Operations Management 300words.docx

(Mt) – Operations Management 300words
Operations Management: Sustainability and Supply Chain Management Thirteenth Edition
Chapter 5 Design of Goods and Services Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved Outline (1 of 2) • Global Company Profile: Regal Marine • Goods and
Services Selection • Generating New Products • Product Development • Issues for Product
Design • Product Development Continuum Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Outline (2 of 2) • Defining a Product • Documents for
Production • Service Design • Application of Decision Trees to Product Design • Transition
to Production Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Regal Marine • Global market • 3-dimensional CAD system – Reduced product development
time – Reduced problems with tooling – Reduced problems in production • Assembly line
production • JIT Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Learning Objectives (1 of 2) When you complete this chapter you should be able to: 5.1
Define product life cycle 5.2 Describe a product development system 5.3 Build a house of
quality 5.4 Explain how time-based competition is implemented by OM Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) When
you complete this chapter you should be able to: 5.5 Describe how goods and services are
defined by OM 5.6 Describe the documents needed for production 5.7 Apply decision trees
to product issues Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Goods and Services Selection (1 of 3) • Organizations exist to provide goods or services to
society • Great products are the key to success • Top organizations typically focus on core
products • Customers buy satisfaction, not just a physical good or particular service •
Fundamental to an organization’s strategy with implications throughout the operations
function Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Goods
and Services Selection (2 of 3) • Limited and predictable life cycles requires constantly
looking for, designing, and developing new products • Utilize strong communication among
customer, product, processes, and suppliers • New products generate substantial revenue
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Goods and
Services Selection (3 of 3) Figure 5.1 Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Product Decision The objective of the product decision is to develop and
implement a product strategy that meets the demands of the marketplace with a
competitive advantage Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Product Strategy Options • Differentiation – Shouldice Hospital • Low cost – Taco
Bell • Rapid response – Toyota Copyright © 2020, 2017, 2014 Pearson Education, Inc. All

Rights Reserved Product Life Cycles • May be any length from a few days to decades • The
operations function must be able to introduce new products successfully Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Product Life Cycle Figure 5.2
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Life Cycle and
Strategy Introductory Phase • Fine tuning may warrant unusual expenses for 1. Research 2.
Product development 3. Process modification and enhancement 4. Supplier development
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Product Life
Cycle (1 of 3) Growth Phase • Product design begins to stabilize • Effective forecasting of
capacity becomes necessary • Adding or enhancing capacity may be necessary Copyright ©
2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Product Life Cycle (2 of 3)
Maturity Phase • Competitors now established • High volume, innovative production may
be needed • Improved cost control, reduction in options, paring down of product line
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Product Life
Cycle (3 of 3) Decline Phase • Unless product makes a special contribution to the
organization, management must plan to terminate offering Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Product Life Cycle Costs Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Product-by-Value Analysis • Lists
products in descending order of their individual dollar contribution to the firm • Lists the
total annual dollar contribution of the product • Helps management evaluate alternative
strategies Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Generating New Products 1. Understanding the customer 2. Economic change 3. Sociological
and demographic change 4. Technological change 5. Political and legal change 6. Market
practice, professional standards, suppliers, distributors Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Product Development Stages Figure 5.3
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Quality Function
Deployment (1 of 2) • Quality function deployment (QFD) – Determine what will satisfy the
customer – Translate those customer desires into the target design • House of quality –
Utilize a planning matrix to relate customer wants to how the firm is going to meet those
wants Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Quality
Function Deployment (2 of 2) 1. Identify customer wants 2. Identify how the good/service
will satisfy customer wants 3. Relate customer wants to product hows 4. Identify
relationships between the firm’s hows 5. Develop our importance ratings 6. Evaluate
competing products 7. Compare performance to desirable technical attributes Copyright ©
2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved QFD House of Quality
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved House of Quality
Example (1 of 9) Your team has been charged with designing a new camera for Great
Cameras, Inc. The first action is to construct a House of Quality Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved House of Quality Example (2 of 9)
Example (3 of 9) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
House of Quality Example (4 of 9) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved House of Quality Example (5 of 9) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved House of Quality Example (6 of 9) Copyright © 2020,

2017, 2014 Pearson Education, Inc. All Rights Reserved House of Quality Example (7 of 9)
House of Quality Example (9 of 9) Completed House of Quality Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved House of Quality Sequence Deploying
resources through the organization in response to customer requirements Figure 5.4
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Organizing for
Product Development (1 of 4) • Traditionally – distinct departments – Duties and
responsibilities are defined – Difficult to foster forward thinking • A Champion – Product
manager drives the product through the product development system and related
organizations Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Organizing for Product Development (2 of 4) • Team approach – Cross functional –
representatives from all disciplines or functions – Product development teams, design for
manufacturability teams, value engineering teams • Japanese “whole organization”
approach – No organizational divisions Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved Organizing for Product Development (3 of 4) • Product
development teams – Market requirements to product success – Cross-functional teams
often involving vendors – Open, highly participative environment Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Organizing for Product Development (4 of
4) • Concurrent engineering – Simultaneous performance of product development stages –
Speedier product development – Facilitated by cross-functional teams Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Manufacturability and Value
Engineering • Benefits: 1. Reduced complexity of the product 2. Reduction of environmental
impact 3. Additional standardization of components 4. Improvement of functional aspects of
the product 5. Improved job design and job safety 6. Improved maintainability
(serviceability) of the product 7. Robust design Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Cost Reduction of a Bracket via Value Engineering Figure
5.5 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Issues for
Product Design • Robust design • Modular design • Computer-aided design (CAD) •
Computer-aided manufacturing (CAM) • Virtual reality technology • Value analysis •
Sustainability and Life Cycle Assessment (LCA) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Robust Design • Product is designed so that small
variations in production or assembly do not adversely affect the product • Typically results
in lower cost and higher quality Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Modular Design • Products designed in easily segmented components •
Adds flexibility to both production and marketing • Improved ability to satisfy customer
requirements Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Computer Aided Design (CAD) • Using computers to design products and prepare
engineering documentation • Shorter development cycles, improved accuracy, lower cost •
Information and designs can be deployed worldwide Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Extensions of CAD • 3D Object Modeling – Small
prototype development • Design for Manufacturing and Assembly (DFMA) – Solve
manufacturing problems during the design stage • CAD through the Internet • International

data exchange through STEP • 3D printing Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Computer-Aided Manufacturing (CAM) • Utilizing
specialized computers and program to control manufacturing equipment • Often driven by
the CAD system (CAD/CAM) • Additive manufacturing – Extension of CAD that builds
products by adding material layer upon layer Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Additive Manufacturing Figure 5.5 Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Benefits of CAD/CAM 1. Product
quality 2. Shorter design time 3. Production cost reductions 4. Database availability 5. New
range of capabilities Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Virtual Reality Technology • A visual form of communication in which images
substitute for reality and typically allow the user to respond interactively • Allows people to
‘see’ the finished design before a physical model is built • Very effective in large-scale
designs such as plant layout Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Augmented Reality • The integration of digital information with the user’s
environment in real time – Digital information or images superimposed on an existing
image – Useful in product design, assembly and maintenance operations, tool or
specification information Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Value Analysis • Focuses on design improvement during production • Seeks
improvements leading either to a better product or a product that can be produced more
economically with less environmental impact Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Sustainability and Life Cycle Assessment (LCA) •
Sustainability means meeting the needs of the present without compromising the ability of
future generations to meet their needs • LCA is a formal evaluation of the environmental
impact of a product Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Product Development Continuum (1 of 4) • Product life cycles are becoming
shorter and the rate of technological change is increasing • Developing new products faster
can result in a competitive advantage • Time-based competition Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Product Development Continuum (2 of 4)
Figure 5.6 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Product Development Continuum (3 of 4) • Purchasing technology by acquiring a firm –
Speeds development – Issues concern the fit between the acquired organization and
product and the host • Joint Ventures – Both organizations learn – Risks are shared
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Product
Development Continuum (4 of 4) • Alliances – Cooperative agreements between
independent organizations – Useful when technology is developing – Reduces risks
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Defining a
Product • First definition is in terms of functions • Rigorous specifications are developed
during the design phase • Manufactured products will have an engineering drawing • Bill of
material (BOM) lists the components of a product Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Monterey Jack Cheese Figure 5.7 Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Product Documents • Engineering
drawing – Shows dimensions, tolerances, and materials – Shows codes for Group
Technology • Bill of Material – Lists components, quantities, and where used – Shows

product structure Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Engineering Drawings Figure 5.8 Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Bills of Material (1 of 2) BOM for a Panel Weldment
Figure 5.9 (a) NUMBER DESCRIPTION QTY A 60-71 PANEL WELDM’T 1 A 60-7 R 60-17 R
60-428 P 60-2 LOWER ROLLER ASSM. ROLLER PIN LOCKNUT 1 1 1 1 A 60-72 R 60-57-1 A
60-4 02-50-1150 GUIDE ASSM. REAR SUPPORT ANGLE ROLLER ASSM. BOLT 1 1 1 1 A 60-
73 A 60-74 R 60-99 02-50-1150 GUIDE ASSM. FRONT SUPPORT WELDM’T WEAR PLATE
BOLT 1 1 1 1 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Bills of Material (2 of 2) Hard Rock Cafe’s Hickory BBQ Bacon Cheeseburger Figure 5.9 (b)
DESCRIPTION Bun Hamburger patty Cheddar cheese Bacon BBQ onions Hickory BBQ sauce
Burger set Lettuce Tomato Red onion Pickle French fries Seasoned salt 11-inch plate HRC
flag QTY 1 8 oz. 2 slices 2 strips 1/2 cup 1 oz. blank 1 leaf 1 slice 4 rings 1 slice 5 oz. 1 tsp. 1
1 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Make-or-Buy
Decisions • Produce components themselves or buy from an outside source • Variations in –
Quality – Cost – Delivery schedules • Critical to product definition Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Group Technology • Parts grouped into
families with similar characteristics • Coding system describes processing and physical
characteristics • Part families can be produced in dedicated manufacturing cells Copyright
© 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Group Technology Scheme
Figure 5.10 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Group Technology Benefits 1. Improved design 2. Reduced raw material and purchases 3.
Simplified production planning and control 4. Improved layout, routing, and machine
loading 5. Reduced tooling setup time, work-in-process, and production time Copyright ©
2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Documents for Production •
Assembly drawing • Assembly chart • Route sheet • Work order • Engineering change
notices (ECNs) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Assembly Drawing Figure 5.11 (a) • Shows exploded view of product • Details relative
locations to show how to assemble the product Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Assembly Chart Figure 5.11 (b) Identifies the point of
production where components flow into subassemblies and ultimately into the final
product Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Route
Sheet Lists the operations and times required to produce a component Process Machine 1
Auto Insert 2 2 Manual Insert 1 3 Wave Solder 4 Test 4 Setup Operations Time Insert
Component 1.5 Set 56 Insert Component .5 Set 12C Solder all 1.5 components to board
Circuit integrity .25 test 4GY Operation Time/Unit .4 2.3 4.1 .5 Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Work Order Instructions to produce a
given quantity of a particular item, usually to a schedule Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Engineering Change Notice (ECN) • A correction
or modification to a product’s definition or documentation – Engineering drawings – Bill of
material Quite common with long product life cycles, long manufacturing lead times, or
rapidly changing technologies Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Configuration Management • The need to manage ECNs has led to the
development of configuration management systems • A product’s planned and changing

components are accurately identified • Control and accountability for change are identified
and maintained Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Product Life-Cycle Management (PLM) • Integrated software that brings together most, if
not all, elements of product design and manufacture – – – – – Product design CAD/CAM
DFMA Product routing Materials – – – – Layout Assembly Maintenance Environmental
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Service Design •
Many aspects of services are intangible • Service typically includes direct interaction with
the customer • Service productivity is notoriously low partially because of customer
involvement in the design or delivery of the service, or both • Complicates product design
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Designing More
Efficient Services (1 of 2) • Limit the options – Improves efficiency and ability to meet
customer expectations • Delay customization • Modularization – Eases customization of a
service Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Designing More Efficient Services (2 of 2) • Automation – Reduces cost, increases customer
service • Moment of truth – Critical moments between the customer and the organization
that determine customer satisfaction Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Documents for Services • High levels of customer interaction
necessitate different documentation • Often explicit job instructions • Scripts and
storyboards are other techniques Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved First Bank Corp. Drive-up Teller Service Guidelines • Say “please” and
“thank you” in all conversations. • Be discrete when speaking into the microphone as others
may hear the conversation. • Give customers written instructions if they need to fill out
forms you give them. • Look directly at customers if there is a line of sight. • If the customer
needs to park and enter the bank, apologize for the inconvenience. Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Application of Decision Trees to Product
Design (1 of 2) • Particularly useful when there are a series of decisions and outcomes that
lead to other decisions and outcomes Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Application of Decision Trees to Product Design (2 of 2) Procedure 1.
Include all possible alternatives and states of nature – including “doing nothing” 2. Enter
payoffs at end of branch 3. Determine the expected value of each branch and “prune” the
tree to find the alternative with the best expected value Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Decision Tree Example (1 of 4) Figure 5.12
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Decision Tree
Example (2 of 4) Figure 5.12 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Decision Tree Example (3 of 4) Figure 5.12 Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Decision Tree Example (4 of 4) Figure 5.12
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Transition to
Production (1 of 2) • Know when to move to production – Product development can be
viewed as evolutionary and never complete – Product must move from design to production
in a timely manner • Most products have a trial production period to insure producibility –
Develop tooling, quality control, training – Ensures successful production Copyright ©
2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Transition to Production (2 of
2) • Responsibility must also transition as the product moves through its life cycle – Line

management takes over from design • Three common approaches to managing transition –
Project managers – Product development teams – Integrate product development and
manufacturing organizations Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Copyright This work is protected by United States copyright laws and is
provided solely for the use of instructors in teaching their courses and assessing student
learning. Dissemination or sale of any part of this work (including on the World Wide Web)
will destroy the integrity of the work and is not permitted. The work and materials from it
should never be made available to students except by instructors using the accompanying
text in their classes. All recipients of this work are expected to abide by these restrictions
and to honor the intended pedagogical purposes and the needs of other instructors who
rely on these materials. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Operations Management: Sustainability and Supply Chain Management Thirteenth
Edition Chapter 4 Forecasting Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Outline (1 of 2) • Global Company Profile: Walt Disney Parks & Resorts •
What Is Forecasting? • The Strategic Importance of Forecasting • Seven Steps in the
Forecasting System • Forecasting Approaches Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Outline (2 of 2) • Time-Series Forecasting • Associative
Forecasting Methods: Regression and Correlation Analysis • Monitoring and Controlling
Forecasts • Forecasting in the Service Sector Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Forecasting Provides a Competitive Advantage for
Disney (1 of 4) • Global portfolio includes parks in Shanghai, Hong Kong, Paris, Tokyo,
Orlando, and Anaheim • Revenues are derived from people – how many visitors and how
they spend their money • Daily management report contains only the forecast and actual
attendance at each park Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Forecasting Provides a Competitive Advantage for Disney (2 of 4) • Disney
generates daily, weekly, monthly, annual, and 5year forecasts • Forecast used by labor
management, maintenance, operations, finance, and park scheduling • Forecast used to
adjust opening times, rides, shows, staffing levels, and guests admitted Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Forecasting Provides a Competitive
Advantage for Disney (3 of 4) • 20% of customers come from outside the USA • Economic
model includes gross domestic product, crossexchange rates, arrivals into the USA • A staff
of 35 analysts and 70 field people survey 1 million park guests, employees, and travel
professionals each year Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Forecasting Provides a Competitive Advantage for Disney (4 of 4) • Inputs to the
forecasting model include airline specials, Federal Reserve policies, Wall Street trends,
vacation/holiday schedules for 3,000 school districts around the world • Average forecast
error for the 5-year forecast is 5% • Average forecast error for annual forecasts is between
0% and 3% Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Learning Objectives (1 of 2) When you complete this chapter you should be able to: 4.1
Understand the three time horizons and which models apply for each 4.2 Explain when to
use each of the four qualitative models 4.3 Apply the naive, moving-average, exponential
smoothing, and trend methods Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Learning Objectives (2 of 2) When you complete this chapter you should be

able to: 4.4 Compute three measures of forecast accuracy 4.5 Develop seasonal indices 4.6
Conduct a regression and correlation analysis 4.7 Use a tracking signal Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved What is Forecasting? • Process of
predicting a future event • Underlying basis of all business decisions – Production –
Inventory – Personnel – Facilities Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Forecasting Time Horizons 1. Short-range forecast – Up to 1 year, generally
less than 3 months – Purchasing, job scheduling, workforce levels, job assignments,
production levels 2. Medium-range forecast – 3 months to 3 years – Sales and production
planning, budgeting 3. Long-range forecast – 3+ years – New product planning, facility
location, capital expenditures, research and development Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Distinguishing Differences 1. Medium/long
range forecasts deal with more comprehensive issues and support management decisions
regarding planning and products, plants and processes 2. Short-term forecasting usually
employs different methodologies than longer-term forecasting 3. Short-term forecasts tend
to be more accurate than longerterm forecasts Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Influence of Product Life Cycle Introduction – Growth –
Maturity – Decline • Introduction and growth require longer forecasts than maturity and
decline • As product passes through life cycle, forecasts are useful in projecting – Staffing
levels – Inventory levels – Factory capacity Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Product Life Cycle (1 of 2) Figure 2.5 Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Product Life Cycle (2 of 2) Figure
2.5 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Types of
Forecasts 1. Economic forecasts – Address business cycle – inflation rate, money supply,
housing starts, etc. 2. Technological forecasts – Predict rate of technological progress –
Impacts development of new products 3. Demand forecasts – Predict sales of existing
products and services Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Strategic Importance of Forecasting • Supply Chain Management – Good supplier
relations, advantages in product innovation, cost and speed to market • Human Resources –
Hiring, training, laying off workers • Capacity – Capacity shortages can result in
undependable delivery, loss of customers, loss of market share Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Seven Steps in Forecasting 1. Determine
the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of
the forecast 4. Select the forecasting model(s) 5. Gather the data needed to make the
forecast 6. Make the forecast 7. Validate and implement the results Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved The Realities! • Forecasts are seldom
perfect; unpredictable outside factors may impact the forecast • Most techniques assume an
underlying stability in the system • Product family and aggregated forecasts are more
accurate than individual product forecasts Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Forecasting Approaches (1 of 2) Qualitative Methods •
Used when situation is vague and little data exist – New products – New technology •
Involves intuition, experience – e.g., forecasting sales on Internet Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Forecasting Approaches (2 of 2)
Quantitative Methods • Used when situation is ‘stable’ and historical data exist – Existing

products – Current technology • Involves mathematical techniques – e.g., forecasting sales
of color televisions Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Overview of Qualitative Methods (1 of 2) 1. Jury of executive opinion – Pool
opinions of high-level experts, sometimes augmented by statistical models 2. Delphi method
– Panel of experts, queried iteratively Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved Overview of Qualitative Methods (2 of 2) 3. Sales force composite –
Estimates from individual salespersons are reviewed for reasonableness, then aggregated 4.
Market Survey – Ask the customer Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Jury of Executive Opinion • Involves small group of high-level experts
and managers • Group estimates demand by working together • Combines managerial
experience with statistical models • Relatively quick • ‘Group-think’ disadvantage Copyright
© 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Delphi Method • Iterative
group process, continues until consensus is reached • Three types of participants – Decision
makers – Staff – Respondents Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Sales Force Composite • Each salesperson projects his or her sales •
Combined at district and national levels • Sales reps know customers’ wants • May be overly
optimistic Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Market Survey • Ask customers about purchasing plans • Useful for demand and product
design and planning • What consumers say and what they actually do may be different •
May be overly optimistic Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Overview of Quantitative Approaches Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Time-Series Forecasting • Set of evenly spaced
numerical data – Obtained by observing response variable at regular time periods •
Forecast based only on past values, no other variables important – Assumes that factors
influencing past and present will continue influence in future Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Time-Series Components Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Components of Demand Figure 4.1
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Trend
Component • Persistent, overall upward or downward pattern • Changes due to population,
technology, age, culture, etc. • Typically several years duration Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Seasonal Component • Regular pattern of
up and down fluctuations • Due to weather, customs, etc. • Occurs within a single year
PERIOD LENGTH “SEASON” LENGTH NUMBER OF “SEASON” IN PATTERN Week Day 7
Month Week 4 – 4.5 Month Day 28 – 31 Year Quarter 4 Year Month 12 Year Week 52
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Cyclical
Component • Repeating up and down movements • Affected by business cycle, political, and
economic factors • Multiple years duration • Often causal or associative relationships
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Random
Component • Erratic, unsystematic, ‘residual’ fluctuations • Due to random variation or
unforeseen events • Short duration and nonrepeating Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Naive Approach • Assumes demand in next
period is the same as demand in most recent period – e.g., If January sales were 68, then
February sales will be 68 • Sometimes cost effective and efficient • Can be good starting

point Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Moving
Averages • MA is a series of arithmetic means • Used if little or no trend • Used often for
smoothing – Provides overall impression of data over time demand in previous n periods å
Moving average = n Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Moving Average Example Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Weighted Moving Average (1 of 3) • Used when some trend might be
present – Older data usually less important • Weights based on experience and intuition (
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Weighted
Moving Average (2 of 3) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Weighted Moving Average (3 of 3) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Potential Problems With Moving Average (1 of 2) 1.
Increasing n smooths the forecast but makes it less sensitive to changes 2. Does not forecast
trends well 3. Requires extensive historical data Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Graph of Moving Averages Figure 4.2 Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Potential Problems With Moving
Average (2 of 2) • Form of weighted moving average – Weights decline exponentially – Most
recent data weighted most • Requires smoothing constant (α) – Ranges from 0 to 1 –
Subjectively chosen • Involves little record keeping of past data Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Exponential Smoothing New forecast =
Last period’s forecast + α (Last period’s actual demand − Last period’s forecast) Ft = Ft – 1+
α ( At – 1 – Ft – 1 ) where Ft = new forecast Ft – 1 = previous period’s forecast α =
smoothing (or weighting) constant (0 ≤ α ≤ 1) At – 1 = previous period’s actual demand
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Exponential
Smoothing Example (1 of 3) • Predicted demand = 142 Ford Mustangs • Actual demand =
153 • Smoothing constant α = .20 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Exponential Smoothing Example (2 of 3) Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Exponential Smoothing Example (3 of 3)
Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20
New forecast = 142 + .2(153 − 142) = 142 + 2.2 = 144.2 ≈ 144 cars Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Effect of Smoothing Constants •
Smoothing constant generally .05 ≤ α ≤ .50 • As α increases, older values become less
significant Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Impact of Different α (1 of 2) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Impact of Different α (2 of 2) • Choose high values of α when underlying
average is likely to change • Choose low values of α when underlying average is stable
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Selecting the
Smoothing Constant The objective is to obtain the most accurate forecast no matter the
technique We generally do this by selecting the model that gives us the lowest forecast
error according to one of three preferred measures: • Mean Absolute Deviation (MAD) •
Mean Squared Error (MSE) • Mean Absolute Percent Error (MAPE) Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Common Measures of Error (1 of 3) Mean
Absolute Deviation (MAD) Actual – Forecast å MAD = n Copyright © 2020, 2017, 2014

Pearson Education, Inc. All Rights Reserved Determining the MAD (1 of 2) QUARTER
ACTUAL TONNAGE UNLOADED 1 180 175 2 168 175.50 = 175.00 + .10(180 − 175) 177.50 3
159 174.75 = 175.50 + .10(168 − 175.50) 172.75 4 175 173.18 = 174.75 + .10(159 − 174.75)
165.88 5 190 173.36 = 173.18 + .10(175 − 173.18) 170.44 6 205 175.02 = 173.36 + .10(190
− 173.36) 180.22 7 180 178.02 = 175.02 + .10(205 − 175.02) 192.61 8 182 178.22 = 178.02
+ .10(180 − 178.02) 186.30 9 ? 178.59 = 178.22 + .10(182 − 178.22) 184.15 FORECAST
WITH α = .10 FORECAST WITH α = .50 175 Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Determining the MAD (2 of 2) Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Common Measures of Error (2 of 3) Mean
Educa
1,526.52/8 = 190.8 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Common Measures of Error (3 of 3) Mean Absolute Percent Error (MAPE) n MAPE
100 Actual − Forecast /Actual i =1 i i i n Copyright © 2020, 2017, 2014 Pearson
MAPE = = = 5.59% n 8 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Comparison of Measures Table 4.1 Comparison of Measures of Forecast Error
MEASURE MEANING APPLICATION TO CHAPTER EXAMPLE Mean absolute deviation
(MAD) How much the forecast missed the target For α = .10 in Example 4, the forecast for
grain unloaded was off by an average of 10.31 tons. Mean squared error (MSE) The square
of how much the forecast missed the target For α = .10 in Example 5, the square of the
forecast error was 190.8. This number does not have a physical meaning, but is useful when
compared to the MSE of another forecast. Mean absolute percent error (MAPE) The average
percent error For α = .10 in Example 6, the forecast is off by 5.59% on average. As in
Examples 4 and 5, some forecasts were too high, and some were low. Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Comparison of Forecast Error (1 of
5) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Comparison
of Forecast Error (2 of 5) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Comparison of Forecast Error (3 of 5) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Comparison of Forecast Error (4 of 5) Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Comparison of Forecast Error (5 of
5) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Exponential
Smoothing with Trend Adjustment (1 of 3) When a trend is present, exponential smoothing
must be modified FORECAST (Ft) FOR MONTHS 1 – 5 MONTH ACTUAL DEMAND 1 100 F1 =
100 (given) 2 200 F2 = F1 + α(A1 − F1) = 100 + .4(100 − 100) = 100 3 300 F3 = F2 + α(A2 −
F2) = 100 + .4(200 − 100) = 140 4 400 F4 = F3 + α(A3 − F3) = 140 + .4(300 − 140) = 204 5
500 F5 = F4 + α(A4 − F4) = 204 + .4(400 − 204) = 282 Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Exponential Smoothing with Trend Adjustment
(2 of 3) Forecast Exponentially Exponentially including ( FITt ) = smoothed ( Ft ) =
- 1 ) + (1 – a)( Ft- 1 + Tt- – Ft
– 1 ) + (1 – - 1 where Ft = exponentially smoothed forecast average Tt = exponentially
smoothed trend At = actual demand α = smoothing constant for average (0 ≤ α ≤ 1) β =
smoothing constant for trend (0 ≤ β ≤ 1) Copyright © 2020, 2017, 2014 Pearson Education,

Inc. All Rights Reserved Exponential Smoothing with Trend Adjustment (3 of 3) Step 1:
Compute Ft Step 2: Compute Tt Step 3: Calculate the forecast FITt = Ft + Tt Copyright ©
2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Exponential Smoothing with
Trend Adjustment Example MONTH (t) ACTUAL DEMAND (At) MONTH (t) ACTUAL
DEMAND (At) 1 12 6 21 2 17 7 31 3 20 8 28 4 19 9 36 5 24 10 ? α = .2 β = .4 Copyright ©
Trend Adjustment Example (1 of 5) Table 4.2 Forecast with α = .2 and β = .4 Copyright ©
Trend Adjustment Example (4 of 5) Table 4.2 Forecast with α = .2 and β = .4 MONTH
SMOOTHED ACTUAL FORECAST DEMAND AVERAGE, Ft SMOOTHED TREND, Tt FORECAST
INCLUDING TREND, FITt 1 12 11 2 13.00 2 17 12.80 1.92 14.72 3 20 15.18 2.10 17.28 4 19
17.82 2.32 20.14 5 24 19.91 2.23 22.14 6 21 22.51 2.38 24.89 7 31 24.11 2.07 26.18 8 28
27.14 2.45 29.59 9 36 29.28 2.32 31.60 10 blank 32.48 2.68 35.16 Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Exponential Smoothing with Trend
Adjustment Example (5 of 5) Figure 4.3 Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved Trend Projections (1 of 2) • Fitting a trend line to historical data
points to project into the medium to long-range • Linear trends can be found using the
least-squares technique yˆ = a + bx where yˆ = computed value of the variable to be
predicted ( dependent variable) a = y-axis intercept b = slope of the regression line x = the
independent variable Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Least Squares Method (1 of 2) Figure 4.4 Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Least Squares Method (2 of 2) Equations to calculate the
regression variables ŷ = a + bx xy – nxy å b= å x – nx 2 2 a = y – bx Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Least Squares Example (1 of 4) YEAR
ELECTRICAL POWER DEMAND YEAR ELECTRICAL POWER DEMAND 1 74 5 105 2 79 6 142
3 80 7 122 4 90 blank blank Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Least Squares
Least Squares Example (4 of 4) Figure 4.5 Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Least Squares Requirements 1. We always plot the data
to insure a linear relationship 2. We do not predict time periods far beyond the database 3.
Deviations around the least squares line are assumed to be random Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Seasonal Variations In Data (1 of 2)
The multiplicative seasonal model can adjust trend data for seasonal variations in demand
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Seasonal
Variations In Data (2 of 2) Steps in the process for monthly seasons: 1. Find average
historical demand for each month 2. Compute the average demand over all months 3.
Compute a seasonal index for each month 4. Estimate next year’s total demand 5. Divide this

estimate of total demand by the number of months, then multiply it by the seasonal index
for that month Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Seasonal Index Example (1 of 6) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Seasonal Index Example (2 of 6) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Seasonal Index Example (3 of 6) Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Seasonal Index Example (4 of 6)
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Seasonal Index
Example (5 of 6) Seasonal forecast for Year 4 Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Seasonal Index Example (6 of 6) Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved San Diego Hospital (1 of 5) Figure
4.6 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved San Diego
Hospital (2 of 5) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
San Diego Hospital (3 of 5) Figure 4.7 Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved San Diego Hospital (4 of 5) Period 67 68 69 70 71 72 Month Jan Feb
Mar Apr May June 9,911 9,265 9,764 9,691 9,520 9,542 Period 73 74 75 76 77 78 Month
July Aug Sept Oct Nov Dec 9,949 10,068 9,411 9,724 9,355 9,572 Forecast with Trend &
Seasonality Forecast with Trend & Seasonality Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved San Diego Hospital (5 of 5) Figure 4.8 Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Adjusting Trend Data yˆseasonal =
Index yˆ trend forecast Quarter I: ŷ I = (1.30)($100,000) = $130,000 Quarter II: ŷ II =
(.90)($120,000) = $108,000 Quarter III: ŷ III = (.70)($140,000) = $98,000 Quarter IV: ŷ IV =
(1.10)($160,000) = $176,000 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Cyclical Variations • Cycles – patterns in the data that occur every several
years – Forecasting is difficult – Wide variety of factors Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Associative Forecasting Used when changes in
one or more independent variables can be used to predict the changes in the dependent
variable Most common technique is linear-regression analysis We apply this technique just
as we did in the time-series example Copyright © 2020, 2017, 2014 Pearson Education, Inc.
All Rights Reserved Trend Projections (2 of 2) Forecasting an outcome based on predictor
variables using the least squares technique yˆ = a + bx where yˆ = value of the dependent
variable ( in our example, sales) a = y-axis intercept b = slope of the regression line x = the
independent variable Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Associative Forecasting Example (1 of 6) Copyright © 2020, 2017, 2014 Pearson
= y − bx = 2.5 − (.25)(3) = 1.75 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
(.25)(3) = 1.75 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
earson
Education, Inc. All Rights Reserved Associative Forecasting Example (5 of 6) If payroll next
year is estimated to be $6 billion, then: Sales (in $ millions) = 1.75 + .25(6) = 1.75 + 1.5 =

3.25 Sales = $3,250,000 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Associative Forecasting Example (6 of 6) Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Standard Error of the Estimate (1 of 4) • A forecast is
just a point estimate of a future value • This point is actually the mean or expected value of a
probability distribution Figure 4.9 Copyright © 2020, 2017, 2014 Pearson Education, Inc.
= y-value of each data point yc = computed value of the dependent variable, from the
regression equation n = number of data points Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Standard Error of the Estimate (3 of 4) Computationally,
this equation is cons
standard error to set up prediction intervals around the point estimate Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Standard Error of the Estimate (4 of
4) S
millions ) The standard error of the estimate is $306,000 in sales Copyright © 2020, 2017,
2014 Pearson Education, Inc. All Rights Reserved Correlation (1 of 2) • How strong is the
linear relationship between the variables? • Correlation does not necessarily imply
causality! • Coefficient of correlation, r, measures degree of association – Values range from
−1 to +1 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Correlation Coefficient (1 of 4) Figure 4.10 Copyright © 2020, 2017, 2014 Pearson
–
2017, 2014 Pearson Education,
1,872 43.3 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Correlation (2 of 2) • Coefficient of Determination, r2, measures the percent of change in y
predicted by the change in x – Values range from 0 to 1 – Easy to interpret For the Nodel
Construction example: r = .901 r2 = .81 Copyright © 2020, 2017, 2014 Pearson Education,
Inc. All Rights Reserved Multiple-Regression Analysis (1 of 2) If more than one independent
variable is to be used in the model, linear regression can be extended to multiple regression
to accommodate several independent variables ŷ = a + b1x1 + b 2 x 2 Computationally, this
is quite complex and generally done on the computer Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved Multiple-Regression Analysis (2 of 2) In the
Nodel example, including interest rates in the model gives the new equation: ŷ = 1.80 + .30
x1 − 5.0 x2 An improved correlation coefficient of r = .96 suggests this model does a better
job of predicting the change in construction sales Sales = 1.80 + .30 ( 6 ) − 5.0 (.12 ) = 3.00
Sales = $3,000,000 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights
Reserved Monitoring and Controlling Forecasts (1 of 2) Tracking Signal • Measures how
well the forecast is predicting actual values • Ratio of cumulative forecast errors to mean
absolute deviation (MAD) – Good tracking signal has low values – If forecasts are
continually high or low, the forecast has a bias error Copyright © 2020, 2017, 2014 Pearson
Education, Inc. All Rights Reserved Monitoring and Controlling Forecasts (2 of 2) Tracking
signal = Cumulative error MAD (Actual demand in period i –

Reserved Correlation Coefficient (4 of 4) Figure 4.11 Copyright © 2020, 2017, 2014 Pearson
quarter 6, MAD = n Tracking signal = = 85 = 14.2 6 Cumulative error 35 = = 2.5 MADs MAD
14.2 Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Adaptive
Smoothing • It’s possible to use the computer to continually monitor forecast error and
adjust the values of the α and β coefficients used in exponential smoothing to continually
minimize forecast error • This technique is called adaptive smoothing Copyright © 2020,
2017, 2014 Pearson Education, Inc. All Rights Reserved Focus Forecasting • Developed at
American Hardware Supply, based on two principles: 1. Sophisticated forecasting models
are not always better than simple ones 2. There is no single technique that should be used
for all products or services • Uses historical data to test multiple forecasting models for
individual items • Forecasting model with the lowest error used to forecast the next
demand Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved
Forecasting in the Service Sector • Presents unusual challenges – Special need for short-
term records – Needs differ greatly as function of industry and product – Holidays and other
calendar events – Unusual events Copyright © 2020, 2017, 2014 Pearson Education, Inc. All
Rights Reserved Fast Food Restaurant Forecast Figure 4.12a Copyright © 2020, 2017, 2014
Pearson Education, Inc. All Rights Reserved FedEx Call Center Forecast Figure 4.12b
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Copyright This
work is protected by United States copyright laws and is provided solely for the use of
instructors in teaching their courses and assessing student learning. Dissemination or sale
of any part of this work (including on the World Wide Web) will destroy the integrity of the
work and is not permitted. The work and materials from it should never be made available
to students except by instructors using the accompanying text in their classes. All recipients
of this work are expected to abide by these restrictions and to honor the intended
pedagogical purposes and the needs of other instructors who rely on these materials.
Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved

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