60) = 62 4 58 59.2 62 + .40(55 – 62) = 59.2 5 64 58.72 59.2 + .40(58 – 59.2) = 58.72 6 60.83 58.72 + .40(64 – 58.72) = 60.83

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Problem 2 Using seasonal relatives. Apple’s Citrus Fruit Farm ships boxed fruit anywhere in the world. Using the following information, a manager wants to forecast shipments for the first four months of next year.

Month Seasonal Relative Month

Seasonal Relative

Jan. 1.2 Jul. 0.8

Feb. 1.3 Aug. 0.6

Mar. 1.3 Sep. 0.7

Apr. 1.1 Oct. 1.0

May. 0.8 Nov. 1.1

Jun. 0.7 Dec. 1.4

The monthly forecast equation being used is:

Ft = 402 + 3t

where

t0 = January of last year Ft = Forecast of shipments for month t

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You also can obtain the forecasts and a plot using an Excel template, as shown:

Solution a. Determine trend amounts for the first four months of next year: January, t = 24; February, t = 25; etc. Thus, F Jan = 402 + 3(24) = 474

F Feb = 402 + 3(25) = 477

F Mar = 402 + 3(26) = 480

F Apr = 402 + 3(27) = 483 b. Multiply each monthly trend by the corresponding seasonal relative for that month.

Month Seasonal Relative Forecast

Jan. 1.2 474(1.2) = 568.8

Feb. 1.3 477(1.3) = 620.1

Mar. 1.3 480(1.3) = 624.0

Apr. 1.1 483(1.1) = 531.3

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Linear trend line. Plot the data on a graph, and verify visually that a linear trend line is appropriate. Develop a linear trend equation for the following data. Then use the equation to predict the next two values of the series.

Problem 3

2 4 6 8 10

Period

D e

m a

n d

1 3 5 7 9 11

Trend line

SolutionPeriod Demand 1 44

2 52

3 50

4 54

5 55

6 55

7 60

8 56

9 62

A plot of the data indicates that a linear trend line is appropriate:

Period, t t2

Demand, y ty

1 1 44 44

2 4 52 104

3 9 50 150 Σt = 45 and Σt2 = 285 4 16 54 216

5 25 55 275

6 36 55 330

7 49 60 420

8 64 56 448

9 81 62 558

45 285 488 2,545

b =

n∑ ty − ∑ t∑ y _____________

n∑ t 2 − (∑ t ) 2 =

9(2, 545) − 45(488) ________________

9(285) − 45(45) = 1.75

a = ∑ y − b∑ t

_________ n =

488 − 1.75(45) _____________

9 = 45.47

Thus, the trend equation is Ft = 45.47 + 1.75t. The next two forecasts are:

F 10 = 45.47 + 1.75(10) = 62.97 F 11 = 45.47 + 1.75(11) = 64.72

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Problem 4

You also can use an Excel template to obtain the coefficients and a plot. Simply replace the existing data in the template with your data.

Seasonal relatives. Obtain estimates of quarter relatives for these data using the centered moving average method:

YEAR 1 2 3 4 Quarter: 1 2 3 4 1 2 3 4 1 2 3 4 1

Demand: 14 18 35 46 28 36 60 71 45 54 84 88 58

Solution Note that each season has an even number of data points. When an even-numbered moving average is used (in this case, a four-period moving average), the “centered value” will not correspond to an actual data point; the center of 4 is between the second and third data points. To correct for this, a second set of moving averages must be computed using the MA4 values. The MA2 values are centered between the MA4 and “line up” with actual data points. For example, the first MA4 value is 28.25. It is centered between 18 and 35 (i.e., between quarter 2 and quarter 3). When the average of the first two MA4 values is taken (i.e., MA2) and centered, it lines up with the 35 and, hence, with quarter 3.

So, whenever an even-numbered moving average is used as a centered moving average (e.g., MA4, MA12), a second moving average, a two-period moving average, is used to achieve correspon- dence with periods. This procedure is not needed when the number of periods in the centered moving average is odd. Year Quarter Demand MA4 MA2 Demand/MA2 1 1 14

2 18 28.25

3 35 31.75 30.00 1.17

4 46 36.25 34.00 1.35

2 1 28 42.50 39.38 0.71

2 36 48.75 45.63 0.79

3 60 53.00 50.88 1.18

4 71 57.50 55.25 1.29

3 1 45 63.50 60.50 0.74

2 54 67.75 65.63 0.82

3 84 71.00 69.38 1.21 4 88 4 1 58

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QUARTER 1 2 3 4

0.71 0.79 1.17 1.35

0.74 0.82 1.18 1.29

1.45 1.61 1.21 2.64

3.56

Average for the quarter: 0.725 0.805 1.187 1.320

The sum of these relatives is 4.037. Multiplying each by 4.00/4.037 will standardize the relatives, making their total equal 4.00. The resulting relatives are quarter 1, .718; quarter 2, .798; quarter 3, 1.176; quarter 4, 1.308.

Problem 5Regression line. A large midwestern retailer has developed a graph that summarizes the effect of advertising expenditures on sales volume. Using the graph, determine an equation of the form y = a + bx that describes this relationship.

2 4 6 8 10 x

Advertising ($ thousands)

S a

le s

($ m

ill io

n s)

The linear equation has the form y = a + bx, where a is the value of y when x = 0 (i.e., where the line intersects the y axis) and b is the slope of the line (the amount by which y changes for a one-unit change in x).

Accordingly, a = 1 and b = (3 − 1)/(10 − 0) = .2, so y = a + bx becomes y = 1 + .2x. [Note: (3 − 1) is the change in y, and (10 − 0) is the change in x.]

Solution

Problem 6Regression analysis. The owner of a small hardware store has noted a sales pattern for window locks that seems to parallel the number of break-ins reported each week in the newspaper. The data are:

Sales: 46 18 20 22 27 34 14 37 30

Break-ins: 9 3 3 5 4 7 2 6 4

a. Plot the data to determine which type of equation, linear or nonlinear, is appropriate. b. Obtain a regression equation for the data. c. Estimate average sales when the number of break-ins is five.

Solution

The graph supports a linear relationship.

0 2 4 6 8 10

Number of break-ins

S a

le s

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b. You can obtain the regression coefficients using the appropriate Excel template. Simply replace the existing data for x and y with your data. Note: Be careful to enter the values for the variable you want to predict as y values. In this problem, the objective is to predict sales, so the sales val- ues are entered in the y column. The equation is yc = 7.129 + 4.275x.

c. For x = 5, yc = 7.129 + 4.275(5) = 28.50.

Problem 7 Accuracy of forecasts. The manager of a large manufacturer of industrial pumps must choose between two alternative forecasting techniques. Both techniques have been used to prepare forecasts for a six- month period. Using MAD as a criterion, which technique has the better performance record?

FORECAST

Month Demand Technique 1 Technique 2

1 492 488 495

2 470 484 482

3 485 480 478

4 493 490 488

5 498 497 492

6 492 493 493

Solution Check that each forecast has an average error of approximately zero. (See computations that follow.) Month Demand Technique 1 e |e| Technique 2 e |e|

1 492 488 4 4 495 −3 3

2 470 484 −14 14 482 −12 12

3 485 480 5 5 478 7 7

4 493 490 3 3 488 5 5

5 498 497 1 1 492 6 6

6 492 493 −1 1 493 −1 1

−2 28 +2 34

MAD 1 =

∑ | e | ____ n =

28 ___

6 = 4.67

MAD 2 =

∑ | e | ____ n =

34 ___

6 = 5.67

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Technique 1 is superior in this comparison because its MAD is smaller, although six observations would generally be too few on which to base a realistic comparison.

Problem 8Control chart. Given the demand data that follow, prepare a naive forecast for periods 2 through 10. Then determine each forecast error, and use those values to obtain 2s control limits. If demand in the next two periods turns out to be 125 and 130, can you conclude that the forecasts are in control?

Period: 1 2 3 4 5 6 7 8 9 10

Demand: 118 117 120 119 126 122 117 123 121 124

SolutionFor a naive forecast, each period’s demand becomes the forecast for the next period. Hence, the forecasts and errors are:

Period Demand Forecast Error Error2

1 118 — — —

2 117 118 −1 1

3 120 117 3 9

4 119 120 −1 1

5 126 119 7 49

6 122 126 −4 16

7 117 122 −5 25

8 123 117 6 36

9 121 123 −2 4

10 124 121 3 9

+6 150

s = √ _______

∑ Error 2

_______ n − 1

= √ ____

150

____ 9 − 1

= 4.33 (n = Number of errors)

The control limits are 2(4.33) = ± 8.66. The forecast for period 11 was 124. Demand turned out to be 125, for an error of 125 − 124 = +1.

This is within the limits of ± 8.66. If the next demand is 130 and the naive forecast is 125 (based on the period 11 demand of 125), the error is + 5. Again, this is within the limits, so you cannot con- clude the forecast is not working properly. With more values—at least five or six—you could plot the errors to see whether you could detect any patterns suggesting the presence of nonrandomness.

1. What are the main advantages that quantitative techniques for forecasting have over qualitative techniques? What limitations do quantitative techniques have?

2. What are some of the consequences of poor forecasts? Explain. 3. List the specific weaknesses of each of these approaches to developing a forecast:

a. Consumer surveys. b. Salesforce composite. c. Committee of managers or executives.

4. Forecasts are generally wrong. a. Why are forecasts generally wrong? b. Explain the term “wrong” as it pertains to a good forecast.

5. What is the purpose of establishing control limits for forecast errors? 6. What factors would you consider in deciding whether to use wide or narrow control limits for forecasts? 7. Contrast the use of MAD and MSE in evaluating forecasts. 8. What advantages as a forecasting tool does exponential smoothing have over moving averages? 9. How does the number of periods in a moving average affect the responsiveness of the forecast? 10. What factors enter into the choice of a value for the smoothing constant in exponential smoothing? 11. How accurate is your local five-day weather forecast? Support your answer with actual data.

DISCUSSION AND REVIEW QUESTIONS

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12. Explain how using a centered moving average with a length equal to the length of a season elimi- nates seasonality from a time series.

13. Contrast the terms sales and demand. 14. Contrast the reactive and proactive approaches to forecasting. Give several examples of types of

organizations or situations in which each type is used. 15. Explain how flexibility in production systems relates to the forecast horizon and forecast accuracy. 16. How is forecasting in the context of a supply chain different from forecasting for just a single

organization? List possible supply chain benefits and discuss potential difficulties in doing supply chain forecasting.

17. Which type of forecasting approach, qualitative or quantitative, is better? 18. Suppose a software producer is about to release a new version of its popular software. What infor-

mation do you think it would take into account in forecasting initial sales? 19. Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend, or

associative) that would be most appropriate for predicting: a. Demand for Mother’s Day greeting cards. b. Popularity of a new television series. c. Demand for vacations on the moon. d. The impact a price increase of 10 percent would have on sales of orange marmalade. e. Demand for toothpaste in a particular supermarket.

1. Explain the trade-off between responsiveness and stability in a forecasting system that uses time-series data.

2. Who needs to be involved in preparing forecasts? 3. How has technology had an impact on forecasting?

TAKING STOCK

PROBLEMS

1. It has been said that forecasting using exponential smoothing is like driving a car by looking in the rear-view mirror. What are the conditions that would have to exist for driving a car that are analo- gous to the assumptions made when using exponential smoothing?

2. What capability would an organization have to have to not need forecasts? 3. When a new business is started, or a patent idea needs funding, venture capitalists or investment

bankers will want to see a business plan that includes forecast information related to a profit and loss statement. What type of forecasting information do you suppose would be required?

4. Discuss how you would manage a poor forecast. 5. Omar has heard from some of his customers that they will probably cut back on order sizes in the

next quarter. The company he works for has been reducing its sales force due to falling demand and he worries that he could be next if his sales begin to fall off. Believing that he may be able to con- vince his customers not to cut back on orders, he turns in an optimistic forecast of his next quarter sales to his manager. What are the pros and cons of doing that?

6. Give three examples of unethical conduct involving forecasting and the ethical principle each violates.

CRITICAL THINKING EXERCISES

1. A commercial bakery has recorded sales (in dozens) for three products, shown as follows:

Day Blueberry

Muffins Cinnamon

Buns Cupcakes 1 30 18 45

2 34 17 26

3 32 19 27

4 34 19 23

5 35 22 22

6 30 23 48

7 34 23 29

8 36 25 20

9 29 24 14

10 31 26 18

11 35 27 47

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Day Blueberry

Muffins Cinnamon

Buns Cupcakes 12 31 28 26

13 37 29 27

14 34 31 24

15 33 33 22

a. Predict orders for the following day for each of the products using an appropriate naive method. Hint: Plot each data set.

b. What should the use of sales data instead of demand imply? 2. National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven-month period

were as follows:

Month Sales (000 units) Feb. 19

Mar. 18

Apr. 15

May 20

Jun. 18

Jul. 22

Aug. 20

a. Plot the monthly data on a sheet of graph paper. b. Forecast September sales volume using each of the following: (1) The naive approach (2) A five month moving average (3) A weighted average using .60 for August, .30 for July, and .10 for June (4) Exponential smoothing with a smoothing constant equal to .20, assuming a a March forecast of

19(000) (5) A linear trend equation c. Which method seems least appropriate? Why? (Hint: Refer to your plot from part a.) d. What does use of the term sales rather than demand presume?

3. A dry cleaner uses exponential smoothing to forecast equipment usage at its main plant. August usage was forecasted to be 88 percent of capacity; actual usage was 89.6 percent of capacity. A smoothing constant of .1 is used. a. Prepare a forecast for September. b. Assuming actual September usage of 92 percent, prepare a forecast for October usage.

4. An electrical contractor’s records during the last five weeks indicate the number of job requests:

Week 1 2 3 4 5

Requests 20 22 18 21 22

Predict the number of requests for week 6 using each of these methods: a. Naive b. A four-period moving average c. Exponential smoothing with α = .30; use 20 for week 2 forecast

5. A cosmetics manufacturer’s marketing department has developed a linear trend equation that can be used to predict annual sales of its popular Hand & Foot Cream. Ft = 80 + 15t

where F t = Annual sales (000 bottles) t is in years a. Are annual sales increasing or decreasing? By how much? b. Predict annual sales for year 6 using the equation.

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600

500

400

300

200

100

Year

S a

le s

(u n

its )

1 2 3 4 5 6 7 8 9 100

Annual Sales, Glib Sales, Inc.

7. Freight car loadings over a 12-year period at a busy port are as follows:

Week Number Week Number Week Number

1 220 7 350 13 460

2 245 8 360 14 475

3 280 9 400 15 500

4 275 10 380 16 510

5 300 11 420 17 525

6 310 12 450 18 541

a. Determine a linear trend line for expected freight car loadings. b. Use the trend equation to predict expected loadings for weeks 20 and 21. c. The manager intends to install new equipment when the volume exceeds 800 loadings per week.

Assuming the current trend continues, the loading volume will reach that level in approximately what week?

8. Air travel on Mountain Airlines for the past 18 weeks was:

Week Passengers 1 405 2 410 3 420 4 415 5 412 6 420 7 424 8 433 9 438

10 440 11 446 12 451 13 455 14 464 15 466 16 474 17 476 18 482

a. Explain why an averaging technique would not be appropriate for forecasting. b. Use an appropriate technique to develop a forecast for the expected number of passengers for

the next three weeks.

6. From the following graph, determine the equation of the linear trend line for time-share sales for Glib Marketing, Inc.

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9. a. Obtain the linear trend equation for the following data on new checking accounts at Fair Savings Bank and use it to predict expected new checking accounts for periods 16 through 19.

Period New Accounts Period New Accounts Period New Accounts 1 200 6 232 11 281

2 214 7 248 12 275

3 211 8 250 13 280

4 228 9 253 14 288

5 235 10 267 15 310

b. Use trend-adjusted smoothing with α = .3 and β = .2 to smooth the new account data in part a. What is the forecast for period 16?

10. After plotting demand for four periods, an emergency room manager has concluded that a trend- adjusted exponential smoothing model is appropriate to predict future demand. The initial estimate of trend is based on the net change of 30 for the three periods from 1 to 4, for an average of +10 units. Use α = .5 and β = .4, and TAF of 250 for period 5. Obtain forecasts for periods 6 through 10.

Period Actual Period Actual 1 210 6 265

2 224 7 272

3 229 8 285

4 240 9 294

5 255 10 11. A manager of a store that sells and installs spas wants to prepare a forecast for January, February,

and March of next year. Her forecasts are a combination of trend and seasonality. She uses the fol- lowing equation to estimate the trend component of monthly demand: Ft = 70 + 5t, where t = 0 in June of last year. Seasonal relatives are 1.10 for January, 1.02 for February, and .95 for March. What demands should she predict?

12. The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that. Ft = 40 – 6.5t + 2t

where

F t

= Unit Sales

= 0 at, the first quarter of last year

Quarter Relative 1 1.1

2 1.0

3 .6

4 1.3

13. Compute seasonal relatives for this data using the SA method:

Quarter Year 1 Year 2 Year 3 Year 4 1 2 3 7 4

2 6 10 18 14

3 2 6 8 8

4 5 9 15 11

14. A tourist center is open on weekends (Friday, Saturday, and Sunday). The owner-manager hopes to improve scheduling of part-time employees by determining seasonal relatives for each of these days. Data on recent traffic at the center have been tabulated and are shown in the following table:

WEEK 1 2 3 4 5 6 Friday 149 154 152 150 159 163 Saturday 250 255 260 268 273 276 Sunday 166 162 171 173 176 183

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a. Develop seasonal relatives for the shop using the centered moving average method. b. Develop seasonal relatives for the shop using the SA method (see Example 8B). c. Explain why the results of the two methods correlate the way they do.

15. The manager of a fashionable restaurant open Wednesday through Saturday says that the restaurant does about 35 percent of its business on Friday night, 30 percent on Saturday night, and 20 percent on Thursday night. What seasonal relatives would describe this situation?

16. Obtain estimates of daily relatives for the number of customers at a restaurant for the evening meal, given the following data. a. Use the centered moving average method. (Hint: Use a seven-day moving average.) b. Use the SA method.

Day Number Served 1 80

2 75

3 78

4 95

5 130

6 136

7 40

8 82

9 77

10 80

11 94

12 131

13 137

14 42

15 84

16 78

17 83

18 96

19 135

20 140

21 44

22 87

23 82

24 88

25 99

26 144

27 144

28 48

17. A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales dur- ing the last 15 days were as follows:

Day 1 2 3 4 5 6 7 8 9

Number sold 36 38 42 44 48 49 50 49 52

Day 10 11 12 13 14 15

Number sold 48 52 55 54 56 57

a. Which method would you suggest using to predict future sales—a linear trend equation or trend-adjusted exponential smoothing? Why?

b. If you learn that on some days the store ran out of the specific pain reliever, would that knowl- edge cause you any concern? Explain.

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c. Assume that the data refer to demand rather than sales. Using trend-adjusted smoothing with an initial forecast of 50 for day 8, an initial trend estimate of 2, and α = β = .3, develop forecasts for days 9 through 16. What is the MSE for the eight forecasts for which there are actual data?

18. New car sales for a dealer in Cook County, Illinois, for the past year are shown in the following table, along with monthly indexes (seasonal relatives), which are supplied to the dealer by the regional distributor.

Month Units Sold Index Jan. 640 0.80

Feb. 648 0.80

Mar. 630 0.70

Apr. 761 0.94

May. 735 0.89

Jun. 850 1.00

Jul. 765 0.90

Aug. 805 1.15

Sept. 840 1.20

Oct. 828 1.20

Nov. 840 1.25

Dec. 800 1.25

a. Plot the data. Does there seem to be a trend? b. Deseasonalize car sales. c. Plot the deseasonalized data on the same graph as the original data. Comment on the two

graphs. d. Assuming no proactive approach on the part of management, discuss (no calculations neces-

sary) how you would forecast sales for the first three months of the next year. e. What action might management consider based on your findings in part b?

19. The following table shows a tool and die company’s quarterly sales for the current year. What sales would you predict for the first quarter of next year? Quarter relatives are SR1 = 1.10, SR2 = .99, SR3 = .90, and SR4 = 1.01.

Quarter 1 2 3 4

Sales 88 99 108 141.4

20. An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a linear trend equation and the naive approach. The linear trend equation is Ft = 124 + 2t, and it was developed using data from periods 1 through 10. Based on data for periods 11 through 20 as shown in the table, which of these two methods has the greater accuracy if MAD and MSE are used?

t Units Sold 11 147

12 148

13 151

14 145

15 155

16 152

17 155

18 157

19 160

20 165

21. Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows:

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PREDICTED DEMAND Period Demand F1 F2

1 68 66 66

2 75 68 68

3 70 72 70

4 74 71 72

5 69 72 74

6 72 70 76

7 80 71 78

8 78 74 80

a. Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? Explain.

b. Compute the MSE for each set of forecasts. Given your results, which forecast appears to be more accurate?

c. In practice, either MAD or MSE would be employed to compute forecast errors. What factors might lead a manager to choose one rather than the other?

d. Compute MAPE for each data set. Which forecast appears to be more accurate? 22. Two independent methods of forecasting based on judgment and experience have been prepared

each month for the past 10 months. The forecasts and actual sales are as follows:

Month Sales Forecast 1 Forecast 2 1 770 771 769

2 789 785 787

3 794 790 792

4 780 784 798

5 768 770 774

6 772 768 770

7 760 761 759

8 775 771 775

9 786 784 788

10 790 788 788

a. Compute the MSE and MAD for each forecast. Does either forecast seem superior? Explain. b. Compute MAPE for each forecast. c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of

the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. How do the naive results compare with the other two forecasts?

23. Long-Life Insurance has developed a linear model that it uses to determine the amount of term life insurance a family of four should have, based on the current age of the head of the household. The equation is: y = 850 − .1x where y = Insurance needed ($000) x = Current age of head of household a. Plot the relationship on a graph. b. Use the equation to determine the amount of term life insurance to recommend for a family of

four if the head of the household is 30 years old. 24. Timely Transport provides local delivery service for a number of downtown and suburban

businesses. Delivery charges are based on distance and weight involved for each delivery: 10 cents per pound and 15 cents per mile. Also, there is a $10 handling fee per parcel. a. Develop an expression that summarizes delivery charges. b. Determine the delivery charge for transporting a 40-pound parcel 26 miles.

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25. The manager of a seafood restaurant was asked to establish a pricing policy on lobster dinners. Experimenting with prices produced the following data:

Average Number Sold per Day, y Price, x

200 $6.00 155 $8.25

190 6.50 156 8.50

188 6.75 148 8.75

180 7.00 140 9.00

170 7.25 133 9.25

162 7.50

160 8.00

a. Plot the data and a regression line on the same graph. b. Determine the correlation coefficient and interpret it.

26. The following data were collected during a study of consumer buying patterns:

Observation x y 1 15 74

2 25 80

3 40 84

4 32 81

5 51 96

6 47 95

7 30 83

8 18 78

9 14 70

10 15 72

11 22 85

12 24 88

13 33 90

1. Plot the data. 2. Obtain a linear regression line for the data. 3. What percentage of the variation is explained by the regression line? 4. Use the equation determined in part b to predict the expected value of y for x = 41.

27. Lovely Lawns, Inc., intends to use sales of lawn fertilizer to predict lawn mower sales. The store manager estimates a probable six-week lag between fertilizer sales and mower sales. The pertinent data are:

Period Fertilizer

Sales (tons) Number of Mowers Sold (six-week lag)

1 1.6 10

2 1.3 8

3 1.8 11

4 2.0 12

5 2.2 12

6 1.6 9

7 1.5 8 8 1.3 7

9 1.7 10

10 1.2 6

11 1.9 11

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Period Fertilizer

Sales (tons) Number of Mowers Sold (six-week lag)

12 1.4 8

13 1.7 10

14 1.6 9

a. Determine the correlation between the two variables. Does it appear that a relationship between these variables will yield reasonable predictions? Explain.

b. Obtain a linear regression line for the data. c. Predict expected lawn mower sales for the first week in August, given fertilizer sales six weeks

earlier of 2 tons. 28. The manager of a travel agency has been using a seasonally adjusted forecast to predict demand for

packaged tours. The actual and predicted values are as follows:

Period Demand Predicted 1 129 124

2 194 200

3 156 150

4 91 94

5 85 80

6 132 140

7 126 128

8 126 124

9 95 100

10 149 150

11 98 94

12 85 80

13 137 140

14 134 128

a. Compute MAD for the fifth period, then update it period by period using exponential smooth- ing with α = .3.

b. Compute a tracking signal for periods 5 through 14 using the initial and updated MADs. If lim- its of ± 4 are used, what can you conclude?

29. Refer to the data in problem 22. a. Compute a tracking signal for the 10th month for each forecast using the cumulative error for

months 1 to 10. Use action limits of ± 4. Is there bias present? Explain. b. Compute 2s control limits for each forecast.

30. The classified department of a monthly magazine has used a combination of quantitative and quali- tative methods to forecast sales of advertising space. Results over a 20-month period are as follows:

Month Error 1 −8

2 −2

3 4

4 7

5 9

6 5

7 0

8 −3

9 −9

10 −4

11 1

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Month Error 12 6

13 8

14 4

15 1

16 −2

17 −4

18 −8

19 −5

20 −1

a. Compute a tracking signal for months 11 through 20. Compute an initial value of MAD for month 11, and then update it for each month using exponential smoothing with α = .1. What can you conclude? Assume limits of ± 4.

b. Using the first half of the data, construct a control chart with 2s limits. What can you conclude? c. Plot the last 10 errors on the control chart. Are the errors random? What is the implication of

this? 31. A textbook publishing company has compiled data on total annual sales of its business texts for the

preceding nine years:

Year 1 2 3 4 5 6 7 8 9

Sales (000) 40.2 44.5 48.0 52.3 55.8 57.1 62.4 69.0 73.7

a. Using an appropriate model, forecast textbook sales for each of the next five years. b. Prepare a control chart for the forecast errors using the original data. Use 2s limits. c. Suppose actual sales for the next five years turn out as follows:

Year 10 11 12 13 14

Sales (000) 77.2 82.1 87.8 90.6 98.9 Is the forecast performing adequately? Explain.

32. A manager has just received an evaluation from an analyst on two potential forecasting alternatives. The analyst is indifferent between the two alternatives, saying that they should be equally effective.

Period 1 2 3 4 5 6 7 8 9 10

Data 37 39 37 39 45 49 47 49 51 54

Alt. 1 36 38 40 42 46 46 46 48 52 55

Alt. 2 36 37 38 38 41 52 47 48 52 53

a. What would cause the analyst to reach this conclusion? b. What information can you add to enhance the analysis?

33. A manager uses this equation to predict demand for landscaping services: Ft = 10 + 5t. Over the past eight periods, demand has been as follows:

Period, t 1 2 3 4 5 6 7 8

Demand 15 21 23 30 32 38 42 47 Is the forecast performing adequately? Explain.

34. A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives are SR1 = .90, SR2 = .95, SR3 = 1.05, and SR4 = 1.10. The trend equation is: Ft = 10 + 5t. Over the past nine quarters, demand has been as follows:

Period, t 1 2 3 4 5 6 7 8 9

Demand 14 20 24 31 31 37 43 48 52 Is the forecast performing adequately? Explain.

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M&L Manufacturing makes various components for printers and copiers. In addition to supplying these items to a major manufac- turer, the company distributes these and similar items to office supply stores and computer stores as replacement parts for print- ers and desktop copiers. In all, the company makes about 20 dif- ferent items. The two markets (the major manufacturer and the replacement market) require somewhat different handling. For example, replacement products must be packaged individually whereas products are shipped in bulk to the major manufacturer.

The company does not use forecasts for production planning. Instead, the operations manager decides which items to produce and the batch size, based on orders and the amounts in inventory. The products that have the fewest amounts in inventory get the highest priority. Demand is uneven, and the company has experi- enced being overstocked on some items and out of others. Being understocked has occasionally created tensions with the manag- ers of retail outlets. Another problem is that prices of raw mate- rials have been creeping up, although the operations manager thinks that this might be a temporary condition.

Because of competitive pressures and falling profits, the man- ager has decided to undertake a number of changes. One change is to introduce more formal forecasting procedures in order to improve production planning and inventory management.

With that in mind, the manager wants to begin forecasting for two products. These products are important for several reasons. First, they account for a disproportionately large share of the company’s profits. Second, the manager believes that one of these products will become increasingly important to future growth plans; and third, the other product has experienced periodic out-of-stock instances.

The manager has compiled data on product demand for the two products from order records for the previous 14 weeks. These are shown in the following table.

Week Product 1 Product 2

1 50 40

2 54 38

3 57 41

4 60 46

5 64 42

6 67 41

7 90* 41

8 76 47

9 79 42

10 82 43

11 85 42

12 87 49

13 92 43

14 96 44

*Unusual order due to flooding of customer’s warehouse.

Questions 1. What are some of the potential benefits of a more formalized

approach to forecasting?

2. Prepare a weekly forecast for the next four weeks for each product. Briefly explain why you chose the methods you used. (Hint: For product 2, a simple approach, possibly some sort of naive/intuitive approach, would be preferable to a technical approach in view of the manager’s disdain of more technical methods.)

CASE M&L MANUFACTURING

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Highline Financial Services provides three categories of service to its clients. Managing partner Freddie Mack is getting ready to pre- pare financial and personnel hiring (or layoff) plans for the coming year. He is a bit perplexed by the following printout he obtained, which seems to show oscillating demand for the three categories of services over the past eight quarters:

Service

Year Quarter A B C

1 1 60 95 93

2 45 85 90

3 100 92 110

4 75 65 90

Examine the demand that this company has experienced for the three categories of service it offers over the preceding two years. Assuming nothing changes in terms of advertising

or promotion, and competition doesn’t change, predict demand for the services the company offers for the next four quarters. Note that there are not enough data to develop seasonal rela- tives. Nonetheless, you should be able to make reasonably good, approximate intuitive estimates of demand. What general obser- vations can you make regarding demand? Should Freddie have any concerns? Explain.

Service Year Quarter A B C

2 1 72 85 102

2 51 75 75

3 112 85 110

4 85 50 100

CASE

Acar, Yavuc, and Everette S. Gardner, Jr. “Forecasting Method Selection in a Global Supply Chain.” Inter- national Journal of Forecasting 28, no. 4 (October– December 2012), 842–48.

Bonomo, Charles. “Forecasting from the Center of the Supply Chain.” Journal of Business Forecasting Methods and Systems 22, no. 1 (Spring 2003), p. 3.

Byrne, Robert F. “Forecasting Performance for North American Consumer Products.” Journal of Business Forecasting 31, no. 3 (Fall 2012), p. 12.

SELECTED BIBLIOGRAPHY AND FURTHER READINGS

Hanke, John, and Dean Wichern. Business Forecast- ing, 9th ed. Upper Saddle River, NJ: Pearson, 2009.

Hopp, Wallace J., and Mark I. Spearman. Factory Physics, 3rd ed. New York: McGraw-Hill, 2008.

Wilson, J. Holton, Barry Keating, and John Galt Solu- tions. Business Forecasting with ForecastX, 6th ed. New York: McGraw-Hill, 2009.

HIGHLINE FINANCIAL SERVICES, LTD.

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4 Product and Service Design

4.1 Introduction, 138 What Does Product and Service Design Do?, 138 Key Questions, 138 Reasons for Product or Service Design or Redesign, 139

4.2 Idea Generation, 140

4.3 Legal and Ethical Considerations, 143

4.4 Human Factors, 144

4.5 Cultural Factors, 145

4.6 Global Product and Service Design, 145

4.8 Other Design Considerations, 151 Strategies for Product or Service Life Stages, 151 Product Life Cycle Management, 152 Degree of Standardization, 153

Designing for Mass Customization, 154 Reliability, 155 Robust Design, 156 Degree of Newness, 157 Quality Function Deployment, 157 The Kano Model, 160

4.9 Phases in Product Design and Development, 161

4.10 Designing for Production, 162 Concurrent Engineering, 162 Computer-Aided Design, 163 Production Requirements, 164 Component Commonality, 164

C H A P T E R O U T L I N E

L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO4.1 Explain the strategic importance of product and service design.

LO4.2 Describe what product and service design does.

LO4.3 Name the key questions of product and service design.

LO4.4 Identify some reasons for design or redesign.

LO4.5 List some of the main sources of design ideas.

LO4.6 Discuss the importance of legal, ethical, and sustainability considerations in product and service design.

LO4.7 Explain the purpose and goal of life cycle assessment.

LO4.8 Explain the phrase “the 3 Rs.”

LO4.9 Briefly describe the phases in product design and development.

LO4.10 Discuss several key issues in product or service design.

LO4.11 Discuss the two key issues in service design.

LO4.12 List the characteristics of well-designed service systems.

LO4.13 List some guidelines for successful service design.

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In this chapter you will discover insights into the design process that apply to both product and service design.

LO4.1 Explain the strate- gic importance of product and service design.

4.11 Service Design, 165 Overview of Service Design, 165 Differences between Service Design and Product Design, 165 Phases in the Service Design Process, 166

Service Blueprinting, 167 Characteristics of Well-Designed Service Systems, 168 Challenges of Service Design, 168 Guidelines for Successful Service Design, 168

4.12 Operations Strategy, 169

Operations Tour: High Acres Landfill, 173

Chapter Supplement: Reliability, 174

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4.1 INTRODUCTION

This section discusses what product and service designers do, the reasons for design (or rede- sign), and key questions that management must address.

What Does Product and Service Design Do? The various activities and responsibilities of product and service design include the following (functional interactions are shown in parentheses):

1. Translate customer wants and needs into product and service requirements (marketing, operations)

2. Refine existing products and services (marketing) 3. Develop new products and/or services (marketing, operations) 4. Formulate quality goals (marketing, operations) 5. Formulate cost targets (accounting, finance, operations) 6. Construct and test prototypes (operations, marketing, engineering) 7. Document specifications 8. Translate product and service specifications into process specifications (engineering,

operations)

Product and service design involves or affects nearly every functional area of an organiza- tion. However, marketing and operations have major involvement.

Key Questions From a buyer’s standpoint, most purchasing decisions entail two fundamental considerations; one is cost and the other is quality or performance. From the organization’s standpoint, the key questions are:

1. Is there demand for it? What is the potential size of the market, and what is the expected demand profile (will demand be long term or short term, will it grow slowly or quickly)?

2. Can we do it? Do we have the necessary knowledge, skills, equipment, capacity, and supply chain capability? For products, this is known as manufacturability; for services, this is known as serviceability. Also, is outsourcing some or all of the work an option?

3. What level of quality is appropriate? What do customers expect? What level of quality do competitors provide for similar items? How would it fit with our current offerings?

4. Does it make sense from an economic standpoint? What are the potential liability issues, ethical considerations, sustainability issues, costs, and profits? For nonprofits, is the cost within budget?

Manufacturability The capa- bility of an organization to produce an item at an accept- able profit.

Serviceability The capability of an organization to provide a service at an acceptable cost or profit.

As businesses continue to reduce costs to achieve competitive advantage, design issues are becoming increasingly important aspects of business strategy. Because product and service design touches every part of a business organization, from operations and supply chains to finance, marketing, accounting, and informa- tion systems, design decisions have far-reaching implications for the organization and its success in the marketplace. Product and service innovation is becoming a key avenue in pursuing a com- petitive edge, and sustainability issues are being given increasing importance in business decisions.

READING DESIGN AS A BUSINESS STRATEGY

Some companies, such as Steelcase, Inc., have adopted “design thinking” to integrate design strategy throughout the company. The idea is to predicate design on insights into user wants and needs—not really a new concept, but one that is put forth in a way that becomes the focal point of how the company makes design decisions.

Source: Based on “Sustaining the Dream,” BusinessWeek, October 15, 2007, p. 60.

LO4.2 Describe what product and service design does.

LO4.3 Name the key questions of product and service design.

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Reasons for Product and Service Design or Redesign Product and service design has typically had strategic implications for the success and pros- perity of an organization. Furthermore, it has an impact on future activities. Consequently, decisions in this area are some of the most fundamental that managers must make.

Organizations become involved in product and service design or redesign for a variety of reasons. The main forces that initiate design or redesign are market opportunities and threats. The factors that give rise to market opportunities and threats can be one or more changes:

• Economic (e.g., low demand, excessive warranty claims, the need to reduce costs) • Social and demographic (e.g., aging baby boomers, population shifts) • Political, liability, or legal (e.g., government changes, safety issues, new regulations) • Competitive (e.g., new or changed products or services, new advertising/promotions) • Cost or availability (e.g., of raw materials, components, labor, water, energy) • Technological (e.g., in product components, processes)

While each of these factors may seem obvious, let’s reflect a bit on technological changes, which can create a need for product or service design changes in several different ways. An obvious way is new technology that can be used directly in a product or service (e.g., a faster,

Redesign, not offshoring production to countries such as China, holds the greatest promise of cost advantage for U.S. manufactur- ers, according to a new study that attacks longstanding assump- tions about product costs that have troubled manufacturers for decades, both in the United States and worldwide.

The study, “Improved Product Design Practices Would Make U.S. Manufacturing More Cost Effective: A Case to Consider Before Outsourcing to China,” suggests that U.S. companies should do a much better job of integrating cost analysis into product design. If rigorous cost analysis were to be instituted as a foundation for product design, U.S. manufacturers would be able to develop innovative products that are more economical to produce in the United States, assert the report’s coauthors, Nicholas P. Dewhurst and David G. Meeker.

Dewhurst is executive vice president of Boothroyd Dewhurst, a solution provider specializing in software for design for manufac- ture and assembly (DFMA). Meeker is a DFMA consultant.

One of the more provocative conclusions of the study is that it can be more advantageous for U.S. manufacturers to lower costs by redesigning products than by outsourcing production to other countries such as China. In many instances, the study shows, redesigning a product and manufacturing it in the United States is a better option for saving money.

To support this idea, the authors identify two principles of design best practices that, they said, many U.S. manufacturing companies overlook when making outsourcing decisions:

• First, it is possible to redesign products to reduce part count and cost. As an illustration, the study features an in-depth, quantitative analysis showing how the redesign of an electric drill would eliminate the cost advantage of offshore manufac- turing and justify a decision not to outsource production.

READING PRODUCT REDESIGN, NOT OFFSHORING, HOLDS COST ADVANTAGE FOR U.S. MANUFACTURERS

• And second, it is necessary to account for all the additional costs associated with offshore manufacturing and to apply those additional costs to the product. The study warns that companies may not be accounting for the full costs of out- sourcing when they consider sending production overseas to countries with very low labor rates such as China. The authors identify a number of hidden costs, including shipping and logis- tics, that can add an estimated 24 percent to labor and mate- rial costs at the offshore location.

The study seeks to demonstrate that if companies consider the potential for design improvement along with a realistic estimate of the full costs of outsourcing, it often makes more sense to manu- facture products in the United States.

“We know, from years of consulting with design engineers, that U.S. manufacturers have very little visibility into what their prod- ucts should cost to make,” said Dewhurst. “Companies histori- cally do a poor job of integrating cost analysis into early product design. Many companies now rushing to outsource manufacturing still do not understand that the design of the product determines the final cost.”

Dewhurst warned that outsourcing should not be the first step in lowering product costs. “Considering the competitive nature of the global economy and the many hidden risks associated with outsourcing ventures, U.S. companies should scour product designs for efficiency before resorting to offshore production,” he said. “We hope this study encourages the U.S. manufacturing industry to take another look at design cost analysis.”

Source: “Product Redesign, Not Offshoring, Holds Cost Advantage for U.S. Manu- facturers,” Supply & Demand Chain Executive, September 8, 2004. Copyright © 2004 Cygnus Business Media and/or its suppliers, 1233 Janesville Ave., Fort Atkinson, WI 53538 U.S.A. All rights reserved.

LO4.4 Identify some reasons for design or redesign.

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The following reading suggests another potential benefit of product redesign.

4.2 IDEA GENERATION

READING DUTCH BOY BRUSHES UP ITS PAINTS

of a recess in the bottom that mates with the lid’s top ring. “The new design allows for one additional shelf facing on an eight-foot rack or shelf area.”

Courtesy of Dutch Boy

LO4.5 List some of the main sources of design ideas.

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Input from suppliers, distributors, and employees can be obtained from interviews, direct or indirect suggestions, and complaints.

One of the strongest motivators for new and improved products or services is competitors’ products and services. By studying a competitor’s products or services and how the competi- tor operates (pricing policies, return policies, warranties, location strategies, etc.), an organi- zation can glean many ideas. Beyond that, some companies purchase a competitor’s product and then carefully dismantle and inspect it, searching for ways to improve their own product. This is called reverse engineering. The Ford Motor Company used this tactic in develop- ing its highly successful Taurus model: It examined competitors’ automobiles, searching for best-in-class components (e.g., best hood release, best dashboard display, best door handle). Sometimes reverse engineering can enable a company to leapfrog the competition by devel- oping an even better product. Suppliers are still another source of ideas, and with increased emphasis on supply chains and supplier partnerships, suppliers are becoming an important source of ideas.

Research is another source of ideas for new or improved products or services. Research and development (R&D) refers to organized efforts that are directed toward increasing scientific knowledge and product or process innovation. Most of the advances in semi- conductors, medicine, communications, and space technology can be attributed to R&D efforts at colleges and universities, research foundations, government agencies, and private enterprises.

R&D efforts may involve basic research, applied research, or development.

Basic research has the objective of advancing the state of knowledge about a subject, without any near-term expectation of commercial applications.

Applied research has the objective of achieving commercial applications. Development converts the results of applied research into useful commercial appli cations.

Basic research, because it does not lead to near-term commercial applications, is gener- ally underwritten by the government and large corporations. Conversely, applied research and development, because of the potential for commercial applications, appeals to a wide spec- trum of business organizations.

The benefits of successful R&D can be tremendous. Some research leads to patents, with the potential of licensing and royalties. However, many discoveries are not patentable, or companies don’t wish to divulge details of their ideas so they avoid the patent route. Even so, the first organization to bring a new product or service to the market generally stands to profit from it before the others can catch up. Early products may be priced higher because a tempo- rary monopoly exists until competitors bring their versions out.

The costs of R&D can be high. Some companies spend more than $1 million a day on R&D. Large companies in the automotive, computer, communications, and pharmaceuti- cal industries spend even more. For example, IBM spends about $5 billion a year, Hewlett- Packard about $4 billion a year, and Toshiba about $3 billion a year. Even so, critics say that many U.S. companies spend too little on R&D, a factor often cited in the loss of competitive advantage.

It is interesting to note that some companies are now shifting from a focus primarily on products to a more balanced approach that explores both product and process R&D. Also, there is increasing recognition that technologies often go through life cycles, the same way that many products do. This can impact R&D efforts on two fronts. Sustained economic growth requires constant attention to competitive factors over a life cycle, and it also requires planning to be able to participate in the next-generation technology.

In certain instances, however, research may not be the best approach. The following read- ing illustrates a research success.

Reverse engineering Dis- mantling and inspecting a competitor’s product to dis- cover product improvements.

Research and development (R&D) Organized efforts to increase scientific knowledge or product innovation.

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Michele Darnell Many were skeptical of Frank Meczkowski’s plan to develop a pickle so big that a single slice could cover a hamburger.

After all, whoever saw a pickle that big—except maybe in the Guinness Book of World Records?

Meczkowski and his team of food researchers at Vlasic Foods International were convinced the project—given the code name Frisbee—could fly.

For about four years, they labored to cultivate a jumbo cucum- ber with the taste, shape, and crunch to be a perfect pickle.

Made only at the company’s plant in Millsboro, the monster- sized slices seem to have captured the pickle lover’s fancy. They’ve become one of Vlasic’s best-selling products since their introduction in supermarkets. And the better-than-anticipated sales have helped to reverse a three-year decline in consumption of Vlasic pickles.

Hamburger Stackers are about 10 times bigger than traditional pickle chips and come in dill and bread-and-butter varieties.

“They said it just couldn’t be done.” Making a bigger pickle may not sound like that big of a deal.

You just grow a bigger cucumber, right? There is more to it than that. The folks at Vlasic soon learned

how tough it was to deal with gigantic cucumbers as they devel- oped the new product and as they retooled the Delaware plant.

Meczkowski came up with the idea for the mammoth pickle slices soon after Vlasic’s introduction of its Sandwich Stackers— regular-size pickles sliced lengthwise so they can be draped on sandwiches.

Sandwich Stackers currently account for 20 percent of all Vlasic pickle sales.

Vlasic is the No. 1 seller of pickles in the United States, with a 32 percent share of the $800 million retail pickle market, beating out brands such as Claussen, Heinz, and Peter Piper’s.

To develop Hamburger Stackers, Meczkowski worked with seed researchers and others to scour the globe looking for oversized varieties of cucumbers. Most weren’t in commercial production.

Vlasic’s team grew different varieties in greenhouses, looking for one that would get big enough yet still make a good pickle.

It had to taste like a regular cucumber, stay crisp when pickled, have a small seed cavity and be straight enough so that it could be cut mechanically.

“We wanted it to really be a cucumber,” said Meczkowski, who has worked as a food researcher for 22 years and is based at Vlasic’s headquarters in New Jersey.

He said Vlasic also had to decide just how big Hamburger Stackers should be. At one point, it asked consumers who were participating in focus groups to bring in their own homemade burgers so the company could determine the perfect size for its new pickles.

Eventually, Vlasic officials found what they were looking for—a now-patented cucumber that grows 3.25 inches in diameter, easily reaches 12 to 16 inches in length, and weighs about five pounds.

READING VLASIC ON A ROLL WITH HUGE PICKLE SLICES

It looks like the watermelon’s skinny runt brother. Once the company settled on a cucumber, it had to work out

details of how to get Hamburger Stackers into commercial produc- tion. One challenge was to grow the cucumbers in fields, rather than in a greenhouse.

Randy Spence, Vlasic’s manager of manufacturing services, said the jumbo cucumbers grew quicker than anyone expected.

“Early on, we expected the bigger ones to grow slower, but that hasn’t been the case,” he said.

These days, most of the gigantic cucumbers are grown in Florida, where they are handpicked because of their size. Depending on the weather, they take about 54 days from seed to harvest.

Once harvested, they’re shipped to Vlasic’s plant in Sussex County. The plant employs about 260 workers year-round and 300 to 400 others from April to November.

Steven McNulty, director of plant operations at the nearly 30-year-old Millsboro facility, said the size of the new cucumbers meant they couldn’t be handled in the same manner as the smaller versions used to make pickle spears and sweet gherkins.

That became obvious when Vlasic tried to process its first batch of the somewhat fragile, jumbo-sized cucumbers.

Officials didn’t end up with the Hamburger Stackers they envisioned. Instead, they ended up with a batch of broken big cucumbers.

“On the first run, we broke every one,” Spence said. But it taught the company a lot about some of the retooling

they’d have to do to the plant in Millsboro. Officials at the plant began making months worth of adjust-

ments so one of the facility’s four production lines could handle the jumbo cucumbers.

“We’ve learned a lot,” McNulty said. “And we’re still learning.” Making Hamburger Stackers requires a mix of automation and

the human touch. The process starts when the big cucumbers

Courtesy of Pinnacle Foods Corporation

The Hamburger Stacker on the burger at left dwarfs a traditional pickle slice. Stackers are genetically designed using cucumbers that grow to over 3 inches in diameter and weigh 5 pounds.

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4.3 LEGAL AND ETHICAL CONSIDERATIONS

Designers must be careful to take into account a wide array of legal and ethical consider- ations. Generally, they are mandatory. Moreover, if there is a potential to harm the environ- ment, then those issues also become important. Most organizations are subject to numerous government agencies that regulate them. Among the more familiar federal agencies are the Food and Drug Administration, the Occupational Health and Safety Administration, the Envi- ronmental Protection Agency, and various state and local agencies. Bans on cyclamates, red food dye, phosphates, and asbestos have sent designers scurrying back to their drawing boards to find alternative designs that were acceptable to both government regulators and custom- ers. Similarly, automobile pollution standards and safety features, such as seat belts, air bags, safety glass, and energy-absorbing bumpers and frames, have had a substantial impact on automotive design. Much attention also has been directed toward toy design to remove sharp edges, small pieces that can cause choking, and toxic materials. The government further regu- lates construction, requiring the use of lead-free paint, safety glass in entranceways, access to public buildings for individuals with disabilities, and standards for insulation, electrical wiring, and plumbing.

Product liability can be a strong incentive for design improvements. Product liability is the responsibility of a manufacturer for any injuries or damages caused by a faulty product because of poor workmanship or design. Many business firms have faced lawsuits related to their products, including Firestone Tire & Rubber, Ford Motor Company, General Motors, tobacco companies, and toy manufacturers. Manufacturers also are faced with the implied warranties created by state laws under the Uniform Commercial Code, which says that prod- ucts carry an implication of merchantability and fitness; that is, a product must be usable for its intended purposes.

The suits and potential suits have led to increased legal and insurance costs, expensive settlements with injured parties, and costly recalls. Moreover, increasing customer awareness of product safety can adversely affect product image and subsequent demand for a product.

Thus, it is extremely important to design products that are reasonably free of hazards. When hazards do exist, it is necessary to install safety guards or other devices for reduc- ing accident potential, and to provide adequate warning notices of risks. Consumer groups,

Product liability The respon- sibility of a manufacturer for any injuries or damages caused by a faulty product.

Uniform Commercial Code A product must be suitable for its intended purpose.

arrive by truck and are rushed into a cold-storage facility to pre- serve their flavor.

Once cooled, the cucumbers can be loaded onto the produc- tion line and checked for bad spots and other flaws.

They’re washed by machine a couple times and sliced. Then they’re sized. Jiggling along a conveyor belt, slices that

are too small are weeded out by a worker and a machine. Those that are too big also are sorted out.

Too big? Yes, the monster-sized cucumbers can get a little too big to fit

in the jar. The cucumber slices that make the cut are mechanically

stacked into jars and then topped off by hand. Ella Mae Wilkerson, who has worked at the Vlasic plant in Mills-

boro for 17 years, said it takes some fast hands to make certain that outgoing jars have enough pickles packed in.

“The bigger the jar, the harder it is,” she said as containers of sweet gherkins being jarred on another production line zipped by on a conveyor belt.

After being packed with pickle slices, the jars of Hamburger Stackers are filled with a combination of water, vinegar, salt, and other flavorings and colorings. They are capped, vacuum-sealed, and pasteurized before being labeled and packed for global distribution.

Some details of how Hamburger Stackers are made are kept secret. McNulty said that is because the company is certain its rivals would love to figure out how to make their own Hamburger Stackers.

Vlasic is the only pickle-making company with such a product on the market. “We think the competition loves the idea,” McNulty said.

Apparently, so does the pickle-eating public. About $13 million worth of Hamburger Stackers were sold in

the first five months after they were introduced. The company is optimistic that the product will continue to

grow in popularity with U.S. consumers who eat about 3.5 billion hamburgers at home annually.

Source: Rochester Democrat and Chronicle, December 13, 1999.

LO4.6 Discuss the importance of legal, ethical, and sustainability considerations in product and service design.

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business firms, and various government agencies often work together to develop industrywide standards that help avoid some of the hazards.

Ethical issues often arise in the design of products and services; it is important for manag- ers to be aware of these issues and for designers to adhere to ethical standards. Designers are often under pressure to speed up the design process and to cut costs. These pressures often require them to make trade-off decisions, many of which involve ethical considerations. One example of what can happen is “vaporware,” when a software company doesn’t issue a release of software as scheduled as it struggles with production problems or bugs in the software. The company faces the dilemma of releasing the software right away or waiting until most of the bugs have been removed—knowing that the longer it waits, the more time will be needed before it receives revenues and the greater the risk of damage to its reputation.

Organizations generally want designers to adhere to guidelines such as the following:

• Produce designs that are consistent with the goals of the organization. For instance, if the company has a goal of high quality, don’t cut corners to save cost, even in areas where it won’t be apparent to the customer.

• Give customers the value they expect. • Make health and safety a primary concern. At risk are employees who will produce

goods or deliver services, workers who will transport the products, customers who will use the products or receive the services, and the general public, which might be endan- gered by the products or services.

4.4 HUMAN FACTORS

Human factor issues often arise in the design of consumer products. Safety and liability are two critical issues in many instances, and they must be carefully considered. For example, the crashworthiness of vehicles is of much interest to consumers, insurance companies, automo- bile producers, and the government.

Another issue for designers to take into account is adding new features to their products or services. Companies in certain businesses may seek a competitive edge by adding new

The much anticipated Boeing 787 Dreamliner aircraft resumed flights after being grounded for more than three months for a battery defect. Aviation safety depends on well-maintained equipment and an expert flight crew who can handle emergencies as they come up.

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features. Although this can have obvious benefits, it can sometimes be “too much of a good thing,” and be a source of customer dissatisfaction. This “creeping featurism” is particularly evident in electronic products such as handheld devices that continue to offer new features, and more complexity, even while they are shrinking in size. This may result in low consumer ratings in terms of “ease of use.”

4.5 CULTURAL FACTORS

Product designers in companies that operate globally also must take into account any cultural differences of different countries or regions related to the product. This can result in different designs for different countries or regions, as illustrated by the following reading.

4.6 GLOBAL PRODUCT AND SERVICE DESIGN

Traditionally, product design has been conducted by members of the design team who are located in one facility or a few nearby facilities. However, organizations that operate glob- ally are discovering advantages in global product design, which uses the combined efforts of a team of designers who work in different countries and even on different continents. Such virtual teams can provide a range of comparative advantages over traditional teams such as engaging the best human resources from around the world without the need to assemble them all in one place, and operating on a 24-hour basis, thereby decreasing the time-to- market. The use of global teams also allows for customer needs assessment to be done in more than one country with local resources, opportunities, and constraints to be taken into account. Global product design can provide design outcomes that increase the marketability and utility of a product. The diversity of an international team may yield different points of view and ideas and information to enrich the design process. However, care must be taken in managing the diversity, because if it is mismanaged, that can lead to conflicts and miscommunications.

A good example of cultural variations in fast food is McDonald’s menus in various countries. Here is a sampling from around the world, including the slice of pickled beets in New Zealand:

Country Menu Items Argentina McNifica: A hamburger sandwich with lettuce, tomato, onion, and cheese.

China Hong Dou Pie: A dessert pie filled with red bean paste. Egypt: McHalafel: Fried ground bean patties flavored with spices. France Croque McDo: Toasted ham and cheese sandwich. Germany Gemuse Mac: A veggie burger with lettuce, tomato, and a special sauce. Greece Greek Mac: Pita bread sandwich with yogurt sauce. India: McAloo Tikki Burger: A vegetarian sandwich with a

crispy, spicy potato and vegetable patty, tomato and onion, and eggless tomato mayonnaise,

READING DO YOU WANT PICKLED BEETS WITH THAT?

Italy McPink: Two pork patties and a slice of yellow cheese. Japan Teriyaki burger: Chicken cutlet marinated in teriyaki

sauce, with sweet mayonnaise and lettuce on a sesame bun. New Zealand Kiwi burger: A hamburger with a fried egg and a

slice of a pickled beet.

Questions

1. What effects do cultural differences have on the design of fast- food offerings in this reading?

2. What functions in the organization are impacted by the differ- ences in product offerings among different countries?

Source: Based on The Washington Post, December 2, 2002 © 2002. The Washington Post. All rights reserved. Used by permission and protected by the Copyright Laws of the United States. The printing, copying, redistribution or retransmission of the Material without express written permission is prohibited.

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Advances in information technology have played a key role in the viability of global prod- uct design teams by enabling team members to maintain continual contact with each other and to instantaneously share designs and progress, and to transmit engineering changes and other necessary information.

4.7 ENVIRONMENTAL FACTORS: SUSTAINABILITY

Product and service design is a focal point in the quest for sustainability. Key aspects include cradle-to-grave assessment, end-of-life programs, reduction of costs and materials used, reuse of parts of returned products, and recycling.

Cradle-to-Grave Assessment Cradle-to-grave assessment, also known as life cycle analysis, is the assessment of the envi- ronmental impact of a product or service throughout its useful life, focusing on such factors as global warming (the amount of carbon dioxide released into the atmosphere), smog for- mation, oxygen depletion, and solid waste generation. For products, cradle-to-grave analysis takes into account impacts in every phase of a product’s life cycle, from raw material extrac- tion from the earth, or the growing and harvesting of plant materials, through fabrication of parts and assembly operations, or other processes used to create products, as well as the use or consumption of the product, and final disposal at the end of a product’s useful life. It also considers energy consumption, pollution and waste, and transportation in all phases. Although services generally involve less use of materials, cradle-to-grave assessment of ser- vices is nonetheless important, because services consume energy and involve many of the same or similar processes that products involve.

The goal of cradle-to-grave assessment is to choose products and services that have the least environmental impact while still taking into account economic considerations. The pro- cedures of cradle-to-grave assessment are part of the ISO 14000 environmental management standards, which are discussed in Chapter 9.

End-of-Life Programs End-of-life (EOL) programs deal with products that have reached the end of their useful lives. The products include both consumer products and business equipment. The purpose of these programs is to reduce the dumping of products, particularly electronic equipment, in landfills or third-world countries, as has been the common practice, or incineration, which converts materials into hazardous air and water emissions and generates toxic ash. Although the pro- grams are not limited to electronic equipment, that equipment poses problems because the equipment typically contains toxic materials such as lead, cadmium, chromium, and other heavy metals. IBM provides a good example of the potential of EOL programs. Over the last 15 years, it has collected about 2 billion pounds of product and product waste.

The Three Rs: Reduce, Reuse, and Recycle Designers often reflect on three particular aspects of potential cost saving and reducing envi- ronmental impact: reducing the use of materials through value analysis; refurbishing and then reselling returned goods that are deemed to have additional useful life, which is referred to as remanufacturing; and reclaiming parts of unusable products for recycling.

Reduce: Value Analysis Value analysis refers to an examination of the function of parts and materials in an effort to reduce the cost and/or improve the performance of a product. Typical questions that would be asked as part of the analysis include: Could a cheaper part or material be used? Is the function necessary? Can the function of two or more parts or components be performed by a single

LO4.7 Explain the pur- pose and goal of life cycle assessment.

Cradle-to-grave assessment The assessment of the envi- ronmental impact of a product or service throughout its use- ful life.

LO4.8 Explain the phrase “the 3 Rs.”

Value analysis Examina- tion of the function of parts and materials in an effort to reduce cost and/or improve product performance.

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part for a lower cost? Can a part be simplified? Could product specifications be relaxed, and would this result in a lower price? Could standard parts be substituted for nonstandard parts? Table 4.1 provides a checklist of questions that can guide a value analysis.

The following reading describes how Kraft Foods is working to reduce water and energy use, CO2 and plant waste, and packaging.

Electronic junk, that is. The giant electronics retailer sees its recycling program as a way to get customers into its stores while building a green reputation. While old TVs, desktop computers, and other outmoded electronics come in, out go flat-screen TVs, netbooks, and iPhones.

The company decided that being a good corporate citizen makes business sense. Best Buy’s commitment to corporate responsibility fits nicely with the company’s strategy and business model. To set itself apart from its biggest competitors, Walmart and Amazon, the company wants to do more than sell consumer electronics. It would like to help customers get better use out of technology, whether they are buying, installing, fixing, or dispos- ing of their hardware. Since 2009, when Best Buy began offer- ing free recycling of electronic equipment, millions of pounds of In Store Take Back (ISTB) have found their way to the company’s 1,044 U.S. stores, making Best Buy America’s biggest collector of electronic junk.

Best Buy’s interest in the social and environmental impact of electronic goods that had reached the end of their useful life came at the urging of its workers and customers. “Employees wanted to know what Best Buy was doing to become more environmentally sustainable.” Some customers—enough to matter—said they pre- ferred buying from retailers that demonstrated an interest in their community. The company audits the factories of its suppliers to make sure they don’t exploit workers or pollute the environment.

READING BEST BUY WANTS YOUR JUNK

It actually severed ties with 26 of about 200 factories in 2008. Best Buy CEO, Brian Dunn, strives to stay connected to staff and customers, posting questions on an employee website called the Water Cooler, tracking consumer sentiment on Facebook and Twitter (BBY-CEO), attending focus groups, and inviting customers to the company’s leadership meetings.

TVs account for the majority of items. This is somewhat of a mixed blessing. TVs with picture tubes can’t be resold; they have to be taken away and disassembled before the materials are melted down for reuse. Unlike plastics, which might end up in lawn furniture or other products, the innards of TVs aren’t of much value. In many states manufacturers are required by take-back laws to help finance recycling, and companies like Samsung and Sony share the costs of disposing of old TVs.

Even though there may not be a clear economic case for doing so, at least for TVs, the take-back program could also be benefi- cial by encouraging innovative design and new business models. Manufacturers that know they will be responsible for the end of life of their products will design them so that they can be disas- sembled and recycled easily. Computer maker Dell, for instance, reduced the number of screws in its computers to make them easier to dismantle.

Source: Based on “Best Buy Wants Your Electronic Junk,” Fortune magazine, December 7, 2009.

1. Select an item that has a high annual dollar volume. This can be material, a purchased item, or a service.

2. Identify the function of the item. 3. Obtain answers to these kinds of questions:

a. Is the item necessary; does it have value; can it be eliminated? b. Are there alternative sources for the item? c. Can the item be provided internally? d. What are the advantages of the present arrangement? e. What are the disadvantages of the present arrangement? f. Could another material, part, or service be used instead? g. Can specifications be less stringent to save cost or time? h. Can two or more parts be combined? i. Can more/less processing be done on the item to save cost or time? j. Do suppliers/providers have suggestions for improvements? k. Do employees have suggestions for improvements? l. Can packaging be improved or made less costly?

4. Analyze the answers obtained as well as answers to other questions that arise, and make recommendations.

TABLE 4.1 Overview of value analysis

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Reuse: Remanufacturing An emerging concept in manufacturing is the remanufacturing of products. Remanufacturing refers to refurbishing used products by replacing worn-out or defective components, and reselling the products. This can be done by the original manufacturer, or another company. Among the products that have remanufactured components are automobiles, printers, copiers, cameras, computers, and telephones.

There are a number of important reasons for doing this. One is that a remanufactured product can be sold for about 50 percent of the cost of a new product. Another is that the pro- cess requires mostly unskilled and semiskilled workers. And in the global market, European lawmakers are increasingly requiring manufacturers to take back used products, because this means fewer products end up in landfills and there is less depletion of natural resources such as raw materials and fuel.

Remanufacturing Refurbish- ing used products by replac- ing worn-out or defective components.

The threat of global warming and the desire to protect the envi- ronment has many companies embracing sustainability initiatives. And they are finding that in many instances, there are cost savings in doing so.

Among them was the Kraft Foods Company prior to the spin off of its North American grocery business as Mondelez, and its merger with the H.J. Heinz Company. The Kraft Heinz Company is now one of the largest food and beverage company in the world. Its brands include Kraft, Heinz, ABC, Capri Sun, Jell-O, Kool-Aid, Lunchables, Maxwell House, Ore-Ida, Oscar Mayer, Philadelphia, Planters, Quero, Weight Watchers Smart Ones, and Velveeta. According the company’s web site, it “is dedicated to the sustainable health of our people, our planet and our Com- pany.” (kraftheinzcompany.com)

Prior to the merger of the two companies, both Kraft Foods and the H.J. Heinz Company were recognized for their sustainability efforts. Here the focus is on some of Kraft’s accomplishments prior to the merger. They provide insight into some of the cost savings that can stem from sustainability efforts, and serve as examples that others might wish to follow.

Some of Kraft’s successes came from redesigned packaging. The goal was ambitious. It required more efficient packaging and a reduction in the amount of packaging material used. Kraft believed that the greatest opportunity to reduce the environmental impact of a package is early in the design phase. Their packaging designers worldwide critically considered the amount of packag- ing used, how much post-consumer material could be used, how much energy was used to create the packaging materials, how much CO2 is generated as the materials are created and formed, and how well the package fit the product physically. According to Kraft’s press releases at the time examples and benefits of some packaging redesigns included:

• DiGiorno and California Pizza Kitchen pizzas: Using slimmer cartons that allow shipment of two extra pizza boxes per case and 14 percent more pizzas per pallet. This led to a savings of approximately 1.4 million pounds of packaging per year, and the ability to load more pizzas on each truck meant there were fewer trucks on the road and less fuel consumed.

READING KRAFT FOODS’ RECIPE FOR SUSTAINABILITY

• Oscar Mayer Deli Creations: Using 30 percent less paperboard than the previous design resulted in 1.2 million fewer pounds of packaging going to landfills.

• Kraft salad dressing: Using 19 percent less plastic per bottle translated to 3 million pounds fewer annually. Additionally the new design allowed more bottles to be shipped per truckload, leading to an increase in transportation efficiency of 18 percent.

The company also worked to reduce water pollution/soil ero- sion and support biodiversity. Considering those successes, Kraft’s recipe for sustainability is one that other companies should emulate.

Kraft Natural Cheese packaging zipper eliminates more than one million pounds of packaging per year.

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By recycling yet one more five-pound toner cartridge from a Xerox multifunction system, Xerox Corporation announced today it has surpassed a major sustainability milestone by diverting billions of pounds of electronic waste from landfills around the world through waste-free initiatives that create sustainability benefits for the company and its customers.

Launched in 1991, long before sustainability was on most com- panies’ radar screens, Xerox’s environmental program achieved waste avoidance in two areas: reuse and recycling in imaging supplies and product take-back and recycling and parts reuse. In addition, Xerox integrates innovative environmental priorities into manufacturing operations to add to its recycling efforts.

READING XEROX DIVERTS 2 BILLION POUNDS OF WASTE FROM LANDFILLS THROUGH GREEN INITIATIVES

“Xerox’s experience with reuse, recycling, and remanufactur- ing has not only kept waste out of landfills, but saved the com- pany billions of dollars as it did so,” said Patricia Calkins, Xerox vice president, Environment, Health and Safety. “If that amount of waste were loaded into garbage trucks, it would fill more than 160,000 trucks, stretching more than 1,000 miles, from Seattle to the Mexican border. We believe sustainability is an integral part of developing products, serving customers, and posting profits.”

Source: Xerox Press Release, November 9, 2007.

Maria’s Market is the main supermarket in Recycle City. Maria tries to stock items and provide services in her store that reduce the amount of material going into the waste stream and encourage reuse and recycling.

Maria realized that the first and best thing she should do was to reduce the amount of waste her customers had to throw away after they bought products at her market.

Maria To reduce the amount of waste and its impact on the environment, Maria began to stock items in the store that contained fewer harm- ful ingredients and used less packaging. To reduce packaging and wasted food, she created a section in the store where shoppers

READING RECYCLE CITY: MARIA’S MARKET

could buy food in bulk, measuring out the exact amounts they needed.

Maria also set up a program to reuse those things that could be reused, such as cardboard boxes that shoppers could use to carry their purchases and bring back to the store on their next visit. She also gave customers discounts for returning their plastic bags the next time they shopped and for bringing their own cloth sacks to carry groceries home.

Finally, Maria made sure that many of the items in the store could be easily recycled. She set up well-marked collection con- tainers to make it easy for shoppers to participate in the market’s recycling program. Maria knows that recycling keeps useful mate- rials from going into landfills, helping to preserve the land in and around Recycle City for other uses, like parks and schools.

(continued)

Designing products so that they can be more easily taken apart has given rise to yet another design consideration: Design for disassembly (DFD).

Recycle Recycling is sometimes an important consideration for designers. Recycling means recover- ing materials for future use. This applies not only to manufactured parts but also to materials used during production, such as lubricants and solvents. Reclaimed metal or plastic parts may be melted down and used to make different products.

Companies recycle for a variety of reasons, including

1. Cost savings 2. Environment concerns 3. Environmental regulations

An interesting note: Companies that want to do business in the European Union must show that a specified proportion of their products are recyclable.

The pressure to recycle has given rise to the term design for recycling (DFR), referring to product design that takes into account the ability to disassemble a used product to recover the recyclable parts.

Design for disassembly (DFD) Design so that used products can be easily taken apart.

Recycling Recovering materi- als for future use.

Design for recycling (DFR) Design that facilitates the recovery of materials and components in used products for reuse.

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Paper or Plastic? Should you ask for a paper or plastic bag at the checkout counter? There’s no easy answer. The materials needed to make either bag come from our natural resources.

• Paper comes from wood, which comes from trees, which grow in the earth’s soil.

• Plastic is made from petroleum, also known as fossil fuel. Petroleum is made by the decomposition (breaking down) of ancient plants and animals inside the earth.

The trees needed to make paper are considered renewable resources. That means more trees can be planted to take the place of trees that are cut down to make paper and other products. But, trees take many years to replace because they grow slowly. Once paper is made, it can be recycled and used to create more paper goods. Making it into new paper, however, uses water and energy.

Petroleum needed to make plastic is considered a non- renewable resource. Like aluminum, tin, and steel, petroleum is not renewable because it is the result of geological processes that take millions of years to complete. When used up, the earth’s petroleum reserves will be gone for a long, long time. While plas- tic bags are easy to reuse, they’re seldom recycled, and lots and lots of them get dumped into landfills.

The best solution is to use a cloth bag or knapsack for grocery shopping, or to bring your old plastic or paper bag back to the store when you shop again. (Some stores, like Maria’s Market in Recycle City, credit your grocery bill for reusing old bags because they don’t have to buy as many new ones.) If you only purchase one or two items, you might not need a bag at all.

Recycling Igloos In many parts of the country, supermarkets place recycling con- tainers near the store to encourage their customers to recycle. (They can be any shape really, but Recycle City uses these brightly colored igloos because they’re fun.)

These igloos are used to collect bottles, cans, and plastic from Maria’s Market shoppers. Twice a week, trucks from the local Materials Recovery Facility come by to empty the igloos and take the items for recycling.

Cardboard Boxes The cardboard boxes used to ship food to Maria’s Market can be put to a variety of other uses once the food has been unpacked. The folks at the market let Recycle City residents come by and pick up cartons for storing things or moving to a new home. Any cartons that aren’t claimed by the residents are broken down and put into a pile so they can be collected, recycled, and made into other things, like new boxes, paper bags, building insulation, ani- mal bedding, or packaging materials.

Reduced Packaging When the buyers at Maria’s Market place orders to restock the store, they try to order items with very little packaging, or that use ecological packaging (ones requiring as little energy and as few resources as possible to produce).

Maria’s buyers also try to stock products that come in refillable containers. Products that don’t harm the environment and come in ecologically friendly packages are called green products.

Packaging that isn’t environmentally friendly includes products that are wrapped in several layers of plastic, use plastic foam, or have individually wrapped packages inside of a larger wrapped package.

Maria’s buyers let the manufacturers who make products for the grocery shelves know that they and their Recycle City cus- tomers would rather buy products wrapped in environmentally friendly packages than ones that aren’t. Using this kind of packag- ing is good for the manufacturer’s business.

Bulk and Fresh Foods Packaging materials make up more than 30 percent of all con- sumer waste. Maria’s Market offers shoppers many fresh foods and bulk foods to help reduce the amount of waste from too much packaging.

Fresh foods, such as bananas, oranges, and nuts, come in their own natural packaging and are excellent sources of nutrition.

Bulk items and food purchased in bulk quantities allow Maria’s shoppers to decide exactly how much they want to keep on hand. For small needs, folks measure out the exact quantity they want, helping to reduce food waste. For larger needs, they can buy bulk quantities, which usually use less packaging material and cost less.

When purchasing fresh foods or buying in bulk, shoppers can put their purchases into refillable containers they bring to the store or into the recyclable or reusable bags Maria provides.

Paper Towels and Other Paper Items Many paper products on the shelves today have already been recycled. Buying recycled products saves valuable natural res- ources and helps to create a market for those materials. When manufacturers know that shoppers want recyclable goods, they will make more of them.

In Maria’s Market, the popularity of paper towels and toilet paper made from recycled materials ensures that fewer new trees have to be cut down to produce new products.

Source: Excerpted from https://www3.epa.gov/recyclecity/market.htm

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4.8 OTHER DESIGN CONSIDERATIONS

Aside from legal, ethical, environmental, and human considerations designers must also take into account product or service life cycles, how much standardization to incorporate, product or service reliability, and the range of operating conditions under which a product or service must function. These topics are discussed in this section. We begin with life cycles.

Strategies for Product or Service Life Stages Most, but not all, products and services go through a series of stages over their useful life, sometimes referred to as their life cycle, as shown in Figure 4.1. Demand typically varies by phase. Different phases call for different strategies. In every phase, forecasts of demand and cash flow are key inputs for strategy.

When a product or service is introduced, it may be treated as a curiosity item. Many poten- tial buyers may suspect that all the bugs haven’t been worked out and that the price may drop

LO4.9 Briefly describe the phases in product design and development.

FIGURE 4.1 Products or services often go through stages over time

Time 0

D e

m a

n d

Intr od

uc tio

G ro

w th

Mat urity Decline

Hettich/Rehau/Dmitriy Sergeev

International Design Excellence Award Winners: The Technogym Recline Personal is a recumbent bike that integrates ergonomic design, cardiovascular exercise, and entertainment, making it suitable for the home or office. Designed by Dimitriy Sergeev from Bauman Moscow State Technical University, the CookPit is a complex kitchen cooker system that was developed for small kitchens, eliminating the need for bulky appliances. The various saucepans can be nested as an effective way to save space. It can be used as an oven, steamer, slow cooker, and deep fryer.

Courtesy of Technogym

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after the introductory period. Strategically, companies must carefully weigh the trade-offs in getting all the bugs out versus getting a leap on the competition, as well as getting to the market at an advantageous time. For example, introducing new high-tech products or features during peak back-to-school buying periods or holiday buying periods can be highly desirable.

It is important to have a reasonable forecast of initial demand so an adequate supply of product or an adequate service capacity is in place.

Over time, design improvements and increasing demand yield higher reliability and lower costs, leading the growth in demand. In the growth phase, it is important to obtain accurate projections of the demand growth rate and how long that will persist, and then to ensure that capacity increases coincide with increasing demand.

In the next phase, the product or service reaches maturity, and demand levels off. Few, if any, design changes are needed. Generally, costs are low and productivity is high. New uses for products or services can extend their life and increase the market size. Examples include baking soda, duct tape, and vinegar. The maker of Legos has found a way to grow its market as described in the following reading.

In the decline phase, decisions must be made on whether to discontinue a product or ser- vice and replace it with new ones or abandon the market, or to attempt to find new uses or new users for the existing product or service. For example, duct tape and baking soda are two products that have been employed well beyond their original uses of taping heating and cooling ducts and cooking. The advantages of keeping existing products or services can be tremendous. The same workers can produce the product or provide the service using much of the same equipment, the same supply chain, and perhaps the same distribution channels. Con- sequently, costs tend to be very low, and additional resource needs and training needs are low.

Some products do not exhibit life cycles: wooden pencils; paper clips; nails; knives, forks, and spoons; drinking glasses; and similar items. However, most new products do.

Some service life cycles are related to the life cycles of products. For example, as older products are phased out, services such as installation and repair of the older products also phase out.

Wide variations exist in the amount of time a particular product or service takes to pass through a given phase of its life cycle: some pass through various stages in a relatively short period; others take considerably longer. Often it is a matter of the basic need for the item and the rate of technological change. Some toys, novelty items, and style items have a life cycle of less than one year, whereas other, more useful items, such as clothes washers and dryers, may last for many years before yielding to technological change.

Product Life Cycle Management Product life cycle management (PLM) is a systematic approach to managing the series of changes a product goes through, from its conception, design, and development, through production and any redesign, to its end of life. PLM incorporates everything related to a particular product. That includes data pertaining to production processes, business processes, people, and anything else related to the product.

PLM software can be used to automate the management of product-related data and inte- grate the data with other business processes such as enterprise resource planning (discussed in

LO4.10 Discuss several key issues in product or service design.

Product life cycle manage- ment (PLM) A systematic approach to managing the series of changes a product goes through, from its con- ception to its end-of-life.

Lego A/S overcame the recent doldrums in the toy market as well as new competition in the building-block segment to continue its market success, increasing revenues and achieving a close tie for the No. 2 slot in the global toy business.

“The Danish toy maker enjoyed sustained success for its popu- lar Lego City and Lego Star Wars sets. Its new Lego Friends theme, targeting girls, sold twice as well as initial expectations and helped triple sales to girls.”

READING LEGO A/S IN THE PINK

Questions Can you think of other companies that have used new colors to extend or grow the market for their products?

Source. “Lego Shrugs Off Toy-Market Blues,” The Wall Street Journal, February 21, 2013.

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Chapter 12). A goal of PLM is to eliminate waste and improve efficiency. For example, PLM is considered to be an integral part of lean production (discussed in Chapter 14).

There are three phases of PLM application:

• Beginning of life, which involves design and development; • Middle of life, which involves working with suppliers, managing product information

and warranties; and • End of life, which involves strategies for product discontinuance, disposal, or recycling.

Although PLM is generally associated with manufacturing, the same management struc- ture can be applied to software development and services.

Degree of Standardization An important issue that often arises in both product/service design and process design is the degree of standardization. Standardization refers to the extent to which there is absence of vari- ety in a product, service, or process. Standardized products are made in large quantities of identi- cal items; calculators, computers, and 2 percent milk are examples. Standardized service implies that every customer or item processed receives essentially the same service. An automatic car wash is a good example; each car, regardless of how clean or dirty it is, receives the same ser- vice. Standardized processes deliver standardized service or produce standardized goods.

Standardization carries a number of important benefits as well as certain disadvantages. Standardized products are immediately available to customers. Standardized products mean interchangeable parts, which greatly lower the cost of production while increasing produc- tivity and making replacement or repair relatively easy compared with that of customized parts. Design costs are generally lower. For example, automobile producers standardize key components of automobiles across product lines; components such as brakes, electrical sys- tems, and other “under-the-skin” parts would be the same for all car models. By reducing vari- ety, companies save time and money while increasing quality and reliability of their products.

Another benefit of standardization is reduced time and cost to train employees and reduced time to design jobs. Similarly, scheduling of work, inventory handling, and purchasing and accounting activities become much more routine, and quality is more consistent.

Lack of standardization can at times lead to serious difficulties and competitive struggles. For example, the use of the English system of measurement by U.S. manufacturers, while most of the rest of the world’s manufacturers use the metric system, has led to problems in selling U.S. goods in foreign countries and in buying foreign machines for use in the United States. This may make it more difficult for U.S. firms to compete in the European Union.

Standardization also has disadvantages. A major one relates to the reduction in variety. This can limit the range of customers to whom a product or service appeals. And that creates a risk that a competitor will introduce a better product or greater variety and realize a competi- tive advantage. Another disadvantage is that a manufacturer may freeze (standardize) a design prematurely and, once the design is frozen, find compelling reasons to resist modification.

Obviously, designers must consider important issues related to standardization when mak- ing choices. The major advantages and disadvantages of standardization are summarized in Table 4.2.

Standardization Extent to which a product, service, or process lacks variety.

TABLE 4.2 Advantages and disadvantages of standardization

Advantages 1. Fewer parts to deal with in inventory and in manufacturing. 2. Reduced training costs and time. 3. More routine purchasing, handling, and inspection procedures. 4. Orders fillable from inventory. 5. Opportunities for long production runs and automation. 6. Need for fewer parts justifies increased expenditures on perfecting designs and

improving quality control procedures.

Disadvantages 1. Designs may be frozen with too many imperfections remaining. 2. High cost of design changes increases resistance to improvements. 3. Decreased variety results in less consumer appeal.

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Designing for Mass Customization Companies like standardization because it enables them to produce high volumes of relatively low-cost products, albeit products with little variety. Customers, on the other hand, typically prefer more variety, although they like the low cost. The question for producers is how to resolve these issues without (1) losing the benefits of standardization, and (2) incurring a host of problems that are often linked to variety. These include increasing the resources needed to achieve design variety; increasing variety in the production process, which would add to the skills necessary to produce products, causing a decrease in productivity; creating an addi- tional inventory burden during and after production, by having to carry replacement parts for the increased variety of parts; and adding to the difficulty of diagnosing and repairing product failures. The answer, at least for some companies, is mass customization, a strategy of pro- ducing standardized goods or services, but incorporating some degree of customization in the final product or service. Several tactics make this possible. One is delayed differentiation, and another is modular design.

Delayed differentiation is a postponement tactic: the process of producing, but not quite completing, a product or service, postponing completion until customer preferences or speci- fications are known. There are a number of variations of this. In the case of goods, almost- finished units might be held in inventory until customer orders are received, at which time customized features are incorporated, according to customer requests. For example, furniture makers can produce dining room sets, but not apply stain, allowing customers a choice of stains. Once the choice is made, the stain can be applied in a relatively short time, thus elimi- nating a long wait for customers, giving the seller a competitive advantage. Similarly, various e-mail or Internet services can be delivered to customers as standardized packages, which can then be modified according to the customer’s preferences. Hewlett-Packard printers that are made in the United States but intended for foreign markets are mostly completed in domestic assembly plants and then finalized closer to the country of use. The result of delayed differen- tiation is a product or service with customized features that can be quickly produced, appealing to the customers’ desire for variety and speed of delivery, and yet one that for the most part is standardized, enabling the producer to realize the benefits of standardized production. This technique is not new. Manufacturers of men’s clothing, for example, produce suits with pants that have legs that are unfinished, allowing customers to tailor choices as to the exact length and whether to have cuffs or no cuffs. What is new is the extent to which business organiza- tions are finding ways to incorporate this concept into a broad range of products and services.

Modular design is a form of standardization. Modules represent groupings of component parts into subassemblies, usually to the point where the individual parts lose their separate identity. One familiar example of modular design is computers, which have modular parts that can be replaced if they become defective. By arranging modules in different configu- rations, different computer capabilities can be obtained. For mass customization, modular design enables producers to quickly assemble products with modules to achieve a customized configuration for an individual customer, avoiding the long customer wait that would occur if individual parts had to be assembled. Dell Computers has successfully used this concept to become a dominant force in the PC industry by offering consumers the opportunity to config- ure modules according to their own specifications. Many other computer manufacturers now use a similar approach. Modular design also is found in the construction industry. One firm in Rochester, New York, makes prefabricated motel rooms complete with wiring, plumbing, and even room decorations in its factory and then moves the complete rooms by rail to the construction site, where they are integrated into the structure.

One advantage of modular design of equipment compared with nonmodular design is that failures are often easier to diagnose and remedy because there are fewer pieces to investi- gate. Similar advantages are found in ease of repair and replacement; the faulty module is conveniently removed and replaced with a good one. The manufacture and assembly of mod- ules generally involve simplifications: Fewer parts are involved, so purchasing and inventory control become more routine, fabrication and assembly operations become more standard- ized, and training costs often are relatively low.

LO4.10 Discuss several key issues in product or service design.

Mass customization A strat- egy of producing basically standardized goods, but incorporating some degree of customization.

Delayed differentiation The process of producing, but not quite completing, a product or service until customer prefer- ences are known.

Modular design A form of standardization in which com- ponent parts are grouped into modules that are easily replaced or interchanged.

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The main disadvantages of modular design stem from the decrease in variety: The number of possible configurations of modules is much less than the number of possible configurations based on individual components. Another disadvantage that is sometimes encountered is the inability to disassemble a module in order to replace a faulty part; the entire module must be scrapped—usually at a higher cost.

Reliability Reliability is a measure of the ability of a product, a part, a service, or an entire system to per- form its intended function under a prescribed set of conditions. The importance of reliability is underscored by its use by prospective buyers in comparing alternatives and by sellers as one determinant of price. Reliability also can have an impact on repeat sales, reflect on the prod- uct’s image, and, if it is too low, create legal implications. Reliability is also a consideration for sustainability; the higher the reliability of a product, the fewer the resources that will be needed to maintain it, and the less frequently it will involve the three Rs.

The term failure is used to describe a situation in which an item does not perform as intended. This includes not only instances in which the item does not function at all, but also

LO4.10 Discuss several key issues in product or service design.

Reliability The ability of a product, part, or system to perform its intended function under a prescribed set of conditions.

Failure Situation in which a product, part, or system does not perform as intended.

Pulled pork sandwiches are proving popular at many chain res- taurants, including Wendy’s, Buffalo Wild Wings, and Burger King. Because pulled pork typically takes about 4 hours to cook, it’s not a food most folks are likely to cook at home. And once cooked, sandwiches can easily be assembled to order (i.e., delayed differentiation) using any of a large number of sauces or seasonings. Customer appeal is obvious. And advantages for fast-food restaurants include simplified menus, minimal training requirements, and little need for new equipment.

READING FAST-FOOD CHAINS ADOPT MASS CUSTOMIZATION

Questions

1. What two major benefits do customers get from delayed differentiation?

2. Can you think of another food product that might lend itself to delayed differentiation, and therefore end up as a popular fast- food item?

Source: Based on “Fast-Food Chains Are Pigging Out,” Businessweek, October 12–October 18, 2015, pp. 22-3.

Employees on the production line at the new Dell Global Operation Facility in China. With the capacity to produce up to seven million units a year, the site will manufacture electronics for the Chinese market as well as markets in Europe and the United States.

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instances in which the item’s performance is substandard or it functions in a way not intended. For example, a smoke alarm might fail to respond to the presence of smoke (not operate at all), it might sound an alarm that is too faint to provide an adequate warning (substandard perfor- mance), or it might sound an alarm even though no smoke is present (unintended response).

Reliabilities are always specified with respect to certain conditions, called normal operating conditions. These can include load, temperature, and humidity ranges as well as operating procedures and maintenance schedules. Failure of users to heed these conditions often results in premature failure of parts or complete systems. For example, using a pas- senger car to tow heavy loads will cause excess wear and tear on the drive train; driving over potholes or curbs often results in untimely tire failure; and using a calculator to drive nails might have a marked impact on its usefulness for performing mathematical operations.

Improving Reliability. Reliability can be improved in a number of ways, some of which are listed in Table 4.3.

Because overall system reliability is a function of the reliability of individual components, improvements in their reliability can increase system reliability. Unfortunately, inadequate pro- duction or assembly procedures can negate even the best of designs, and this is often a source of failures. System reliability can be increased by the use of backup components. Failures in actual use often can be reduced by upgrading user education and refining maintenance recommenda- tions or procedures. Finally, it may be possible to increase the overall reliability of the system by simplifying the system (thereby reducing the number of components that could cause the system to fail) or altering component relationships (e.g., increasing the reliability of interfaces).

A fundamental question concerning improving reliability is: How much reliability is needed? Obviously, the reliability needed for a household light bulb isn’t in the same category as the reliability needed for an airplane. So the answer to the question depends on the poten- tial benefits of improvements and on the cost of those improvements. Generally speaking, reliability improvements become increasingly costly. Thus, although benefits initially may increase at a much faster rate than costs, the opposite eventually becomes true. The optimal level of reliability is the point where the incremental benefit received equals the incremental cost of obtaining it. In the short term, this trade-off is made in the context of relatively fixed parameters (e.g., costs). However, in the longer term, efforts to improve reliability and reduce costs can lead to higher optimal levels of reliability.

Robust Design Some products or services will function as designed only within a narrow range of conditions, while others will perform as designed over a much broader range of conditions. The latter have robust design. Consider a pair of fine leather boots—obviously not made for trekking through mud or snow. Now consider a pair of heavy rubber boots—just the thing for mud or snow. The rubber boots have a design that is more robust than that of the fine leather boots.

The more robust a product or service, the less likely it will fail due to a change in the envi- ronment in which it is used or in which it is performed. Hence, the more designers can build robustness into the product or service, the better it should hold up, resulting in a higher level of customer satisfaction.

A similar argument can be made for robust design as it pertains to the production process. Environmental factors can have a negative effect on the quality of a product or service. The more resistant a design is to those influences, the less likely is a negative effect. For example,

Normal operating conditions The set of conditions under which an item’s reliability is specified.

LO4.10 Discuss several key issues in product or service design.

Robust design Design that results in products or services that can function over a broad range of conditions.

TABLE 4.3 Potential ways to improve reliability

1. Improve component design. 2. Improve production and/or assembly techniques. 3. Improve testing. 4. Use backups. 5. Improve preventive maintenance procedures. 6. Improve user education. 7. Improve system design.

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many products go through a heating process: food products, ceramics, steel, petroleum prod- ucts, and pharmaceutical products. Furnaces often do not heat uniformly; heat may vary either by position in an oven or over an extended period of production. One approach to this problem might be to develop a superior oven; another might be to design a system that moves the prod- uct during heating to achieve uniformity. A robust-design approach would develop a product that is unaffected by minor variations in temperature during processing.

Taguchi’s Approach. Japanese engineer Genichi Taguchi’s approach is based on the concept of robust design. His premise is that it is often easier to design a product that is insensitive to envi- ronmental factors, either in manufacturing or in use, than to control the environmental factors.

The central feature of Taguchi’s approach—and the feature used most often by U.S. companies—is parameter design. This involves determining the specification settings for both the product and the process that will result in robust design in terms of manufacturing variations, product deterioration, and conditions during use.

The Taguchi approach modifies the conventional statistical methods of experimental design. Consider this example. Suppose a company will use 12 chemicals in a new product it intends to produce. There are two suppliers for these chemicals, but the chemical concentra- tions vary slightly between the two suppliers. Classical design of experiments would require 212 = 4,096 test runs to determine which combination of chemicals would be optimum. Tagu- chi’s approach would involve only testing a portion of the possible combinations. Relying on experts to identify the variables that would be most likely to affect important performance, the number of combinations would be dramatically reduced, perhaps to, say, 32. Identify- ing the best combination in the smaller sample might be a near-optimal combination instead of the optimal combination. The value of this approach is its ability to achieve major advances in product or process design fairly quickly, using a relatively small number of experiments.

Critics charge that Taguchi’s methods are inefficient and incorrect, and often lead to non- optimal solutions. Nonetheless, his methods are widely used and have been credited with helping to achieve major improvements in U.S. products and manufacturing processes.

Degree of Newness Product or service design change can range from the modification of an existing product or service to an entirely new product or service:

1. Modification of an existing product or service 2. Expansion of an existing product line or service offering 3. Clone of a competitor’s product or service 4. New product or service

The degree of change affects the newness to the organization and the newness to the mar- ket. For the organization, a low level of newness can mean a fairly quick and easy transition to producing the new product, while a high level of newness would likely mean a slower and more difficult, and therefore more costly, transition. For the market, a low level of newness would mean little difficulty with market acceptance, but possibly low profit potential. Even in instances of low profit potential, organizations might use this strategy to maintain market share. A high level of newness, on the other hand, might mean more difficulty with accep- tance, or it might mean a rapid gain in market share with a high potential for profits. Unfor- tunately, there is no way around these issues. It is important to carefully assess the risks and potential benefits of any design change, taking into account clearly identified customer wants.

Quality Function Deployment Obtaining input from customers is essential to assure that they will want what is offered for sale. Although obtaining input can be informal through discussions with customers, there is a formal way to document customer wants. Quality function deployment (QFD) is a struc- tured approach for integrating the “voice of the customer” into both the product and service development process. The purpose is to ensure that customer requirements are factored into

LO4.10 Discuss several key issues in product or service design.

Quality function deployment (QFD) An approach that inte- grates the “voice of the cus- tomer” into both product and service development.

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every aspect of the process. Listening to and understanding the customer is the central feature of QFD. Requirements often take the form of a general statement such as, “It should be easy to adjust the cutting height of the lawn mower.” Once the requirements are known, they must be translated into technical terms related to the product or service. For example, a statement about changing the height of the lawn mower may relate to the mechanism used to accomplish that, its position, instructions for use, tightness of the spring that controls the mechanism, or materials needed. For manufacturing purposes, these must be related to the materials, dimen- sions, and equipment used for processing.

The structure of QFD is based on a set of matrices. The main matrix relates customer requirements (what) and their corresponding technical requirements (how). This matrix is illustrated in Figure 4.2. The matrix provides a structure for data collection.

Additional features are usually added to the basic matrix to broaden the scope of analysis. Typical additional features include importance weightings and competitive evaluations. A corre- lational matrix is usually constructed for technical requirements; this can reveal conflicting tech- nical requirements. With these additional features, the set of matrices has the form illustrated in Figure 4.3. It is often referred to as the house of quality because of its house-like appearance.

An analysis using this format is shown in Figure 4.4. The data relate to a commercial printer (customer) and the company that supplies the paper. At first glance, the display appears complex. It contains a considerable amount of information for product and process planning. Therefore, let’s break it up into separate parts and consider them one at a time. To start, a key part is the list of customer requirements on the left side of the figure. Next, note the technical requirements, listed vertically near the top. The key relationships and their degree of importance are shown in the center of the figure. The circle with a dot inside indicates the strongest positive relationship; that is, it denotes the most important technical requirements for satisfying customer requirements. Now look at the “importance to customer” numbers that are shown next to each customer requirement (3 is the most important). Designers will take into account the importance values and the strength of correlation in determining where to focus the greatest effort.

Next, consider the correlation matrix at the top of the “house.” Of special interest is the strong negative correlation between “paper thickness” and “roll roundness.” Designers will have to find some way to overcome that or make a trade-off decision.

On the right side of the figure is a competitive evaluation comparing the supplier’s per- formance on the customer requirements with each of the two key competitors (A and B). For example, the supplier (X) is worst on the first customer requirement and best on the third customer requirement. The line connects the X performances. Ideally, design will cause all of the Xs to be in the highest positions.

Across the bottom of Figure 4.4 are importance weightings, target values, and technical evaluations. The technical evaluations can be interpreted in a manner similar to that of the competitive evaluations (note the line connecting the Xs). The target values typically contain technical specifications, which we will not discuss. The importance weightings are the sums

FIGURE 4.2 An example of the house of quality: the main QFD matrix

Source: Ernst and Young Consulting Group, Total Quality (Homewood, IL: Dow-Jones Irwin, 1991), p. 121. Reprinted by permission.

Technical requirements

Importance to customer

Customer requirements

Relationship matrix

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FIGURE 4.3 The house of quality

Customer requirements

Relationship matrix

Competitive assessment

Specifications or

target values

Design requirements

Correlation matrix

FIGURE 4.4 An example of the house of quality

Technical requirements

Importance to

customer

Customer requirements

Paper will not tear

Consistent finish

No ink bleed

Prints cleanly

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X = Us A = Competitor A B = Competitor B

X A B

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Importance weighting 3 27 36 36 27 9

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Positive

Negative

Strong negative

Correlations

Strong = 9

Medium = 3

Small = 1

Relationships

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of values assigned to the relationships (see the lower right-hand key for relationship weights). The 3 in the first column is the product of the importance to the customer, 3, and the small (∆) weight, 1. The importance weightings and target evaluations help designers focus on desired results. In this example, the first technical requirement has the lowest importance weighting, while the next four technical requirements all have relatively high importance weightings.

The house of quality approach involves a sequence of “houses,” beginning with design char- acteristics, which leads to specific components, then production processes, and finally, a quality plan. The sequence is illustrated in Figure 4.5. Although the details of each house are beyond the scope of this text, Figure 4.5 provides a conceptual understanding of the progression involved.

The Kano Model The Kano model is a theory of product and service design developed by Dr. Noriaki Kano, a Japanese professor, who offered a perspective on customer perceptions of quality different from the traditional view that “more is better.” Instead, he proposed different categories of quality and posited that understanding them would better position designers to assess and address quality needs. His model provides insights into the attributes that are perceived to be important to customers. The model employs three definitions of quality: basic, performance, and excitement.

Basic quality refers to customer requirements that have only a limited effect on customer satisfaction if present, but lead to dissatisfaction if not present. For example, putting a very short cord on an electrical appliance will likely result in customer dissatisfaction, but beyond a certain length (e.g., 4 feet), adding more cord will not lead to increased levels of customer satisfaction. Performance quality refers to customer requirements that generate satisfaction or dissatisfaction in proportion to their level of functionality and appeal. For example, increasing the tread life of a tire or the amount of time house paint will last will add to customer satisfac- tion. Excitement quality refers to a feature or attribute that was unexpected by the customer and causes excitement (the “wow” factor), such as a voucher for dinner for two at the hotel restaurant when checking in. Figure 4.6A portrays how the three definitions of quality influ- ence customer satisfaction or dissatisfaction relative to the degree of implementation. Note that features that are perceived by customers as basic quality result in dissatisfaction if they are missing or at low levels, but do not result in customer satisfaction if they are present, even at high levels. Performance factors can result in satisfaction or dissatisfaction, depending on the degree to which they are present. Excitement factors, because they are unexpected, do not result in dissatisfaction when they are absent or at low levels, but have the potential for dispro- portionate levels of satisfaction if they are present.

Over time, features that excited become performance features, and performance features soon become basic quality features, as illustrated in Figure 4.6B. The rates at which various design elements are migrating is an important input from marketing that will enable design- ers to continue to satisfy and delight customers and not waste efforts on improving what have become basic quality features.

LO4.10 Discuss several key issues in product or service design.

FIGURE 4.5 The house of quality sequence

House 1 House 2 House 3 House 4

Design characteristics

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The lesson of the Kano model is that design elements that fall into each aspect of quality must first be determined. Once basic needs have been met, additional efforts in those areas should not be pursued. For performance features, cost–benefit analysis comes into play, and these features should be included as long as the benefit exceeds the cost. Excitement features pose somewhat of a challenge. Customers are not likely to indicate excitement factors in sur- veys because they don’t know that they want them. However, small increases in such fac- tors produce disproportional increases in customer satisfaction and generally increase brand loyalty, so it is important for companies to strive to identify and include these features when economically feasible.

The Kano model can be used in conjunction with QFD as well as in Six Sigma projects (see Chapter 9 for a discussion of Six Sigma).

4.9 PHASES IN PRODUCT DESIGN AND DEVELOPMENT

Product design and development generally proceeds in a series of phases (see Table 4.4).

Feasibility analysis. Feasibility analysis entails market analysis (demand), economic analysis (development cost and production cost, profit potential), and technical analy- sis (capacity requirements and availability, and the skills needed). Also, it is necessary

FIGURE 4.6A The Kano model

Excitement quality

Satisfied

Dissatisfied

Low functionality and appeal

High functionality and appeal

Performance quality

Basic quality

FIGURE 4.6B As time passes, excitement factors become performance factors, and performance factors become basic factors

Excitement quality

Satisfied

Tim e

Dissatisfied

Low functionality and appeal

High functionality and appeal

Performance quality

Basic quality

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to answer the question: Does it fit with the mission? It requires collaboration among marketing, finance, accounting, engineering, and operations. Product specifications. This involves detailed descriptions of what is needed to meet (or exceed) customer wants, and requires collaboration between legal, marketing, and operations. Process specifications. Once product specifications have been set, attention turns to specifications for the process that will be needed to produce the product. Alternatives must be weighed in terms of cost, availability of resources, profit potential, and quality. This involves collaboration between accounting and operations. Prototype development. With product and process specifications complete, one (or a few) units are made to see if there are any problems with the product or process specifications. Design review. At this stage, any necessary changes are made or the project is aban- doned. Marketing, finance, engineering, design, and operations collaborate to determine whether to proceed or abandon. Market test. A market test is used to determine the extent of consumer acceptance. If unsuccessful, the product returns to the design review phase. This phase is handled by marketing. Product introduction. The new product is promoted. This phase is handled by marketing. Follow-up evaluation. Based on user feedback, changes may be made or forecasts refined. This phase is handled by marketing.

4.10 DESIGNING FOR PRODUCTION

In this section, you will learn about design techniques that have greater applicability for the design of products than the design of services. Even so, you will see that they do have some rel- evance for service design. The topics include concurrent engineering, computer-assisted design, designing for assembly and disassembly, and the use of components for similar products.

Concurrent Engineering To achieve a smoother transition from product design to production, and to decrease product development time, many companies are using simultaneous development, or concurrent engi- neering. In its narrowest sense, concurrent engineering means bringing design and manu- facturing engineering people together early in the design phase to simultaneously develop the product and the processes for creating the product. More recently, this concept has been enlarged to include manufacturing personnel (e.g., materials specialists) and marketing and purchasing personnel in loosely integrated, cross-functional teams. In addition, the views of suppliers and customers are frequently sought. The purpose, of course, is to achieve product designs that reflect customer wants as well as manufacturing capabilities.

Traditionally, designers developed a new product without any input from manufacturing, and then turned over the design to manufacturing, which would then have to develop a process for making the new product. This “over-the-wall” approach created tremendous challenges for manufacturing, generating numerous conflicts and greatly increasing the time needed to suc- cessfully produce a new product. It also contributed to an “us versus them” mentality.

Concurrent engineering Bringing engineering design and manufacturing personnel together early in the design phase.

1. Feasibility analysis 5. Design review

2. Product specifications 6. Market test

3. Process specifications 7. Product introduction

4. Prototype development 8. Follow-up evaluation

TABLE 4.4 Phases in the product development process

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For these and similar reasons, the simultaneous development approach has great appeal. Among the key advantages of this approach are the following:

1. Manufacturing personnel are able to identify production capabilities and capacities. Very often, they have some latitude in design in terms of selecting suitable materials and processes. Knowledge of production capabilities can help in the selection process. In addition, cost and quality considerations can be greatly influenced by design, and conflicts during production can be greatly reduced.

2. Design or procurement of critical tooling, some of which might have long lead times, can occur early in the process. This can result in a major shortening of the product development process, which could be a key competitive advantage.

3. The technical feasibility of a particular design or a portion of a design can be assessed early on. Again, this can avoid serious problems during production.

4. The emphasis can be on problem resolution instead of conflict resolution.

However, despite the advantages of concurrent engineering, a number of potential difficul- ties exist in this co-development approach. Two key ones are the following:

1. Long-standing boundaries between design and manufacturing can be difficult to over- come. Simply bringing a group of people together and thinking that they will be able to work together effectively is probably naive.

2. There must be extra communication and flexibility if the process is to work, and these can be difficult to achieve.

Hence, managers should plan to devote special attention if this approach is to work.

Computer-Aided Design (CAD) Computers are increasingly used for product design. Computer-aided design (CAD) uses computer graphics for product design. The designer can modify an existing design or create a new one on a monitor by means of a light pen, a keyboard, a joystick, or a similar device. Once the design is entered into the computer, the designer can maneuver it on the screen: It can be rotated to provide the designer with different perspectives, it can be split apart to give the designer a view of the inside, and a portion of it can be enlarged for closer examination. The

Computer-aided design (CAD) Product design using com- puter graphics.

An architect is using an iPad CAD (computer-aided design) application to model a 3D layout design of a new house.

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designer can obtain a printed version of the completed design and file it electronically, making it accessible to people in the firm who need this information (e.g., marketing, operations).

A growing number of products are being designed in this way, including transformers, automobile parts, aircraft parts, integrated circuits, and electric motors.

A major benefit of CAD is the increased productivity of designers. No longer is it neces- sary to laboriously prepare mechanical drawings of products or parts and revise them repeat- edly to correct errors or incorporate revisions. A rough estimate is that CAD increases the productivity of designers from 3 to 10 times. A second major benefit of CAD is the creation of a database for manufacturing that can supply needed information on product geometry and dimensions, tolerances, material specifications, and so on. It should be noted, however, that CAD needs this database to function and that this entails a considerable amount of effort.

Some CAD systems allow the designer to perform engineering and cost analyses on pro- posed designs. For instance, the computer can determine the weight and volume of a part and do stress analysis as well. When there are a number of alternative designs, the computer can quickly go through the possibilities and identify the best one, given the designer’s criteria. CAD that includes finite element analysis (FEA) capability can greatly shorten the time to market of new products. It enables developers to perform simulations that aid in the design, analysis, and commercialization of new products. Designers in industries such as aeronautics, biomechanics, and automotives use FEA.

Production Requirements As noted earlier in the chapter, designers must take into account production capabilities. Design needs to clearly understand the capabilities of production (e.g., equipment, skills, types of materials, schedules, technologies, special abilities). This helps in choosing designs that match capabilities. When opportunities and capabilities do not match, management must consider the potential for expanding or changing capabilities to take advantage of those opportunities.

Forecasts of future demand can be very useful, supplying information on the timing and volume of demand, and information on demands for new products and services.

Manufacturability is a key concern for manufactured goods: Ease of fabrication and/or assembly is important for cost, productivity, and quality. With services, ease of providing the service, cost, productivity, and quality are of great concern.

The term design for manufacturing (DFM) is used to indicate the designing of products that are compatible with an organization’s capabilities. A related concept in manufacturing is design for assembly (DFA). A good design must take into account not only how a product will be fabricated, but also how it will be assembled. Design for assembly focuses on reducing the number of parts in an assembly, as well as on the assembly methods and sequence that will be employed. Another, more general term, manufacturability, is sometimes used when referring to the ease with which products can be fabricated and/or assembled.

Component Commonality Companies often have multiple products or services to offer customers. Often, these products or services have a high degree of similarity of features and components. This is particularly true of product families, but it is also true of many services. Companies can realize significant benefits when a part can be used in multiple products. For example, car manufacturers employ this tactic by using internal components such as water pumps, engines, and transmissions on several automobile nameplates. In addition to the savings in design time, companies reap benefits through standard training for assembly and installation, increased opportunities for savings by buying in bulk from suppliers, and commonality of parts for repair, which reduces the inventory dealers and auto parts stores must carry. Similar benefits accrue in services. For example, in automobile repair, component commonality means less training is needed because the variety of jobs is reduced. The same applies to appliance repair, where com- monality and substitutability of parts are typical. Multiple-use forms in financial and medical services is another example. Computer software often comprises a number of modules that are commonly used for similar applications, thereby saving the time and cost to write the code

Design for manufacturing (DFM) The designing of prod- ucts that are compatible with an organization’s capabilities.

Design for assembly (DFA) Design that focuses on reduc- ing the number of parts in a product and on assembly methods and sequence.

Manufacturability The capa- bility of an organization to produce an item at an accept- able profit.

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for major portions of the software. Tool manufacturers use a design that allows tool users to attach different power tools to a common power source. Similarly, Hewlett-Packard has a uni- versal power source that can be used with a variety of computer hardware.

4.11 SERVICE DESIGN

There are many similarities between product and service design. However, there are some important differences as well, owing to the nature of services. One major difference is that unlike manufacturing, where production and delivery are usually separated in time, services are usually created and delivered simultaneously.

Service refers to an act, something that is done to or for a customer (client, patient, etc.). It is provided by a service delivery system, which includes the facilities, processes, and skills needed to provide the service. Many services are not pure services, but part of a product bundle—the combination of goods and services provided to a customer. The service com- ponent in products is increasing. The ability to create and deliver reliable customer-oriented service is often a key competitive differentiator. Successful companies combine customer- oriented service with their products.

System design involves development or refinement of the overall service package:1

1. The physical resources needed. 2. The accompanying goods that are purchased or consumed by the customer, or provided

with the service. 3. Explicit services (the essential/core features of a service, such as tax preparation). 4. Implicit services (ancillary/extra features, such as friendliness, courtesy).

Overview of Service Design Service design begins with the choice of a service strategy, which determines the nature and focus of the service, and the target market. This requires an assessment by top management of the potential market and profitability (or need, in the case of a nonprofit organization) of a particular service, and an assessment of the organization’s ability to provide the service. Once decisions on the focus of the service and the target market have been made, the customer requirements and expectations of the target market must be determined.

Two key issues in service design are the degree of variation in service requirements and the degree of customer contact and customer involvement in the delivery system. These have an impact on the degree to which service can be standardized or must be customized. The lower the degree of customer contact and service requirement variability, the more standardized the ser- vice can be. Service design with no contact and little or no processing variability is very much like product design. Conversely, high variability and high customer contact generally mean the service must be highly customized. A related consideration in service design is the opportunity for selling: The greater the degree of customer contact, the greater the opportunities for selling.

Differences between Service Design and Product Design Service operations managers must contend with issues that may be insignificant or nonexis- tent for managers in a production setting. These include the following:

1. Products are generally tangible; services are generally intangible. Consequently, service design often focuses more on intangible factors (e.g., peace of mind, ambiance) than does product design.

2. In many instances services are created and delivered at the same time (e.g., a haircut, a car wash). In such instances there is less latitude in finding and correcting errors before

Service Something that is done to or for a customer.

Service delivery system The facilities, processes, and skills needed to provide a service.

Product bundle The combi- nation of goods and services provided to a customer.

1Adapted from James A. Fitzsimmons and Mona J. Fitzsimmons, Service Management for Competitive Advantage (New York: McGraw-Hill, 1994). Copyright © 1994 McGraw-Hill Companies, Inc. Used with permission.

Service package The physical resources needed to perform the service, the accompanying goods, and the explicit and implicit services included.

LO4.11 Discuss the two key issues in service design.

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the customer has a chance to discover them. Consequently, training, process design, and customer relations are particularly important.

3. Services cannot be inventoried. This poses restrictions on flexibility and makes capacity issues very important.

4. Services are highly visible to consumers and must be designed with that in mind; this adds an extra dimension to process design, one that usually is not present in product design.

5. Some services have low barriers to entry and exit. This places additional pressures on service design to be innovative and cost-effective.

6. Location is often important to service design, with convenience as a major factor. Hence, design of services and choice of location are often closely linked.

7. Service systems range from those with little or no customer contact to those that have a very high degree of customer contact. Here are some examples of those different types:

Insulated technical core; little or no customer contact (e.g., software development) Production line; little or no customer contact (e.g., automatic car wash) Personalized service (e.g., haircut, medical service) Consumer participation (e.g., diet program, dance lessons) Self-service (e.g., supermarket) If there is little or no customer contact, service system design is like product system design. 8. Demand variability alternately creates waiting lines or idle service resources.

When demand variability is a factor, designers may approach service design from one of two perspectives. One is a cost and efficiency perspective, and the other is a customer perspective. Waiting line analysis (see Chapter 18) can be especially useful in this regard.

Basing design objectives on cost and efficiency is essentially a “product design approach” to service design. Because customer participation makes both quality and demand variability more difficult to manage, designers may opt to limit customer participation in the process where possible. Alternatively, designers may use staff flexibility as a means of dealing with demand variability.

In services, a significant aspect of perceived quality relates to the intangibles that are part of the service package. Designers must proceed with caution because attempts to achieve a high level of efficiency tend to depersonalize service and to create the risk of negatively alter- ing the customer’s perception of quality. Such attempts may involve the following:

1. Reducing consumer choices makes service more efficient, but it can be both frustrat- ing and irritating for the customer. An example would be a cable company that bundles channels, rather than allowing customers to pick only the channels they want.

2. Standardizing or simplifying certain elements of service can reduce the cost of pro- viding a service, but it risks eliminating features that some customers value, such as personal attention.

3. Incorporating flexibility in capacity management by employing part-time or temporary staff may involve the use of less-skilled or less-interested people, and service quality may suffer.

Design objectives based on customer perspective require understanding the customer expe- rience, and focusing on how to maintain control over service delivery to achieve customer sat- isfaction. The customer-oriented approach involves determining consumer wants and needs in order to understand relationships between service delivery and perceived quality. This enables designers to make enlightened choices in designing the delivery system.

Of course, designers must keep in mind that while depersonalizing service delivery for the sake of efficiency can negatively impact perceived quality, customers may not want or be willing to pay for highly personalized service either, so trade-offs may have to be made.

Phases in the Service Design Process Table 4.5 lists the phases in the service design process. As you can see, they are quite similar to the phases of product design, except that the delivery system also must be designed.

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Service Blueprinting A useful tool for conceptualizing a service delivery system is the service blueprint, which is a method for describing and analyzing a service process. A service blueprint is much like an architectural drawing, but instead of showing building dimensions and other construc- tion features, a service blueprint shows the basic customer and service actions involved in a service operation. Figure 4.7 illustrates a simple service blueprint for a restaurant. At the top of the figure are the customer actions, and just below are the related actions of the direct contact service people. Next are what are sometimes referred to as “backstage contacts”—in this example, the kitchen staff—and below those are the support, or “backroom,” operations. In this example support operations include the reservation system, ordering of food and sup- plies, cashier, and the outsourcing of laundry service. Figure 4.7 is a simplified illustration; typically time estimates for actions and operations would be included.

The major steps in service blueprinting are as follows:

1. Establish boundaries for the service and decide on the level of detail needed. 2. Identify and determine the sequence of customer and service actions and interactions.

A flowchart can be a useful tool for this.

Service blueprint A method used in service design to describe and analyze a pro- posed service.

TABLE 4.5 Phases in service design process

1. Conceptualize. Idea generation Assessment of customer wants/needs (marketing) Assessment of demand potential (marketing) 2. Identify service package components needed (operations and marketing). 3. Determine performance specifications (operations and marketing). 4. Translate performance specifications into design specifications. 5. Translate design specifications into delivery specifications.

Customer actions

line of information

Contact persons

line of visibility

Backstage contacts

Support

Arrive Seated Order Eat Pay and

leave

Greeted by hostess

Hostess checks reservation

Hostess escorts customers to their table

Greeted by server Server provides

menus Server fills

water glasses

Server describes specials

Server takes orders

Dinners are served

Server occasionally checks to see if any problems

Server brings the check

Server receives payment

Busboy clears table

Kitchen sta� prepares food

Reservation system

Ordering food Cashier Laundry service

Dishes are

washed

line of internal interaction

FIGURE 4.7 A simple service blueprint for a restaurant

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3. Develop time estimates for each phase of the process, as well as time variability. 4. Identify potential failure points and develop a plan to prevent or minimize them, as well

as a plan to respond to service errors.

Characteristics of Well-Designed Service Systems There are a number of characteristics of well-designed service systems. They can serve as guidelines in developing a service system. They include the following:

1. Being consistent with the organization’s mission. 2. Being user-friendly. 3. Being robust if variability is a factor. 4. Being easy to sustain. 5. Being cost-effective. 6. Having value that is obvious to customers. 7. Having effective linkages between back-of-the-house operations (i.e., no contact with

the customer) and front-of-the-house operations (i.e., direct contact with customers). Front operations should focus on customer service, while back operations should focus on speed and efficiency.

8. Having a single, unifying theme, such as convenience or speed. 9. Having design features and checks that will ensure service that is reliable and of high quality.

Challenges of Service Design Variability is a major concern in most aspects of business operations, and it is particu- larly so in the design of service systems. Requirements tend to be variable, both in terms of differences in what customers want or need, and in terms of the timing of customer requests. Because services generally cannot be stored, there is the additional challenge of balancing supply and demand. This is less of a problem for systems in which the timing of services can be scheduled (e.g., doctor’s appointment), but not so in others (e.g., emergency room visit).

Another challenge is that services can be difficult to describe precisely and are dynamic in nature, especially when there is a direct encounter with the customer (e.g., personal services), due to the large number of variables.

Guidelines for Successful Service Design 1. Define the service package in detail. A service blueprint may be helpful for this. 2. Focus on the operation from the customer’s perspective. Consider how customer

expectations and perceptions are managed during and after the service. 3. Consider the image that the service package will present both to customers and to

prospective customers. 4. Recognize that designers’ familiarity with the system may give them a quite different

perspective than that of the customer, and take steps to overcome this. 5. Make sure that managers are involved and will support the design once it is

implemented. 6. Define quality for both tangibles and intangibles. Intangible standards are more difficult

to define, but they must be addressed. 7. Make sure that recruitment, training, and reward policies are consistent with service

expectations. 8. Establish procedures to handle both predictable and unpredictable events. 9. Establish systems to monitor, maintain, and improve service.

LO4.12 List the charac- teristics of well-designed service systems.

LO4.13 List some guide- lines for successful service design.

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4.12 OPERATIONS STRATEGY

Product and service design is a fertile area for achieving competitive advantage and/or increas- ing customer satisfaction. Potential sources of such benefits include the following:

• Packaging products and ancillary services to increase sales. Examples include sell- ing PCs at a reduced cost with a two-year Internet access sign-up agreement, offering extended warranties on products, offering installation and service, and offering training with computer software.

• Using multiple-use platforms. Auto manufacturers use the same platform (basic chassis, say) for several nameplates (e.g., Jaguar S type, Lincoln LS, and Ford Thunderbird have shared the same platform). There are two basic computer platforms, PC and Mac, with many variations of computers using a particular platform.

• Implementing tactics that will achieve the benefits of high volume while satisfying cus- tomer needs for variety, such as mass customization.

• Continually monitoring products and services for small improvements rather than the “big bang” approach. Often the “little” things can have a positive, long-lasting effect on consumer attitudes and buying behavior.

• Shortening the time it takes to get new or redesigned goods and services to market.

A key competitive advantage of some companies is their ability to bring new products to market more quickly than their competitors. Companies using this “first-to-market” approach are able to enter markets ahead of their competitors, allowing them to set higher selling prices than otherwise due to absence of competition. Such a strategy is also a defense against compe- tition from cheaper “clones” because the competitors always have to play “catch up.”

From a design standpoint, reducing the time to market involves:

• Using standardized components to create new but reliable products. • Using technology such as computer-aided design (CAD) equipment to rapidly design

new or modified products. • Concurrent engineering to shorten engineering time.

Services can pose a variety of managerial challenges for managers—challenges that in manufacturing are either much less or nonexistent. And because services represent an increasing share of the economy, this places added importance on under- standing and dealing with the challenges of managing services. Here are some of the main factors:

1. Jobs in service environments are often less structured than in manufacturing environments.

2. Customer contact is usually much higher in services. 3. In many services, worker skill levels are low compared to those

of manufacturing workers. 4. Services are adding many new workers in low-skill, entry-level

positions. 5. Employee turnover is often higher, especially in the low-skill jobs.

READING THE CHALLENGES OF MANAGING SERVICES

6. Input variability tends to be higher in many service environ- ments than in manufacturing.

7. Service performance can be adversely affected by workers’ emotions, distractions, customers’ attitudes, and other factors, many of which are beyond managers’ control.

Because of these factors, quality and costs are more difficult to control, productivity tends to be lower, the risk of customer dis- satisfaction is greater, and employee motivation is more difficult.

Questions 1. What managerial challenges do services present that manufac-

turing does not? 2. Why does service management present more challenges than

manufacturing?

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Product and service design is a key factor in satisfying the customer. To be successful in product and service design, organizations must be continually aware of what customers want, what the competition is doing, what government regulations are, and what new technologies are available.

The design process involves motivation, ideas for improvement, organizational capabilities, and fore- casting. In addition to product life cycles, legal, environmental, and ethical considerations influence design choices. What degree of standardization designers should incorporate into designs is also an important consideration. A key objective for designers is to achieve a product or service design that will meet or exceed customer expectations, within cost or budget and taking into account the capabilities of operations. Although product design and service design are similar in some respects, a number of key differences exist between products and services that influence the way they are designed.

Successful design often incorporates many of these basic principles: Determine what customers want as a starting point; minimize the number of parts needed to manufacture an item or the number of steps to provide a service; simplify assembly or service, standardize as much as possible; and make the design robust. Trade-off decisions are common in design, and they involve such things as development time and cost, product or service cost, special features/performance, and product or service complexity.

Research and development efforts can play a significant role in product and process innovations, although these are sometimes so costly that only large companies or governments can afford to under- write them.

Reliability of a product or service is often a key dimension in the eyes of the customer. Measuring and improving reliability are important aspects of product and service design, although other areas of the organization also have an influence on reliability.

Quality function deployment is one approach for getting customer input for product or service design.

SUMMARY

1. A range of factors can cause an organization to design or redesign a product or service, includ- ing economic, legal, political, social, technological, and competitive pressures. Furthermore, an important cause of operations failures can be traced to faulty design.

2. Every area of a business organization and its supply chain is connected to, and influenced by, its products and/or services, so the potential impact on each area must be taken into account when products or services are redesigned or new products or services are to be designed.

3. Central issues relate to the actual or expected demand for a product or service, the organization’s capabilities, the cost to produce or provide, the desired quality level, and the cost and availability of necessary resources.

4. Among considerations that are generally important are legal, ethical, and environmental. 5. Although there are some basic differences between product design and service design, there are

many similarities between the two.

KEY POINTS

1. What are some of the factors that cause organizations to redesign their products or services? 2. Contrast applied research and basic research. 3. What is CAD? Describe some of the ways a product designer can use it. 4. Name some of the main advantages and disadvantages of standardization. 5. What is modular design? What are its main advantages and disadvantages?

DISCUSSION AND REVIEW QUESTIONS

computer-aided design (CAD), 163

concurrent engineering, 162 cradle-to-grave assessment,

146 delayed differentiation, 154 design for assembly (DFA), 164 design for disassembly

(DFD), 147 design for manufacturing

(DFM), 164 design for recycling (DFR), 148 failure, 155

KEY TERMS manufacturability, 138, 164 mass customization, 154 modular design, 154 normal operating conditions,

156 product bundle, 165 product liability, 143 product life cycle management

(PLM), 152 quality function deployment

(QFD), 157 recycling, 147 reliability, 155

remanufacturing, 147 research and development

(R&D), 141 reverse engineering, 141 robust design, 156 service, 165 serviceability, 138 service blueprint, 167 service delivery system, 165 service package, 165 standardization, 153 Uniform Commercial Code, 143 value analysis, 146

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6. Explain the term design for manufacturing and briefly explain why it is important. 7. What are some of the competitive advantages of concurrent engineering? 8. Explain the term remanufacturing. 9. a. What is meant by the term life cycle?

b. Why would this be a consideration in product or service design? c. Name three ways that each of these products has found new uses: baking soda, duct tape, and vinegar.

10. Why is R&D a key factor in productivity improvement? Name some ways R&D contributes to pro- ductivity improvements.

11. What is mass customization? 12. Name two factors that could make service design much different than product design. 13. Explain the term robust design. 14. Explain what quality function deployment is and how it can be useful. 15. What is reverse engineering? Do you feel this is unethical? 16. What is the purpose of value analysis? 17. What is life cycle assessment, and what is its overall goal? 18. Explain the term “three Rs” and how the three Rs relate to sustainability. 19. a. Select an electronic device you are familiar with. What standard feature does it have that was

once a “wow” feature? What “wow” feature does it have that you think will soon be a standard feature on new versions?

b. Answer part a for a service you are familiar with.

1. Describe some of the trade-offs that are encountered in product and service design. 2. Who needs to be involved in the design of products and services? 3. How has technology had an impact on product and service design?

TAKING STOCK

1. A number of fast-food chains, after their success with offering their customers fresh salads, and in an effort to downplay the image of selling unhealthy food, began adding fresh fruit plates to their menus. At about the same time, and seemingly in direct conflict with this “healthy” strategy, several other fast-food chains began offering fat- and calorie-laden items to their menus. Compare these two widely different approaches, and predict the chances of each one’s success. Name some other products that are popular, despite known health risks.

2. In wintry conditions, highway safety is improved by treating road surfaces with substances that will provide traction and/or melt snow and ice. Sand and rock salt are two widely used substances. Recently, a combination of beet juice and rock salt is being used in some parts of the country to treat road surfaces. Suppose you have been asked to provide a list of factors to consider for a switch from rock salt alone to using a combination of beet juice and rock salt. Name the major consider- ations you would take into account in making a decision in the following categories: cost consider- ations, environmental considerations, both positive and negative, and other considerations.

3. How were food producers impacted by the U.S. government’s requirement to identify the trans fat content on product labels?

4. Suppose a company intends to offer a new service to some of its internal customers. Briefly discuss how the fact that the customers are internal would change the process of managing the four phases of the service life cycle.

5. A few days before the end of the term of a two-year NDA (nondisclosure agreement) he signed with a startup company related to a possible patent, Frank interviewed with another startup and divulged information covered by the agreement. The interview had been scheduled for a week later, in which case it wouldn’t have been an issue, but had been moved up when another job applicant dropped out and the company had an opening for an earlier interview. Frank reasoned that he had met the spirit of the NDA, and a few days early wouldn’t really matter. Besides, as it turned out, the company he interviewed with wasn’t interested in that information, although they did hire him. What would you have done if you were Frank?

6. Give two examples of unethical conduct involving product or service design and the ethical prin- ciples (see Chapter 1) that are violated.

CRITICAL THINKING EXERCISES

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1. Examine and compare one of the following product sets. Base your comparison on such factors as features, costs, convenience, ease of use, and value. a. GPS versus maps b. Cell phones versus landlines c. Online shopping versus “bricks and mortar” shopping d. Standard gasoline automobile engines versus hybrids e. Online course versus classroom f. Satellite television versus cable

2. Use the Internet to obtain recent crash-safety ratings for passenger vehicles. Then answer these questions: a. Which vehicles received the highest ratings? The lowest ratings? b. How important are crash-safety ratings to new car buyers? Does the degree of importance

depend on the circumstances of the buyer? c. Which types of buyers would you expect to be the most concerned with crash-safety ratings? d. Are there other features of a new car that might sway a buyer from focusing solely on crash

safety? If so, what might they be? 3. Prepare a service blueprint for each of these banking transactions:

a. Make a savings deposit using a teller b. Apply for a home equity loan

4. Prepare a service blueprint for each of these post office transactions: a. Buy stamps from a machine b. Buy stamps from a postal clerk

5. List the steps involved in getting gasoline into your car for full service and for self-service. Assume that paying cash is the only means of payment. For each list, identify the potential trouble points and indicate a likely problem.

6. Construct a list of steps for making a cash withdrawal from an automated teller machine (ATM). Assume that the process begins at the ATM with your bank card in hand. Then identify the poten- tial failure points (i.e., where problems might arise in the process). For each failure point, state one potential problem.

7. a. Refer to Figure 4.4. What two technical requirements have the highest impact on the customer requirement that the paper not tear?

b. The following table presents technical requirements and customer requirements for the output of a laser printer. First, decide if any of the technical requirements relate to each customer requirement. Decide which technical requirement, if any, has the greatest impact on that cus- tomer requirement.

TECHNICAL REQUIREMENTS

Customer Requirements Type of Paper Internal Paper Feed Print Element

Paper doesn’t wrinkle

Prints clearly

Easy to use

8. Prepare a table similar to that shown in Problem 7b for cookies sold in a bakery. List what you believe are the three most important customer requirements (not including cost) and the three most relevant technical requirements (not including sanitary conditions). Next, indicate by a checkmark which customer requirements and which technical requirements are related.

PROBLEMS

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The High Acres Landfill is located on a 218-acre site outside Fairport, New York. Opened in 1971, it is licensed to handle residential, commer- cial, and industrial nonhazardous waste. The landfill has 27 employ- ees, and it receives approximately 3,000 tons of waste per day.

The public often has certain preconceived notions about a landfill, chief among them that landfills are dirty and unpleasant. However, a visit to the landfill dispelled some of those miscon- ceptions. The entrance is nicely landscaped. Most of the site is planted with grass and a few trees. Although unpleasant odors can emanate from arriving trucks or at the dump site, the remain- der of the landfill is relatively free of odors.

A major portion of the landfill consists of a large hill within which the waste is buried. Initially, the landfill began not as a hill but as a large hole in the ground. After a number of years of depos- iting waste, the hole eventually was filled. From that point on, as additional layers were added, the landfill began to take the shape of a flattop hill. Each layer is a little narrower than the preceding one, giving the hill a slope. The sides of the hill are planted with grass. Only the “working face” along the top remains unplanted. When the designated capacity is exhausted (this may take another

HIGH ACRES LANDFILLOPERATIONS TOUR

10 years), the landfill will be closed to further waste disposal. The site will be converted into a public park with hiking trails and pic- nic and recreation areas, and given to the town.

The construction and operation of landfills are subject to numerous state and federal regulations. For example, nonperme- able liners must be placed on the bottom and sides of the landfill to prevent leakage of liquids into the groundwater. (Independent firms monitor groundwater to determine if there is any leakage into wells placed around the perimeter of the hill.) Mindful of pub- lic opinion, every effort is made to minimize the amount of time that waste is left exposed. At the end of each day, the waste that has been deposited in the landfill is compacted and covered with six inches of soil.

The primary source of income for the landfill is the fees it charges users. The landfill also generates income from methane gas, a byproduct of organic waste decomposition, that accumu- lates within the landfill. A collection system is in place to capture and extract the gas from the landfill, and it is then sold to the local power company. Also, the landfill has a composting operation in which leaves and other yard wastes are converted into mulch.

Davis, Mark M., and Janelle Heineke. Managing Ser- vices: Using Technology to Create Value. New York: McGraw-Hill/Irwin, 2003.

Fitzsimmons, James A., and Mona J. Fitzsimmons. Service Management: Operations, Strategy, and Information Technology, 7th ed. New York: McGraw-Hill, 2011.

Gilmore, James, and B. Joseph Pine II. Markets of One: Creating Customer-Unique Value through Mass Customization. Boston: Harvard Business School Press, 2000.

Gorman, Michael E. Transforming Nature: Ethics, Invention, and Design. Boston: Kluwer Academic Publishers, 1998.

Groover, Mikell P. Automation, Production Systems, and Computer-Aided Manufacturing, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 2008.

Lovelock, Christopher H. Services Marketing: People, Technology, Strategy, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 2010.

Ulrich, Karl T., and Steven D. Eppinger. Product Design and Development, 3rd ed. New York: McGraw-Hill, 2004.

Vicente, Kim. The Human Factor. New York: Routledge, 2004.

SELECTED BIBLIOGRAPHY AND FURTHER READINGS

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174

Introduction, 174

Quantifying Reliability, 174 Finding the Probability of Functioning When Activated, 174 Finding the Probability of Functioning for a Given Length of Time, 176

Availability, 180

4 Reliability S U P P L E M E N T

L E A R N I N G O B J E C T I V E S After completing this supplement, you should be able to:

LO4S.1 Define reliability

LO4S.2 Perform simple reliability computations.

LO4S.3 Explain the term availability and perform simple calculations.

S U P P L E M E N T O U T L I N E

4S.1 INTRODUCTION

Reliability is a measure of the ability of a product, service, part, or system to perform its intended function under a prescribed set of conditions. In effect, reliability is a probability.

Suppose that an item has a reliability of .90. This means that it has a 90 percent probability of functioning as intended. The probability it will fail is 1 − .90 = .10, or 10 percent. Hence, it is expected that, on the average, 1 of every 10 such items will fail or, equivalently, that the item will fail, on the average, once in every 10 trials. Similarly, a reliability of .985 implies 15 failures per 1,000 parts or trials.

4S.2 QUANTIFYING RELIABILITY

Engineers and designers have a number of techniques at their disposal for assessing reliability. A discussion of those techniques is not within the scope of this text. Instead, let us turn to the issue of quantifying overall product or system reliability. Probability is used in two ways:

1. The probability that the product or system will function when activated. 2. The probability that the product or system will function for a given length of time.

The first of these focuses on one point in time and is often used when a system must operate for one time or a relatively few number of times. The second of these focuses on the length of service. The distinction will become more apparent as each of these approaches is described in more detail.

Finding the Probability of Functioning When Activated The probability that a system or a product will operate as planned is an important concept in system and product design. Determining that probability when the product or system consists of a number of independent components requires the use of the rules of probability for inde- pendent events. Independent events have no relation to the occurrence or nonoccurrence of

LO4S.1 Define reliability.

Reliability The ability of a product, part, or system to perform its intended function under a prescribed set of conditions.

Independent events Events whose occurrence or nonoc- currence does not influence each other.

LO4S.2 Perform simple reliability computations.

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each other. What follows are three examples illustrating the use of probability rules to deter- mine whether a given system will operate successfully.

Rule 1. If two or more events are independent and success is defined as the probability that all of the events occur, then the probability of success is equal to the product of the probabili- ties of the events.

Example Suppose a room has two lamps, but to have adequate light both lamps must work (success) when turned on. One lamp has a probability of working of .90, and the other has a probability of working of .80. The probability that both will work is .90 × .80 = .72. Note that the order of multiplication is unimportant: .80 × .90 = .72. Also note that if the room had three lamps, three probabilities would have been multiplied.

This system can be represented by the following diagram:

.90 .80

Lamp 1 Lamp 2

Even though the individual components of a system might have high reliabilities, the sys- tem as a whole can have considerably less reliability because all components that are in series (as are the ones in the preceding example) must function. As the number of components in a series increases, the system reliability decreases. For example, a system that has eight compo- nents in a series, each with a reliability of .99, has a reliability of only .998 = .923.

Obviously, many products and systems have a large number of component parts that must all operate, and some way to increase overall reliability is needed. One approach is to use redundancy in the design. This involves providing backup parts for some items.

Rule 2. If two events are independent and success is defined as the probability that at least one of the events will occur, the probability of success is equal to the probability of either one plus 1.00 minus that probability multiplied by the other probability.

Example There are two lamps in a room. When turned on, one has a probability of working of .90 and the other has a probability of working of .80. Only a single lamp is needed to light for success. If one fails to light when turned on, the other lamp is turned on. Hence, one of the lamps is a backup in case the other one fails. Either lamp can be treated as the backup; the probability of success will be the same. The probability of success is .90 + (1 − .90) × .80 = .98. If the .80 light is first, the computation would be .80 + (1 − .80) × .90 = .98.

This system can be represented by the following diagram:

.90 Lamp 1

.80 Lamp 2 (backup)

Rule 3. If two or more events are involved and success is defined as the probability that at least one of them occurs, the probability of success is 1 − p (all fail).

Example Three lamps have probabilities of .90, .80, and .70 of lighting when turned on. Only one lighted lamp is needed for success; hence, two of the lamps are considered to be backups. The probability of success is

1 − [(1 − .90) × (1 − .80) × (1 − .70)] = .994

Redundancy The use of backup components to increase reliability.

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This system can be represented by the following diagram:

.90 Lamp 1

.80 Lamp 2 (backup for Lamp 1)

.70 Lamp 3 (backup for Lamp 2)

S O L U T I O N

Computing Reliability Determine the reliability of the following system.

.98 .90 .95

.90 .92 mhhe.com/stevenson13e

E X A M P L E 4 S - 1

The system can be reduced to a series of three components:

.98 .90 + .90(1 - .90) .95 + .92(1 - .95)

The system reliability is, then, the product of these:

.98 × .99 × .996 = .966

Finding the Probability of Functioning for a Given Length of Time The second way of looking at reliability considers the incorporation of a time dimension: Probabilities are determined relative to a specified length of time. This approach is com- monly used in product warranties, which pertain to a given period of time after purchase of a product.

A typical profile of product failure rate over time is illustrated in Figure 4S.1. Because of its shape, it is sometimes referred to as a bathtub curve. Frequently, a number of products fail shortly after they are put into service, not because they wear out, but because they are defec- tive to begin with. The rate of failures decreases rapidly once the truly defective items are weeded out. During the second phase, there are fewer failures because most of the defective items have been eliminated, and it is too soon to encounter items that fail because they have worn out. In some cases, this phase covers a relatively long time. In the third phase, failures occur because the products are worn out, and the failure rate increases.

Information on the distribution and length of each phase requires the collection of histori- cal data and analysis of those data. It often turns out that the mean time between failures (MTBF) in the infant mortality phase can be modeled by a negative exponential distribution, such as that depicted in Figure 4S.2. Equipment failures as well as product failures may occur

Mean time between failures (MTBF) The average length of time between failures of a product or component.

LO4S.2 Perform simple reliability computations.

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in this pattern. In such cases, the exponential distribution can be used to determine various probabilities of interest. The probability that equipment or a product put into service at time 0 will fail before some specified time, T, is equal to the area under the curve between 0 and T. Reliability is specified as the probability that a product will last at least until time T; reliabil- ity is equal to the area under the curve beyond T. (Note that the total area under the curve in each phase is treated as 100 percent for computational purposes.) Observe that as the speci- fied length of service increases, the area under the curve to the right of that point (i.e., the reliability) decreases.

Determining values for the area under a curve to the right of a given point, T, becomes a relatively simple matter using a table of exponential values. An exponential distribution is completely described using a single parameter, the distribution mean, which reliability engi- neers often refer to as the mean time between failures. Using the symbol T to represent length of service, the probability that failure will not occur before time T (i.e., the area in the right tail) is easily determined:

P(no failure before T) = e−T/MTBF

where

e = 2.7183... T = Length of service before failure MTBF = Mean time between failures

The probability that failure will occur before time T is:

P(failure before T) = 1 − e−T/MTBF

Selected values of e−T/MTBF are listed in Table 4S.1.

FIGURE 4S.2 An exponential distribution

Time

Reliability = e -T /MTBF

1 - e -T /MTBF

f (T)

FIGURE 4S.1 Failure rate is a function of time

Infant

mortality

Few (random)

failures

Failures due

to wear-out

Time, T

F a

ilu re

r a

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TABLE 4S.1 Values of e−T/MTBF T/MTBF e

−T/MTBF T/MTBF e−T/MTBF T/MTBF e−T/MTBF

0.10 .9048 2.60 .0743 5.10 .0061

0.20 .8187 2.70 .0672 5.20 .0055

0.30 .7408 2.80 .0608 5.30 .0050

0.40 .6703 2.90 .0550 5.40 .0045

0.50 .6065 3.00 .0498 5.50 .0041

0.60 .5488 3.10 .0450 5.60 .0037

0.70 .4966 3.20 .0408 5.70 .0033

0.80 .4493 3.30 .0369 5.80 .0030

0.90 .4066 3.40 .0334 5.90 .0027

1.00 .3679 3.50 .0302 6.00 .0025

1.10 .3329 3.60 .0273 6.10 .0022

1.20 .3012 3.70 .0247 6.20 .0020

1.30 .2725 3.80 .0224 6.30 .0018

1.40 .2466 3.90 .0202 6.40 .0017

1.50 .2231 4.00 .0183 6.50 .0015

1.60 .2019 4.10 .0166 6.60 .0014

1.70 .1827 4.20 .0150 6.70 .0012

1.80 .1653 4.30 .0136 6.80 .0011

1.90 .1496 4.40 .0123 6.90 .0010

2.00 .1353 4.50 .0111 7.00 .0009

2.10 .1255 4.60 .0101

2.20 .1108 4.70 .0091

2.30 .1003 4.80 .0082

2.40 .0907 4.90 .0074

2.50 .0821 5.00 .0067

S O L U T I O N

Computing Product Life Probability By means of extensive testing, a manufacturer has determined that its Super Sucker Vac- uum Cleaner models have an expected life that is exponential with a mean of four years. Find the probability that one of these cleaners will have a life that ends

a. After the initial four years of service. b. Before four years of service are completed. c. Not before six years of service.

mhhe.com/stevenson13e

E X A M P L E 4 S - 2

MTBF = 4 years

a. T = 4 years:

T/MTBF = 4 years

______ 4 years

= 1.0

From Table 4S.1, e−1.0 = .3679.

b. The probability of failure before T = 4 years is 1− e−1, or 1 − .3679 = .6321. c. T = 6 years:

T/MTBF = 6 years

______ 4 years

= 1.50

From Table 4S.1, e−1.5 = .2231.

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Product failure due to wear-out can sometimes be modeled by a normal distribution. Obtaining probabilities involves the use of a table (refer to Appendix Table B.2). The table provides areas under a normal curve from (essentially) the left end of the curve to a specified point z, where z is a standardized value computed using the formula

z = T − Mean wear-out time

_____________________________ Standard deviation of wear-out time

Thus, to work with the normal distribution, it is necessary to know the mean of the distribu- tion and its standard deviation. A normal distribution is illustrated in Figure 4S.3. Appendix Table B.2 contains normal probabilities (i.e., the area that lies to the left of z). To obtain a probability that service life will not exceed some value T, compute z and refer to the table. To find the reliability for time T, subtract this probability from 100 percent. To obtain the value of T that will provide a given probability, locate the nearest probability under the curve to the left in Appendix Table B.2. Then use the corresponding z in the preceding formula and solve for T.

FIGURE 4S.3 A normal curve

Reliability

0 z

z scale

Mean life Years

S O L U T I O N

Computing Life Probability and Service Life The mean life of a certain ball bearing can be modeled using a normal distribution with a mean of six years and a standard deviation of one year. Determine each of the following:

a. The probability that a ball bearing will wear out before seven years of service. b. The probability that a ball bearing will wear out after seven years of service (i.e., find

its reliability). c. The service life that will provide a wear-out probability of 10 percent.

mhhe.com/stevenson13e

E X A M P L E 4 S - 3

Wear-out life mean = 6 years. Wear out life standard deviation = 1 year. Wear-out life is normally distributed.

a. Compute z and use it to obtain the probability directly from Appen- dix Table B.2 (see diagram).

z = 7 − 6

_____ 1 = +1.00

Thus, P (T < 7) = .8413.

0 +1.00 z scale

6 Years

.8413

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b. Subtract the probability determined in part a from 100 percent (see diagram).

1.00 − .8413 = .1587

c. Use the normal table and find the value of z that corresponds to an area under the curve of 10 percent (see diagram).

z = −1.28 = T − 6

_____ 1

Solving for T, we find T = 4.72 years.

0 +1.00 z scale

6 Years

.1587

0z = -1.28 z scale

6 Years

90%

10%

4.72

4S.3 AVAILABILITY

A related measure of importance to customers, and hence to designers, is availability. It measures the fraction of time a piece of equipment is expected to be operational (as opposed to being down for repairs). Availability can range from zero (never available) to 1.00 (always available). Companies that can offer equipment with a high availability factor have a com- petitive advantage over companies that offer equipment with lower availability values. Avail- ability is a function of both the mean time between failures and the mean time to repair. The availability factor can be computed using the following formula:

Availability = MTBF _____________

MTBF + MTR

where

MTBF = Mean time between failures MTR = Mean time to repair, including waiting time

LO4S.3 Define the term availability and perform simple calculations.

Availability The fraction of time a piece of equipment is expected to be available for operation.

Computing Availability A copier is able to operate for an average of 200 hours between repairs, and the mean repair time is two hours. Determine the availability of the copier.

mhhe.com/stevenson13e

E X A M P L E 4 S - 4

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S O L U T I O NMTBF = 200 hours and MTR = 2 hours

Availability = MTBF _____________

MTBF + MTR =

200 _______

200 + 2 = .99

Two implications for design are revealed by the availability formula. One is that avail- ability increases as the mean time between failures increases. The other is that availability also increases as the mean repair time decreases. It would seem obvious that designers would want to design products that have a long time between failures. However, some design options enhance repairability, which can be incorporated into the product. Ink-jet printers, for exam- ple, are designed with print cartridges that can easily be replaced.

availability, 180 independent events, 174 KEY TERMS

mean time between failures (MTBF), 176

redundancy, 175 reliability, 174

A product design engineer must decide if a redundant component is cost-justified in a certain system. The system in question has a critical component with a probability of .98 of operating. System failure would involve a cost of $20,000. For a cost of $100, a switch could be added that would automati- cally transfer the system to the backup component in the event of a failure. Should the backup be added if the backup probability is also .98?

Problem 1

SOLVED PROBLEMS

.94

.90

.80

Because no probability is given for the switch, we will assume its probability of operating when needed is 100 percent. The expected cost of failure (i.e., without the backup) is $20,000 × (1 − .98) = $400.

With the backup, the probability of not failing would be:

.98 + 0.2(.98) = .9996

Hence, the probability of failure would be 1 − .9996 = .0004. The expected cost of failure with the backup would be the added cost of the backup component plus the failure cost:

$100 + $20,000(.0004) = $108

Because this is less than the cost without the backup, it appears that adding the backup is definitely cost justifiable.

Solution

Due to the extreme cost of interrupting production, a firm has two standby machines available in case a particular machine breaks down. The machine in use has a reliability of .94, and the backups have reliabilities of .90 and .80. In the event of a failure, either backup can be pressed into service. If one fails, the other backup can be used. Compute the system reliability.

Problem 2

R1 = .94, R2 = .90, and R3 = .80

The system can be depicted in this way: Solution

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Rsystem = R1 + R2(1 − R1) + R3(1 − R2) (1 − R1) = .94 + .90(1 − .94) + .80(1 − .90)(1 − .94) = .9988

A hospital has three independent fire alarm systems, with reliabilities of .95, .97, and .99. In the event of a fire, what is the probability that a warning would be given?

Problem 3

A warning would not be given if all three alarms failed. The probability that at least one alarm would operate is 1 − P (none operate):

P(none operate) = (1 − .95)(1 − .97)(1 − .99) = .000015

P(warning) = 1 − .000015 = .999985

Solution

A weather satellite has an expected life of 10 years from the time it is placed into earth orbit. Deter- mine its probability of no wear-out before each of the following lengths of service. Assume the exponential distribution is appropriate.

a. 5 years b. 12 years c. 20 years d. 30 years

Problem 4

MTBF is 10 years. Compute the ratio T/MTBF for T = 5, 12, 20, and 30, and obtain the values of e−T/MTBF from Table 4S.1. The solutions are summarized in the following table.

Solution

T MTBF T/MTBF e−T/MTBF

a. 5 10 0.50 .6065

b. 12 10 1.20 .3012

c. 20 10 2.00 .1353

d. 30 10 3.00 .0498

What is the probability that the satellite described in Solved Problem 4 will fail between 5 and 12 years after being placed into earth orbit?

Problem 5

P(5 years < failure < 12 years) = P(failure after 5 years) − P(failure after 12 years)

Using the probabilities shown in the previous solution, you obtain:

P(failure after = years) = .6065 −P(failure after 12 years) = .3012 .3053

The corresponding area under the curve is illustrated as follows.

Solution

125

Years

f( t)

.3053

P (5 < Failure < 12)

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One line of radial tires produced by a large company has a wear-out life that can be modeled using a normal distribution with a mean of 25,000 miles and a standard deviation of 2,000 miles. Determine each of the following: a. The percentage of tires that can be expected to wear out within ± 2,000 miles of the average (i.e.,

between 23,000 miles and 27,000 miles). b. The percentage of tires that can be expected to fail between 26,000 miles and 29,000 miles. c. For what tire life would you expect 4 percent of the tires to have worn out?

Problem 6

Notes: (1) Miles are analogous to time and are handled in exactly the same way; (2) the term percent- age refers to a probability. a. The phrase “within ± 2,000 miles of the average” translates to within one standard deviation of

the mean since the standard deviation equals 2,000 miles. Therefore the range of z is z = −1.00, to z = +1.00, and the area under the curve between those points is found as the difference between P(z < + 1.00) and P(z < − 1.00), using values obtained from Appendix Table B.2.

P(z < +1.00) = .8413 −P(z < −1.00) = .1587

P(−1.00 < z < +1.00) = .6826

Solution

0 + .50 + 2.00

.6915

.9772

.2857

0 +1.00

.6826

–1.00

.1587

.8413

b. Wear-out mean = 25,000 miles Wear-out standard deviation = 2,000 miles P(26,000 < Wear-out < 29,000) = P(z < z29,000) − P(z < z26,000)

z29,000 = 29,000 − 25,000

______________ 2,000

= +2.00 From Appendix Table B P = .9772

z26,000 = 26,000 − 25,000

______________ 2,000

= +.50 From Appendix Table B P = .6915

The difference is .9772 − .6915 = .2857, which is the expected percent of tires that will wear out between 26,000 miles and 29,000 miles.

c. Use Appendix Table B.1 to find z for 4 percent: z = − 1.75. Find tire life using μ + zσ: 25,000 − 1.75(2,000) = 21,500 miles.

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1. Define the term reliability. 2. Explain why a product or system might have an overall reliability that is low even though it is com-

prised of components that have fairly high reliabilities. 3. What is redundancy and how can it improve product design?

DISCUSSION & REVIEW QUESTIONS

PROBLEMS 1. Consider the following system:

.90 .90

Determine the probability that the system will operate under each of these conditions: a. The system as shown. b. Each system component has a backup with a probability of .90 and a switch that is 100 percent

reliable. c. Backups with .90 probability and a switch that is 99 percent reliable.

2. A product is composed of four parts. In order for the product to function properly in a given situa- tion, each of the parts must function. Two of the parts have a .96 probability of functioning, and two have a probability of .99. What is the overall probability that the product will function properly?

3. A system consists of three identical components. In order for the system to perform as intended, all of the components must perform. Each has the same probability of performance. If the system is to have a .92 probability of performing, what is the minimum probability of performing needed by each of the individual components?

4. A product engineer has developed the following equation for the cost of a system component: C = (10P)2, where C is the cost in dollars and P is the probability that the component will operate as expected. The system is composed of two identical components, both of which must operate for the system to operate. The engineer can spend $173 for the two components. To the nearest two decimal places, what is the largest component probability that can be achieved?

5. The guidance system of a ship is controlled by a computer that has three major modules. In order for the computer to function properly, all three modules must function. Two of the modules have reliabilities of .97, and the other has a reliability of .99. a. What is the reliability of the computer? b. A backup computer identical to the one being used will be installed to improve overall reli-

ability. Assuming the new computer automatically functions if the main one fails, determine the resulting reliability.

c. If the backup computer must be activated by a switch in the event that the first computer fails, and the switch has a reliability of .98, what is the overall reliability of the system? (Both the switch and the backup computer must function in order for the backup to take over.)

6. One of the industrial robots designed by a leading producer of servomechanisms has four major components. Components’ reliabilities are .98, .95, .94, and .90. All of the components must func- tion in order for the robot to operate effectively. a. Compute the reliability of the robot. b. Designers want to improve the reliability by adding a backup component. Due to space limita-

tions, only one backup can be added. The backup for any component will have the same reli- ability as the unit for which it is the backup. Which component should get the backup in order to achieve the highest reliability?

c. If one backup with a reliability of .92 can be added to any one of the main components, which component should get it to obtain the highest overall reliability?

7. A production line has three machines A, B, and C, with reliabilities of .99, .96, and .93, respec- tively. The machines are arranged so that if one breaks down, the others must shut down. Engineers are weighing two alternative designs for increasing the line’s reliability. Plan 1 involves adding an identical backup line, and plan 2 involves providing a backup for each machine. In either case, three machines (A, B, and C) would be used with reliabilities equal to the original three. a. Which plan will provide the higher reliability? b. Explain why the two reliabilities are not the same. c. What other factors might enter into the decision of which plan to adopt?

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8. Refer to the previous problem. a. Assume that the single switch used in plan 1 is 98 percent reliable, while reliabilities of the

machines remain the same. Recalculate the reliability of plan 1. Compare the reliability of this plan with the reliability of plan 1 calculated in solving the original problem. How much did the reliability of plan 1 decrease as a result of a 98 percent reliable switch?

b. Assume that the three switches used in plan 2 are all 98 percent reliable, while reliabilities of the machines remain the same. Recalculate the reliability of plan 2. Compare the reliability of this plan with the reliability of plan 2 calculated in solving the original problem. How much did the reliability of plan 2 decrease?

9. A Web server has five major components that must all function in order for it to operate as intended. Assuming that each component of the system has the same reliability, what is the mini- mum reliability each one must have in order for the overall system to have a reliability of .98?

10. Repeat Problem 9 under the condition that one of the components will have a backup with a reli- ability equal to that of any one of the other components.

11. Hoping to increase the chances of reaching a performance goal, the director of a research project has assigned three separate research teams the same task. The director estimates that the team probabilities are .9, .8, and .7 for successfully completing the task in the allotted time. Assuming that the teams work independently, what is the probability that the task will not be completed in time?

12. An electronic chess game has a useful life that is exponential with a mean of 30 months. Deter- mine each of the following: a. The probability that any given unit will operate for at least (1) 39 months, (2) 48 months,

(3) 60 months. b. The probability that any given unit will fail sooner than (1) 33 months, (2) 15 months,

(3) 6 months. c. The length of service time after which the percentage of failed units will approximately equal

(1) 50 percent, (2) 85 percent, (3) 95 percent, (4) 99 percent. 13. A manufacturer of programmable calculators is attempting to determine a reasonable free-service

period for a model it will introduce shortly. The manager of product testing has indicated that the calculators have an expected life of 30 months. Assume product life can be described by an expo- nential distribution. a. If service contracts are offered for the expected life of the calculator, what percentage of those

sold would be expected to fail during the service period? b. What service period would result in a failure rate of approximately 10 percent?

14. Lucky Lumen light bulbs have an expected life that is exponentially distributed with a mean of 20,000 hours. Determine the probability that one of these light bulbs will last a. At least 24,000 hours b. No longer than 4,000 hours c. Between 4,000 hours and 24,000 hours

15. Planetary Communications, Inc., intends to launch a satellite that will enhance reception of televi- sion programs in Alaska. According to its designers, the satellite will have an expected life of six years. Assume the exponential distribution applies. Determine the probability that it will function for each of the following time periods: a. More than 9 years b. Less than 12 years c. More than 9 years but less than 12 years d. At least 21 years

16. An office manager has received a report from a consultant that includes a section on equipment replacement. The report indicates that scanners have a service life that is normally distributed with a mean of 41 months and a standard deviation of 4 months. On the basis of this information, deter- mine the percentage of scanners that can be expected to fail in the following time periods: a. Before 38 months of service b. Between 40 and 45 months of service c. Within ± 2 months of the mean life

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17. A major television manufacturer has determined that its 40-inch LED televisions have a mean service life that can be modeled by a normal distribution with a mean of six years and a standard deviation of one-half year. a. What probability can you assign to service lives of at least (1) five years? (2) six years? (3)

seven and one-half years? b. If the manufacturer offers service contracts of four years on these televisions what percentage

can be expected to fail from wear-out during the service period? 18. Refer to Problem 17. What service period would achieve an expected wear-out rate of (1) 2 per-

cent? (2) 5 percent? 19. Determine the availability for each of these cases:

a. MTBF = 40 days, average repair time = 3 days. b. MTBF = 300 hours, average repair time = 6 hours.

20. A machine can operate for an average of 10 weeks before it needs to be overhauled, a process which takes two days. The machine is operated five days a week. Compute the availability of this machine. (Hint: All times must be in the same units.)

21. A manager must decide between two machines. The manager will take into account each machine’s operating costs and initial costs, and its breakdown and repair times. Machine A has a projected average operating time of 142 hours and a projected average repair time of 7 hours. Projected times for machine B are an average operating time of 65 hours and a repair time of 2 hours. What are the projected availabilities of each machine?

22. A designer estimates that she can (a) increase the average time between failures of a part by 5 per- cent at a cost of $450, or (b) reduce the average repair time by 10 percent at a cost of $200. Which option would be more cost-effective? Currently, the average time between failures is 100 hours and the average repair time is 4 hours.

23. Auto batteries have an average life of 2.7 years. Battery life is normally distributed with a mean of 2.7 years and a standard deviation of .3 year. The batteries are warranted to operate for a minimum of 2 years. If a battery fails within the warranty period, it will be replaced with a new battery at no charge. The company sells and installs the batteries. Also, the usual $5 installation charge will be waived. a. What percentage of batteries would you expect to fail before the warranty period expires? b. A competitor is offering a warranty of 30 months on its premium battery. The manager of this

company is toying with the idea of using the same battery with a different exterior, labeling it as a premium battery, and offering a 30-month warranty on it. How much more would the com- pany have to charge on its “premium” battery to offset the additional cost of replacing batteries?

c. What other factors would you take into consideration besides the price of the battery?

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5.1 Introduction, 189

5.2 Capacity Decisions Are Strategic, 191

5.3 Defining and Measuring Capacity, 192

5.4 Determinants of Effective Capacity, 193

5.5 Strategy Formulation, 195 Steps in the Capacity Planning Process, 195

5.6 Forecasting Capacity Requirements, 196 Calculating Processing Requirements, 197

5.7 Additional Challenges of Planning Service Capacity, 198

5.8 Do It In-House or Outsource It?, 199

5.9 Developing Capacity Strategies, 200

5.10 Constraint Management, 205

5.11 Evaluating Alternatives, 205 Cost–Volume Analysis, 206 Financial Analysis, 209 Decision Theory, 210 Waiting-Line Analysis, 210 Simulation, 210

5.12 Operations Strategy, 211 Case: Outsourcing of Hospital Services, 219 Supplement: Decision Theorym, 220

Strategic Capacity Planning for Products and Services

L E A R N I N G O B J E C T I V E S After completing this chapter, you should be able to:

LO5.1 Name the three key questions in capacity planning.

LO5.2 Explain the importance of capacity planning.

LO5.3 Describe ways of defining and measuring capacity.

LO5.4 Name several determinants of effective capacity.

LO5.5 Discuss factors to consider when deciding whether to perform in-house or outsource.

LO5.6 Discuss the major considerations related to developing capacity alternatives.

LO5.7 Describe the steps that are used to resolve constraint issues.

LO5.8 Briefly describe approaches that are useful for evaluating capacity alternatives.

C H A P T E R O U T L I N E

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Capacity planning is a key strategic component in designing the system. It encompasses many basic decisions with long-term consequences for the organization. In this chapter, you will learn about the importance of capacity decisions, the measurement of capacity, how capacity requirements are determined, and the development and evaluation of capacity alternatives. Note that decisions made in the product or service design stage have major implications for capacity planning. Designs have processing requirements related to volume and degree of customization that affect capacity planning.

5.1 INTRODUCTION

Hospitals that not too long ago had what could be described as “facility oversupply” are now experiencing what can be described as a “capacity crisis” in some areas. The way hospitals plan for capacity will be critical to their future success. And the same applies to all sorts of organizations, and at all levels of these organizations. Capacity refers to an upper limit or ceiling on the load that an operating unit can handle. The load might be in terms of the number of physical units produced (e.g., bicycles assembled per hour) or the number of services per- formed (e.g., computers upgraded per hour). The operating unit might be a plant, department, machine, store, or worker. Capacity needs include equipment, space, and employee skills.

The goal of strategic capacity planning is to achieve a match between the long-term sup- ply capabilities of an organization and the predicted level of long-term demand. Organiza- tions become involved in capacity planning for various reasons. Among the chief reasons are changes in demand, changes in technology, changes in the environment, and perceived threats or opportunities. A gap between current and desired capacity will result in capacity that is out of balance. Overcapacity causes operating costs that are too high, while undercapacity causes strained resources and possible loss of customers.

Capacity The upper limit or ceiling on the load that an operating unit can handle.

Demand forecast

Capacity planning

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The key questions in capacity planning are the following:

1. What kind of capacity is needed? 2. How much is needed to match demand? 3. When is it needed?

The question of what kind of capacity is needed depends on the products and services that management intends to produce or provide. Hence, in a very real sense, capacity planning is governed by those choices.

Forecasts are key inputs used to answer the questions of how much capacity is needed and when is it needed.

Related questions include:

1. How much will it cost, how will it be funded, and what is the expected return? 2. What are the potential benefits and risks? These involve the degree of uncertainty

related to forecasts of the amount of demand and the rate of change in demand, as well as costs, profits, and the time to implement capacity changes. The degree of accuracy that can be attached to forecasts is an important consideration. The likelihood and impact of wrong decisions also need to be assessed.

3. Are there sustainability issues that need to be addressed? 4. Should capacity be changed all at once, or through several (or more) small changes? 5. Can the supply chain handle the necessary changes? Before an organization commits to

ramping up its input, it is essential to confirm that its supply chain will be able to handle related requirements.

Because of uncertainties, some organizations prefer to delay capacity investment until demand materializes. However, such strategies often inhibit growth because adding capacity takes time and customers won’t usually wait. Conversely, organizations that add capacity in anticipation of growth often discover that the new capacity actually attracts growth. Some organizations “hedge their bets” by making a series of small changes and then evaluating the results before committing to the next change.

In some instances, capacity choices are made very infrequently; in others, they are made regularly, as part of an ongoing process. Generally, the factors that influence this frequency are the stability of demand, the rate of technological change in equipment and product design, and competitive factors. Other factors relate to the type of product or ser- vice and whether style changes are important (e.g., automobiles and clothing). In any case, management must review product and service choices periodically to ensure that the com- pany makes capacity changes when they are needed for cost, competitive effectiveness, or other reasons.

Today, huge gaps between supply and demand have many com- panies struggling. Excess capacity abounds in such major indus- tries as telecom, airline, and auto manufacturing. The bad news is that some companies are losing millions of dollars a year because of this. In the telecom industry, the increasing reach of cellular technology and other kinds of wireless access is continuing to cre- ate more and more supply, requiring telecom companies to cut prices and offer incentives to increase demand.

READING EXCESS CAPACITY CAN BE BAD NEWS!

In the airline industry, air travel is way down, leaving airline companies awash in capacity. And even much of the currently mothballed aircraft are only in storage. Companies have elimi- nated flights to save money and cut prices to the bone trying to lure passengers.

The auto producers don’t have it quite so bad, but for years they’ve been offering their customers incentives and interest-free financing—in order to keep their excess plants running.

LO5.1 Name the three key questions in capacity planning.

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5.2 CAPACITY DECISIONS ARE STRATEGIC

For a number of reasons, capacity decisions are among the most fundamental of all the design decisions that managers must make. In fact, capacity decisions can be critical for an organization.

1. Capacity decisions have a real impact on the ability of the organization to meet future demands for products and services; capacity essentially limits the rate of output possible. Having capacity to satisfy demand can often allow a company to take advantage of tremen- dous benefits. When Microsoft introduced its new Xbox in late 2005, there were insufficient supplies, resulting in lost sales and unhappy customers. And shortages of flu vaccine in some years due to production problems affected capacity, limiting the availability of the vaccine.

2. Capacity decisions affect operating costs. Ideally, capacity and demand requirements will be matched, which will tend to minimize operating costs. In practice, this is not always achieved because actual demand either differs from expected demand or tends to vary (e.g., cyclically). In such cases, a decision might be made to attempt to balance the costs of over- and undercapacity.

3. Capacity is usually a major determinant of initial cost. Typically, the greater the capac- ity of a productive unit, the greater its cost. This does not necessarily imply a one-for- one relationship; larger units tend to cost proportionately less than smaller units.

4. Capacity decisions often involve long-term commitment of resources and the fact that, once they are implemented, those decisions may be difficult or impossible to modify without incurring major costs.

5. Capacity decisions can affect competitiveness. If a firm has excess capacity, or can quickly add capacity, that fact may serve as a barrier to entry by other firms. Then, too, capacity can affect delivery speed, which can be a competitive advantage.

6. Capacity affects the ease of management; having appropriate capacity makes manage- ment easier than when capacity is mismatched.

7. Globalization has increased the importance and the complexity of capacity decisions. Far-flung supply chains and distant markets add to the uncertainty about capacity needs.

8. Because capacity decisions often involve substantial financial and other resources, it is necessary to plan for them far in advance. For example, it may take years for a new power-generating plant to be constructed and become operational. However, this increases the risk that the designated amount of capacity will not match actual demand when the capacity becomes available.

LO5.2 Explain the impor- tance of capacity planning.

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Business Inputs Outputs

Auto manufacturing Labor hours, machine hours Number of cars per shift

Steel mill Furnace size Tons of steel per day

Oil refinery Refinery size Gallons of fuel per day

Farming Number of acres, number of cows

Bushels of grain per acre per year, gallons of milk per day

Restaurant Number of tables, seating capacity Number of meals served per day

Theater Number of seats Number of tickets sold per performance

Retail sales Square feet of floor space Revenue generated per day

5.3 DEFINING AND MEASURING CAPACITY

Capacity often refers to an upper limit on the rate of output. Even though this seems simple enough, there are subtle difficulties in actually measuring capacity in certain cases. These difficulties arise because of different interpretations of the term capacity and problems with identifying suitable measures for a specific situation.

In selecting a measure of capacity, it is important to choose one that does not require updating. For example, dollar amounts are often a poor measure of capacity (e.g., capacity of $30 million a year) because price changes necessitate updating of that measure.

Where only one product or service is involved, the capacity of the productive unit may be expressed in terms of that item. However, when multiple products or services are involved, as is often the case, using a simple measure of capacity based on units of output can be mislead- ing. An appliance manufacturer may produce both refrigerators and freezers. If the output rates for these two products are different, it would not make sense to simply state capacity in units without reference to either refrigerators or freezers. The problem is compounded if the firm has other products. One possible solution is to state capacities in terms of each prod- uct. Thus, the firm may be able to produce 100 refrigerators per day or 80 freezers per day. Sometimes this approach is helpful, sometimes not. For instance, if an organization has many different products or services, it may not be practical to list all of the relevant capacities. This is especially true if there are frequent changes in the mix of output, because this would neces- sitate a frequently changing composite index of capacity. The preferred alternative in such cases is to use a measure of capacity that refers to availability of inputs. Thus, a hospital has a certain number of beds, a factory has a certain number of machine hours available, and a bus has a certain number of seats and a certain amount of standing room.

No single measure of capacity will be appropriate in every situation. Rather, the measure of capacity must be tailored to the situation. Table 5.1 provides some examples of commonly used measures of capacity.

Up to this point, we have been using a general definition of capacity. Although it is func- tional, it can be refined into two useful definitions of capacity:

1. Design capacity: The maximum output rate or service capacity an operation, process, or facility is designed for.

2. Effective capacity: Design capacity minus allowances such as personal time, and maintenance.

Design capacity is the maximum rate of output achieved under ideal conditions. Effective capacity is always less than design capacity owing to realities of changing product mix, the need for periodic maintenance of equipment, lunch breaks, coffee breaks, problems in schedul- ing and balancing operations, and similar circumstances. Actual output cannot exceed effective capacity and is often less because of machine breakdowns, absenteeism, shortages of materials, and quality problems, as well as factors that are outside the control of the operations managers.

Design capacity The maximum designed service capacity or output rate.

Effective capacity Design capacity minus allowances such as personal time, equipment maintenance, delays due to scheduling problems, and changing the mix of products.

TABLE 5.1 Measures of capacity

LO5.3 Describe ways of defining and measuring capacity.

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These different measures of capacity are useful in defining two measures of system effectiveness: efficiency and utilization. Efficiency is the ratio of actual output to effective capacity. Capacity utilization is the ratio of actual output to design capacity.

Efficiency = Actual output

_______________ Effective capacity

× 100% (5–1)

Utilization = Actual output

_____________ Design capacity

× 100% (5–2)

Both measures are expressed as percentages. It is not unusual for managers to focus exclusively on efficiency, but in many instances this

emphasis can be misleading. This happens when effective capacity is low compared to design capacity. In those cases, high efficiency would seem to indicate effective use of resources when it does not. The following example illustrates this point.

Computing Efficiency and Utilization Given the following information, compute the efficiency and the utilization of the vehicle repair department:

Design capacity

50 trucks per day

Effective capacity = 40 trucks per day Actual output

36 trucks per day

E X A M P L E

S O L U T I O N

Efficiency =

Actual output _______________

Effective capacity × 100% =

36 trucks per day ______________

40 trucks per day × 100% = 90%

Utilization =

Actual output _____________

Design capacity × 100% =

36 trucks per day ______________

50 trucks per day × 100% = 72%

Compared to the effective capacity of 40 units per day, 36 units per day looks pretty good. However, compared to the design capacity of 50 units per day, 36 units per day is much less impressive although probably more meaningful.

Because effective capacity acts as a lid on actual output, the real key to improving capacity utilization is to increase effective capacity by correcting quality problems, maintaining equip- ment in good operating condition, fully training employees, and fully utilizing bottleneck equipment.

Hence, increasing utilization depends on being able to increase effective capacity, and this requires a knowledge of what is constraining effective capacity.

The following section explores some of the main determinants of effective capacity. It is important to recognize that the benefits of high utilization are realized only in instances where there is demand for the output. When demand is not there, focusing exclusively on utilization can be counterproductive, because the excess output not only results in additional variable costs but also generates the costs of having to carry the output as inventory. Another disadvan- tage of high utilization is that operating costs may increase because of increasing waiting time due to bottleneck conditions.

5.4 DETERMINANTS OF EFFECTIVE CAPACITY

Many decisions about system design have an impact on capacity. The same is true for many operating decisions. This section briefly describes some of these factors, which are then elab- orated on elsewhere in the book. The main factors relate to facilities, products or services, processes, human considerations, operational factors, the supply chain, and external forces.

LO5.4 Name several determinants of effective capacity.

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Facilities. The design of facilities, including size and provision for expansion, is key. Loca- tional factors, such as transportation costs, distance to market, labor supply, energy sources, and room for expansion, are also important. Likewise, layout of the work area often deter- mines how smoothly work can be performed, and environmental factors such as heating, lighting, and ventilation also play a significant role in determining whether personnel can perform effectively or whether they must struggle to overcome poor design characteristics.

Product and Service Factors. Product or service design can have a tremendous influ- ence on capacity. For example, when items are similar, the ability of the system to produce those items is generally much greater than when successive items differ. Thus, a restaurant that offers a limited menu can usually prepare and serve meals at a faster rate than a restaurant with an extensive menu. Generally speaking, the more uniform the output, the more opportu- nities there are for standardization of methods and materials, which leads to greater capacity. The particular mix of products or services rendered also must be considered since different items will have different rates of output.

In only 48 hours, Solectron in San Jose, California, can build to order, ship, and install a complex computer system. Suppliers hold inventory until it is pulled, thereby increasing manufacturing flexibility.

Making a violin requires precision and skill from an artisan. Capacity is highly limited when items are specialized and produced one at a time.

Process Factors. The quantity capability of a process is an obvious determinant of capac- ity. A more subtle determinant is the influence of output quality. For instance, if quality of output does not meet standards, the rate of output will be slowed by the need for inspection and rework activities. Productivity also affects capacity. Process improvements that increase quality and productivity can result in increased capacity. Also, if multiple products or mul- tiple services are processed in batches, the time to change over equipment settings must be taken into account.

Human Factors. The tasks that make up a job, the variety of activities involved, and the training, skill, and experience required to perform a job all have an impact on the potential and actual output. In addition, employee motivation has a very basic relationship to capacity, as do absenteeism and labor turnover.

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Policy Factors. Management policy can affect capacity by allowing or not allowing capac- ity options such as overtime or second or third shifts.

Operational Factors. Scheduling problems may occur when an organization has differ- ences in equipment capabilities among alternative pieces of equipment or differences in job requirements. Inventory stocking decisions, late deliveries, purchasing requirements, accept- ability of purchased materials and parts, and quality inspection and control procedures also can have an impact on effective capacity.

Inventory shortages of even one component of an assembled item (e.g., computers, refrig- erators, automobiles) can cause a temporary halt to assembly operations until the compo- nents become available. This can have a major impact on effective capacity. Thus, insufficient capacity in one area can affect overall capacity.

Supply Chain Factors. Supply chain factors must be taken into account in capacity plan- ning if substantial capacity changes are involved. Key questions include: What impact will the changes have on suppliers, warehousing, transportation, and distributors? If capacity will be increased, will these elements of the supply chain be able to handle the increase? Conversely, if capacity is to be decreased, what impact will the loss of business have on these elements of the supply chain?

External Factors. Product standards, especially minimum quality and performance stan- dards, can restrict management’s options for increasing and using capacity. Thus, pollution standards on products and equipment often reduce effective capacity, as does paperwork required by government regulatory agencies by engaging employees in nonproductive activi- ties. A similar effect occurs when a union contract limits the number of hours and type of work an employee may do.

Table 5.2 summarizes these factors. In addition, inadequate planning can be a major limit- ing determinant of effective capacity.

5.5 STRATEGY FORMULATION

The three primary strategies are leading, following, and tracking. A leading capacity strategy builds capacity in anticipation of future demand increases. If capacity increases involve a long lead time, this strategy may be the best option. A following strategy builds capacity when demand exceeds current capacity. A tracking strategy is similar to a following strategy, but it adds capacity in relatively small increments to keep pace with increasing demand.

A. Facilities 1. Design 2. Location 3. Layout 4. Environment B. Product/service 1. Design 2. Product or service mix C. Process 1. Quantity capabilities 2. Quality capabilities D. Human factors 1. Job content 2. Job design 3. Training and experience 4. Motivation

5. Compensation 6. Learning rates 7. Absenteeism and labor turnover E. Policy F. Operational 1. Scheduling 2. Materials management 3. Quality assurance 4. Maintenance policies 5. Equipment breakdowns G. Supply chain H. External factors 1. Product standards 2. Safety regulations 3. Unions 4. Pollution control standards

TABLE 5.2 Factors that determine effective capacity

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An organization typically bases its capacity strategy on assumptions and predictions about long-term demand patterns, technological changes, and the behavior of its competitors. These typically involve (1) the growth rate and variability of demand, (2) the costs of building and operating facilities of various sizes, (3) the rate and direction of technological innovation, (4) the likely behavior of competitors, and (5) availability of capital and other inputs.

In some instances a decision may be made to incorporate a capacity cushion, which is an amount of capacity in excess of expected demand when there is some uncertainty about demand. Capacity cushion = capacity − expected demand. Typically, the greater the degree of demand uncertainty, the greater the amount of cushion used. Organizations that have stan- dard products or services generally have smaller capacity cushions. Cost and competitive pri- orities are also key factors.

Steps in the Capacity Planning Process

1. Estimate future capacity requirements. 2. Evaluate existing capacity and facilities and identify gaps. 3. Identify alternatives for meeting requirements. 4. Conduct financial analyses of each alternative. 5. Assess key qualitative issues for each alternative. 6. Select the alternative to pursue that will be best in the long term. 7. Implement the selected alternative. 8. Monitor results.

Capacity planning can be difficult at times due to the complex influence of market forces and technology.

5.6 FORECASTING CAPACITY REQUIREMENTS

Capacity planning decisions involve both long-term and short-term considerations. Long- term considerations relate to overall level of capacity, such as facility size; short-term con- siderations relate to probable variations in capacity requirements created by such things as seasonal, random, and irregular fluctuations in demand. Because the time intervals covered by each of these categories can vary significantly from industry to industry, it would be mis- leading to put times on the intervals. However, the distinction will serve as a framework within which to discuss capacity planning.

Long-term capacity needs require forecasting demand over a time horizon and then con- verting those forecasts into capacity requirements. Figure 5.1 illustrates some basic demand patterns that might be identified by a forecast. In addition to basic patterns there are more complex patterns, such as a combination of cycles and trends.

When trends are identified, the fundamental issues are (1) how long the trend might per- sist, because few things last forever, and (2) the slope of the trend. If cycles are identified, interest focuses on (1) the approximate length of the cycles and (2) the amplitude of the cycles (i.e., deviation from average).

Short-term capacity needs are less concerned with cycles or trends than with seasonal vari- ations and other variations from average. These deviations are particularly important because they can place a severe strain on a system’s ability to satisfy demand at some times and yet result in idle capacity at other times.

An organization can identify seasonal patterns using standard forecasting techniques. Although commonly thought of as annual fluctuations, seasonal variations are also reflected in monthly, weekly, and even daily capacity requirements. Table 5.3 provides some examples of items that tend to exhibit seasonal demand patterns.

When time intervals are too short to have seasonal variations in demand, the analysis can often describe the variations by probability distributions such as a normal, uniform, or Poisson

Capacity cushion Extra capacity used to offset demand uncertainty.

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distribution. For example, we might describe the amount of coffee served during the midday meal at a luncheonette by a normal distribution with a certain mean and standard deviation. The number of customers who enter a bank branch on Monday mornings might be described by a Poisson distribution with a certain mean. It does not follow, however, that every instance of random variability will lend itself to description by a standard statistical distribution. Ser- vice systems in particular may experience a considerable amount of variability in capacity requirements unless requests for service can be scheduled. Manufacturing systems, because of their typical isolation from customers and the more uniform nature of production, are likely to experience fewer variations. Waiting-line models and simulation models can be useful when analyzing service systems. These models are described in Chapter 18.

Irregular variations are perhaps the most troublesome: They are difficult or impossible to predict. They are created by such diverse forces as major equipment breakdowns, freak storms that disrupt normal routines, foreign political turmoil that causes oil shortages, discovery of health hazards (nuclear accidents, unsafe chemical dumping grounds, carcinogens in food and drink), and so on.

The link between marketing and operations is crucial to realistic determination of capacity requirements. Through customer contracts, demographic analyses, and forecasts, marketing can supply vital information to operations for ascertaining capacity needs for both the long term and the short term.

Calculating Processing Requirements A necessary piece of information is the capacity requirements of products that will be pro- cessed. To get this information, one must have reasonably accurate demand forecasts for each product and know the standard processing time per unit for each product, the number of work- days per year, and the number of shifts that will be used.

FIGURE 5.1 Common demand patterns

Time

V o

lu m

e V

o lu

m e

V o

lu m

e V

o lu

m e

Growth Decline

Cyclical Stable

TABLE 5.3 Examples of seasonal demand patterns

Period Items

Year Beer sales, toy sales, airline traffic, clothing, vacations, tourism, power usage, gasoline consumption, sports and recreation, education

Month Welfare and social security checks, bank transactions

Week Retail sales, restaurant meals, automobile traffic, automotive rentals, hotel registrations

Day Telephone calls, power usage, automobile traffic, public transportation, classroom utilization, retail sales, restaurant meals

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The task of determining capacity requirements should not be taken lightly. Substantial losses can occur when there are misjudgments on capacity needs. One key reason for those misjudg- ments can be overly optimistic projections of demand and growth. Marketing personnel are gen- erally optimistic in their outlook, which isn’t necessarily a bad thing. But care must be taken so that that optimism doesn’t lead to overcapacity, because the resulting underutilized capacity will create an additional cost burden. Another key reason for misjudgments may be focusing exclu- sively on sales and revenue potential, and not taking into account the product mix that will be needed to generate those sales and revenues. To avoid that, marketing and operations personnel must work closely to determine the optimal product mix needed and the resulting cost and profit.

A reasonable approach to determining capacity requirements is to obtain a forecast of future demand, translate demand into both the quantity and the timing of capacity requirements, and then decide what capacity changes (increased, decreased, or no changes) are needed.

Long-term capacity alternatives include expansion or contraction of an existing facility, opening or closing branch facilities, and relocation of existing operations. At this point, a decision must be made on whether to make or buy a good, or provide or buy a service.

5.7 ADDITIONAL CHALLENGES OF PLANNING SERVICE CAPACITY

While the foregoing discussion relates generally to capacity planning for both goods and ser- vices, it is important to note that capacity planning for services can present special challenges due to the nature of services. Three very important factors in planning service capacity are (1) there may be a need to be near customers, (2) the inability to store services, and (3) the degree of volatility of demand.

Convenience for customers is often an important aspect of service. Generally, a service must be located near customers. For example, hotel rooms must be where customers want to stay; having a vacant room in another city won’t help. Thus, capacity and location are closely tied.

Capacity also must be matched with the timing of demand. Unlike goods, services can- not be produced in one period and stored for use in a later period. Thus, an unsold seat on an airplane, train, or bus cannot be stored for use on a later trip. Similarly, inventories of goods allow customers to immediately satisfy wants, whereas a customer who wants a service may have to wait. This can result in a variety of negatives for an organization that provides the ser- vice. Thus, speed of delivery, or customer waiting time, becomes a major concern in service

Determining Needed Capacity A department works one 8-hour shift, 250 days a year, and has these figures for usage of a machine that is currently being considered:

Product

Annual Demand

Standard Processing Time per Unit (hr)

Processing Time Needed (hr)

1 400 5.0 2,000 2 300 8.0 2,400 3 700 2.0 1,400 5,800

Units of capacity needed = Processing time needed

___________________________ Processing time capacity per unit

(5–3)

Working one 8-hour shift 250 days a year provides an annual capacity of 8 × 250 = 2,000 hours per year. Consequently, three of these machines would be needed to handle the required volume:

5, 800 hours

__________________ 2, 000 hours/machine

= 2.90 machines

E X A M P L E 2

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capacity planning. For example, deciding on the number of police officers and fire trucks to have on duty at any given time affects the speed of response and brings into issue the cost of maintaining that capacity. Some of these issues are addressed in the chapter on waiting lines.

Demand volatility presents problems for capacity planners. Demand volatility tends to be higher for services than for goods, not only in timing of demand, but also in the amount of time required to service individual customers. For example, banks tend to experience higher volumes of demand on certain days of the week, and the number and nature of transactions tend to vary substantially for different individuals. Then, too, a wide range of social, cultural, and even weather factors can cause major peaks and valleys in demand. The fact that services can’t be stored means service systems cannot turn to inventory to smooth demand require- ments on the system the way goods-producing systems are able to. Instead, service planners have to devise other methods of coping with demand volatility and cyclical demand. For example, to cope with peak demand periods, planners might consider hiring extra workers, hiring temporary workers, outsourcing some or all of a service, or using pricing and promo- tion to shift some demand to slower periods.

In some instances, demand management strategies can be used to offset capacity limita- tions. Pricing, promotions, discounts, and similar tactics can help to shift some demand away from peak periods and into slow periods, allowing organizations to achieve a closer match in supply and demand.

5.8 DO IT IN-HOUSE OR OUTSOURCE IT?

Once capacity requirements have been determined, the organization must decide whether to pro- duce a good or provide a service itself, or to outsource from another organization. Many orga- nizations buy parts or contract out services, for a variety of reasons. Among those factors are:

1. Available capacity. If an organization has available the equipment, necessary skills, and time, it often makes sense to produce an item or perform a service in-house. The addi- tional costs would be relatively small compared with those required to buy items or sub- contract services. On the other hand, outsourcing can increase capacity and flexibility.

2. Expertise. If a firm lacks the expertise to do a job satisfactorily, buying might be a rea- sonable alternative.

3. Quality considerations. Firms that specialize can usually offer higher quality than an organization can attain itself. Conversely, unique quality requirements or the desire to closely monitor quality may cause an organization to perform a job itself.

4. The nature of demand. When demand for an item is high and steady, the organization is often better off doing the work itself. However, wide fluctuations in demand or small orders are usually better handled by specialists who are able to combine orders from multiple sources, which results in higher volume and tends to offset individual buyer fluctuations.

5. Cost. Any cost savings achieved from buying or making must be weighed against the preceding factors. Cost savings might come from the item itself or from transportation cost savings. If there are fixed costs associated with making an item that cannot be real- located if the service or product is outsourced, that has to be recognized in the analysis. Conversely, outsourcing may help a firm avoid incurring fixed costs.

6. Risks. Buying goods or services may entail considerable risks. Loss of direct control over operations, knowledge sharing, and the possible need to disclose proprietary infor- mation are three risks. And liability can be a tremendous risk if the products or services of other companies cause harm to customers or the environment, as well as damage to an organization’s reputation. Reputation can also be damaged if the public discovers that a supplier operates with substandard working conditions.

In some cases, a firm might choose to perform part of the work itself and let others handle the rest in order to maintain flexibility and to hedge against loss of a subcontractor. If part or all of the work will be done in-house, capacity alternatives will need to be developed.

LO5.5 Discuss factors to consider when deciding whether to perform in-house or outsource.

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Outsourcing brings with it a host of supply chain considerations. These are described in Chapter 15.

The following reading describes outsourcing that might surprise you.

5.9 DEVELOPING CAPACITY STRATEGIES

There are a number of ways to enhance development of capacity strategies: 1. Design flexibility into systems. The long-term nature of many capacity decisions and

the risks inherent in long-term forecasts suggest potential benefits from designing flexible systems. For example, provision for future expansion in the original design of a structure fre- quently can be obtained at a small price compared to what it would cost to remodel an existing structure that did not have such a provision. Hence, if future expansion of a restaurant seems likely, water lines, power hookups, and waste disposal lines can be put in place initially so that if expansion becomes a reality, modification to the existing structure can be minimized. Similarly, a new golf course may start as a nine-hole operation, but if provision is made for future expansion by obtaining options on adjacent land, it may progress to a larger (18-hole) course. Other considerations in flexible design involve layout of equipment, location, equip- ment selection, production planning, scheduling, and inventory policies, which will be dis- cussed in later chapters.

2. Take stage of life cycle into account. Capacity requirements are often closely linked to the stage of the life cycle that a product or service is in. At the introduction phase, it can be difficult to determine both the size of the market and the organization’s eventual share of that market. Therefore, organizations should be cautious in making large and/or inflexible capac- ity investments.

In the growth phase, the overall market may experience rapid growth. However, the real issue is the rate at which the organization’s market share grows, which may be more or less than the market rate, depending on the success of the organization’s strategies. Organiza- tions generally regard growth as a good thing. They want growth in the overall market for their products or services, and in their share of the market, because they see this as a way of increasing volume, and thus, increasing profits. However, there can also be a downside to this because increasing output levels will require increasing capacity, and that means increasing investment and increasing complexity. In addition, decision makers should take into account

Ever wonder how some sit-down restaurants are able to offer a huge variety of menu items, and how they are able to serve every- thing on that menu quickly? Could they have humongous kitchens and a battery of chefs scurrying around? Or maybe a few amaz- ing chefs whose hands are almost quicker than the eye? Maybe, and maybe not. In fact, that great-tasting restaurant entrée or des- sert you are served might have been prepared in a distant kitchen, where it was partially cooked, then flash-frozen or vacuum-packed, and shipped to your restaurant, awaiting your order. Then the entrée was finished cooking, perhaps in a microwave oven, and soon it was served to you—fresh made, so to speak. Surprised? Don’t be. Many restaurants, from chains like Fuddruckers and Per- kins, to top-quality restaurants, are going the outsourcing route. And companies such as Sara Lee, Land O’ Lakes, and Stockpot Soup Company of Redwood, Washington, are only too happy to oblige

READING MY COMPLIMENTS TO THE CHEF, ER, BUYER

them. Advertisements in restaurant trade magazines abound, with taglines such as “Hours versus ours” and “Just heat and serve.”

Not exactly like mother used to make, but then mother never had to contend with labor costs that run about 30 percent of rev- enue, or worry about keeping up with the competition.

Questions 1. Explain the meaning of the phrase “Hours versus ours.” 2. What advantages are there when restaurants outsource? 3. What are some important disadvantages or limitations of out-

sourcing for restaurants? 4. Do you consider restaurant outsourcing to be dishonest?

Unethical? Explain. 5. Does restaurant outsourcing increase capacity? Explain.

LO5.6 Discuss the major considerations related to developing capacity alternatives.

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possible similar moves by competitors, which would increase the risk of overcapacity in the market, and result in higher unit costs of the output. Another strategy would be to compete on some nonprice attribute of the product by investing in technology and process improvements to make differentiation a competitive advantage.

In the maturity phase, the size of the market levels off, and organizations tend to have stable market shares. Organizations may still be able to increase profitability by reducing costs and making full use of capacity. However, some organizations may still try to increase profitability by increasing capacity if they believe this stage will be fairly long, or the cost to increase capacity is relatively small.

In the decline phase, an organization is faced with underutilization of capacity due to declining demand. Organizations may eliminate the excess capacity by selling it, or by intro- ducing new products or services. An option that is sometimes used in manufacturing is to transfer capacity to a location that has lower labor costs, which allows the organization to continue to make a profit on the product for a while longer.

3. Take a “big-picture” (i.e., systems) approach to capacity changes. When develop- ing capacity alternatives, it is important to consider how parts of the system interrelate. For example, when making a decision to increase the number of rooms in a motel, one should also take into account probable increased demands for parking, entertainment and food, and housekeeping. Also, will suppliers be able to handle the increased volume?

Capacity changes inevitably affect an organization’s supply chain. Suppliers may need time to adjust to their capacity, so collaborating with supply chain partners on plans for capacity increases is essential. That includes not only suppliers, but also distributors and transporters.

The risk in not taking a big-picture approach is that the system will be unbalanced. Evi- dence of an unbalanced system is the existence of a bottleneck operation. A bottleneck operation is an operation in a sequence of operations whose capacity is lower than the capaci- ties of other operations in the sequence. As a consequence, the capacity of the bottleneck operation limits the system capacity; the capacity of the system is reduced to the capacity of the bottleneck operation. Figure 5.2 illustrates this concept: Four operations generate work that must then be processed by a fifth operation. The four different operations each have a capacity of 10 units per hour, for a total capacity of 40 units per hour. However, the fifth operation can only process 30 units per hour. Consequently, the output of the system will only be 30 units per hour. If the other operations operate at capacity, a line of units waiting to be processed by the bottleneck operation will build up at the rate of 10 per hour.

Here is another perspective. The following diagram illustrates a three-step process, with capacities of each step shown. However, the middle process, because its capacity is lower than that of the others, constrains the system to its capacity of 10 units per hour. Hence it is a bottleneck. In order to increase the capacity of the entire process, it would be necessary to increase the capacity of this bottleneck operation. Note, though, that the potential for increas- ing the capacity of the process is only 5 units, to 15 units per hour. Beyond that, operation 3’s capacity would limit process capacity to 15 units per hour.

Bottleneck operation An operation in a sequence of operations whose capacity is lower than that of the other operations.

FIGURE 5.2 Bottleneck operation

Operation 1

Operation 2

Bottleneck operation

30/hr

10/hr

Operation 3

Operation 4

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4. Prepare to deal with capacity “chunks.” Capacity increases are often acquired in fairly large chunks rather than smooth increments, making it difficult to achieve a match between desired capacity and feasible capacity. For instance, the desired capacity of a certain operation may be 55 units per hour, but suppose that machines used for this operation are able to produce 40 units per hour each. One machine by itself would cause capacity to be 15 units per hour short of what is needed, but two machines would result in an excess capacity of 25 units per hour. The illustration becomes even more extreme if we shift the topic—to open- hearth furnaces or to the number of airplanes needed to provide a desired level of capacity.

5. Attempt to smooth out capacity requirements. Unevenness in capacity requirements also can create certain problems. For instance, during periods of inclement weather, public transportation ridership tends to increase substantially relative to periods of pleasant weather. Consequently, the system tends to alternate between underutilization and overutilization. Increasing the number of buses or subway cars will reduce the burden during periods of heavy demand, but this will aggravate the problem of overcapacity at other times and certainly add to the cost of operating the system.

We can trace the unevenness in demand for products and services to a variety of sources. The bus ridership problem is weather related to a certain extent, but demand could be consid- ered to be partly random (i.e., varying because of chance factors). Still another source of vary- ing demand is seasonality. Seasonal variations are generally easier to cope with than random variations because they are predictable. Consequently, management can make allowances in planning and scheduling activities and inventories. However, seasonal variations can still pose problems because of their uneven demands on the system: At certain times the system

Operation 1 Operation 2 Operation 3

20/hr 10/hr 15/hr

10/hr

Capacity requirements are affected by seasonal variations. One approach is to identify products that offset each other such as demand for water skis and demand for snow skis.

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will tend to be overloaded, while at other times it will tend to be underloaded. One possible approach to this problem is to identify products or services that have complementary demand patterns, that is, patterns that tend to offset each other. For instance, demand for snow skis and demand for water skis might complement each other: Demand for water skis is greater in the spring and summer months, and demand for snow skis is greater in the fall and winter months. The same might apply to heating and air-conditioning equipment. The ideal case is one in which products or services with complementary demand patterns involve the use of the same resources but at different times, so that overall capacity requirements remain fairly stable and inventory levels are minimized. Figure 5.3 illustrates complementary demand patterns.

Variability in demand can pose a problem for managers. Simply adding capacity by increasing the size of the operation (e.g., increasing the size of the facility, the workforce, or the amount of processing equipment) is not always the best approach, because that reduces flexibility and adds to fixed costs. Consequently, managers often choose to respond to higher than normal demand in other ways. One way is through the use of overtime work. Another way is to subcontract some of the work. A third way is to draw down finished goods invento- ries during periods of high demand and replenish them during periods of slow demand. These options and others are discussed in detail in the chapter on aggregate planning.

6. Identify the optimal operating level. Production units typically have an ideal or opti- mal level of operation in terms of unit cost of output. At the ideal level, cost per unit is the lowest for that production unit. If the output rate is less than the optimal level, increas- ing the output rate will result in decreasing average unit costs. This is known as economies of scale. However, if output is increased beyond the optimal level, average unit costs will become increasingly larger. This is known as diseconomies of scale. Figure 5.4 illustrates these concepts.

Economies of scale If the output rate is less than the optimal level, increasing the output rate results in decreas- ing average unit costs.

Diseconomies of scale If the output rate is more than the optimal level, increasing the output rate results in increas- ing average unit costs.

FIGURE 5.3 A and B have complementary demand patterns

Time

D e

m a

n d

A + B

FIGURE 5.4 Production units have an optimal rate of output for minimum cost

A ve

ra g

e c

o st

p e

r u

n it

0 Rate of output

Minimum cost

Optimal rate

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Reasons for economies of scale include the following: a. Fixed costs are spread over more units, reducing the fixed cost per unit. b. Construction costs increase at a decreasing rate with respect to the size of the facil-

ity to be built. c. Processing costs decrease as output rates increase because operations become more

standardized, which reduces unit costs. Reasons for diseconomies of scale include the following: a. Distribution costs increase due to traffic congestion and shipping from one large

centralized facility instead of several smaller, decentralized facilities. b. Complexity increases costs; control and communication become more problematic. c. Inflexibility can be an issue. d. Additional levels of bureaucracy exist, slowing decision making and approvals for

changes. The explanation for the shape of the cost curve is that at low levels of output, the costs of

facilities and equipment must be absorbed (paid for) by very few units. Hence, the cost per unit is high. As output is increased, there are more units to absorb the “fixed” cost of facilities and equipment, so unit costs decrease. However, beyond a certain point, unit costs will start to rise. To be sure, the fixed costs are spread over even more units, so that does not account for the increase, but other factors now become important: worker fatigue; equipment break- downs; the loss of flexibility, which leaves less of a margin for error; and, generally, greater difficulty in coordinating operations.

Both optimal operating rate and the amount of the minimum cost tend to be a func- tion of the general capacity of the operating unit. For example, as the general capacity of a plant increases, the optimal output rate increases and the minimum cost for the optimal rate decreases. Thus, larger plants tend to have higher optimal output rates and lower minimum costs than smaller plants. Figure 5.5 illustrates these points.

In choosing the capacity of an operating unit, management must take these relationships into account along with the availability of financial and other resources and forecasts of expected demand. To do this, it is necessary to determine enough points for each size facility to be able to make a comparison among different sizes. In some instances, facility sizes are givens, whereas in others, facility size is a continuous variable (i.e., any size can be selected). In the latter case, an ideal facility size can be selected. Usually, management must make a choice from given sizes, and none may have a minimum at the desired rate of output. 7. Choose a strategy if expansion is involved. Consider whether incremental expansion

or single step is more appropriate. Factors include competitive pressures, market oppor- tunities, costs and availability of funds, disruption of operations, and training require- ments. Also, decide whether to lead or follow competitors. Leading is more risky, but it may have greater potential for rewards.

FIGURE 5.5 Minimum cost and optimal operating rate are functions of size of a production unit

Output rate

A ve

ra g

e c

o st

p e

r u

n it

Small plant

Medium plant

Large plant

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5.10 CONSTRAINT MANAGEMENT

A constraint is something that limits the performance of a process or system in achieving its goals. Constraint management is often based on the work of Eli Goldratt (The Theory of Constraints), and Eli Schragenheim and H. William Dettmer (Manufacturing at Warp Speed). There are seven categories of constraints:

Market: Insufficient demand Resource: Too little of one or more resources (e.g., workers, equipment, and space), as illustrated in Figure 5.2 Material: Too little of one or more materials Financial: Insufficient funds Supplier: Unreliable, long lead time, substandard quality Knowledge or competency: Needed knowledge or skills missing or incomplete Policy: Laws or regulations interfere

There may only be a few constraints, or there may be more than a few. Constraint issues can be resolved by using the following five steps:1

1. Identify the most pressing constraint. If it can easily be overcome, do so, and return to Step 1 for the next constraint. Otherwise, proceed to Step 2.

2. Change the operation to achieve the maximum benefit, given the constraint. This may be a short-term solution.

3. Make sure other portions of the process are supportive of the constraint (e.g., bottleneck operation).

4. Explore and evaluate ways to overcome the constraint. This will depend on the type of constraint. For example, if demand is too low, advertising or price change may be an option. If capacity is the issue, working overtime, purchasing new equipment, and out- sourcing are possible options. If additional funds are needed, working to improve cash flow, borrowing, and issuing stocks or bonds may be options. If suppliers are a problem, work with them, find more desirable suppliers, or do in-house. If knowledge or skills are needed, seek training or consultants, or outsource. If laws or regulations are the issue, working with lawmakers or regulators may be an option.

5. Repeat the process until the level of constraints is acceptable.

5.11 EVALUATING ALTERNATIVES

An organization needs to examine alternatives for future capacity from a number of different perspectives. Most obvious are economic considerations: Will an alternative be economically feasible? How much will it cost? How soon can we have it? What will operating and mainte- nance costs be? What will its useful life be? Will it be compatible with present personnel and present operations?

Less obvious, but nonetheless important, is possible negative public opinion. For instance, the decision to build a new power plant is almost sure to stir up reaction, whether the plant is coal-fired, hydroelectric, or nuclear. Any option that could disrupt lives and property is bound to generate hostile reactions. Construction of new facilities may necessitate moving personnel to a new location. Embracing a new technology may mean retraining some people and termi- nating some jobs. Relocation can cause unfavorable reactions, particularly if a town is about to lose a major employer. Conversely, community pressure in a new location may arise if the presence of the company is viewed unfavorably (noise, traffic, pollution).

Constraint Something that limits the performance of a process or system in achiev- ing its goals.

1 Adapted from Eli Schragenheim and H. William Dettmer, Manufacturing at Warp Speed (Boca Raton: St. Lucie Press, 2000).

LO5.7 Describe the steps that are used to resolve constraint issues.

LO5.8 Briefly describe approaches that are useful for evaluating capacity alternatives.

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A number of techniques are useful for evaluating capacity alternatives from an economic standpoint. Some of the more common are cost–volume analysis, financial analysis, decision theory, and waiting-line analysis. Cost–volume analysis is described in this section. Financial analysis is mentioned briefly, decision analysis is described in the chapter supplement, and waiting-line analysis is described in Chapter 18.

Cost–Volume Analysis Cost–volume analysis focuses on relationships between cost, revenue, and volume of output. The purpose of cost–volume analysis is to estimate the income of an organization under differ- ent operating conditions. It is particularly useful as a tool for comparing capacity alternatives.

Use of the technique requires identification of all costs related to the production of a given product. These costs are then designated as fixed costs or variable costs. Fixed costs tend to remain constant regardless of volume of output. Examples include rental costs, property taxes, equipment costs, heating and cooling expenses, and certain administrative costs. Vari- able costs vary directly with volume of output. The major components of variable costs are generally materials and labor costs. We will assume that variable cost per unit remains the same regardless of volume of output, and that all output can be sold.

Table 5.4 summarizes the symbols used in the cost–volume formulas. The total cost associated with a given volume of output is equal to the sum of the fixed cost

and the variable cost per unit times volume:

TC = FC + VC (5–4)

VC = Q × v (5–5)

where v = variable cost per unit. Figure 5.6A shows the relationship between volume of out- put and fixed costs, total variable costs, and total (fixed plus variable) costs.

Revenue per unit, like variable cost per unit, is assumed to be the same regardless of quantity of output. Total revenue will have a linear relationship to output, as illustrated in Figure 5.6B. The total revenue associated with a given quantity of output, Q, is

TR = R × Q (5–6)

Figure 5.6C describes the relationship between profit—which is the difference between total revenue and total (i.e., fixed plus variable) cost—and volume of output. The volume at which total cost and total revenue are equal is referred to as the break-even point (BEP). When volume is less than the break-even point, there is a loss; when volume is greater than the break-even point, there is a profit. The greater the deviation from this point, the greater the profit or loss. Figure 5.6D shows total profit or loss relative to the break-even point. Figure 5.6D can be obtained from Figure 5.6C by drawing a horizontal line through the point where the total cost and total revenue lines intersect. Total profit can be computed using the formula

P = TR − TC = R × Q − ( FC + v × Q )

Rearranging terms, we have

P = Q ( R − v ) − FC (5–7)

The difference between revenue per unit and variable cost per unit, R − v, is known as the contribution margin.

The required volume, Q, needed to generate a specified profit is

Q = P + FC

_______ R − v

(5–8)

A special case of this is the volume of output needed for total revenue to equal total cost. This is the break-even point, computed using the formula

Break-even point (BEP) The volume of output at which total cost and total revenue are equal.

FC = Fixed cost

VC = Total variable cost

v = Variable cost per unit

TC = Total cost

TR = Total revenue

R = Revenue per unit

Q = Quantity or volume of output

QBEP = Break-even quantity

P = Profit

TABLE 5.4 Cost–volume symbols

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Q BEP = FC

_____ R − v

(5–9)

Different alternatives can be compared by plotting the profit lines for the alternatives, as shown in Figure 5.6E.

Figure 5.6E illustrates the concept of an indifference point: the quantity at which a deci- sion maker would be indifferent between two competing alternatives. In this illustration, a quantity less than the point of indifference would favor choosing alternative B because its profit is higher in that range, while a quantity greater than the point of indifference would favor choosing alternative A.

Indifference point The quantity that would make two alternatives equivalent.

FIGURE 5.6 Cost–volume relationships

Q (volume in units)

A m

o u

n t

($ )

A. Fixed, variable, and total costs

Q (volume in units) A

m o

u n

t ($

)

B. Total revenue increases linearly with output

BEP units Q (volume in units)

A m

o u

n t

($ )

C. Profit = TR - TC

To tal

co st =

VC +

To tal

va ria

ble co

st ( VC

)

Fixed cost (FC)

To ta

l r ev

en ue

To ta

l r ev

en ue

To tal

cos t

Pr ofi

Lo ss

Q (volume in units)

A m

o u

n t

($ )

D. Profit versus loss

BEP

Pro fit

Lo ss

A m

o u

n t

($ )

Pro fit f

rom alt

ern ativ

e A

Profit from a lternative B

Q (volume in units)

Point of indi�erence

E. Point of indi�erence for two alternatives

Break-even Point and Profit Analysis The owner of Old-Fashioned Berry Pies, S. Simon, is contemplating adding a new line of pies, which will require leasing new equipment for a monthly payment of $6,000. Variable costs would be $2 per pie, and pies would retail for $7 each.

a. How many pies must be sold in order to break even? b. What would the profit (loss) be if 1,000 pies are made and sold in a month? c. How many pies must be sold to realize a profit of $4,000? d. If 2,000 can be sold, and a profit target is $5,000, what price should be charged per pie?

E X A M P L E 3

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Capacity alternatives may involve step costs, which are costs that increase stepwise as potential volume increases. For example, a firm may have the option of purchasing one, two, or three machines, with each additional machine increasing the fixed cost, although perhaps not linearly. (See Figure 5.7A.) Then fixed costs and potential volume would depend on the number of machines purchased. The implication is that multiple break-even quantities may occur, possibly one for each range. Note, however, that the total revenue line might not inter- sect the fixed-cost line in a particular range, meaning that there would be no break-even point in that range. This possibility is illustrated in Figure 5.7B, where there is no break-even point in the first range. In order to decide how many machines to purchase, a manager must con- sider projected annual demand (volume) relative to the multiple break-even points and choose the most appropriate number of machines, as Example 4 shows.

FC = $6,000,VC = $2 per pie, R = $7 per pie

a. Q BEP = FC _____ R − VC =

$6,000 ______ $7 − $2

= 1,200 pies/month

b. For Q = 1,000, P = Q ( R − v ) − FC = 1,000 ($7 − $2) − $6,000 = − $1,000

$4,000; solve for Q using Formula 5–8:

$4,000 + $6,000

_______________ $7 − $2

= 2,000 pies

Profit =

Q(R − v) − FC

$5,000 = 2,000(R − $2) − $6, 000 R

$7.50

S O L U T I O N

Determining the Break-even Point A manager has the option of purchasing one, two, or three machines. Fixed costs and poten- tial volumes are as follows:

Number of Machines

Total Annual Fixed Costs

Corresponding Range of Output

1 $ 9,600 0 to 300 2 15,000 301 to 600 3 20,000 601 to 900

Variable cost is $10 per unit, and revenue is $40 per unit.

a. Determine the break-even point for each range. b. If projected annual demand is between 580 and 660 units, how many machines should

the manager purchase?

E X A M P L E 4

FIGURE 5.7 Break-even problem with step fixed costs

Quantity

A. Step fixed costs and variable costs

FC + V

C = TC

1 machine

FC + V

C = TC

2 machines

FC + V

C = TC

3 machines

Quantity

B. Multiple break-even points

BEP3

BEP2

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Cost–volume analysis can be a valuable tool for comparing capacity alternatives if certain assumptions are satisfied:

1. One product is involved. 2. Everything produced can be sold. 3. The variable cost per unit is the same regardless of the volume. 4. Fixed costs do not change with volume changes, or they are step changes. 5. The revenue per unit is the same regardless of volume. 6. Revenue per unit exceeds variable cost per unit.

As with any quantitative tool, it is important to verify that the assumptions on which the technique is based are reasonably satisfied for a particular situation. For example, revenue per unit or variable cost per unit is not always constant. In addition, fixed costs may not be constant over the range of possible output. If demand is subject to random variations, one must take that into account in the analysis. Also, cost–volume analysis requires that fixed and variable costs can be separated, and this is sometimes exceedingly difficult to accomplish. Cost–volume analysis works best with one product or a few products that have the same cost characteristics.

A notable benefit of cost–volume considerations is the conceptual framework it provides for integrating cost, revenue, and profit estimates into capacity decisions. If a proposal looks attractive using cost–volume analysis, the next step would be to develop cash flow models to see how it fares with the addition of time and more flexible cost functions.

Financial Analysis Operations personnel need to have the ability to do financial analysis. A problem that is univer- sally encountered by managers is how to allocate scarce funds. A common approach is to use financial analysis to rank investment proposals, taking into account the time value of money.

Two important terms in financial analysis are cash flow and present value:

Cash flow refers to the difference between the cash received from sales (of goods or services) and other sources (e.g., sale of old equipment) and the cash outflow for labor, materials, overhead, and taxes. Present value expresses in current value the sum of all future cash flows of an invest- ment proposal.

The three most commonly used methods of financial analysis are payback, present value, and internal rate of return.

Cash flow The difference between cash received from sales and other sources, and cash outflow for labor, mate- rial, overhead, and taxes.

Present value The sum, in current value, of all future cash flows of an investment proposal.

a. Compute the break-even point for each range using the formula QBEP = FC/(R − v).

For one machine :

QBEP

$9,600

_________________ $40 / unit − $10 / unit

= 320 units [not in range, so there is no BEP]

For two machines : QBEP = $15,000

_________________ $40 / unit − $10 / unit

= 500 units

For three machines :

QBEP

$20,000

_________________ $40 / unit − $10 / unit

= 666.67 units

b. Comparing the projected range of demand to the two ranges for which a break-even point occurs (see Figure 5.7B), you can see that the break-even point is 500, which is in the range 301 to 600. This means that even if demand is at the low end of the range, it would be above the break-even point and thus yield a profit. That is not true of range 601 to 900. At the top end of projected demand, the volume would still be less than the break-even point for that range, so there would be no profit. Hence, the manager should choose two machines.

S O L U T I O N

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Payback is a crude but widely used method that focuses on the length of time it will take for an investment to return its original cost. For example, an investment with an original cost of $6,000 and a monthly net cash flow of $1,000 has a payback period of six months. Payback ignores the time value of money. Its use is easier to rationalize for short-term than for long- term projects.

S O L U T I O N

Determining Payback Time A new machine will cost $2,000, but it will result in savings of $500 per year. What is the payback time in years?

E X A M P L E 5

Initial cost = $2,000 Annual savings = $500

The payback time is initial cost divided by annual savings. Thus, the payback time is

Paybacktime = Initial cost

_____________ Annual Saving

= $2,000

_______ $500 / yr

= 4 years

The present value (PV) method summarizes the initial cost of an investment, its estimated annual cash flows, and any expected salvage value in a single value called the equivalent cur- rent value, taking into account the time value of money (i.e., interest rates).

The internal rate of return (IRR) summarizes the initial cost, expected annual cash flows, and estimated future salvage value of an investment proposal in an equivalent interest rate. In other words, this method identifies the rate of return that equates the estimated future returns and the initial cost.

These techniques are appropriate when there is a high degree of certainty associated with estimates of future cash flows. In many instances, however, operations managers and other managers must deal with situations better described as risky or uncertain. When conditions of risk or uncertainty are present, decision theory is often applied.

Decision Theory Decision theory is a helpful tool for financial comparison of alternatives under conditions of risk or uncertainty. It is suited to capacity decisions and to a wide range of other decisions managers must make. It involves identifying a set of possible future conditions that could influence results, listing alternative courses of action, and developing a financial outcome for each alternative–future condition combination. Decision theory is described in the supple- ment to this chapter.

Waiting-Line Analysis Analysis of lines is often useful for designing or modifying service systems. Waiting lines have a tendency to form in a wide variety of service systems (e.g., airport ticket counters, tele- phone calls to a cable television company, hospital emergency rooms). The lines are symp- toms of bottleneck operations. Analysis is useful in helping managers choose a capacity level that will be cost-effective through balancing the cost of having customers wait with the cost of providing additional capacity. It can aid in the determination of expected costs for various levels of service capacity.

This topic is described in Chapter 18.

Simulation Simulation can be a useful tool in evaluating what-if scenarios. Simulation is described on the book’s website.

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5.12 OPERATIONS STRATEGY

Bottleneck management can be a way to increase effective capacity, by scheduling non- bottleneck operations to achieve maximum utilization of bottleneck operations.

Three key inputs to capacity planning are the kind of capacity that will be needed, how much will be needed, and when it will be needed. Accurate forecasts are critical to the planning process.

SUMMARY

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bottleneck operation 201 break-even point (BEP) 206 capacity 189 capacity cushion 209

KEY TERMS cash flow 209 constraint 205 design capacity 192 diseconomies of scale 203

economies of scale 203 effective capacity 192 indifference point 207 present value 209

A firm’s manager must decide whether to make or buy a certain item used in the production of vending machines. Making the item would involve annual lease costs of $150,000. Cost and volume estimates are as follows:

Make Buy

Annual fixed cost $150,000 None Variable cost/unit $ 60 $ 80 Annual volume (units) 12,000 12,000

a. Given these numbers, should the firm buy or make this item? b. There is a possibility that volume could change in the future. At what volume would the manager

be indifferent between making and buying?

Problem 1

SOLVED PROBLEMS

Development of capacity alternatives is enhanced by taking a systems approach to planning, by rec- ognizing that capacity increments are often acquired in chunks, by designing flexible systems, and by considering product/service complements as a way of dealing with various patterns of demand.

In evaluating capacity alternatives, a manager must consider both quantitative and qualitative aspects. Quantitative analysis usually reflects economic factors, and qualitative considerations include intangibles such as public opinion and personal preferences of managers. Cost–volume analysis can be useful for analyzing alternatives.

1. Capacity decisions can be critical to the success of a business organization because capacity is the supply side of the supply-demand equation, and too little or too much capacity is costly.

2. The key issues in capacity planning relate to determining what kind of capacity is needed, how much is needed, and when it is needed.

3. Volatile demand and long lead times to achieve capacity changes can be challenging. 4. One or more constraints can adversely affect the overall capacity of a system (see, for example,

Figure 5.2). Capacity increases can only be achieved by loosening those constraints, not by increasing other resources, so it is essential to identify constraining resources and focus efforts on overcoming them.

KEY POINTS

Solution a. Determine the annual cost of each alternative: Total cost = Fixed cost + Volume × Variable cost

Make: $150, 000 + 12,000($60) = $870,000 Buy: 0 + 12,000($80) = $960,000

Because the annual cost of making the item is less than the annual cost of buying it, the manager would reasonably choose to make the item. Note: If the unit cost to buy had been less than the vari- able cost to make, there would be no need to even consider fixed costs; it would simply have been better to buy. b. To determine the volume at which the two choices would be equivalent, set the two total costs equal

to each other and solve for volume: TCmake = TCbuy. Thus, $150,000 + Q($60) = 0 + Q($80). Solving, Q = 7,500 units. Therefore, at a volume of 7,500 units a year, the manager would be indif- ferent between making and buying. For lower volumes, the choice would be to buy, and for higher volumes, the choice would be to make.

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A small firm produces and sells automotive items in a five-state area. The firm expects to consolidate assembly of its battery chargers line at a single location. Currently, operations are in three widely scattered locations. The leading candidate for location will have a monthly fixed cost of $42,000 and variable costs of $3 per charger. Chargers sell for $7 each. Prepare a table that shows total profits, fixed costs, variable costs, and revenues for monthly volumes of 10,000, 12,000, and 15,000 units. What is the break-even point?

Problem 2

Solution

Revenue

$7 per unit

Variable cost

$3 per unit

Fixed cost = $42, 000 per month Profit

Q ( R − v ) − FC

Total cost

FC + v + Q

Problem 3

Problem 4

Volume Total

Revenue Total VC

Fixed Cost

Total Cost

Total Profit

10,000 $ 70,000 $30,000 $42,000 $72,000 $(2,000)

12,000 84,000 36,000 42,000 78,000 6,000

15,000 105,000 45,000 42,000 87,000 18,000

Q BEP = FC

____ R − v

= $42,000

_______ $7 − $3

= 10,500 units per month

Refer to Problem 2. Determine profit when volume equals 22,000 units.

Profit = Q(R − v) − FC = Q($7 − $3) − $42,000 = $4Q − $42,000

For Q = 22,000, profit is

$4(22,000) − $42, 000 = $46, 000

A manager must decide which type of equipment to buy, Type A or Type B. Type A equipment costs $15,000 each, and Type B costs $11,000 each. The equipment can be operated 8 hours a day, 250 days a year. Either machine can be used to perform two types of chemical analysis, C1 and C2. Annual service requirements and processing times are shown in the following table. Which type of equipment should be purchased, and how many of that type will be needed? The goal is to minimize total purchase cost.

? Units (Indi�erent volume)

$150,000

TCmake

FCmake

TC b uy

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Total processing time (Annual volume × Processing time per analysis) needed by type of equipment:Solution

PROCESSING TIME PER ANALYSIS (hr)

Analysis Type Annual Volume A B

C1 1,200 1 2 C2 900 3 2

Analysis Type A B

C1 1,200 2,400 C2 2,700 1,800

Total 3,900 4,200

Total processing time available per piece of equipment is 8 hours/day × 250 days/year = 2,000. Hence, one piece can handle 2,000 hours of analysis, two pieces of equipment can handle 4,000 hours, and so on.

Given the total processing requirements, two of Type A would be needed, for a total cost of 2 × $15,000 = $30,000, or three of Type B, for a total cost of 3 × $11,000 = $33,000. Thus, two pieces of Type A would have sufficient capacity to handle the load at a lower cost than three of Type B.

Problem 5 Processes are commonly represented in either of two ways. One style uses blocks to represent opera- tions. A similar style uses circles rather than blocks:

Solution The attainable output of each portion of the system is equal to the output of the slowest operation. So the output of the upper portion is 10 units per hour and the output of the lower portion is 9 units per hour. Together the two portions can produce 10 + 9 = 19 units per hour. Although operation #7 can handle 20 units per hour, only 19 units per hour come from the two previous portions of the system, so the system output can only be 19 units per hour.

1 2 3

4 5 6

Process can operate in series, as shown in the preceding diagram, or in parallel, as shown here:

Question: What is the capacity (attainable output) of this system?

Operation Operation Operation

2 3

10 units/hr 15 units/hr 12 units/hr

11 units/hr 14 units/hr 9 units/hr

2 3

4 5 6

20 units/hr

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1. Contrast design capacity and effective capacity. 2. List and briefly explain three factors that may inhibit capacity utilization. 3. How do long-term and short-term capacity considerations differ? 4. Give an example of a good and a service that exhibit these seasonal demand patterns:

a. Annual b. Monthly c. Weekly d. Daily

5. Give some examples of building flexibility into system design. 6. Why is it important to adopt a big-picture approach to capacity planning? 7. What is meant by “capacity in chunks,” and why is that a factor in capacity planning? 8. What kinds of capacity problems do many elementary and secondary schools periodically experi-

ence? What are some alternatives to deal with those problems? 9. How can a systems approach to capacity planning be useful? 10. How do capacity decisions influence productivity? 11. Why is it important to match process capabilities with product requirements? 12. Briefly discuss how uncertainty affects capacity decisions. 13. Discuss the importance of capacity planning in deciding on the number of police officers or fire

trucks to have on duty at a given time. 14. Why is capacity planning one of the most critical decisions a manager has to make? 15. Why is capacity planning for services more challenging than it is for goods production? 16. What are some capacity measures for each of the following?

a. University b. Hospital c. Computer repair shop d. Farm

17. What is the benefit to a business organization of having capacity measures?

DISCUSSION AND REVIEW QUESTIONS

1. What are the major trade-offs in capacity planning? 2. Who needs to be involved in capacity planning? 3. In what ways does technology have an impact on capacity planning?

TAKING STOCK

1. A computer repair service has a design capacity of 80 repairs per day. Its effective capacity, how- ever, is 64 repairs per day, and its actual output is 62 repairs per day. The manager would like to increase the number of repairs per day. Which of the following factors would you recommend that the manager investigate: quality problems, absenteeism, or scheduling and balancing? Explain your reasoning.

2. Compared to manufacturing, service requirements tend to be more time dependent, location depen- dent, and volatile. In addition, service quality is often directly observable by customers. Find a recent article in a business magazine that describes how a service organization is struggling with one or more of these issues and make recommendations on what an organization needs to do to overcome these difficulties.

3. Identify four potential unethical actions or inactions related to capacity planning, and the ethical principle each violates (see Chapter 1).

4. Any increase in efficiency also increases utilization. Although the upper limit on efficiency is 100 percent, what can be done to achieve still higher levels of utilization?