Business Forecasting with ForecastX

526 Pages·2008·8.585 MB·English

by  J. Holton Wilson, Barry Keating

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Business Forecasting
With
ForecastX™
Sixth Edition
J. Holton Wilson Barry Keating
Central Michigan University University of Notre Dame
John Galt Solutions, Inc.
Chicago
Boston Burr Ridge, IL Dubuque, IA New York San Francisco St. Louis
Bangkok Bogotá Caracas Kuala Lumpur Lisbon London Madrid Mexico City
Milan Montreal New Delhi Santiago Seoul Singapore Sydney Taipei Toronto
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BUSINESS FORECASTING: WITH FORECASTX™
Published by McGraw-Hill/Irwin, a business unit of The McGraw-Hill Companies, Inc., 1221
Avenue of the Americas, New York, NY, 10020. Copyright © 2009, 2007, 2002, 1998, 1994,
1990 by The McGraw-Hill Companies, Inc. All rights reserved. No part of this publication may
be reproduced or distributed in any form or by any means, or stored in a database or retrieval
system, without the prior written consent of The McGraw-Hill Companies, Inc., including,
but not limited to, in any network or other electronic storage or transmission, or broadcast
for distance learning.
Some ancillaries, including electronic and print components, may not be available to customers
outside the United States.
This book is printed on acid-free paper.
1 2 3 4 5 6 7 8 9 0 DOC/DOC 0 9 8
ISBN: 978-0-07-337364-5
MHID: 0-07-337364-8
Vice president and editor-in-chief: BrentGordon
Editorial director:StewartMattson
Executive editor:RichardT.Hercher, Jr.
Editorial coordinator:RebeccaMann
Marketing manager:JaimeHalteman
Senior project manager:SusanneRiedell
Full service project manager:LoriHazzard,ICC Macmillan Inc.
Senior production supervisor:DebraR.Sylvester
Design coordinator:JoanneMennemeier
Media project manager:BalajiSundararaman, Hurix Systems Pvt. Ltd.
Cover design:JoanneMennemeier
Typeface: 10/12 Times New Roman
Compositor: Macmillan Publishing Solutions
Printer: R. R. Donnelley
Library of Congress Cataloging-in-Publication Data
Wilson, J. Holton, 1942-
Business forecasting : with forecastX™ / J. Holton Wilson, Barry Keating.—6th ed.
p. cm.
Includes index.
ISBN-13: 978-0-07-337364-5 (alk. paper)
ISBN-10: 0-07-337364-8 (alk. paper)
1. Business forecasting. I. Keating, Barry, 1945- II. Title.
HD30.27.W56 2009
658.4(cid:2)03550285554—dc22 2008046165
www.mhhe.com
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To Eva, Ronnie, and Clara
To Maryann, John, Ingrid, Vincent,
Katy, Alice, Casey, and Jill Keating
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Preface
The sixth edition of Business Forecastingwith ForecastX™ builds on the success
of the first five editions. While a number of significant changes have been made in
this sixth edition, it remains a book about forecasting methods for managers, fore-
casting practitioners, and students who will one day become business profession-
als and have a need to understand practical issues related to forecasting. Our
emphasis is on authentic learning of the forecasting methods that practicing fore-
casters have found most useful. Business Forecastingwith ForecastX™ is written
for students and others who want to know how forecasting is really done.
The major change to the sixth edition of the text is a new chapter on data min-
ing as a tool in business forecasting. As with the fifth edition, we again use the
ForecastX™ software as the tool to implement the methods described in the text.
This software is included on a CD with each copy of the text and has been made
available through an agreement with John Galt Solutions, Inc. Every forecasting
method discussed in the text can be implemented with this software (the data
mining techniques, however, require separate software). Based on our own expe-
riences and those of other faculty members who have used the fifth edition, we
know that students find the ForecastX™ software easy to use, even without a man-
ual or other written instructions. However, we have provided a brief introduction
to the use of ForecastX™ at the end of each relevant chapter. There is also a User’s
Guide on the CD with the software for those who may want more extensive
coverage, including information on advanced issues not covered in the text, but
included in the software.
John Galt Solutions provides us with the ForecastX software that does con-
tainproprietaryalgorithms,whichinsomesituationsdonotmatchexactlywith
the results one would get if the calculations were done “by hand.”Their meth-
ods, however, have proven successful in the marketplace as well as in forecast
competitions.
Weareconfidentthatfacultyandstudentswillenjoyusingthiswidelyadopted,
commerciallysuccessfulsoftware.However,thetextalsocanbeusedwithoutre-
lianceonthisparticularpackage.AlldatafilesareprovidedonthestudentCDin
Excelformatsothattheycanbeeasilyusedwithalmostanyforecastingorstatis-
ticalsoftware.Aswithpreviouseditions,nearlyalldatainthetextisreal,suchas
jewelrysales,bookstoresales,andtotalhousessold.Inaddition,wehavecontin-
uedtheuseofanongoingcaseinvolvingforecastingsalesofTheGap,Inc.,atthe
endofeachchaptertoprovideaconsistentlink.Additionally,anumberofexcel-
lentsourcesofdataarereferencedinthetext.Theseareespeciallyusefulforstu-
dentprojectsandforadditionalexercisesthatinstructorsmaywishtodevelop.
Comments from the Field by forecasting practitioners provide quick insights
into issues and problems faced daily by individuals who are actively engaged in
the forecasting process. These offer a practical perspective from the “real world”
to help students appreciate the relevance of the concepts presented in the text.
iv
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Preface v
Today, most business planning routinely begins with a sales forecast. Whether
you are an accountant, a marketer, a human resources manager, or a financial an-
alyst, you will have to forecast something sooner or later. This book is designed to
lead you through the most helpful techniques to use in any forecasting effort. The
examples we offer are, for the most part, based on actual historical data, much like
that you may encounter in your own forecasts. The techniques themselves are
explained as procedures that you may replicate with your own data.
The Online Learning Center accompanying the book includes all data used in
the text examples and chapter-ending problems. In addition, Excel sheets with
suggested answers to these problems are on this Web site.
The authors would like to thank the students at the University of Notre Dame
and Central Michigan University for their help in working with materials included
in this book during its development. Their comments were invaluable in preparing
clear expositions and meaningful examples for this sixth edition. Comments from
students at other universities both in the United States and elsewhere have also
been appreciated. It has been particularly gratifying to hear from students who
have found what they learned from a course using this text to be useful in their
professional careers.
The final product owes a great debt to the inspiration and comments of our
colleagues, especially ProfessorsThomas Bundt of Hillsdale College, andTunga
KiyakatMichiganStateUniversity.Inaddition,wewouldliketothankthestaffat
JohnGaltSolutionsforfacilitatingouruseoftheForecastX™software. We also
thank Professor Eamonn Keogh at the University of California, Riverside, for
sharing with us his illuminating examples of data mining techniques.
Adopters of the first five editions who have criticized, challenged, encouraged,
and complimented our efforts deserve our thanks. The authors are particularly
grateful to the following faculty and professionals who used earlier editions of
the text and/or have provided comments that have helped to improve this sixth
edition.
Paul Altieri Ali Dogramaci
Central Connecticut State Rutgers, the State University
University of New Jersey
Peter Bruce Farzad Farsio
Statistics.com Montana State University
Margaret M.Capen Robert Fetter
East Carolina University Yale University
Thomas P.Chen Benito Flores
St. John’s University Texas A & M University
Ronald L.Coccari Kenneth Gaver
Cleveland State University Montana State University
Lewis Coopersmith Rakesh Gupta
Rider University Adelphi University
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vi Preface
Joseph Kelley Nitin Patel
CaliforniaStateUniversity,Sacramento Massachusetts Institute
Thomas Kelly of Technology
BMW of Canada Gerald Platt
Eamonn Keogh San Francisco State University
University of California, Riverside Melissa Ramenofsky
Krishna Kool University of Southern Alabama
University of Rio Grande Helmut Schneider
John Mathews Louisiana State University
University of Wisconsin, Madison Stanley Schultz
Joseph McCarthy Cleveland State University
Bryant College Nancy Serafino
Elam McElroy United Telephone
Marquette University Galit Shmueli
Rob Roy McGregor University of Maryland
University of North Carolina, Donald N.Stengel
Charlotte California State University, Fresno
John C.Nash Kwei Tang
University of Ottawa Louisiana State University
Thomas Needham Dick Withycomb
US Bancorp University of Montana
We are especially grateful to have worked with the following publishing pro-
fessionals on our McGraw-Hill/Irwin book team: Dick Hercher, Rebecca Mann,
Rhonda Seelinger, Lori Hazzard, Joanne Mennemeier, Debra Sylvester, and Balaji
Sundararaman.
We hope that all of the above, as well as all new faculty, students, and business
professionals who use the text, will be pleased with the sixth edition.
J.HoltonWilson
[email protected]
BarryKeating
[email protected]
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Brief Contents
1 Introduction to Business 6 Times-Series Decomposition 298
Forecasting 1
7 ARIMA (Box-Jenkins)–Type
2 The Forecast Process, Data Forecasting Models 343
Considerations, and Model
8 Combining Forecast
Selection 56
Results 402
3 Moving Averages and Exponential
9 Data Mining 439
Smoothing 101
10 Forecast Implementation 482
4 Introduction to Forecasting with
Regression Methods 160
INDEX 507
5 Forecasting with Multiple
Regression 225
vii
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Contents
Chapter 1 Analog Forecasts 22
New Product and Penetration Curves
Introduction to Business
for VCR Sales 23
Forecasting 1
Test Marketing 24
Introduction 1 Product Clinics 24
Comments from the Field 2 Type of Product Affects New-Product
Quantitative Forecasting Has Become Widely Forecasting 25
Accepted 2 The Bass Model for New-Product
Forecasting in Business Today 3 Forecasting 25
Krispy Kreme 5 Forecasting Sales for New Products That
Bell Atlantic 5 Have Short Product Life Cycles 27
Columbia Gas 6 Two Simple Naive Models 30
Segix Italia 6 Evaluating Forecasts 34
Pharmaceuticals in Singapore 6 Using Multiple Forecasts 36
Fiat Auto 7 Sources of Data 37
Brake Parts, Inc. 7 Forecasting Total Houses Sold 37
Some Global Forecasting Issues: Overview of the Text 39
Examples from Ocean Spray Comments from the Field 41
Cranberries 7 Integrative Case: Forecasting Sales
Forecasting in the Public and Not-for-Profit of The Gap 41
Sectors 8 Comments from the Field 47
Forecasting and Supply Chain John Galt Partial Customer List 48
Management 10 An Introduction to ForecastX 7.0 49
Collaborative Forecasting 12 Forecasting with the ForecastX Wizard™ 49
Computer Use and Quantitative Forecasting 15 Using the Five Main Tabs on the Opening
Qualitative or Subjective Forecasting ForecastX Screen 49
Methods 16 Suggested Readings and Web Sites 52
Sales Force Composites 16 Exercises 53
Surveys of Customers and the General
Population 18
Jury of Executive Opinion 18 Chapter 2
The Delphi Method 18 The Forecast Process,Data
Some Advantages and Disadvantages
Considerations,and Model
of Subjective Methods 19
Selection 56
New-Product Forecasting 20
Using Marketing Research to Aid New-Product Introduction 56
Forecasting 20 The Forecast Process 56
The Product Life Cycle Concept Aids in Trend, Seasonal, and Cyclical Data
New-Product Forecasting 21 Patterns 59
viii
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Contents ix
Data Patterns and Model Forecasting Jewelry Sales and Houses
Selection 62 Sold with Exponential Smoothing 143
A Statistical Review 64 Jewelry Sales 143
Descriptive Statistics 64 Houses Sold 145
The Normal Distribution 69 Summary 146
The Student’s t-Distribution 71 Integrative Case: The Gap 147
From Sample to Population: Using ForecastX™ to Make Exponential
Statistical Inference 74 Smoothing Forecasts 149
Hypothesis Testing 76 Suggested Readings 151
Correlation 81 Exercises 152
Correlograms: Another Method of Data
Exploration 84
Total Houses Sold: Exploratory Data Chapter 4
Analysis and Model Selection 87
Introduction to Forecasting with
Business Forecasting: A Process,
Regression Methods 160
Not an Application 89
Integrative Case: The Gap 89 The Bivariate Regression Model 160
Comments from the Field 92 Visualization of Data: An Important Step in
Using ForecastX™ to Find Autocorrelation Regression Analysis 161
Functions 93 A Process for Regression Forecasting 164
Suggested Readings 95 Forecasting with a Simple Linear Trend 165
Exercises 96 Using a Causal Regression Model
to Forecast 171
A Jewelry Sales Forecast Based on Disposable
Chapter 3 Personal Income 173
Statistical Evaluation of Regression
Moving Averages and Exponential
Models 178
Smoothing 101
Basic Diagnostic Checks for Evaluating
Moving Averages 101 Regression Results 178
Simple Exponential Smoothing 107 Using the Standard Error of the Estimate 184
Holt’s Exponential Smoothing 112 Serial Correlation 185
Winters’Exponential Smoothing 118 Heteroscedasticity 190
The Seasonal Indices 120 Cross-Sectional Forecasting 191
Adaptive–Response-Rate Single Exponential Forecasting Total Houses Sold with Two
Smoothing 121 Bivariate Regression Models 193
Using Single, Holt’s, or ADRESSmoothing to Comments from the Field 200
Forecast a Seasonal Data Series 124 Integrative Case: The Gap 200
New-Product Forecasting (Growth Curve Comments from the Field 204
Fitting) 125 Using ForecastX™ to Make Regression
Gompertz Curve 129 Forecasts 205
Logistics Curve 133 Further Comments on Regression
Bass Model 135 Models 210
The Bass Model in Action 136 Suggested Readings 213
Event Modeling 139 Exercises 214