MICHAEL GILLILAND is Product Marketing Manager at SAS Institute and has worked in consu-mer products forecasting for more than twenty years. Prior to joining SAS in 2004, Mike held forecasting management positions 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.[...]

Foreword-Tom Wallace xiii

Foreword-Anne G. Robinson xv

Acknowledgments xvii

Prologue 1

Chapter 1 Fundamental Issues in Business Forecasting 5

The Problem of Induction 5

The Realities of Business Forecasting 6

The Contest 7

What Is Demand? 10

Constrained Forecast 13

Demand Volatility 15

Inherent Volatility and Artificial Volatility 17

Evils of Volatility 19

Evaluating Forecast Performance 22

Embarking on Improvement 24

Notes 26

Chapter 2 Worst Practices in Business Forecasting: Part 1 29

Worst Practices in the Mechanics of Forecasting 30

Model "Overfitting" and "Pick-Best" Selection 32

Confusing Model Fit with Forecast Accuracy 41

Accuracy Expectations and Performance Goals 43

Failure to Use a Naïve Model or Assess Forecast Value Added 47

Forecasting Hierarchies 48

Outlier Handling 50

Notes 54

Chapter 3 Worst Practices in Business Forecasting: Part 2 55

Worst Practices in the Process and Practices of Forecasting 55

Politics of Forecasting 57

Blaming the Forecast 60

Adding Variation to Demand 61

Evangelical Forecasting 64

Overinvesting in the Forecasting Function 66

Forecasting Performance Measurement and Reporting 69

Forecasting Software Selection 74

Editorial Comment on Forecasting Practices 76

Notes 78

Chapter 4 Forecast Value Added Analysis 81

What Is Forecast Value Added? 82

The Naïve Forecast 83

Why Is FVA Important? 90

FVA Analysis: Step-by-Step 92

Further Application of FVA Analysis 101

Case Studies 102

Summary: The Lean Approach to Forecasting 107

Notes 108

Chapter 5 Forecasting without History.111

Typical New Product Forecasting Situations 111

New Product Forecasting by Structured Analogy 114

Organizational Realignment 120

Summary 131

Notes 132

Chapter 6 Alternative Approaches to the Problems of Business Forecasting.133

Statistical Approach 134

Collaborative Approach 136

Supply Chain Engineering Approach 142

Pruning Approach 145

Summary 149

Notes 150

Chapter 7 Implementing a Forecasting Solution 151

Why Do Forecasting Implementations Fail? 151

Preproject Assessment 153

Requesting Information or Proposals 154

Evaluating Software Vendors 155

Warning Signs of Failure 157

Notes 159

Chapter 8 Practical First Steps 161

Step 1: Recognize the Volatility versus Accuracy Relationship 161

Step 2: Determine Inherent and Artifi cial Volatility 165

Step 3: Understand What Accuracy Is Reasonable to Expect 166

Step 4: Use Forecast Value Added Analysis to Eliminate Wasted Efforts 167

Step 5: Utilize Meaningful Performance Metrics and Reporting 168

Step 6: Eliminate Worst Practices 169

Step 7: Consult Forecasting Resources 170

Notes 173

Chapter 9 What Management Must Know About Forecasting 175

Aphorism 1: Forecasting Is a Huge Waste of Management Time 175

Aphorism 2: Accuracy Is Determined More by the Nature of the Behavior Being Forecast than by the Specific Method Being Used to Forecast It 177

Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness 179

Aphorism 4: You May Not Control the Accuracy Achieved, But You Can Control the Process Used and the Resources You Invest 180

Aphorism 5: The Surest Way to Get a Better Forecast Is to Make the Demand Forecastable 182

Aphorism 6: Minimize the Organization's Reliance on Forecasting 183

Aphorism 7: Before Investing in a New System or Process, Put It to the Test 184

Notes 185

Epilogue 187

Glossary 189

Appendix Forecasting FAQs 193

Accuracy Expectations 193

Performance Benchmarks 196

Performance Measurement and Reporting 198

The Naïve Forecast 208

Forecast Value Added Analysis 211

Forecast Modeling 220

Politics and Practices of Forecasting 224

Demand Volatility 227

Forecasting Process 230

Judgment 237

Forecasting Organization 238

Low Volume/Intermittent Demand 239

New Product Forecasting 241

Forecasting Hierarchy 242

Software Selection 245

Index 247