The Business Forecasting Deal Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions

by Gilliland, Michael

• ISBN13: 9780470574430
• ISBN10: 0470574437
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-06-08
• Publisher: Wiley

Summary

There are many books that discuss statistical forecasting modeling; this book is not one of them. This book provides a practical approach for the nonstatistical problems that forecasters face (e.g., what needs to be forecasted, how often a forecast should take place, what is volatility, and problems in business forecasting). The tentative contents is:Chapter 1: Fundamental Issues of Business Forecasting (Basic Principles, Definition of Demand, What to Forecast, What Accuracy is Required, The Naive Forecast, The Politics of Forecasting)Chapter 2: The Problems of Business Forecasting (Demand Volatility, Performance Measurement, Forecasting Without History)Chapter 3: Alternative Solutions to the Forecasting Problem (Standard Forecasting Methods, Failings of the Statistical Approach, Evangelical Forecasting, Supply Chain Engineering, Collaborative Planning, Forecasting, and Replenishment, Demand Smoothing, Proactive Collaboration, Pruning)Chapter 4: Forecast Value Added Analysis (The Lean Approach to Forecasting, Data Requirements, Analysis and Reporting, Presenting Results to Management, Streamlining the Process)Chapter 5: Worst Practices in Business Forecasting (Inappropriate Performance Goals, Overfitting Statistical Models, Handling Outliers, Gaming the Metrics, Software Selection, Forecasting When You Shouldn't, Blaming the Forecast)Chapter 6: The Aphorisms of Business ForecastingChapter 7: Practical First Steps

Author Biography

Michael Gilliland is Product Marketing Manager at SAS Institute and has worked in consumer products forecasting for more than twenty years. Prior to joining SAS in 2004, Mike held forecasting management positins in the food, electronics, and apparel industries and served as a consultant. He is a frequent speaker at industry events, has published articles in Supply Chain Management Review, Journal of Business Forecasting, Foresight, and APICS magazine, and was a columnist on "Worst Practices in Business Forecasting" for Supply Chain Forecasting Digest. Mike holds a BA in philosophy from Michigan State University, and master's degrees in philosophy and mathematical sciences from Johns Hopkins University. Follow his blog, The Business Forecasting Deal, at blogs.sas.com/forecasting.

Table of Contents

Foreword	p. xiii
Foreword	p. xv
Acknowledgments	p. xvii
Prologue	p. 1
Fundamental Issues in Business Forecasting	p. 5
The Problem of Induction	p. 5
The Realities of Business Forecasting	p. 6
The Contest	p. 7
What Is Demand?	p. 10
Constrained Forecast	p. 13
Demand Volatility	p. 15
Inherent Volatility and Artificial Volatility	p. 17
Evils of Volatility	p. 19
Evaluating Forecast Performance	p. 22
Embarking on Improvement	p. 24
Notes	p. 26
Worst Practices in Business Forecasting: Part 1	p. 29
Worst Practices in the Mechanics of Forecasting	p. 30
Model "Overfitting" and "Pick-Best" Selection	p. 32
Confusing Model Fit with Forecast Accuracy	p. 41
Accuracy Expectations and Performance Goals	p. 43
Failure to Use a Naïve Model or Assess Forecast Value Added	p. 47
Forecasting Hierarchies	p. 48
Outlier Handling	p. 50
Notes	p. 54
Worst Practices in Business Forecasting: Part 2	p. 55
Worst Practices in the Process and Practices of Forecasting	p. 55
Politics of Forecasting	p. 57
Blaming the Forecast	p. 60
Adding Variation to Demand	p. 61
Evangelical Forecasting	p. 64
Overinvesting in the Forecasting Function	p. 66
Forecasting Performance Measurement and Reporting	p. 69
Forecasting Software Selection	p. 74
Editorial Comment on Forecasting Practices	p. 76
Notes	p. 78
Forecast Value Added Analysis	p. 81
What Is Forecast Value Added?	p. 82
The Naïve Forecast	p. 83
Why Is FVA Important?	p. 90
FVA Analysis: Step-by-Step	p. 92
Further Application of FVA Analysis	p. 101
Case Studies	p. 102
Summary: The Lean Approach to Forecasting	p. 107
Notes	p. 108
Forecasting Without History	p. 111
Typical New Product Forecasting Situations	p. 111
New Product Forecasting by Structured Analogy	p. 114
Organizational Realignment	p. 120
Summary	p. 131
Notes	p. 132
Alternative Approaches to the Problems of Business Forecasting	p. 133
Statistical Approach	p. 134
Collaborative Approach	p. 136
Supply Chain Engineering Approach	p. 142
Pruning Approach	p. 145
Summary	p. 149
Notes	p. 150
Implementing a Forecasting Solution	p. 151
Why Do Forecasting Implementations Fail?	p. 151
Preproject Assessment	p. 153
Requesting Information or Proposals	p. 154
Evaluating Software Vendors	p. 155
Warning Signs of Failure	p. 157
Notes	p. 159
Practical First Steps	p. 161
Recognize the Volatility versus Accuracy Relationship	p. 161
Determine Inherent and Artificial Volatility	p. 165
Understand What Accuracy Is Reasonable to Expect	p. 166
Use Forecast Value Added Analysis to Eliminate Wasted Efforts	p. 167
Utilize Meaningful Performance Metrics and Reporting	p. 168
Eliminate Worst Practices	p. 169
Consult Forecasting Resources	p. 170
Notes	p. 173
What Management Must Know About Forecasting	p. 175
Forecasting Is a Huge Waste of Management Time	p. 175
Accuracy Is Determined More by the Nature of the Behavior Being Forecast than by the Specific Method Being Used to Forecast It	p. 177
Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness	p. 179
You May Not Control the Accuracy Achieved, But You Can Control the Process Used and the Resources You Invest	p. 180
The Surest Way to Get a Better Forecast Is to Make the Demand Forecastable	p. 182
Minimize the Organization's Reliance on Forecasting	p. 183
Before Investing in a New System or Process, Put It to the Test	p. 184
Epilogue	p. 187
Glossary	p. 189
Appendix: Forecasting FAQs	p. 193
Accuracy Expectations	p. 193
Performance Benchmarks	p. 196
Performance Measurement and Reporting	p. 198
The Naïve Forecast	p. 208
Forecast Value Added Analysis	p. 211
Forecast Modeling	p. 220
Politics and Practices of Forecasting	p. 224
Demand Volatility	p. 227
Forecasting Process	p. 230
Judgment	p. 237
Forecasting Organization	p. 238
Low Volume/Intermittent Demand	p. 239
New Product Forecasting	p. 241
Forecasting Hierarchy	p. 242
Software Selection	p. 245
Index	p. 247
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