Project_ManagementChapter_88__Copyright__Cengage_Learning_All

Project Management

Chapter 8

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Learning Objectives

Define the term project, list the steps involved in project management, and explain the role of the project manager.

Describe various project management tools and techniques, such as work breakdown structure, critical path method, program evaluation and review technique, cost and time tradeoff, and resource management.

Understand how to execute project successfully and how to avoid risks and failure.

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Elements of Project Management

To identify the elements of project management

we need to answer two questions:

What is a project?

What is Project Management?

And then we need to also consider the role of:

The Project Manager

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What is a Project?

Project: a set of interrelated activities necessary to achieve established goals using a specified amount of time, budget, and resources
The primary characteristics are:

A well-defined goal or objective

Composed of a set of interrelated activities

A specified beginning and ending time

Specified resource and personnel requirements

A specified budget

Uniqueness

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Supplementary
Characteristics for Projects

Projects generally have or include:

Pre-specified deliverables after completion

Pre-established limits and exclusions

Specific intermediate goals or performance milestones.

An element of risk

Teams made up of several individuals who come from different departments or functional areas or who have unique skills

Team members work are working on multiple projects at the same time

Source: © Image Source/Corbis

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Examples of
Operations Management Projects

The development of new product and service offerings such as Nintendo Wii, Sony Playstation, and Microsoft-X-Box.
Quality improvement projects such as implementation of Six Sigma projects at a large service organization like American Express.
Preparation for ISO9000 or ISO14000 certifications.

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What is Product Management

Project management: the application of the knowledge, skills, tools, and techniques necessary to successfully complete a project.
According to the Project Management Institute (www.pmi.org), the body of knowledge of project management can be divided into five categories:

initiation

planning

execution

control

closure

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Project Initiation

During the project initiation phase, a business problem or opportunity is identified, a solution is identified and a project team is established.
The project manager is ultimately responsible for the successful execution of the project.
The Project Management Institution recommends that project managers need to gain expertise in areas such as: information integrations, scope, time, cost, quality, human resources, communications, risk, and procurement.

Source: © Image Source/Corbis

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Discussion Starter

What are Project Champions?

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Project Planning

Involves the creation of a number of planning documents such as:

Project plan

Resource plan

Financial plan

Quality plans

Communications plan

Risk plan

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Project Execution

Involves the actual completion of all activities that are part of the project.
Requires the project manager to start constructing the deliverables.
The deliverables can be sequenced in series so that neither the project team nor the recipient is overburdened by them.

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Project Control

Is the real-time assessment of the execution of a planned project
Requires time, cost, quality, resource, risk, and change management skills
Hardest job for a project manager

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Project Closure

At the conclusion of all project activities and after submission of the required deliverables, a project is formally closed.
Conducting a critical assessment of all project phases that went well and those that did not allows the organization to learn and to improve the execution of the next project.

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The Project Manager

Project manager: the person responsible for delivering the goals of a project
Project time: the amount of time available to complete a project
Project cost: the budgeted amount available for the project
Project scope: the activities that must be completed to achieve a project’s end goal

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Figure 8.1: Three Interrelated
Constraints in Project Management

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Project Management
Tools and Techniques

The discipline of project management has available a number of tools and procedures that enable the project team to organize its work to meet the constraints:

Work Breakdown Structure

Precedence Relationship and Time Estimates

Gantt Chart

Network Diagram

Critical Path Method (CPM)

Cost and Time Tradeoff Analysis

Program Evaluation and Review Technique (PERT)

Resource Management

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Work Breakdown Structure

Work breakdown structure (WBS): an approach that defines a project in terms of its subprojects, tasks, and activities

Most fundamental technique for designing and organizing

Activity: the smallest work package that can be assigned to a single worker or a team
It is essential that care is taken to develop a realistic work breakdown structure.

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Figure 8.2: Work Breakdown Structure

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Precedence Relationship
and Time Estimates

Precedence relationship analysis: identification of the relationships and the sequence of activities within a project
Great care is taken to estimate the approximate completion time for each activity.
The project schedule, cost, and resource requirements depend on the precedence relationships and time estimates for individual tasks.

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Gantt Chart

Gantt chart: a special type of horizontal bar chart used to display the schedule for an entire project
Named after Henry Gantt, who originally developed the chart in the 1910s.
A Gantt chart with different color codes can be used to track performance while the project is in progress.

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Figure 8.3: An
Example of a Gantt Chart

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Network Diagram

Network diagram: a diagram with arrows and nodes (circles) created to display a sequence of activities within a project
Activity on node (AON) approach: a network diagram that shows each activity as a circle (or a node) and connects the activities with arrows
Activity on arrow (AOA) convention: a network diagram in which each activity is represented by an arrow, and the nodes are used to show the beginning and end points

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Figure 8.5: Activity on Node (AON)
and Activity on Arrow (AOA) Conventions
for Representing Network Diagrams

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Figure 8.7: AON Network Diagram
for Sunny Beach Resort Project

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Critical Path Method

Critical path method: an algorithm for scheduling activities within a project for the fastest and most efficient execution
Critical path: the path within a project that takes the longest time to complete

Dictates the project completion time

a.k.a.: the bottleneck path or the binding constraint

Critical activities: the project activities making up a critical path
Slack: the amount of flexibility in scheduling an activity within a project

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Identifying the Critical Path

The algorithm involves calculating four parameters

for each activity:

Early start time (ES): the earliest time at which an activity can start, considering the beginning and ending for each of the preceding activities

Early finish time (EF): the sum of the early start time (ES) and the time required to complete the activity

Late state time (LS): the latest time at which an activity can start, considering all the precedence relationships, without delaying the completion time for the project

Late finish time (LF): the sum of the late start time and the time required to complete the activity

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Figure 8.9: Convention for Displaying the Earliest and Latest Start and Finish Times

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Cost and Time Tradeoff Analysis

Sometimes the most efficient schedule is not sufficient for meeting customer needs.

The scope of the project may need to be changed or additional resources may need to be assigned to speed up the project.

If the scope of the project is changed, the project team can reevaluate the schedule based on the new guidelines using the critical path method.
If additional resources are assigned to speed up the project schedule, a cost and time tradeoff analysis (crashing) is conducted.
Crashing: an approach for identifying the lowest-cost approach for reducing the project duration

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Table 8.2: Sunny Beach Resort: Activity Relationships and Time Estimates

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Figure 8.17: Additional
Project Cost Versus Duration

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Program Evaluation
and Review Technique (PERT)

Program evaluation and review technique: a technique for addressing the impact of uncertainties in activity time estimates on the duration of the entire project
In a project schedule, different estimates for activity times are developed:

Optimistic time (to): the minimum possible time required to complete an activity, assuming that everything proceeds better than is normally expected

Pessimistic time (tp): the maximum possible time required to complete ac activity, assuming that everything proceeds at the slowest possible pace

Most likely time (tm): the best estimate of the time required to accomplish a task assuming that everything proceeds normally

Expected time (te): the best estimate of the time required to accomplish an activity considering the potential impact
of to, tm, and tp

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Figure 8.18: Potential
Distributions of Activity Times

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Resource Management

Two commonly used techniques are:

Resource breakdown structure (RBS): a standardized list of personnel required to complete various activities in a project

Resource leveling: an approach to reduce the amount of fluctuations in day-to-day resource requirements within an organization

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Figure 8.26: Resource Requirements
in Multiproject Environments

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Project Management Software

A large number of concepts, tools and techniques for project management have been introduced since the 1950s, and their use has become quite widespread in recent years.
Several reasons for this increase in the use of project management techniques:

Globally diverse workforce

Multi-project environments

Availability of user-friendly software

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Success Factors
in Project Management

Why Do Projects Fail?
Project Risk Management
Why Do Projects Succeed?

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Project Risk Management

Even with careful planning, it is not possible to anticipate everything that can put a project’s scope, schedule, or budget at risk.
A careful manager develops plans for managing various risks associated with the projects.
Four categories of risks:

Financial Resource Risk

Human Resource Risk

Supply Risk

Quality Risk

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Risk Assessment Plan

Step 1: Identify problems (and map them).

Problems are classified according to:

Severity: What percentage of the project’s scope will be affected by a problem?

Probability: What are the chances that a specific problem will occur?

Timing: At what point in the project is the specific problem likely to appear?

Dynamic risk: As the project proceeds, will the probability of the problem occurrence increase, decrease, or stay constant?

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Table 8.9: Project Risk Map Template

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Risk Assessment Plan continued

Step 2: Analyze each potential problem.

The project manager should quantify the impact of each potential problem such as time delays or cost overruns.

Step 3: Once the risks are presented on the same scale, develop a prioritization scheme.

Step 4: Develop a contingency plan.

Done by the project team

Step 5: Develop a potential upside for the project.

Done by the project manager

Step 6: Assign team members the responsibility for monitoring the signs of each potential problem.

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Risk Register

Projects Teams will define the various risks and document these issues.
Each risk will have a “potential solution.”
One owner will be assigned the risk.
Living document – dynamic – will change

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Why Do Projects Succeed?

Clearly Defined Goals

Is there a clear and written-down objective for the project?

Are the main tasks structured?

Has the scope of the project been agreed upon?

Does the team know and agree with the goals?

Are there clear milestones along the way?

Project Manager Ability

Is the project manager skilled and experienced?

Does the project manager have a plan and a budget?

Does the project manager have technical knowledge in the area of the project?

Does the project manager have leadership skills?

Can the project manager motivate the team?

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Why Do Projects Succeed?

Team Member Skills

Do we know what skills are required on this project?

Does the team have all these skills?

Is there a training program for team members?

Is there a range of skills and experience on the project?

Are people there because of what they bring to the project and not due to their position in the organization?

Top Management Support

Is there support from top management for the project?

Does the project have a champion in top management?

Have adequate resources been allocated to the project?

Does top management have a stake in the outcome of the project?

Does the project fit with organization objectives?

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Why Do Projects Succeed?

Project Planning

Is there a clear method for achieving the project?

Has a plan for the project life been prepared from this method?

Is there good short-term planning?

Is progress measured against plan?

Is the plan adjusted to match progress?

Communication

Are there clear channels of communication to all parties on the project?

Can team members discuss issues openly?

Can team members communicate their opinions on decisions?

Do team members get feedback on performance?

Do team members trust each other enough to communicate freely at all times?

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Why Do Projects Succeed?

User Involvement

Do we know the end users of the project?

Have the end users been involved in setting the project outcomes?

Is it easy for end users to get involved in the project?

Do the end users give feedback on progress?

Do the end users have ownership of the solution?

Commitment of Team

Are team members behind the goals of the project?

Do the team members own the project outcome?

Are team members involved in decision making?

Can team members make suggestions about improving and changing the project?

Do team members go beyond their job description for the good of the project?

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Why Do Projects Succeed?

Control Systems

Does the project have a control system?

Do we check planned time and cost against actual duration and expenditure?

Are checks carried out early enough to detect problems and correct them?

Do we give feedback on progress to the team?

Do we check that action on feedback is effective?

Risk Management

Have key risks on the project been identified?

Has the effect of each risk been measured?

Have responses been decided for key risks?

Have action plans been prepared for each response?

Does the team have a plan for managing unexpected risks?

MIS301—Executive Summary Assignments

Consult the article assigned on Isidore. Select one of the trends noted in the article and produce a concise Executive Summary that describes the opportunities and challenges associated with the trend that you select. The Executive Summary should synthesize the trend you selected for a senior-level audience and propose how the concepts presented could be applied in a business (examples will likely be helpful) as the Executive Summary should not simply a summary of the assigned article itself nor should it simply re-state the conclusions already stated (though such conclusions may be leveraged as appropriate). You will likely need to consult outside reading in order to produce a complete picture of the trend that you select. Please consider the following when writing your Executive Summary:

• In general, your Executive Summary should concisely present a high-level overview of the topic being discussed and then answer a simple question— why should the reader care?

• Focus on practical application. Providing a simple example or scenario for how a business could benefit from applying the concepts, techniques, tools, and/or principles presented in the assigned articles will be helpful. This may be an example that you find from performing additional research, a made up example, or similar. Simply relying on examples provided in the assigned article itself is discouraged.

• Use additional research (other articles, web research, etc.) as appropriate. Provide a reference list when citing work from sources other than the assigned article itself. The reference list may be provided separately and does not count towards the one page document limit.

• Focus on synthesizing and being concise. • Your name and the assignment number (e.g., Executive Summary 3) should

be provided as a cover page to the page you are submitting. • The Executive Summary length is limited to one single-spaced page. If you

submit more than one single-spaced page (not including a cover page and reference list) you will receive a grade of zero for the assignment.

A brief, helpful article summarizing how to write Executive Summaries may be found here: https://www.umuc.edu/writingcenter/writingresources/exec_summaries.cfm

https://www.umuc.edu/writingcenter/writingresources/exec_summaries.cfm

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Top 10 Strategic Technology Trends for 2018 Published: 3 October 2017 ID: G00327329

Analyst(s): David W. Cearley, Brian Burke, Samantha Searle, Mike J. Walker

The intelligent digital mesh is a foundation for future digital business and its ecosystems. To create competitive advantage, enterprise architecture and technology innovation leaders must evaluate these top trends to identify opportunities that their organizations can exploit.

Key Findings ■ Artificial intelligence (AI) delivers value to every industry, enabling new business models. It does

so by supporting key initiatives such as customer engagement, digital production, smart cities, self-driving cars, risk management, computer vision and speech recognition.

■ As people, places, processes and "things" become increasingly digitalized, they will be represented by digital twins. This will provide fertile ground for new event-driven business processes and digitally enabled business models and ecosystems.

■ The way we interact with technology will undergo a radical transformation over the next five to 10 years. Conversational platforms, augmented reality, virtual reality and mixed reality will provide more natural and immersive interactions with the digital world.

■ A digital business is event-centric, which means it must be continuously sensing and adapting. The same applies to the security and risk infrastructure that supports it, which must focus on deceiving potential intruders and predicting security events.

Recommendations Enterprise architecture (EA) and technology innovation leaders using EA to master emerging and strategic trends must:

■ Devise new business scenarios using AI as the enabler for new business designs. Do so by engaging, educating and ideating with senior business leaders about their strategically relevant priorities.

■ Create a more natural and immersive user experience by deploying, where effective, conversational platforms and virtual, augmented and mixed reality.

■ Support Internet of Things (IoT) initiatives by developing and prioritizing targeted, high-value business cases to build digital twins and exploit cloud and edge computing synergistically.

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■ Adopt a strategic approach for security and risk that continuously adapts based on risk and trust. Do so by communicating requirements to developers, achieving a DevSecOps environment.

Table of Contents

Analysis..................................................................................................................................................3

Trend No. 1: AI Foundation...............................................................................................................4

Today's AI Is Narrow AI...............................................................................................................5

Trend No. 2: Intelligent Apps and Analytics....................................................................................... 6

Augmented Analytics Will Enable Users to Spend More Time Acting on Insights.........................8

Trend No. 3: Intelligent Things...........................................................................................................9

Swarms of Intelligent Things Will Work Together....................................................................... 11

Trend No. 4: Digital Twins............................................................................................................... 12

Digital Twins Will Be Linked to Other Digital Entities...................................................................14

Trend No. 5: Cloud to the Edge...................................................................................................... 15

Edge Computing Brings Distributed Computing Into the Cloud Style........................................ 16

Trend No. 6: Conversational Platforms............................................................................................17

Integration With Third-Party Services Will Further Increase Usefulness...................................... 18

Trend No. 7: Immersive Experience.................................................................................................20

VR and AR Can Help Increase Productivity............................................................................... 21

Trend No. 8: Blockchain................................................................................................................. 23

Blockchain Offers Significant Potential Long-Term Benefits Despite Its Challenges....................24

Trend No. 9: Event-Driven Model.................................................................................................... 26

Events Will Become More Important in the Intelligent Digital Mesh............................................ 26

Trend No. 10: Continuous Adaptive Risk and Trust......................................................................... 27

Barriers Must Come Down Between Security and Application Teams....................................... 28

Gartner Recommended Reading.......................................................................................................... 30

List of Figures

Figure 1. Top 10 Strategic Technology Trends for 2018...........................................................................4

Figure 2. Narrow AI's Place in the Long History of AI.............................................................................. 5

Figure 3. Augmented Analytics for Citizen and Professional Data Scientists............................................ 8

Figure 4. Intelligent Things Span Many Sectors.....................................................................................10

Figure 5. Digital Twins Are Digital Representations of Real-World Objects............................................. 13

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Figure 6. Digital-Twin Models Will Expand to More Than Just Things.................................................... 15

Figure 7. Cloud and Edge Computing Are Complementary Concepts...................................................17

Figure 8. Conversational Platforms Include New User Experience Design Elements..............................19

Figure 9. The Future of the User Experience......................................................................................... 22

Figure 10. Key Elements of Blockchain.................................................................................................24

Figure 11. Event-Driven and Request-Driven Application Design Models Are Complementary.............. 27

Figure 12. The DevSecOps Model........................................................................................................ 29

Analysis Digital business blurs the physical and virtual worlds in a way that transforms business designs, industries, markets and organizations. The continuing digital business evolution exploits emerging and strategic technologies to integrate the physical and digital worlds, and create entirely new business models. The future will be defined by smart devices delivering increasingly insightful digital services everywhere. We call this mesh of interconnected people, devices, content and services the intelligent digital mesh. It's enabled by digital business platforms delivering a rich intelligent set of services to support digital business. As an EA or technology innovation leader seeking to exploit the intelligent digital mesh, you must respond to the disruptive technology trends driving this future.

Our top 10 strategic technology trends include three groupings of complementary trends (see Figure 1):

■ The intelligent theme explores how AI is seeping into virtually every existing technology and creating entirely new technology categories. The exploitation of AI will be a major battleground for technology providers through 2022. Using AI for well-scoped and targeted purposes delivers more flexible, insightful and increasingly autonomous systems.

■ The digital theme focuses on blending the digital and physical worlds to create a natural and immersive, digitally enhanced experience. As the amount of data that things produce increases exponentially, compute power shifts to the edge to process stream data and send summary data to central systems. Digital trends, along with opportunities enabled by AI, are driving the next generation of digital business and the creation of digital business ecosystems.

■ The mesh theme refers to exploiting connections between an expanding set of people and businesses — as well as devices, content and services — to deliver digital business outcomes. The mesh demands new capabilities that reduce friction, provide in-depth security and respond to events across these connections.

Our top 10 list highlights strategic trends that aren't yet widely recognized but have broad industry impact and significant potential for disruption. Through 2022, technologies related to these trends will reach a level of maturity that crosses a critical tipping point. And they'll experience significant changes. Examine the business impact of our top 10 strategic technology trends, and seize the opportunities to enhance your existing products, create new ones or adopt new business models.

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Digital business will transform your industry. Prepare for the impact of digital business on your industry and your business.

Figure 1. Top 10 Strategic Technology Trends for 2018

Source: Gartner (October 2017)

Trend No. 1: AI Foundation

Interest in AI is growing, as shown by an increase of more than 500% in the number of inquiry calls

from Gartner clients about topics related to AI in the past year. 1 A 2017 Gartner survey found that

59% of organizations are still gathering information to build their AI strategies, while the rest have

already made progress in piloting or adopting AI solutions. 2 Furthermore, the market indicates

strong investment in startups selling AI technologies. 3

Creating systems that learn, adapt and potentially act autonomously will be a major battleground for technology vendors through at least 2020. The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.

The AI foundation consists of numerous technologies and techniques that have grown over many years. These include expert systems, decision trees, linear regression and neural networks. The level of capability has grown steadily. This is the result of:

■ Ever-more advanced algorithms using supervised, unsupervised and reinforcement-learning techniques

■ The availability of massive amounts of data to feed machine learning

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■ Hardware advances (such as servers based on graphics processing units) delivering massive compute infrastructure to process the huge amount of data and sophisticated algorithms

Advanced machine learning in the form of deep learning has further extended the problem domains that AI addresses. Examine the wide variety of AI-related techniques and exploit them as needed.

Today's AI Is Narrow AI

Today, the focus for AI is on "narrow AI" (see Figure 2). Narrow AI consists of highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment). The algorithms chosen are optimized for that task. All the real- world examples of AI in use or development are examples of narrow AI. Artificial general intelligence refers to the use of machine learning to handle a broad range of use cases. Such systems, were they to exist, would successfully perform any intellectual task that a human could perform and would learn dynamically, much as humans do. These systems may never exist, but interest in them continues in the popular media and among those predicting an "AI doomsday." Focus on business results enabled by applications that exploit narrow AI technologies, both leading-edge and older AI technologies. Leave general AI to the researchers and science fiction writers.

Evaluate a number of business scenarios in which AI could drive specific business value, and consider experimenting with one or two high-impact scenarios. For example, in banking, you could use AI techniques to model current real-time transactions, as well as make predictive models of transactions based on their likelihood of being fraudulent. If you're an early adopter or you're seeking to drive disruptive innovation, begin to implement predictive analytics, ensemble learning and natural-language processing. If you're a mainstream user or have more modest innovation goals, use third parties and packaged solutions with embedded AI (see "Ten Ways AI Will Appear in Your Enterprise — No One Source Can Meet All Your Business Needs").

Figure 2. Narrow AI's Place in the Long History of AI

Source: Gartner (October 2017)

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AI techniques are evolving rapidly. You'll need to invest significantly in skills, processes and tools to successfully exploit these techniques. Investment areas include setup, integration, algorithm/ approach selection, data preparation and model creation. In addition, it can take significant effort to exploit a system's learning capabilities, evaluate the accuracy of findings, and update the algorithms and models to improve results. Effort is required from not only the data scientists creating the system, but also others who have the knowledge needed to "train" the system. You'll need:

■ Data scientists to understand data and AI algorithms, and to formulate coherent questions or problem domains to which to apply these algorithms

■ Application developers to design interfaces, services and process flows

A lack of the relevant data sciences will probably hamper AI adoption in the short term. 4 By 2020,

30% of new development projects will deliver AI through joint teams of data scientists and programmers.

Applied AI gives rise to a range of intelligent implementations. These include physical devices (such as robots, autonomous vehicles and consumer electronics), as well as apps and services (such as virtual personal assistants [VPAs] and smart advisors). These implementations will be delivered as a new class of obviously intelligent apps and things. They'll provide embedded intelligence for a wide range of mesh devices, and existing software and service solutions. The data science needed to create these systems is complex. This means that many organizations will consume applied AI mainly through packaged intelligent apps and things. Alternatively, organizations will consume them through packaged platform services or "models as a service" that they can build into custom applications.

Related Research:

■ "Develop Your Artificial Intelligence Strategy Expecting These Three Trends to Shape Its Future"

■ "AI on the Edge: Fusing Artificial Intelligence and IoT Will Catalyze New Digital Value Creation"

■ "Market Trends: How AI and Affective Computing Deliver More Personalized Interactions With Devices"

■ "Applying Artificial Intelligence to Drive Business Transformation: A Gartner Trend Insight Report"

■ "Innovation Insight for Artificial Intelligence of Things — Machine Learning in the IoT Era"

■ "Where You Should Use Artificial Intelligence — and Why"

■ "Questions to Ask Vendors That Say They Have 'Artificial Intelligence'"

Trend No. 2: Intelligent Apps and Analytics

Organizations are applying AI techniques to create new app categories (such as virtual customer assistants [VCAs]) and improve traditional applications (such as worker performance analysis, sales and marketing, and security). Intelligent apps have the potential to transform the nature of work and

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the structure of the workplace. When building or buying an AI-powered app, consider where its AI impact will be. It's useful to focus on three target domains when exploring how and where to exploit AI:

■ Analytics: AI can be used to create more predictive and prescriptive analytics that can then be presented to users for further evaluation, or plugged into a process to drive autonomous action. AI is also being used for augmented analytics.

■ Process: AI can drive more intelligent actions by an application. For example, you can use AI for intelligent invoice matching or analysis of email documents to improve service flow. In the future, this can be extended further to identify patterns of work, from which process models can be built and executed.

■ User Experience: Natural-language processing used to create VPAs is one application of AI to the user experience. Further examples include facial recognition and other AI applications for understanding user emotions, context or intent, and predicting user needs.

During the next few years, virtually every app, application and service will incorporate some level of AI. Some of these apps will be obvious intelligent apps that couldn't exist without AI and machine learning. Others will be unobtrusive users of AI that provide intelligence behind the scenes.

There is an AI "land grab" from the large vendors making "big bets" and from startups seeking to gain an edge. They all aim to support or replace manual human-based activities with intelligent automation. Vendors such as Salesforce, SAP, Oracle and Microsoft are incorporating more advanced AI functions in their offerings. These vendors are exploiting AI to varying degrees, but they're all focusing on their traditional strongholds. For example, the main enterprise software vendors are emphasizing sales, service, marketing and ERP as particularly valuable areas for applying AI techniques. Microsoft is focusing on Office 365 and a strong developer ecosystem. Challenge your packaged software and service providers to outline how they'll be using AI to add business value in new versions. Explore how much of the new value will come from bleeding-edge, rather than older, AI technologies. Examine how they use AI to deliver advanced analytics, intelligent processes and new user experiences.

VPAs such as Google Now, Microsoft's Cortana and Apple's Siri are becoming smarter and are a rapidly maturing type of intelligent app. Some chatbots, such as Facebook Messenger, can be powered by AI (for example, Wit.ai) to deliver an intelligent app. These intelligent apps feed into the conversational platform trend to create a new intelligent intermediary layer between people and systems. If you're an early adopter or you're seeking to drive disruptive innovation, begin to implement targeted VCAs and VPAs where a high-value target persona (for example, a doctor, marketing leader or high-profit customer) could achieve significant benefit. If you're a mainstream user or have more modest innovation goals, consider more simple rule-based chatbots. Exploit prepackaged assistants or simple mobile assistants based on the VPA capabilities embedded in smartphones.

Intelligent apps can create a new intelligent intermediary layer between people and systems. They have the potential to transform the nature of work and the structure of the workplace, as seen with VCAs and enterprise advisors and assistants. These models free people to build on and extend the

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capabilities of the assistant. For example, in healthcare, advanced advisors and other AI-assisted capabilities have the potential to enhance doctors' understanding and their ability to deliver more personalized treatments. Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people.

Augmented Analytics Will Enable Users to Spend More Time Acting on Insights

Augmented analytics is a particularly strategic, next-generation data and analytics paradigm in which AI is having an impact (see Figure 3). It uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists. Augmented analytics will enable expert data scientists to focus on specialized problems and on embedding enterprise-grade models into applications. Users will spend less time exploring data and more time acting on the most relevant insights. They will do so with less bias than in manual approaches.

Figure 3. Augmented Analytics for Citizen and Professional Data Scientists

NLG = natural-language generation; NLP = natural-language processing; NLQ = natural-language query

Source: Gartner (October 2017)

Enterprises will need to develop a strategy to address the impact of augmented analytics on currently supported data and analytics capabilities, roles, responsibilities and skills. They'll also need to increase their investments in data literacy. Both small startups and large vendors now offer augmented analytics capabilities that could disrupt vendors of business intelligence and analytics, data science, data integration, and embedded analytic applications. Data and analytics leaders

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must review their investments. By 2020, augmented analytics will be the dominant driver for data analysis systems. And by 2020, automation of data science tasks will enable citizen data scientists to produce a higher volume of advanced analysis than specialized data scientists.

Intelligent apps constitute a long-term trend that will evolve and expand the use of AI in apps and services through 2037. Establish a process to continually evaluate where your organization can apply AI today and over time. Use persona-based analysis to determine the most appropriate opportunities. Compare the roadmaps for AI exploitation across your packaged app and service provider portfolio. Proceed with caution if your organization is developing applications — the underlying AI elements for creating intelligent apps aren't ready for most application development projects at scale. Ensure such projects have a very high potential business value. The competitive gaps and missed opportunity costs for laggards could be significant.

Related Research:

■ "Market Guide for Virtual Customer Assistants"

■ "Competitive Landscape: Virtual Personal Assistants, 2016"

■ "Augmented Analytics Is the Future of Data and Analytics"

■ "Hype Cycle for Analytics and Business Intelligence, 2017"

■ "How Enterprise Software Providers Should (and Should Not) Exploit the AI Disruption"

Trend No. 3: Intelligent Things

Intelligent things are physical things that go beyond the execution of rigid programming models and exploit AI to deliver advanced behaviors that interact more naturally with their surroundings and with people. AI is driving advances for new intelligent things, such as autonomous vehicles, robots and drones, and delivering enhanced capability to many existing things, such as IoT-connected consumer and industrial systems (see Figure 4).

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Figure 4. Intelligent Things Span Many Sectors

Source: Gartner (October 2017)

Intelligent things are either semiautonomous or fully autonomous. The word "autonomous," when used to describe intelligent things, is subject to interpretation. When Gartner uses this term to describe intelligent things, we don't mean that these intelligent things have AI-style freedom from external human control or influence. Rather, we mean that these intelligent things can operate unsupervised for a defined period to complete a task. Intelligent things may have various levels of autonomy, as shown by the following examples:

■ Self-directing vacuum cleaners that have limited autonomy and smartness

■ Drones that can autonomously dodge obstacles 5

■ Unmanned aerial vehicles that can fly into buildings through windows and doors

Autonomous drones and robots will undergo significant technical evolution powered by new machine-learning models and algorithms. They will be used mainly in narrowly defined scenarios and controlled environments. Advances in one domain — such as more sophisticated algorithms that enable a robot to learn from its environment — will often have an application in another domain.

The use of autonomous vehicles in controlled settings (for example, farming, mining and warehousing) is a growing area of interest for intelligent things. In industrial settings, vehicles can be fully autonomous. By 2022, it's likely that autonomous vehicles will be used on roadways in limited, well-defined, geofenced and controlled areas. But general use of autonomous cars will probably require a person in the driver's seat in case the technology should fail — several U.S. states have

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passed laws stipulating this. In the near term, high-technology companies and traditional automotive manufacturers (such as Ford, Uber, Alphabet's Google, Volkswagen, Mercedes-Benz, Tesla, Nissan, BMW and Honda) will all be testing autonomous vehicles. Through at least 2022, we expect that semiautonomous scenarios requiring a driver will dominate. During this time, manufacturers will test the technology more rigorously, and the nontechnology issues will be addressed, such as regulations, legal issues and cultural acceptance.

AI will be embedded more often into everyday things, such as appliances, speakers and hospital equipment. This phenomenon is closely aligned with the emergence of conversational platforms, the expansion of the IoT and the trend toward digital twins. Amazon Echo is an example of an intelligent thing. It's a simple speaker connected wirelessly to an assistant, powered by machine learning. As conversational interfaces are delivered through other devices with a speaker or text input option, all these objects will become intelligent things.

Other markets have similar potential for embedded intelligence. For example, today's digital stethoscope can record and store heartbeat and respiratory sounds. Collecting a massive database of such data, relating the data to diagnostic and treatment information, and building an AI-powered doctor assistance app would enable doctors to receive diagnostic support in real time. However, in more advanced scenarios, significant issues such as liability, patient privacy and regulatory constraints must be considered. We expect that these nontechnical issues, and the complexity of creating highly specialized assistants, will slow embedded intelligence in industrial IoT and other business scenarios. Organizations that can address these barriers have the potential for significant competitive advantage.

Swarms of Intelligent Things Will Work Together

As intelligent things proliferate, we expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things. In this model, multiple devices will work together, either independently of people or with human input. For example, if a drone examined a large field and found that it was ready for harvesting, it could dispatch an "autonomous harvester." In the delivery market, the most effective solution may be to use an autonomous vehicle to move packages to the target area. Robots and drones on board the vehicle could then effect final delivery of the package. The military is leading the way in this area and is studying the use of drone swarms to attack or

defend military targets. 6 Other examples include:

■ Intel's use of a drone swarm for the U.S. Super Bowl halftime show in 2017 7

■ A plan for Dubai to use autonomous police vehicles that can deploy their own drones for

surveillance 8

■ Cooperative merge scenarios by Honda and other car manufacturers, in which vehicles

communicate with one another to optimize traffic flows 9

Related Research:

■ "Use Scenarios to Plan for Autonomous Vehicle Adoption"

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■ "Supply Chain Brief: Favorable Regulations Will Accelerate Global Adoption of Autonomous Trucking"

■ "Predicts 2017: Drones"

■ "Hype Cycle for Drones and Mobile Robots, 2017"

■ "Market Trends: Personal Assistant Robots for the Home"

■ "Swarms Will Help CIOs Scale Up Management for Digital Business"

Trend No. 4: Digital Twins

A digital twin is a digital representation of a real-world entity or system (see Figure 5). The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object (see Note 1). Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities. The notion of a digital representation of real-world entities or systems is not new. You can argue that this was a central notion in the IT industry with the creation of computer-aided design representations of physical assets or profiles of individual customers. The difference in the latest iteration of digital twins is:

■ The robustness of the models

■ Digital twins' link to the real world, potentially in real time

■ The application of advanced big data analytics and AI

■ The ability to interact with them and evaluate "what if" scenarios

Digital twins in the context of IoT projects are leading the interest in digital twins today. 10

Well- designed digital twins of assets could significantly improve enterprise decision making. They are linked to their real-world counterparts and are used to understand the state of the thing or system, respond to changes, improve operations, and add value (see Figure 5).

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Figure 5. Digital Twins Are Digital Representations of Real-World Objects

CAD = computer-aided design; FEA = finite element analysis; ML = machine learning

Source: Gartner (October 2017)

By 2020, we estimate there will be more than 20 billion connected sensors and endpoints, 11

and digital twins will exist for potentially billions of things. Benefits will include asset optimization, competitive differentiation and improved user experience in nearly all industries. As OEMs continue to work on connected products, they'll need to do more than just provide digital twins of their assets based on the essential elements described in Note 1. Rather, OEMs will need to think about their customers' evolving use cases and business models. Only by doing this can OEMs ensure that their hardware and software products remain competitive.

Organizations will implement digital twins simply at first. They will evolve them over time, improving their ability to collect and visualize the right data, apply the right analytics and rules, and respond effectively to business objectives. Through 2027, digital-twin use will expand beyond product engineers and data scientists. Operations managers will use them for assets where the cost-benefit analysis of risks in operations makes the case for digital twins compelling. We also expect that digital-twin models will proliferate, with suppliers increasingly providing customers with these models as an integral part of their offering.

Digital twins can enhance data insights and improve decision making, and will eventually help in the development of new business models. Their use will bring numerous benefits in different time frames, including:

■ Short term: Digital twins help in asset monitoring, optimization and improving the user experience, which is vital in nearly all industries. The shift from preventive to predictive (condition-based) maintenance is a particularly high-value use of digital twins. Customer

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benefits include reducing maintenance-driven downtime and lowering operating and maintenance costs.

■ Midterm: Organizations will use digital twins to operate factories and increase operational efficiency. They will use them to plan for equipment service and to predict equipment failure, enabling them to repair equipment to prevent its failure. Organizations will also use digital twins to enhance product development. They will do this by using them to simulate the behavior of new products based on the digital-twin insight from previous products, taking into consideration their cost, environment and performance. Digital twins offer the possibility of business models centered on guaranteed outcomes, such as specific asset performance guarantees.

■ Long term: Digital twins will aid innovation by providing insights into how products and services are used and how they can be improved. New business models may center on proactive advice. For example, automotive engineers could use digital twins, in conjunction with an analytics tool, to analyze how a specific car is driven to suggest new features to reduce accidents. Engineers might also suggest new products to serve the machine as a customer, where the machine and its digital twin have a budget for approved services. Other models may center on potential new marketplaces for digital twins, interfaces and suitably sanitized datasets from digital twins.

Digital Twins Will Be Linked to Other Digital Entities

Digital twins consolidate massive amounts of information on individual assets and groups of assets, often providing control of those assets. As the digital-twin trend evolves, twins will communicate with one another to create "digital factory" models of multiple linked digital twins. Digital twins of assets will be linked to other digital entities for people (digital personas), processes (law enforcement) and spaces (digital cities). Understanding the links across these digital entities, isolating elements where needed and tracking interactions will be vital to support a secure digital environment.

Although much attention is on digital twins of assets as part of an expanding IoT model, more sophisticated digital models of the real world have a much larger impact. Digital twins are built on the concept that virtual asset models coexist and are connected to real assets — they are twins. However, this concept isn't limited to assets (or things). Digital analogs of real-world elements are growing along many dimensions. Like digital twins, these other digital entities often grow from metadata structures and models of things in the real world that are disconnected from it, or are, at most, only loosely connected to it. Over time, these digital representations/models will be connected more tightly to their real-world counterparts. They will be infused with more sophisticated AI-based models, just as we are seeing with digital twins for assets. The following will be used for advanced simulation, operations and analysis (see Figure 6):

■ Future models of humans that could include rich biometric and medical data

■ Business operating system models defining the details of business processes and ecosystem interactions

■ Sophisticated models of buildings, cities and other places

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Figure 6. Digital-Twin Models Will Expand to More Than Just Things

Source: Gartner (October 2017)

Related Research:

■ "Innovation Insight for Digital Twins — Driving Better IoT-Fueled Decisions"

■ "Hype Cycle for the Internet of Things, 2017"

■ "Predicts 2017: IT and OT Convergence Will Create New Challenges and Opportunities"

■ "Digital Twins Will Impact Economic and Business Models"

■ "Create a Digital Twin of Your Organization to Optimize Your Digital Business Transformation Program"

■ "Digital Connectivism Tenet 1: We All Have a Digital Identity"

Trend No. 5: Cloud to the Edge

Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources and sinks of this information. Edge computing draws from the concepts of mesh networking and distributed processing. It tries to keep the traffic and processing local, with the goal being to reduce traffic and latency. As such, the notion of edge content delivery has existed for many years. The "where to process the data" pendulum has swung between highly centralized approaches (such as a mainframe or a centralized cloud service) and more decentralized approaches (such as PCs and mobile devices). Connectivity and latency

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challenges, bandwidth constraints and greater functionality embedded at the edge favor distributed deployment models. The advantages of processing power and low costs of operating at hyperscale, coupled with the complexity of managing and coordinating thousands of geographically separated endpoints, favor the centralized model.

Much of the current focus on edge computing comes from the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world. Widespread application of the topology and explicit application and networking architectures aren't yet common. Systems and networking management platforms will need to be stretched to include edge locations and edge- function-specific technologies. These include data thinning, data compression and protection, and local analytics. Edge computing solves many pressing issues, such as high WAN costs and unacceptable latency. The edge computing topology will enable the specifics of digital business and IT solutions uniquely well in the near future.

Begin using edge design patterns in your mid- to longer-term infrastructure architectures. Immediate actions might include simple trials using colocation and edge-specific networking capabilities. You could also simply place remote-location or branch-office compute functions in a standardized enclosure (for example, "data center in a box"). Some applications, such as client- facing web properties and branch-office solutions, will be simpler to integrate and deploy. Data thinning and cloud interconnection will take more planning and experimentation to get right.

Edge Computing Brings Distributed Computing Into the Cloud Style

Most view cloud and edge computing as competing approaches. They view public cloud deployments as enjoying the economies of hyperscale, centralized data centers, with edge computing mandating processing to be pushed to the edge. But this is a misunderstanding of the two concepts. Cloud computing is a style of computing in which elastically scalable technology capabilities are delivered as a service using internet technologies. Cloud computing doesn't mandate centralization. Edge computing brings the distributed computing aspect into the cloud style. Consider cloud and edge computing as complementary rather than competing concepts (see Figure 7). You can use:

■ Cloud computing as a style of computing to create a service-oriented model and a centralized control and coordination structure

■ Edge computing as a delivery model, allowing for disconnected or distributed process execution of aspects of the cloud service

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Figure 7. Cloud and Edge Computing Are Complementary Concepts

Source: Gartner (October 2017)

Some cloud implementations already use an approach that distributes functionality to the edge (for example, Microsoft Office 365 and AWS Greengrass). We expect this approach will be used more frequently as cloud vendors push further into the IoT market, and IoT solution vendors adopt the cloud style as a way to manage their solutions more effectively. Although the IoT is a strong driver for a cloud-to-the-edge approach, the trend will also benefit mobile and desktop environments. More solutions similar to Office 365 are likely to appear.

Related Research:

■ "Cool Vendors in IoT Edge Computing, 2017"

■ "Expand Your Artificial Intelligence Vision From the Cloud to the Edge"

■ "A Guidance Framework for Architecting the Internet of Things Edge"

■ "Explore the Roles of IoT Gateways in Five Edge Use Cases"

■ "The Edge Manifesto: Digital Business, Rich Media, Latency Sensitivity and the Use of Distributed Data Centers"

■ "Market Guide for Edge Computing Solutions for Industrial IoT"

Trend No. 6: Conversational Platforms

Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world. They will shift the model from technology-literate people to people-literate technology.

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The burden of translating intent will move from the user to the computer. The system takes a question or command from the user in natural language. It responds by executing a function, presenting content or asking for additional input.

A conversational platform provides a high-level design model and execution engine in which user and machine interactions occur. As the term "conversational" implies, these interfaces are implemented mainly in the user's spoken or written natural language. In time, other input/output mechanisms will be added to exploit sight, taste, smell and touch for multichannel interaction. The use of expanded sensory channels will support advanced capabilities, such as emotion detection through facial expression analysis and human health status through olfactory analysis. But exploitation of these other sensory channels will be isolated and limited for the next three to five years.

Over the next few years, conversational interfaces based on natural-language interfaces will become the main design goal for user interaction. Gartner predicts that, by 2019, 20% of users'

interactions with smartphones will be through VPAs. 12

A Gartner survey found that a quarter of

smartphone users had used their VPA in the past month, most on a daily or weekly basis. 13

Conversational platforms are most recognizably implemented in:

■ VPAs, such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft's Cortana

■ VCAs, such as IPsoft's Amelia, Watson Virtual Agent, and VCAs from [24]7, Artificial Solutions, Interactions, Next IT and Nuance

■ Chatbot frameworks, such as Amazon Lex, API.AI from Google, IBM Watson Conversation and Microsoft Bot Framework

Interactions in conversational platforms are typically informal and bidirectional. The interaction may be a simple request or question (such as "What's the weather forecast?" or "What time is it?") with a simple result or answer. Alternatively, it may be a structured interaction (such as that required to book a restaurant table or hotel room). As the technology matures, extremely complex requests will be possible, resulting in highly complex results. For example, the conversational platform may be able to collect oral testimony from crime witnesses, resulting in the creation of a suspect's image.

Integration With Third-Party Services Will Further Increase Usefulness

Conversational platforms have reached a tipping point: the usefulness of the systems has exceeded the friction of using them. But they still fall short. Friction is created when users need to know which domains the UI understands and what its capabilities are within those domains. The challenge that conversational platforms face is that users must communicate in a very structured way. This is often a frustrating experience. Rather than enabling a robust two-way conversation between the person and the computer, most conversational platforms are mainly one-directional query or control systems that produce a very simple response. Over time, more conversational platforms will integrate with growing ecosystems of third-party services that will exponentially drive the usefulness of these systems. A primary differentiator among conversational platforms will be the robustness of their conversational models and the API and event models used to access, invoke and orchestrate third-party services to deliver complex outcomes.

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By YE17, all the major industry players will have delivered their own version of a broadly applicable conversational platform. Some conversational platforms will support the supplier's own applications, and some will be generally available for enterprise buyers and third parties to build on (see Figure 8). Most will serve both purposes. Some platforms will be largely closed, while others will allow for replacement or extension of key components (such as natural-language processing engines and vocabularies). Examine the extensibility and mechanisms to link the conversational platform to other systems as part of any evaluation.

Figure 8. Conversational Platforms Include New User Experience Design Elements

I/O = input/output

Source: Gartner (October 2017)

Through 2020, application vendors will increasingly include conversational platforms in packaged applications. They will do so to maintain a direct channel to their users, rather than being cut off by an intermediary conversational platform they don't control. We expect ongoing battles between application vendors and providers of general-purpose conversational platforms over the next three to five years.

The shifting user experience will create many new digital business opportunities, but will also pose significant IT security and management challenges. The realization of the continuous, immersive and conversational user experience will require a profoundly better appreciation of privacy and permission. Devices that are "always listening" may collect information from users without their consent. Missteps by vendors or questionable ethical use by law enforcement agencies will probably lead to regulation of the collection, storage and permissible uses of data.

Related Research:

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■ "Architecture of Conversational Platforms"

■ "Cool Vendors in AI for Conversational Platforms, 2017"

■ "Innovation Insight for Conversational Commerce"

■ "Architecting and Integrating Chatbots and Conversational User Experiences"

■ "Smart Agents Will Drive the Switch From Technology-Literate People, to People-Literate Technology"

■ "Hype Cycle for Human-Machine Interface, 2017"

Trend No. 7: Immersive Experience

While conversational platforms are changing the way in which people interact with the digital world, virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing the way in which people perceive the digital world. This combined shift in perception and interaction models leads to the future immersive user experience.

VR and AR are separate but related technologies. MR extends both approaches to incorporate the physical world in a more robust way. The visual aspect of the experience is important, but so are other sensory models, such as touch (haptic feedback) and sound (spatial audio). This is particularly so with MR in which the user may interact with digital and real-world objects while maintaining a presence in the physical world (see Note 2).

VR provides a computer-generated 3D environment that surrounds a user and responds to an individual's actions in a natural way. This is usually through an immersive head-mounted display (HMD) that blocks the user's entire field of vision. Gesture recognition or handheld controllers provide hand and body tracking, and touch-sensitive feedback may be incorporated. Room-based systems that provide a deeper sense of immersion deliver a 3D experience for multiple participants or one in which a person can walk in a room untethered.

AR is the real-time use of information in the form of text, graphics, video and other virtual enhancements integrated with real-world objects. It's presented using an HMD or mobile device. This overlaying of virtual-world elements on a real-world background differentiates AR from VR. AR aims to enhance users' interaction with the real physical environment, rather than separating them from it. This definition also applies to MR. In general, MR further combines elements of many types of immersive technologies.

The VR and AR market is adolescent and fragmented. However, investment continues to flow. In 2016, there was a huge amount of funding ($2.09 billion) and this is projected to increase by 3% to

$2.16 billion in 2017. 14

Much of the investment is for core technologies still to be developed or for

technologies advancing to their next stage. In 2017, Apple introduced ARKit 15

and Google

introduced ARCore. 16

These immersive technology platforms are designed for the companies' respective mobile computing devices, and they indicate a very strong long-term interest from market share leaders.

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VR and AR Can Help Increase Productivity

Interest and excitement are high, resulting in multiple, novelty VR applications. Many provide no real business value, other than in advanced entertainment, such as video games and 360-degree spherical videos. For businesses, this means that the market is chaotic. AR and VR are often used as a novelty for customer engagement. Usually this is via smartphone AR (as with Pokémon Go). Sometimes it's as an immersive experience using an HMD (such as Everest VR on HTC Vive, which enables viewers to enjoy the view as they virtually climb Mount Everest). However, 40% of

organizations using or piloting AR find that the technology exceeds their expectations. 17

Examine real-life scenarios in which you can apply VR and AR to make employees more productive. You can use them to enhance design, training, visualization and to provide hands-free information. Only by examining and exploiting real-life scenarios can you drive tangible business benefits with these technologies.

Smartphones can also be an effective platform for mobile VR and AR. As with ARCore and ARKit, Google's Cardboard and Daydream and Samsung's Gear VR also use a smartphone as their computing platform. Snap your smartphone into one of these low-cost HMDs, hold it to your eyes, and see and interact with compelling virtual worlds. You don't even have to use one of these in an HMD configuration to experience AR — it can combine digital overlays on a real-world video experience. The device's screen becomes a "magic window" that displays graphics overlaid on top of real-world things. It superimposes contextual information that blends augmented data on top of real-world objects (such as hidden wiring superimposed on an image of a wall). Although this approach has significant limitations compared with more robust HMD-based approaches, it represents a widely available and cost-effective entry point. We expect the battle for smartphone- based AR to heat up in 2018. This is a result of Apple's release of ARKit and iPhone X, Google's release of ARCore, and the availability of cross-platform AR software development kits, such as Wikitude.

Through 2021, immersive consumer and business content and applications will evolve quickly. In 2018, the market for HMDs will grow and evolve significantly. It will reach 67.2 million shipped units

and $18.8 million in revenue by 2021. 18

In the near term, consumers will be more likely to adopt HMDs. Video games will be the first popular HMD app type, assuming that the game providers can deliver compelling content. More specialized HMDs, and VR and AR content solutions, will become available for businesses. Through 2021, HMD technology will improve dramatically, but mobile AR will be the most widely adopted.

MR is emerging as the immersive user experience of choice (see Figure 9). It provides a compelling technology that optimizes its interface to better match how people view and interact with their world. MR exists along a spectrum and includes HMD for AR and VR, as well as smartphone- and tablet-based AR. MR also encompasses the use of smart mirrors and heads-up displays and projectors. It extends beyond the visual dimension to include auditory, haptic and other sensory input/output channels. MR also includes beacons and sensors embedded in the environment around the user.

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Figure 9. The Future of the User Experience

UX = user experience

Source: Gartner (October 2017)

The integration of VR and AR with multiple mobile, wearable, IoT and sensor-rich environments, and conversational platforms (the mesh) will extend immersive applications beyond isolated and single- person experiences. Rooms and spaces will become active with things, and their connection through the mesh will appear and work in conjunction with immersive virtual worlds. Imagine a warehouse that can not only recognize the presence of workers, but also help them understand the state of its equipment, and can visually point out parts requiring replacement. Although the potential of VR and AR is impressive, there will be many challenges and roadblocks. Identify key target personas and explore targeted scenarios. For example, explore the needs of, and business value for, a target user in different settings, such as at home, in a car, at work, with a customer or traveling.

Related Research:

■ "Getting Started Developing Virtual Reality Experiences"

■ "Market Guide for Augmented Reality"

■ "Best Practices for Using Augmented Reality in Mobile Apps"

■ "Market Insight: Mixed-Reality Immersive Solutions Are the Ultimate User Experience for Everyone"

■ "Immersive Technologies Offer Infinite Possibilities"

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■ "Immersive Digital: The Future of Operations in Oil and Gas"

Trend No. 8: Blockchain

Blockchain is evolving from a digital currency infrastructure into a platform for digital transformation. Blockchain and other distributed-ledger technologies provide trust in untrusted environments, eliminating the need for a trusted central authority. In this research, we use the term "blockchain" as a generic term for all distributed-ledger technologies.

Blockchain technologies offer a radical departure from current centralized transaction and record- keeping mechanisms. They can serve as a foundation of disruptive digital business for both established enterprises and startups. Blockchain will transform the exchange of value, much as http/html transformed the exchange of web-based information.

At its core, blockchain is a shared, distributed, decentralized and tokenized ledger. Blockchain provides business value by removing business friction. It does this by making the ledger independent of individual applications and participants. Everyone with a particular level of permissioned access sees the same information at the same time. Integration is simplified by having a single shared blockchain model. Blockchain also enables a distributed trust architecture that allows untrusted parties to undertake commercial transactions, and create and exchange value using a diverse range of assets (see Figure 10).

Blockchain is a powerful tool for digital business because of its ability to:

■ Remove business and technology friction

■ Enable native asset creation and distribution

■ Provide a managed trust model

More dynamic behavior and business models can be added by:

■ Implementing smart contracts around the blockchain

■ Refining access and control to specific elements of the ledger

■ Creating different trust models

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Figure 10. Key Elements of Blockchain

Source: Gartner (October 2017)

Blockchain is gaining attention because it offers the promise to transform industry operating models. Funding in blockchain projects continues to grow, and one interesting development is the

use of initial coin offerings as a source of funding. 19

The hype surrounding blockchain originally focused on the financial services industry. But blockchain has many potential applications beyond financial services, including government, healthcare, manufacturing, supply chain, content distribution, identity verification and title registry.

A critical aspect of blockchain technology is the unregulated creation and transfer of funds, exemplified by bitcoin. This capability funds much of blockchain development, but also concerns regulators and governments. The debates about permissioned, permissionless, hybrid and private ecosystems, and governance will force a more robust analysis of distributed ledgers. Workable solutions will emerge in 2021 as these analyses are completed.

Blockchain Offers Significant Potential Long-Term Benefits Despite Its Challenges

Key potential benefits of blockchain include:

■ Improved cash flow

■ Lower transaction costs

■ Reduced settlement times

■ Asset provenance

■ Native asset creation

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■ New trust models

Using a public blockchain can remove the need for trusted central authorities in record transactions and dispute arbitrations. This is because trust is built into the model through immutable records on a distributed ledger. The potential of this technology to radically transform economic interactions should raise critical questions for society, governments and enterprises. As yet, there aren't any clear answers to these questions.

Blockchain faces other key challenges that will undermine the delivery of robust scalable solutions through 2022. Blockchain technologies and concepts are immature, poorly understood and unproven in mission-critical, at-scale business operations. This is particularly so with the more complex elements that support more sophisticated scenarios.

Despite the challenges, the significant potential for disruption means you should probably begin evaluating blockchain, even if you don't aggressively adopt the technologies in the next few years. A practical approach to blockchain development demands:

■ A clear understanding of the business opportunity and potential industry impact

■ A clear understanding of the capabilities and limitations of blockchain technology

■ A trust architecture

■ The necessary skills to implement the technology

Develop clear language and definitions for internal discussions about the nature of the technology. Recognize that the terminology surrounding blockchain is in flux. This uncertainty masks the potential suitability of technology solutions to meet business use cases. Consequently, use extreme caution when interacting with vendors that have ill-defined/nonexistent blockchain offerings. Identify exactly how the term "blockchain" is being used, both internally and by providers. Monitor distributed-ledger developments, including related initiatives, such as consensus mechanism development, sidechains and blockchains. Resources permitting, consider distributed ledger as proof-of-concept development. But, before starting a distributed-ledger project, ensure your team has the business and cryptographic skills to understand what is and isn't possible. Identify the integration points with existing infrastructures to determine the necessary future investments, and monitor the platform evolution and maturation.

Related Research:

■ "Understanding Blockchain Platform Architectures and Implementation Styles"

■ "What CIOs Should Tell the Board of Directors About Blockchain"

■ "How to Determine If You Need a Blockchain Project, and If So, What Kind?"

■ "Top 10 Mistakes in Enterprise Blockchain Projects"

■ "Toolkit: Overview of Blockchain Use Cases"

■ "Hype Cycle for Blockchain Technologies, 2017"

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■ "Hype Cycle for Blockchain Business, 2017"

Trend No. 9: Event-Driven Model

Business is always sensing, and ready to exploit, new digital business moments (see "Business Events, Business Moments and Event Thinking in Digital Business"). This is central to digital business. Business events reflect the discovery of notable states or state changes, such as the completion of a purchase order. Some business events, or combinations of events, constitute business moments — detected situations that call for specific business actions. The most significant business moments have implications for multiple parties (for example, separate applications, lines of business or partners).

More business events can be detected more quickly and analyzed in greater detail by using event brokers, the IoT, cloud computing, blockchain, in-memory data management and AI. But technology alone can't deliver the full value of the event-driven model. That requires cultural and leadership change: IT leaders, planners and architects must embrace "event thinking." By 2020, event- sourced, real-time situational awareness will be a required characteristic for 80% of digital business solutions. And 80% of new business ecosystems will require support for event processing.

Event-driven architecture optimizes for agility, resiliency, extensibility, lower cost of change, open- ended design and web scale. A dynamic event-driven approach is required to achieve user goals in conversational platforms. The UI becomes more intelligent with conversational platforms, responding to a dynamic and shifting user context, and integrating various system elements on the back end. Data streams from the IoT represent streams of events. Real-time decision making and situational awareness demand continuous monitoring and assessment of events in real time.

Events Will Become More Important in the Intelligent Digital Mesh

All roads in our expanding intelligent digital mesh push toward greater importance for events. But most organizations use event processing for narrow purposes in isolated application contexts. They don't consider it a prevailing application design model equal to the request-driven service-oriented architecture. This perception must change to accommodate the push to digital business. It's also necessary to enable organizations to choose the most appropriate design model for the task at hand. Technology providers will incorporate more event-driven approaches across their product lines. Examples include Salesforce, with its Platform Events, and SAP, with the SAP Event Stream Processor.

The request-driven and event-driven application design models are complementary (see Figure 11). Both are useful and appropriate, depending on the type of business process being implemented. The request-driven model with its command-driven and structured approach provides more certainty and control of actions between services. But it's relatively rigid and stateful, with limited parallelism, and creates dependencies. The event-driven approach is more flexible, supporting real- time, business-driven event streams and scale. But it requires the introduction of an intermediary layer (event broker) and provides only eventual consistency. Process designers, architects and developers should view the two approaches as first-class and equal. Events will gradually become a preferred default approach because of their flexibility. Request-driven approaches will be applied where extra control and certainty are paramount.

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Figure 11. Event-Driven and Request-Driven Application Design Models Are Complementary

Source: Gartner (October 2017)

Related Research:

■ "Business Events, Business Moments and Event Thinking in Digital Business"

■ "Follow the Leaders: Digital Business Innovation Is Event-Driven"

■ "Assessing Event-Driven Architecture for Scalable and Reactive Web Applications"

■ "Articulating the Business Value of Event-Driven Architecture"

■ "Gartner on Event Processing in Digital Business: Recent Research"

■ "Event-Driven Programming Models Will Disrupt End-User Applications"

■ "Digital Businesses Will Compete and Seek Opportunity in the Span of a Business Moment"

Trend No. 10: Continuous Adaptive Risk and Trust

The intelligent digital mesh and related digital technology platforms and application architectures

create an ever-more-complex world for security. 20

The continuing evolution of the "hacker industry" and its use of increasingly sophisticated tools — including the same advanced technologies available to enterprises — significantly raise the threat potential. Relying on perimeter defense and static rule-based security is inadequate and outdated. This is especially so as organizations exploit more mobile devices, cloud-based services, and open APIs for customers and partners to create business ecosystems. IT leaders must focus on detecting and responding to threats, as well as more traditional measures, such as blocking, to prevent attacks and other abuses. At the same time, digital business will require more advanced access protection when systems and information are opened up to the digital mesh. Security and risk management leaders must adopt a continuous

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adaptive risk and trust assessment (CARTA) strategic approach. This is vital to securely enable access to digital business initiatives in a world of advanced, targeted attacks. It will enable real-

time, risk- and trust-based decision making with adaptive responses. 21

Trust models using ownership and control as a proxy for trust simply won't work in a world of IT- enabled capabilities delivered anytime to users, located anywhere and accessing capabilities from any device. Existing security decision making based on initial one-time block/allow security assessments for access and protection is flawed. It leaves organizations open to zero-day and targeted attacks, credential theft, and insider threats. Trust (and risk) of digital business entities and their actions must be dynamic, and assessed continuously in real time as interactions take place and additional context is gained. A CARTA approach embraces the reality that we can't know the answers to security questions — such as access or blocking — in advance. We can't provide a risk- based answer to these security questions until:

■ The request is made.

■ The context is known.

■ The relative risk and trust scoring of the entity and its requested behavior are assessed.

Barriers Must Come Down Between Security and Application Teams

As part of a CARTA approach, organizations must overcome the barriers between security teams and application teams. This is similar to the way in which DevOps tools and processes overcome the divide between development and operations. Security teams can't afford to wait until the end of the build-and-release pipeline to perform a detailed security scan. Security requirements must be clearly communicated and easily integrated into the processes of the developers, not the other way around. Information security architects must integrate security testing at multiple points into DevOps workflows in a collaborative way. This must be transparent to developers, and must preserve the teamwork, agility and speed of DevOps and agile development environments. This will result in DevSecOps (see Figure 12).

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Figure 12. The DevSecOps Model

Source: Gartner (October 2017)

To move toward this model, start with secure development and training, but don't make developers become security experts or switch tools. Adopt the concept of people-centric security. Empower developers to take personal responsibility for security. Compensate for this with monitoring, following a "trust and verify" mindset. All information security platforms should expose full functionality via APIs. In this way, processes can be integrated into the DevOps process and automated into the developer's preferred toolchain. Use proven version-control practices and tools for all application software and, just as important, for all scripts, templates and blueprints used in DevOps environments. Adopt a fixed infrastructure mindset in which production systems are locked down.

Continuous adaptive risk and trust can also be applied at runtime with approaches such as deception technologies. These technologies are becoming more important in a multilayered process as an alternative to existing tools to improve threat detection and response. Organizations that have chosen deception technologies over other approaches report simpler deployment, lower costs and less operational burden. But this comes at the cost of incomplete coverage. Complementary deployment with tools such as security information and event management, user entity and behavior analytics, and network traffic analytics will provide more complete coverage. However, this will result in a more complex security environment.

Advances in technologies such as virtualization and software-defined networking have made it easier to deploy, manage and monitor "adaptive honeypots" — the basic components of network- based deception. Organizations typically select deception technologies to detect lateral threat movements inside the network. This means that most deployments are internal, rather than in the demilitarized zone. Deception approaches can extend to servers and end-user endpoints, with decoy directories, files, data and credentials to catch an attacker. The idea is that, after a threat has penetrated the organization's external perimeter and is looking for, or moving to, a target, the

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attacker will interact with one of the decoys. This will trigger a high-confidence alert to the defending team.

Focus first on deception technologies for environments (such as the IoT, supervisory control, data acquisition and medical environments) in which technical challenges make it difficult, too expensive or impossible to use other security controls. Expand use as your expertise expands, and as tools mature and are better integrated into overall security frameworks and suites.

Related Research:

■ "Use a CARTA Strategic Approach to Embrace Digital Business Opportunities in an Era of Advanced Threats"

■ "2017 Planning Guide for Security and Risk Management"

■ "DevSecOps: How to Seamlessly Integrate Security Into DevOps"

■ "Applying Deception Technologies and Techniques to Improve Threat Detection and Response"

■ "Cool Vendors in Cloud Security, 2017"

Gartner Recommended Reading Some documents may not be available as part of your current Gartner subscription.

"Hype Cycle for Emerging Technologies, 2017"

"Hype Cycle for Artificial Intelligence, 2017"

"Hype Cycle for Data Science and Machine Learning, 2017"

"Hype Cycle for the Internet of Things, 2017"

"Hype Cycle for Human-Machine Interface, 2017"

"Hype Cycle for Application Development, 2017"

"Hype Cycle for Data Security, 2017"

"Hype Cycle for Application Architecture, 2017"

"Hype Cycle for Platform as a Service, 2017"

Evidence

1 Between June 2016 and June 2017, Gartner analysts took 4,353 inquiries related to AI. This represents a 523% increase year over year.

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2 Between 5 and 21 April 2017, the Gartner Research Circle conducted an online survey on AI development strategies. Of the 83 respondents, it found that:

■ Fifty-nine percent were still gathering knowledge to develop their AI strategies.

■ Twenty-five percent were piloting AI solutions.

■ Six percent were implementing AI solutions.

■ Six percent had deployed AI solutions.

3 The following statistics show the growth in the market:

■ CB Insights reports that more than 550 startups that use AI as their core product raised $5 billion in funding in 2016. There were also 658 deals in 2016.

■ Venture Scanner says it's tracking 1,852 AI companies — 940 of them funded — in 13 categories and 70 countries. It says those companies have raised total funding of $16.8 billion.

■ TechSci Research projects, in a June 2016 report, that the AI market will grow at a compound annual rate of 75% between 2016 and 2021.

4 Between 5 and 21 April 2017, the Gartner Research Circle conducted an online survey on AI development strategies. Of the 83 respondents, 54% believed that a lack of necessary staff skills would be a key challenge for organizations adopting AI.

5 "Autonomous Miniature Aerial Vehicles: Vision-Based Obstacle Avoidance." Cornell University.

6 "How Swarming Drones Could Change the Face of Air Warfare." Defense News.

7 "Intel Powered the Drones During Lady Gaga's Super Bowl Halftime Show." TechCrunch.

8 "Police in Dubai Have Recruited a Self-Driving Robo-Car That Can 'Scan for Undesirables.'" The Verge.

9 "Cooperative Merge." Honda.

10 In June and July 2017, Gartner conducted an online study of 202 organizations in the U.S., Germany, China and Japan. It found that:

■ Twenty-four percent of organizations were already using digital twins.

■ Twenty-four percent planned to use digital twins in the next year.

■ Nineteen percent planned to use digital twins in the next three years.

■ Twenty percent didn't plan to use digital twins.

■ Those organizations that had already implemented the IoT were more likely to use, or plan to use, digital twins.

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http://drones.cs.cornell.edu/

http://www.defensenews.com/2016/05/17/how-swarming-drones-could-change-the-face-of-air-warfare/

https://techcrunch.com/2017/02/05/intel-powered-the-drones-during-lady-gagas-super-bowl-halftime-show/

https://www.theverge.com/2017/6/29/15893802/dubai-police-robot-drone-car

http://hondanews.com/releases/honda-introduces-cooperative-mobility-ecosystem-at-ces-2017/videos/cooperative-merge

This research note is restricted to the personal use of [email protected].

11 "Forecast Analysis: Internet of Things — Endpoints, Worldwide, 2016 Update"

12 "Predicts 2017: Personal Devices"

13 In June and July 2017, Gartner conducted its personal technologies survey online. The 16,537 respondents ranged from 18 to 74 years old. They lived in the U.K., the U.S., Germany, China and India. The survey found that:

■ Twenty-five percent of respondents used their VPA in the past month.

■ Twenty-four percent used their VPA once or twice a day.

■ Sixteen percent used their VPA several times a day.

■ Thirty-two percent used their VPA several times a week.

14 "AR/VR Sets New Records for Deals and Dollars in 2016." CB Insights.

15 "Nine Cool AR Apps You Should Download to Try Out iOS 11's ARKit." The Verge.

16 "Google's ARCore Brings Augmented Reality to Millions of Android Devices." Ars Technica UK.

17 Gartner conducted a survey on the use of digital technologies to drive digital business transformation. Of the 29% of organizations using or piloting AR, 40% reported that it exceeded their expectations. Sixty percent reported that the technology performed as expected. There were 228 respondents.

18 "Forecast: Wearable Electronic Devices, Worldwide, 2017"

19 "Blockchain ICO Funding Gains Steam vs VC Investment." CB Insights.

20 Gartner's Annual Global Risk and Security Survey (fielded online in February to March 2017 with 712 respondents from the U.S., U.K., Germany, Brazil and India) indicated that 86% of respondents feel that the digital world is creating new types and levels of risk for their business.

21 Gartner's Annual Global Risk and Security Survey (fielded online in February to March 2017 with 712 respondents from the U.S., U.K., Germany, Brazil and India) showed that 64% of respondents agree that the agility to sense and respond to unknown and unexpected types of risk is increasing importance (relative to practices for prioritizing, managing, and mitigating known and expected risks).

Note 1 The Elements of a Digital Twin

The essential elements of a digital twin are:

■ Model: The digital twin is a functional, system model of the real-world object. The digital twin includes the real-world object's data structure, metadata and critical variables. More complex, composite digital twins can be assembled from simpler atomic digital twins.

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https://www.cbinsights.com/research/ar-vr-startup-funding/

https://www.theverge.com/2017/9/20/16329366/ios-11-apple-arkit-best-new-ar-apps

https://arstechnica.co.uk/gadgets/2017/08/googles-arcore-brings-augmented-reality-to-millions-of-android-devices/

https://www.cbinsights.com/research/blockchain-ico-vc-funding-pace/

This research note is restricted to the personal use of [email protected].

■ Data: The digital twin's data elements relating to the real-world object include: identity, time series, current data, contextual data and events.

■ Uniqueness: The digital twin corresponds to a unique physical thing.

■ Ability to monitor: You can use the digital twin to query the state of the real-world object or receive notifications (for example, based on an API) in coarse or granular detail.

Note 2 Virtual, Augmented and Mixed Reality

The differences between VR, AR and MR are:

■ VR uses computer-generated (digital) environments to fully immerse users in a virtual "world."

■ AR overlays digital information on the physical world.

■ MR blends the physical and digital worlds in which users may interact with digital and real- world objects while maintaining presence in the physical world.

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Analysis

Trend No. 1: AI Foundation

Today's AI Is Narrow AI

Trend No. 2: Intelligent Apps and Analytics

Augmented Analytics Will Enable Users to Spend More Time Acting on Insights

Trend No. 3: Intelligent Things

Swarms of Intelligent Things Will Work Together

Trend No. 4: Digital Twins

Digital Twins Will Be Linked to Other Digital Entities

Trend No. 5: Cloud to the Edge

Edge Computing Brings Distributed Computing Into the Cloud Style

Trend No. 6: Conversational Platforms

Integration With Third-Party Services Will Further Increase Usefulness

Trend No. 7: Immersive Experience

VR and AR Can Help Increase Productivity

Trend No. 8: Blockchain

Blockchain Offers Significant Potential Long-Term Benefits Despite Its Challenges

Trend No. 9: Event-Driven Model

Events Will Become More Important in the Intelligent Digital Mesh

Trend No. 10: Continuous Adaptive Risk and Trust

Barriers Must Come Down Between Security and Application Teams

Gartner Recommended Reading
List of Figures

Figure 1. Top 10 Strategic Technology Trends for 2018
Figure 2. Narrow AI's Place in the Long History of AI
Figure 3. Augmented Analytics for Citizen and Professional Data Scientists
Figure 4. Intelligent Things Span Many Sectors
Figure 5. Digital Twins Are Digital Representations of Real-World Objects
Figure 6. Digital-Twin Models Will Expand to More Than Just Things
Figure 7. Cloud and Edge Computing Are Complementary Concepts
Figure 8. Conversational Platforms Include New User Experience Design Elements
Figure 9. The Future of the User Experience
Figure 10. Key Elements of Blockchain
Figure 11. Event-Driven and Request-Driven Application Design Models Are Complementary
Figure 12. The DevSecOps Model

Optimization and Simulation Modeling

Chapter 9

9 | *

Learning Objectives

Formulate and solve linear programming problems.

Describe the use of computer simulation modeling in operations decision making.

9 | *

Linear Programming Helps Kellogg’s Optimize Production, Inventory, and Distribution

Kellogg’s must manage a highly complex production, inventory control, and distribution system.
Kellogg’s employs an enterprise resource planning (ERP) system to coordinate its raw material purchases, production, distribution, and demand.
The many different varieties of products and brands, packaged in many different sizes and produced at several different plants, require the use of an optimization approach known as linear programming.
The innovative use of optimization techniques has allowed the company to develop a system that is estimated to save between $35 million and $40 million annually.

9 | *

Discussion Starter

What benefits can we receive from simulations and modeling?

9 | *

Operations Research
or Management Science

Operations research or management science: the use of interdisciplinary scientific methods such as mathematical modeling, statistics, and algorithms that aid decision making for complex real-world problems of coordination and execution of the operations in an organization
The goal is to derive the best possible solution to a problem or to optimize the performance of the organization.

Source: © Image Source/Corbis

9 | *

Introduction

Many management decisions involve making the most effective use of limited resources
Linear programming (LP)

Widely used mathematical modeling technique

Planning and decision making relative to resource allocation

Broader field of mathematical programming

Here programming refers to modeling and solving a problem mathematically

9 | *

Requirements of a
Linear Programming Problem

Four properties in common

Seek to maximize or minimize some quantity (the objective function)

Restrictions or constraints are present

Alternative courses of action are available

Linear equations or inequalities

9 | *

LP Properties and Assumptions

TABLE 7.1

PROPERTIES OF LINEAR PROGRAMS

1. One objective function

2. One or more constraints

3. Alternative courses of action

4. Objective function and constraints are linear – proportionality and divisibility

5. Certainty

6. Divisibility

7. Nonnegative variables

9 | *

Formulating LP Problems

Developing a mathematical model to represent the managerial problem
Steps in formulating a LP problem

Completely understand the managerial problem being faced

Identify the objective and the constraints

Define the decision variables

Use the decision variables to write mathematical expressions for the objective function and the constraints

9 | *

Formulating LP Problems

Common LP application – product mix problem
Two or more products are produced using limited resources
Maximize profit based on the profit contribution per unit of each product
Determine how many units of each product to produce

9 | *

Flair Furniture Company

Flair Furniture produces inexpensive tables and chairs
Processes are similar, both require carpentry work and painting and varnishing

Each table takes 4 hours of carpentry and 2 hours of painting and varnishing

Each chair requires 3 of carpentry and 1 hour of painting and varnishing

There are 240 hours of carpentry time available and 100 hours of painting and varnishing

Each table yields a profit of $70 and each chair a profit of $50

9 | *

Flair Furniture Company

The company wants to determine the best combination of tables and chairs to produce to reach the maximum profit

TABLE 7.2

HOURS REQUIRED TO PRODUCE 1 UNIT
DEPARTMENT	(T) TABLES	(C) CHAIRS	AVAILABLE HOURS THIS WEEK
Carpentry	4	3	240
Painting and varnishing	2	1	100
Profit per unit	$70	$50

9 | *

Flair Furniture Company

The objective is

Maximize profit

The constraints are

The hours of carpentry time used cannot exceed 240 hours per week

The hours of painting and varnishing time used cannot exceed 100 hours per week

The decision variables are

T = number of tables to be produced per week

C = number of chairs to be produced per week

9 | *

Flair Furniture Company

Create objective function in terms of T and C

Maximize profit = $70T + $50C

Develop mathematical relationships for the two constraints

For carpentry, total time used is

(4 hours per table)(Number of tables produced)
+ (3 hours per chair)(Number of chairs produced)

First constraint is

Carpentry time used ≤ Carpentry time available

4T + 3C ≤ 240 (hours of carpentry time)

9 | *

Flair Furniture Company

Similarly

Painting and varnishing time used
≤ Painting and varnishing time available

2 T + 1C ≤ 100 (hours of painting and varnishing time)

This means that each table produced requires two hours of painting and varnishing time

Both of these constraints restrict production capacity and affect total profit

9 | *

Flair Furniture Company

The values for T and C must be nonnegative

T ≥ 0 (number of tables produced is greater than or equal to 0)

C ≥ 0 (number of chairs produced is greater than or equal to 0)

The complete problem stated mathematically

Maximize profit = $70T + $50C

subject to

4T + 3C ≤ 240 (carpentry constraint)

2T + 1C ≤ 100 (painting and varnishing constraint)

T, C ≥ 0 (nonnegativity constraint)

9 | *

Graphical Solution to an
LP Problem

Easiest way to solve a small LP problems is graphically
Only works when there are just two decision variables

Not possible to plot a solution for more than two variables

Provides valuable insight into how other approaches work
Nonnegativity constraints mean that we are always working in the first (or northeast) quadrant of a graph

9 | *

Graphical Representation
of Constraints

This Axis Represents the Constraint T ≥ 0

This Axis Represents the Constraint C ≥ 0

FIGURE 7.1 – Quadrant Containing All Positive Values

100 –

–

80 –

–

60 –

–

40 –

–

20 –

–

| | | | | | | | | | | |

0 20 40 60 80 100

Number of Chairs

Number of Tables

9 | *

Graphical Representation
of Constraints

The first step is to identify a set or region of feasible solutions
Plot each constraint equation on a graph
Graph the equality portion of the constraint equations

4T + 3C = 240

Solve for the axis intercepts and draw the line

9 | *

Graphical Representation
of Constraints

When Flair produces no tables, the carpentry constraint is:

4(0) + 3C = 240

3C = 240

C = 80

Similarly for no chairs:

4T + 3(0) = 240

4T = 240

T = 60

This line is shown on the following graph

9 | *

Graphical Representation
of Constraints

(T = 0, C = 80)

FIGURE 7.2 – Graph of Carpentry Constraint Equation

(T = 60, C = 0)

100 –

–

80 –

–

60 –

–

40 –

–

20 –

–

| | | | | | | | | | | |

0 20 40 60 80 100

Number of Chairs

Number of Tables

9 | *

FIGURE 7.3 – Region that Satisfies the Carpentry Constraint

Graphical Representation
of Constraints

Any point on or below the constraint plot will not violate the restriction
Any point above the plot will violate the restriction

100 –

–

80 –

–

60 –

–

40 –

–

20 –

–

| | | | | | | | | | | |

0 20 40 60 80 100

Number of Chairs

Number of Tables

(30, 40)

(30, 20)

(70, 40)

9 | *

Graphical Representation
of Constraints

The point (30, 40) lies on the line and exactly satisfies the constraint

4(30) + 3(40) = 240

The point (30, 20) lies below the line and satisfies the constraint

4(30) + 3(20) = 180

The point (70, 40) lies above the line and does not satisfy the constraint

4(70) + 3(40) = 400

9 | *

Graphical Representation
of Constraints

(T = 0, C = 100)

FIGURE 7.4 – Region that Satisfies the Painting and Varnishing Constraint

(T = 50, C = 0)

100 –

–

80 –

–

60 –

–

40 –

–

20 –

–

| | | | | | | | | | | |

0 20 40 60 80 100

Number of Chairs

Number of Tables

9 | *

Graphical Representation
of Constraints

To produce tables and chairs, both departments must be used
Find a solution that satisfies both constraints simultaneously
A new graph shows both constraint plots
The feasible region is where all constraints are satisfied

Any point inside this region is a feasible solution

Any point outside the region is an infeasible solution

9 | *

Graphical Representation
of Constraints

FIGURE 7.5 – Feasible Solution Region

Painting/Varnishing Constraint

Carpentry Constraint

Feasible Region

100 –

–

80 –

–

60 –

–

40 –

–

20 –

–

| | | | | | | | | | | |

0 20 40 60 80 100

Number of Chairs

Number of Tables

9 | *

Graphical Representation
of Constraints

For the point (30, 20)

For the point (70, 40)

Carpentry constraint	4T + 3C ≤ 240 hours available (4)(30) + (3)(20) = 180 hours used
Painting constraint	2T + 1C ≤ 100 hours available (2)(30) + (1)(20) = 80 hours used

Carpentry constraint	4T + 3C ≤ 240 hours available (4)(70) + (3)(40) = 400 hours used
Painting constraint	2T + 1C ≤ 100 hours available (2)(70) + (1)(40) = 180 hours used





9 | *

Graphical Representation
of Constraints

For the point (50, 5)

Carpentry constraint	4T + 3C ≤ 240 hours available (4)(50) + (3)(5) = 215 hours used
Painting constraint	2T + 1C ≤ 100 hours available (2)(50) + (1)(5) = 105 hours used





9 | *

Linear Programming

Optimization: arriving at a maximum or minimum point of a mathematical function
Constraints: the necessary conditions that must be met when a mathematical function is being optimized
Linear programming: a special formulation of an optimization problem in which all equations and inequalities are linear

9 | *

Formulation of a
Linear Programming Problem

Five main components of a linear programming

problem:

Objective function: A mathematical formulation of the criterion by which all decisions should be evaluated.

Decision variables: The parameters that can be changed by the decision makers to achieve a higher or lower value of the objective function.

Constraints: The necessary conditions that must be met when a mathematical function is being optimized

Linearity: When formulating linear programming problems, all mathematical equations or inequalities are represented as straight lines.

Non-negativity: Each decision variable within a linear programming formulation is assumed to take only nonnegative values, although this is not essential.

9 | *

Solution of a Linear Programming Problem by a Graphical Method

A simple linear programming problem can be solved by using a graphical method.
Because all the equations within a linear programming problem are either straight lines or inequalities, the constraints are first plotted to find the region that satisfies all conditions.
Once that region is identified, the researcher evaluates the objective function at each corner of the feasible region.

Source: © Image Source/Corbis

9 | *

Mount Sinai Hospital: Scheduling Operating Rooms by Integer Linear Programming

Mount Sinai Hospital in Toronto, Canada, has 14 operating rooms and five departments using the OR.
To address the problem of scheduling operating rooms effectively, Mount Sinai Hospital now uses a constrained-optimization model know as integer linear programming.
Since implementing this approach, the hospital has seen a reduction in the number of conflicts in scheduling operating room times and saves $20,000 annually.
In the end, a better schedule means more effective care for Mount Sinai Hospital’s patients.

9 | *

PLATO Helps Athens Win Gold During 2004 Summer Olympic Games

During the 2004 Olympic Games in Athens, over the course of 16 days, more than 2,000 athletes participated in 300 events in 28 different sports across 36 venues located across the city.
The events were watched by 3.6 million spectators in the stadiums, 22,000 journalists, and 2,500 members of the international committees.

9 | *

PLATO Wins Gold!

The organizing team for the Athens Olympics developed PLATO, the Process Logistics Advanced Technical Optimization approach.

The PLATO project:

Developed business process models for the various venues.
Developed computer simulation models that enabled managers to conduct a variety of what-if analyses.
Developed software that guided the Olympic Committee personnel in using the business process and simulation models.

9 | *

VOLCANO Saves $187 Million for UPS

UPS carries more than 13 million packages to more than 8 million customers in more than 200 countries daily.
UPS, along with a team of researchers from MIT, developed and implemented Volume, Location and Aircraft Network Optimization (VOLCANO), an optimization-based planning system that is transforming the business process within UPS.
The VOLCANO system is an interactive transportation modeling and optimization approach.
Prior to VOLCANO, it used to take planners up to nine months to develop a single transportation plan for UPS airline operations manually.
VOLCANO is expected to save more than $189 million for UPS within the next decade.

9 | *

Simulation Modeling

Computer simulation models are used in decision making because testing proposed new operating procedures in an actual operation is expensive, complicated, and risky.
Computer simulation models allow the user to try out different strategies without actually implementing them in practice.
Simulation models allow managers to evaluate multiple operations designs and perform what-if types of analyses.
Simulation replaces the wasteful and unreliable practice of testing managers’ ideas through trial-and-error methods.

9 | *

Numerical Simulation

Numerical Simulation: simulating outcomes that are controlled by chance, but where the state of the system at specific times is not of interest
Numerical simulations can typically be performed in computer-spreadsheet software.

9 | *

Discrete-Event Simulation

Discrete-event simulation: a type of simulation that is applicable when the state of a system over time is the major concern; the term discrete event describes the nature of such systems, where the system changes at discrete times when particular events occur
Three major uses:

Validating other models

Process design

Management decision-making games

9 | *

Building Simulation Models

Spreadsheet-Based Models
Simulation-Modeling Tools

9 | *

Advantages and Disadvantages
of Simulation-Modeling

Advantages:

The ability to create complex discrete-event models without programming knowledge

The speed at which models can be created by experienced modelers

Disadvantages:

A slow speed of execution

The cost of the software

can be in the tens of thousands of dollars per copy

9 | *

More Disadvantages?

Simulation, like an quantitative-decision aid, is dependent on accurate data.
Because simulation models are relatively easy to create, simulation is often overused.

9 | *

Computer Simulation
of Check Process Operations

One of the largest commercial banks in the U.S. asked one of the authors of this text to develop a simulation model of the upgrade and redesign plan for its check processing operations at its central check processing facility in Chicago.
The objective of the project: Was it worthwhile to spend more than $1 million for new equipment for check process operations?
It was essential that the check processing operation complete its daily work in a timely manner so that customer accounts could be posted and online balance information updated for branch operations.

9 | *

Discussion Starter

What can we use process flowcharts for in companies?

9 | *

Figure 9.10: Check
Processing Workflow Schematic

9 | *

Computer Simulation of
Check Processing Operations

The reject repair and balancing process is used to manually process any check that the automatic sorter is not able to read.
Animated simulation models were developed to understand the old and new reject repair processes.

A model is only an abstraction of reality. Therefore, models should include all essential and relevant elements of the real system and leave the nonessential elements out.

Simulation can be used to model the effects of such new technology.

9 | *

Figure 9.11: Old Check Reject
Repair Process Simulation Model

9 | *

Figure 9.12: New Check Reject
Repair Process Simulation Model

9 | *

Tables 9.9 and 9.10: Summarized Simulation Results for Current and New Process

Dependent
Demand Inventory

Chapter 7

7 | *

Learning Objectives

Contrast dependent and independent demand, and trace the development of material requirements planning (MRP).

Explain the inputs to an MRP system.

Compute single-level MRP records.

Compute multiple-level MRP records and explain the outputs generated.

Describe the evolution of MRP to enterprise resource planning (ERP) and identify ways in which ERP is utilized to integrate all the functions of an organization.

Explain how dependent demand is handled in service organizations and describe the use of technology.

Define three critical features for success with ERP.

7 | *

Dependent Demand at Kellogg’s

Kellogg’s employs dependent demand planning techniques, including material requirements planning.
Every 2 months, a plan is developed for all production items in a given group of plants.
For the morning foods division, Kellogg’s develops a plan for three plants that produce Pop-Tarts.
The plan calls for 61,500 boxes of Hot Fudge Sundae Pop-Tarts and 54,000 boxes of Strawberry Pop-Tarts, along with other varieties during one week.

7 | *

History of Dependent Demand Planning

Independent demand: demand for items that are considered end items that go directly to a customer, and for which demand is influenced by market conditions and not related to inventory decisions for an other item
Dependent demand: demand for items that are used to make another item or are considered to be parts of another item

7 | *

Material Requirements Planning (MRP)

MRP: a computer-based system that develops plans for ordering and producing dependent demand items.

MRP utilizes two basic principles:

Requirements for dependent demand items are derived from the production schedule for their parents (the items that are assembled from component parts).

The production order is offset to account for the lead time.

7 | *

Figure 7.3: Demand Pattern for Independent versus Dependent Items

7 | *

Material Requirements Planning (MRP)

MRP is a technique that
has been employed since the 1940s and 1950s.
Joe Orlicky is known as
the Father of MRP
The use and application
of MRP grew through the 1970s and 1980s as the power of computer hardware and software increased.
MRP gradually evolved into a broader system called manufacturing resource planning (MRP II).

Source: © Image Source/Corbis

7 | *

MRP Inputs

Material requirements plan: a plan that specifies the timing and size of new production orders, adjustments to existing order quantities, and expediting or delay of late/early orders

 Developed through a combination of three inputs:

The Master Schedule

The Bill of Materials

Inventory Records

7 | *

Material Requirements Plan

Material requirements plan: a plan that specifies the timing and size of new production orders, adjustments to existing order quantities, and expediting or delay of late/early orders.
The process of developing the material requirements plan is call MRP explosion; it is a technique for converting the requirements of final products into a material requirements plan that specifies the production/order quantities and timing for all subassemblies, components, and raw materials needed by final products.

7 | *

Figure 7.4: Material
Requirements Plan Inputs

7 | *

The Master Schedule

Master schedule (MS): a document that details the quantity of end items to be produced within a specified period of time
Objectives:

The MS must balance the workload for a given company in terms of not only total capacity, but also capacity at each workstation and for each worker.

The MS seeks to minimize total cost and provides a way of assessing the impact of new orders and providing delivery dates for accepted orders.

The planned production quantities in the MS are intended to satisfy demand, which is estimated based on computer orders and forecasts.

The MS is should be frozen or unchangeable in the near term.

The goal is to plan production but allow some flexibility to change

orders as demand or customer requirements change.

7 | *

Discussion Starter

What may require you to change the MPS?

7 | *

Table 7.1: Master
Schedule for a Family of Bicycles

7 | *

Key Aspects of Master Scheduling

The sums of the quantities in the MS must equal those in the aggregate production plan.
Aggregate production quantities should be planned efficiently over time in order to minimize setup, production, and inventory costs.
Capacity limitations must be considered before finalizing the MS, including labor and machine capacity, storage space, transportation equipment, and other factors.

7 | *

The Bill of Materials

Bill of materials (BOM): a document that specifies all assemblies, subassemblies, parts, and raw materials that are required to produce one unit of the finished product

7 | *

Figure 7.5: Partial Bill
of Materials for a Bicycle

7 | *

Bill of Materials for a Bicycle

Every part in a bill of materials is assigned a level.
End items or finished products that are sold directly to an end customer are Level 0.
The handle bars, frame assembly and sear are Level 1 parts that are components of a complete bicycle.
The wheels and frame are Level 2 parts that are components of the frame assembly.
The spokes and tire rim are Level 3 components of the frame.

7 | *

Figure 7.6: Product
Structure Tree for Item A

7 | *

BOMs

Common parts: parts that are used in more than one place in a single product or in more than one product
Low-level coding: involves assigning a part to the lowest level at which it appears anywhere in the BOM
It is critical that the bill of materials is an accurate representation of the parts required to produce a product, since errors at one level are magnified when they are multiplied by parts requirements at lower levels.
Attention to detail and accuracy, combined with periodic updates and checks of BOMs, are essential if an MRP system is to work effectively.

7 | *

Figure 7.7: Bill of
Materials with a Buried Component

7 | *

Inventory Records

Inventory record: a document that specifies order/lot size policy and lead time and records all transactions made for parts, assemblies, and components

Includes: transactions made for parts, assemblies, and components both from manufacturing within an organization and from purchasing items from external suppliers

Inventory transaction: any change in the quantity of a specific part or material

Includes: receipt of new orders, shipment of complete orders, scrapping of defective parts, release of new orders, adjustment of due dates for scheduled receipts, cancellation of orders, and confirmation of scrap losses and returns.

7 | *

MPR Processing—Creating an
Inventory Record for a Single Item

Developing Inventory Records for Single Items
Determining Planning Factors

7 | *

Developing Inventory
Records for Single Items

Planning factors: three parameters—lot size, lead time, and safety stock—that are chosen by managers utilizing the MRP system
Lot size: the quantity of a part to be produced or ordered when additional inventory is required
Lead time: the time between when an order is placed and when it is expected to arrive or be finished
Safety stock: excess inventory that a company holds to guard against uncertainty in demand, lead time, and supply

7 | *

Planning Factors for an MRP Record

The planning factors for an MRP record are fairly constant—they are entered into the system once and may not be updated for months or years.
Time buckets: the periods of time into which an MRP record is divided
Planning horizon: the time period in the future that the MRP system plans for
Beginning inventory: the amount of inventory that was physically in stock at the end of the most recent time bucket

7 | *

Gross Requirements/Scheduled Receipts

Gross requirements: the total number of units of a part or material derived from all parent production plans
Scheduled receipts: orders that have been placed but not yet received or completed

7 | *

Projected On-hand Inventory

Projected on-hand inventory: the estimated inventory that will be available after the gross requirements have been satisfied, plus any planned or scheduled receipts for that time bucket

Abbreviated: projected OH inventory

Is adjusted according to every inventory transaction

7 | *

Table 7.3: Illustration
of Projected On-hand Inventory

7 | *

Planned Receipts

Planned receipts: future orders that which have not yet been released but are planned in order to avoid a shortage or backlog of inventory

7 | *

Planned Order Release

Planned order release: when an order must be released in order to offset for the lead time so that the order will be received when planned
The difference between a planned and a scheduled receipt: a planned receipt is not firmly committed to and can be changed relatively easily up until the time the order is released.
As soon as the order is released, it becomes a scheduled order, which is much harder to change.

7 | *

Determining Planning Factors

Every MRP record includes three planning factors:

Lead time

Lot size

Safety stock

These are called planning factors because the

decisions managers make regarding these

quantities have a large impact on how well the

MRP system, and by extension the entire inventory

system and supply chain, functions.

7 | *

Lead Time

Lead time is an estimate of the time between releasing an order and receiving that order.
Accuracy in lead times is very important, since early or late orders can greatly affect other items and production schedules through excessive inventory holding costs or shortage, stock-out, and expediting costs.

7 | *

Lead Time

For items that are manufactured or produced within the company, the lead time must take into account a number of factors, including:

Set up time

Processing time

Materials handling time

Waiting time

7 | *

Discussion Starter

Lead Time = Inventory

What do we mean by this?

7 | *

Lot Size

Lot size rules determine:

the size of the order placed, and by extension the timing of orders,

the frequency of set-ups, and

the inventory holding costs for an item.

Three types:

Fixed order quantity

Periodic order quantity

Lot for lot

7 | *

Fixed Order Quantity

Fixed order quantity (FOQ): a lot size rule with a constant order size where the same quantity is ordered every time
The FOQ can be determined by a desire to:

work with equipment capacity, such as when a certain machine has a capacity limit.

mimic the EOQ

make planning consistent

receive a quantity discount

minimize shipping costs

reach a minimum purchase quantity

7 | *

Periodic Order Quantity

Periodic order quantity (POQ): a lot size rule with a variable lot size designed to order exactly the amount required for a specified period of time

Equation:

POQ Lot Size to Arrive in Period t =

(Gross Requirements for P Periods, Including Period t) – (Projected On-Hand Inventory at End of Period t – 1) + (Safety Stock) of time of time

7 | *

Lot for Lot (L4L)

Lot for lot (L4L): a lot size rule that is a special case of the periodic order quantity with the period equal to 1

Equation:

L4L Lot Size to Arrive in Period t =

(Gross Requirements in Period t) – (Projected On-Hand Inventory at End of Period t – 1) + (Safety Stock)

7 | *

Table 7.8: MRP Record with L4L Order

7 | *

Lot Size Rules Summary

The FOQ rule has the highest average inventory because its fixed nature creates inventory remnants.

The POQ rule reduces the amount of OH inventory by matching gross requirements with planned receipts.

The L4L rule always minimizes inventory, but also requires more frequent setups/orders.

7 | *

Safety Stock

It would seem that an MRP inventory system should not require safety stock.

Why is safety stock necessary?

There may be bottlenecks or blockages that prevent orders from being complete on a timely basis.

Quality problems often arise where an order will be only 95 percent filled.

Humans may enter incorrect information into the system.

There is variability in demand, and the master schedule is made to match forecasts.

7 | *

MRP Nervousness

MRP nervousness: a situation in MRP planning where a change at one part level ripples down to affect lower-level parts

7 | *

MRP Explosion

MRP explosion: the process of translating MRP inputs into a plan that specifies required quantities and timing of all subassemblies, components, and raw materials required to produce parent items

7 | *

Action Notices

Action notice: a notice that is generated when an order needs to be released or placed or when the quantity or timing of an order needs to be changed

7 | *

MRP as a Dynamic System

Two approaches to updating:

Periodic update: an approach to updating that involves collecting all new or updated information and processing it once a week or once a day

Net change update: an approach to updating that makes changes as soon as they occur

7 | *

Figure 7.10: Illustration
of a Rolling MRP Schedule

7 | *

Capacity Planning

There are three approaches for managing capacity and ensuring that the MRP plan is feasible:

Capacity requirements planning

Finite capacity scheduling

Input/Output reports

7 | *

Capacity Requirements Planning

Capacity requirements planning: the process of determining short-range capacity requirements based on a tentative MRP plan

Short range generally refers to the next one to three months.

Inputs include the planned order releases generated from the MRP system, workloads at each work center, routing information, and job setup/processing times.
The master schedule and the MRP plan are usually generated by looking at what is needed to support sales, rather than what is possible.
Load report: a report for a department or work center that projects already scheduled and expected future capacity requirements against capacity availability

7 | *

Figure 7.11: MRP with
Capacity Planning Requirements

7 | *

Strategies for Dependent
Demand Inventory

There are strategic keys to making MRP work effectively.

Evolution of MRP to Enterprise Resource Planning

Service Resource Planning

Making MRP/ERP Work

7 | *

Evolution of MRP to
Enterprise Resource Planning

Manufacturing resource planning (MRP II): a system that links the basic MRP system to other company systems, including finance, accounting, purchasing, and logistics
Enterprise resource planning (ERP): a system that provides a complete linkage of all functional areas of a business

Allows manufacturing to see new orders as soon as marketing or sales enters them into the system.

7 | *

Service Resource Planning

Planning and control for manufacturing firms is focused on tangible goods, whereas services require more of a mix of intangible and tangible goods.
However, the concept of dependent demand also applies to services where the demand for a service is based on the demand for a parent item.

7 | *

[Need a heading]

Maintenance, repair and operating supplies (MRO): items that a store or business requires to run the business
Yield management: a management technique that offers customer incentives to shape their demand patterns

7 | *

Customer Relationship Management

Customer relationship management (CRM): a system of planning and control activities and information systems that link an organization with its downstream customers
Is the equivalent of MRP
Consists of 3 components:

Operational CRM

Collaborative CRM

Analytical CRM

7 | *

Making MRP/ERP Work

Dependent demand planning and material requirements

planning are critical components for manufacturing

businesses.

Three key factors contribute to success:

The hardware and software have to be carefully set up to fit with the organization’s method of doing business.

The users of the system (employees) need to be thoroughly trained in the system.

The input data need to be close to 100 percent accurate because MRP will magnify any inconsistencies.

Independent
Demand Inventory

Chapter 6

6 | *

Learning Objectives

List the reasons for and against having inventory.

Learn the basic types of inventory.

Apply the continuous review and periodic review systems. Describe other important inventory systems.

6 | *

The Wal-Mart Effect

Wal-Mart became the world’s largest retailer in large part because of its excellent management of inventory.
The company:

has sales of over $300 billion and operates more than 3,500 stores in the US (and more than 2,500 stores in 15 other countries).

employs more than 1.3 million people.

has more than 6,000 stores and 10,000 stock-keeping units (SKUs) at each stores.

manages 60 million individual stocking locations and at least a quarter of a million line-item orders per day.

6 | *

The Wal-Mart Effect

Wal-Mart’s inventory decisions have been dubbed “the Wal-Mart Effect.”
Wal-Mart announced a major effort to reduce its inventory costs by $6 billion in 2006, or 20 percent of its yearly total, and suppliers took notice.
Wal-Mart accounts for 10 to 30 percent of many suppliers’ sales.
The correction of Wal-Mart’s inventory also affects shippers, with estimates of a $300 to $400 million reduction in freight revenue.
Wal-Mart’s inventory reduction reflects its strategy of cutting costs and improving margins.

6 | *

Inventory

Inventory: the physical stock of any items or resources used in an organization
The objective of an inventory system is to specify:

When items should be ordered.

What quantity of each item should be ordered.

In manufacturing, types of inventory include raw materials, work in process, finished goods, component parts and supplies.
In services, inventory refers to tangible goods that are sold as part of the service and maintenance, repair, and operating (MRO) supplies that are necessary.

6 | *

Inventory Equilibrium

Determining the proper amount of inventory
Similar to balancing a scale
Assesses the benefits of carrying larger amounts of inventory against the drawbacks and the risks of carrying that inventory

6 | *

Discussion Starter

What purpose does inventory serve for companies?

6 | *

Reasons to Carry Inventory

Set-up and ordering costs
Customer service and variation in demand
Labor and equipment utilization
Transportation cost
Costs of materials/quantity discounts

6 | *

Reasons to Reduce Inventory

Storage and Handling
Interest and opportunity cost
Property taxes and insurance premiums
Shrinkage and spoilage

6 | *

Inventory Types

There are several basic types of inventory:

Cycle

Safety stock

Anticipation

Pipeline

Work-in-process

Remanufactured/reconditioned

6 | *

Cycle Inventory

Cycle inventory: a quantity of inventory that varies in proportion to order quantity

6 | *

Figure 6.2: Cycle Inventory

6 | *

Safety Stock Inventory

Safety stock inventory: excess Inventory that a company holds to guard against uncertainty in demand, lead time, and supply

6 | *

Anticipation Inventory

Anticipation inventory: inventory that is held for future use at a time when demand will exceed available capacity

6 | *

Pipeline Inventory

Pipeline inventory: inventory that is in the process of moving from one location in the supply chain to another

6 | *

Work-in-process (WIP) Inventory

Work-in-process inventory: inventory that is in the process of being transformed from one state to another
It cannot be sold to a customer because it is not yet finished.

6 | *

Remanufactured/reconditioned

Remanufactured/reconditioned inventory: products that have been used by a customer and then reacquired by a company and either remanufactured or reconditioned for resale

What are some examples of

remanufactured inventory?

6 | *

Independent versus
Dependent Demand

Independent demand: demand for items that are considered end items that go directly to a customer, and for which demand is influenced by market conditions and not related to inventory decisions for any other item.
Dependent demand: demand for items that are used to make another item or are considered to be component parts

6 | *

Independent/Dependent Demand Items

Penfield Arrow Company makes arrows for the hunting industry.

There are three components that make up it’s arrow: the arrowhead, shaft, and feathers.

Feather Shaft Arrowhead

Is the arrow an independent item, or are the feathers, shaft, and arrowhead independent?

Arrow

6 | *

Inventory Systems

An inventory system provides the structure and operating policies for maintaining and controlling goods to be stocked in inventory.
The system is responsible for ordering, tracking, and receiving goods.

There are two essential policies:
1. How much or what quantity of an item to order?

2. When should an order for that item be placed?

6 | *

Figure 6.3: Inventory
Management as a Water Tank

6 | *

Discussion Starter

Water Tank Analogy – What does the water hide?

6 | *

Two Types of Systems

Continuous review system: an inventory system that always orders the same quantity of items but has differing periods of time between orders
Periodic review system: an inventory system that has a fixed time between orders but has different order quantities from order to order

6 | *

Table 6.1: Differences
Between Inventory Systems

6 | *

Continuous Review Systems

Inventory position: on-hand inventory plus outstanding orders, minus any backorder quantities (items promised to a customer but not yet delivered)

6 | *

Setting the Order Quantity

The goal for the inventory system is to minimize total annual inventory cost:
The total annual inventory cost is the

sum of the cost of holding inventory

and the cost of ordering inventory.

IC = Total Annual Cost

D = Annual Demand Q = Order quantity

S = Setup or ordering cost H = Holding cost

6 | *

Figure 6.4: Annual Inventory Costs

6 | *

Economic Order Quantity

Economic order quantity: the order quantity that minimizes the total annual cost of ordering and holding inventory for a particular item

EOQ = Economic Order Quantity

D = Annual Demand S = Setup or ordering cost H = Holding cost

Go to Page 205 for Example

6 | *

Setting the Reorder
Point with Certain Demand

Reorder point (R): the predetermined level that an inventory position must reach for an order to be placed
Lead time (L): is the time between when an order is placed and when it is expected to arrive or be finished

6 | *

Inventory Position

IP = OH + SR - BO

Inventory Position (IP): The timing or place determined

to be the point an order is to be placed

SR - Scheduled Receipt:

Order has been placed but not yet received

OH – On Hand Delivery:

Amount of a unit that is physically available

BO – Backorder:

Order promised to a customer but is not currently

in inventory

6 | *

Figure 6.5: Cycle
Inventory When Demand is Certain

6 | *

Figure 6.6: Demand, Orders, and Actual versus Calculated Inventory Position

6 | *

Figure 6.7: Reorder
Point When Demand is Uncertain

6 | *

Figure 6.8: Finding Safety
Stock with a Normal Probability
Distribution with Low and High Variance

6 | *

Special Cases of the
Economic Order Quantity

Two relevant costs: ordering/setup (S) and holding (H)
Constant demand
Item independence
Certainty in demand, lead time and supply
Some version of the EOQ is used in almost every company or organization dealing with inventory.
The EOQ works despite the fact that the assumptions are not strictly true for two reasons:

It is relatively insensitive to errors because it involved a square root.

The EOQ works despite its slightly flawed assumptions that adjustments for uncertainty are included in the overall inventory system.

Assumption 3 states that items are independent.

6 | *

EOQ with Quantity Discounts

Many companies offer discounted pricing for items that they sell.
This is done to take advantage of economies of scale in production and in the supply chain.

6 | *

EOQ with Discounts

Calculate the EOQ:

Arrange the prices from lowest to highest. Starting with the lowest price, calculate the EOQ for each price until a feasible EOQ
is found.
If the first feasible EOQ is for the lowest price, this quantity is optimal and should be used.

6 | *

Figure 6.9: Quantity
Discount Cost Curve

6 | *

Technological Applications

The biggest change in inventory management over the past 20 years is the ability of companies to use sophisticated software and hardware to track and monitor inventory very closely.
Enterprise Resource Planning (ERP): a large, integrated information system that supports most enterprise processes and data storage needs across the entire organization
Radio Frequency Identification (RFID): an automatic identification method that relies on storing and remotely retrieving data using devices such as RFID tags or transponders

6 | *

Periodic Review Systems

A periodic review system (P-system) requires a precise counting of inventory only at specific times, often once a week, once a month, or once a quarter.
One situation in which a P-system is useful is when a manufacturer such a Pepsi or Kellogg’s assigns drivers to various stores.

6 | *

VMI and EDI

Vendor-managed inventory (VMI): vendors monitor sales at the retailer and replenish inventories when supplies are low
Electronic data interchange (EDI): a technology that allows companies or units within a company to exchange orders, forecasts, and invoices electronically without human intervention to enter data into the receiving system

6 | *

Figure 6.10: Periodic Review System

6 | *

Determining the Period and Order Quantity for a Periodic Review System

The period (a convenient time period) may be chosen to approximate the average time between orders (TBO).

6 | *

Target Inventory

Target inventory: the desired quantity of inventory that will cover expected demand during the protection interval plus enough safety stock to provide the desired cycle-service level

Q-System (Continuous Review)	P-System (Periodic Review)
How much to order	Q = 104	Q = T – I = 139 – I
When to order	When Inv ≤ R = 27	Every P = 5 weeks
Protection interval	L = 1 week (lead time)	P + L = 6 weeks (period + lead time)
Safety stock	6 units	13 units

6 | *

Other Types of Inventory Systems

Variations on the basic types of continuous and

periodic reviews:

ABC Systems

Bin Systems

Can Order Systems

Base Stock Systems

The Newsvendor Problem

6 | *

ABC Systems

ABC systems: inventory systems that utilize some measure of importance to classify inventory items and allocate control efforts accordingly
They take advantage of what is commonly called the 80/20 rule, which holds that 20 percent of the items usually account for 80 percent of the value.

Category A contains the most important items.

Category B contains moderately important items.

Category C contains the least important items.

6 | *

ABC Systems

A items make up only 10 to 20 percent of the total number of items, yet account for 60 to 80 percent of annual dollar value.
C items account for 50 to 70 percent of the total number of items, yet account for only 10 to 20 percent of annual dollar value.
C items may well be of high importance, but because they account for relatively little annual inventory cost, it may be preferable to order them in large quantities and carry excess safety stock.

6 | *

Bin Systems

Bin system: a type of inventory system that uses either one or two bins to hold a quantity of the item being inventoried; an order is placed when one of two bins is empty or a line on a single bin is reached

6 | *

Can Order Systems

Can order system: a type of inventory system that reviews the inventory position at fixed time intervals and places orders to bring the inventory up to an expected target level, but only if the inventory position is below a minimum quantity, similar to the reorder point in a continuous review system

6 | *

Base Stock System

Base stock system: a type of inventory system that issues an order whenever a withdrawal is made from inventory

6 | *

Newsvendor Problem

Newsvendor problem: a technique that determines how much inventory to order when handling perishable products or items that have a limited life span
Shortage cost: the lost profit from not being able to make a sale, plus any loss of customer goodwill
Excess cost: the different between the purchase cost of an item and its salvage or discounted value

6 | *

Figure 6.11A: Newsvendor Problem for Pink Swimsuits: Shortage Costs Exceed Excess Costs

6 | *

Figure 6.11B: Newsvendor Problem for Pink Swimsuits: Excess Costs Exceed Shortage Costs

6 | *

Inventory Accuracy

The amount of inventory in the system (i.e., the computer) often differs from the amount of physical inventory that is on hand.
Common approaches for minimizing the problem:

Assign specific employees to issue and receive orders and materials and to enter transaction data.

Place inventory in a locked and secured location.

Cycle counting: a system in which employees physically count a percentage of the total number of items stocked in inventory and correct any errors that are found

Forecasting

Chapter 5

5 | *

Learning Objectives

Explain the need for forecasting of independent demand products.
Describe the basic principles of forecasting.
Discuss the fundamental components of demand and types of forecasting methods.
Demonstrate and apply time-series analysis in forecasting.
Evaluate forecast accuracy and determine the best method.
Discuss the application of qualitative and casual methods for forecasting.
Describe forecasting across the broader supply chain.

5 | *

Forecasting

“Forecasting involves using several different methods of estimating to determine possible future outcomes for the business. Planning for these possible outcomes is the job of operations management. Additionally, operations management involves the managing of the processes required to manufacture and distribute products.” Smallbusiness.chron

5 | *

Special K = Special Challenges

Why is it very challenging to make a profit in the market for processed cereals such as Raisin Bran, Cheerios, Rice Krispies or Wheaties?

There is a great deal of competition.

Sales are growing slowly (1-5% yearly).

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Forecasting Kellogg’s Sales

While Kellogg’s sales are good, forecasting needs to be done in order to plan the number of production and distribution facilities to operate, as well as the size and location of such facilities.
Forecasting affects the scheduling of delivery trucks, labor and marketing campaigns.

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Figure 5.1: Kellogg’s Sales of Medium-Sugar Cereal and Cereal Bars in the United States

Source: Breakfast Cereal, U.S. August, 2007 Supply Structure, Mintel International Group Limited

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Why We Need to Forecast

Forecasts are vitally important to organizations.
They are used to plan facilities, production schedules, staffing allocation, capacity planning, and other things.
The goal of a business forecast is not to have a perfect forecast but to have a reasonable forecast that helps us plan.

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Discussion Starter

How accurate are forecasts?

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Principles of Forecasting

Forecasts are wrong.
Forecasts get worse for farther into the future they go.
Aggregated forecasts for product or service groups tend to be more accurate.

a whole formed by combining several (typically disparate) element

formed or calculated by the combination of many separate units or items; total.

form or group into a class or cluster.

Forecasts are not a substitute for derived values.

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Components of Demand

Demand for products or services consists of several components:

average demand

trend

seasonal component

cyclical component

Autocorrelation

Definition of aggregate: General: Collective amount, sum, or mass arrived-at by adding or putting together all components, elements, or parts of an assemblage ...

random variation

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Figure 5.2: Components of Demand

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Forecasting Methods

Time-series analysis: a technique that utilizes past demand data to predict future demand by examining cyclical, trend and seasonal influences
Casual relationships: a technique that identifies a connection between two factors, one that precedes and causes changes in the second or effect factor
Qualitative forecasting: a method of forecasting that is based on subjective factors, estimates and opinions

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Time-Series Analysis

Is based on historical data and the assumption that past patterns will continue in the future.
Goal: to identify the underlying patterns of demand and develop a model to predict these patterns in the future
Five basic techniques:

Naïve forecast

Estimating the average

Moving averages

Weighted moving average

Exponential smoothing

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Naïve Forecast

Naïve forecast: a method of forecasting that uses the demand for the current period as the forecast for the next period
The naïve forecast is very simple and low cost to use.
Works best when demand, trend and seasonal patterns are stable and there is relatively little random variation.
The naïve approach is the simplest of all the possible forecasting methods and works particularly well when there is autocorrelation.

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Estimating the Average

Every series of demand figures includes at least two of the six components of demand:

an average and

random variation.

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Figure 5.3: Daily
Customers at FoodCo Grocery

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Moving Average

Moving average: a technique for estimating the average of a demand series and filtering out the effects of random variation
Developing a moving average involves computing the average of n previous periods of demand and then using this as the estimate for the next period of demand.
The average is updated after every period to include the most recent demand data.

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Moving Average

Ft+1 =

where Dt = actual demand in period t

n = total number of periods in the average

Ft+1 = forecast for period t + 1

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Moving Average: An Example

Daily Customers at FoodCo Grocery

The moving-average forecast at the end of day 4

for the number of customers on day 5 is

Thus, at the end of day 4, we forecast
that there will be 217 customers on day 5.

217.25

Day	Customers
1	228
2	228
3	225
4	188

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The Moving-Average Technique

Technique used for estimating the average of a demand series and filtering out the effects of random variation
Involves computing the average of n previous periods of demand and then using this as the estimate for the next period of demand (or a period farther out in the future)
Average is updated after every period to include the most recent demand data

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Figure 5.4: Comparison of
Moving-Average Forecast with
Two- and Four-Day Moving Average

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Weighted Moving Average

Weighted moving average: a technique that allows periods to have different weights, with the total weight equaling 1.0 (one)
The benefit of a weighted moving average is that it allows a greater emphasis on the most recent demand than on past demand.

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Weighted Moving
Average: An Example

Ft+1 = 0.6Dt + 0.25Dt-1 + 0.15Dt-2

F4 = 0.6D3 + 0.25D2 + 0.15D1 = 0.6 * 228 + 0.25 * 228 + 0.15* 225 = 226.2

Day	Customers	Weight
1	228	.6
2	228	.25
3	225	.15

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Exponential Smoothing

Exponential smoothing: a technique that calculates forecasts by giving more weight to recent demands than to earlier demands
Used mostly because it is often easier to calculate than a weighted moving average and requires less data.

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Exponential Smoothing

F1 = F t-1 + α(A t-1 – F t-1)

Where: F 1 = the New Forecast

F t-1 = Previous period’s forecast

α = (Alpha) a Smoothing Constant

A t-1 = Previous period’s actual demand

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Exponential Smoothing: An Example

In January, the clothing store estimates February demand for boot cut jeans to be 350. Actual February demand was 310. Using a smoothing constant chosen by the clothing store management of α = .20, what is the forecast for March demand using the exponential smoothing model?

March = last period’s forecast + α(last period’s actual demand – last period’s forecast)

March = 350 + .20(310 – 350) = 350 - 8 = 342

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Including a Trend

Weighted moving averages and exponential smoothing will adjust to a trend if high weights are given to more recent periods.
Trend-adjusted exponential smoothing is a method for including a trend component in an exponentially smoothed forecast.

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Including Seasonality

Many organizations sell products or services that have a seasonal demand.
A seasonal demand is characterized by regular repetition of increases or decreases in demand as measured in time periods of less than a year (quarters, months, weeks, days, or hours).

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Figure 5.7: An Illustration
of Demand for Mousetraps

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Seasonal Components

Airline travel in the United States has substantially higher demand in the summer and holidays.
Forecasting at an aggregate level is often easier than forecasting at a more detailed level.

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Figure 5.8: Airline
Traffic for All U.S. Airlines, 2003–2007

Source: Data drawn from http://www.bts.gov/.

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Multiplicative Seasonal Method

Multiplicative seasonal method: a forecasting method that calculates seasonal factors that are multiplied by an estimate of the average demand to develop a seasonal forecast
Seasonality is measured as a percentage or seasonal index of the average demand for a particular season, which is used to multiply the average value of the series.

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Seasonal Indices

Utilized to decompose the data and to include seasonality in the forecast.
Decomposing the data involves separating the data into a seasonal, an average, and a trend component. This is done by dividing each data point by its corresponding seasonal index (i.e., divide July demand by the July index, August demand by the August index, and so on).
The seasonal indices are then combined with data on average demand and the trend component using a two-step process:

Develop trend estimates for the desired periods.

Add seasonality to the trend estimates by multiplying these trend estimates by the corresponding seasonal index.

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Reducing Seasonality

The highly variable nature of demand for products with high seasonality often influences organizations to be more proactive in trying to reduce seasonality.
One method: advertising during slower-demand periods.
Another method: discounting the product or service in periods of slower demand to increase sales.

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Figure 5.10: Illustration
of Reducing Seasonality

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Measuring Errors and
Selecting a Time-Series Method

The best forecast is less wrong than the next best forecast.
All forecasts are wrong to some extent, so how do we choose the “best” forecast?
Generally, managers will examine a range of forecast types over a period of time and choose the one with the least amount of error.

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Forecast Error

Forecast error: the difference between the forecast and the actual demand for a given period
Forecast errors can be classified as either bias errors or random errors.

Bias errors occur when the forecast is consistently over or under the actual demand.

Random errors result from unpredictable factors and do not exhibit a distinct pattern.

Forecasters try to eliminate as much of both types of error as possible, but some error always remains.

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Measures of Forecast Error

Calculating forecast = the difference between actual demand and the forecast:

Et = Dt – Ft

Et = Forecast error for period T

Dt = Actual Demand for period T

Ft = Forecast for period T

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Cumulative Sum of Forecast Errors

The cumulative sum of forecast errors (CFE) measures total forecast error:

CFE = ΣEt

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Mean Absolute Percentage Error

A measure that reports error in proportion to the demand

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Figure 5.11: Comparison
of Three Forecasting Methods

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Additional Forecasting Methods

Qualitative Methods
Casual Methods
Linear Regression

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Qualitative Methods

Market Research
Delphi method
Sales Force Planning

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Market Research

Market research: a systematic approach to measuring customer interest in a service or product through data-gathering surveys
Market research is widely used for new products, but it also has a high degree of uncertainty and must be interpreted with caution.

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Delphi Method

A forecasting method that uses a team of experts to develop a consensus forecast.
The Delphi method is useful for long-range forecasts of demand and technological forecasting.

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Sales Force Planning

Sales force planning: gathering the opinions of salespeople and managers for a particular product or family of products.
Frequent contact with customers often provides insight into what customers may be considering for the future and also into customer perceptions of the company and its products.

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Casual Methods

When historical data is available and there is a relationship between the item to be forecasted and some other factor (such as advertising expenditure, sales of another product, or government regulations), then a casual method is used.
Casual methods employ mathematical techniques to relate one or more independent variables to the variable being forecast.

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Linear Regression

Linear regression: a statistical technique that expresses the forecast variable as a linear function of one or more independent variables

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Figure 5.12: Linear
Regression Line with Raw Data

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Linear Regression

Three measures usually reported:

Correlation coefficient: a measure of the strength and direction of the relationship between the independent variable x and the dependent variable y
Coefficient of determination: a measure of the amount of variation in the dependent variable that the regression line explains
Standard error of the estimate: a measure of the distance between the dependent variable and the regression line

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Supply Chain Forecasting

The biggest gains over the last 10 years have

come with the development of more powerful

information technology.

Can capture and process enormous amounts of data
Can quickly connect numerous players within a supply chain at fairly low cost in terms of time and effort.

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Information Systems for
Sharing Data Across the Supply Chain

Selecting the “best” technology for a specific business or situation is a continuous challenge.
IT, hardware, and techniques are always changing.
Numerous factors affect the performance of any particular choice.

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Radio Frequency Identification

Radio frequency identification: a technology that utilizes an integrated circuit and a tag antenna printed on a tag to transmit and record information on a product
RFID addresses two of the key limitations of bar codes:

RFID can allow indirect reads and permit multiple items to be read simultaneously.

RFID has the ability to carry substantially more information than most bar codes, as well as to both read and write information on a tag.

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Collaborative Planning, Forecasting and Replenishment (CPFR)

CPFR: a group of business processes supported by information technology where supply chain members agree to shared business objectives and measures, develop joint sales and operational plans, and collaborate electronically to generate and revise forecasts and production plans

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Wal-mart’s Application of RFID and
CPFR: Sharing Data Across the Supply Chain to Maximize Forecast Accuracy

Wal-mart’s IT system has 99.992 percent uptime for the processing of 20 million customers per day at more than 5,000 stores around the world.
The system maintains inventory data on more than 693 million items, of which 335 million are reviewed for potential reordering each day.
Wal-mart has been one of the leaders in adopting RFID, issuing a mandate that required its top 100 suppliers to start tagging all cases and pallets of merchandise by January 2005.
Wal-mart has estimated that RFID cut the incidence of out-of-stock products by 30 percent and improved the efficiency of moving products from backrooms to store shelves by 60 percent.

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Building a Responsive Organization/Supply Chain

Forecasting is always necessary for planning purposes, but when innovative products with unpredictable demand are ordered, accuracy tends to be greatly compromised.
When faced with an innovative product with unpredictable demand, a company should seek to make its supply chain more responsive by:

Deploying excess buffer capacity.

Deploying buffer stocks of parts or finished goods.

Investing in lead-time reduction.

Selecting suppliers on the basis of speed, flexibility, and quality rather than cost.

Employing a modular design.

demand

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total

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188

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