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.sas.com/forecasting.

• Foreword—Tom Wallace xiiiForeword—Anne G. Robinson xvAcknowledgments xviiPrologue 1Chapter 1 Fundamental Issues in Business Forecasting 5The Problem of Induction 5The Realities of Business Forecasting 6The Contest 7What Is Demand? 10Constrained Forecast 13Demand Volatility 15Inherent Volatility and Artificial Volatility 17Evils of Volatility 19Evaluating Forecast Performance 22Embarking on Improvement 24Notes 26Chapter 2 Worst Practices in Business Forecasting: Part 1 29Worst Practices in the Mechanics of Forecasting 30Model “Overfitting” and “Pick-Best” Selection 32Confusing Model Fit with Forecast Accuracy 41Accuracy Expectations and Performance Goals 43Failure to Use a Naïve Model or Assess Forecast Value Added 47Forecasting Hierarchies 48Outlier Handling 50Notes 54Chapter 3 Worst Practices in Business Forecasting: Part 2 55Worst Practices in the Process and Practices of Forecasting 55Politics of Forecasting 57Blaming the Forecast 60Adding Variation to Demand 61Evangelical Forecasting 64Overinvesting in the Forecasting Function 66Forecasting Performance Measurement and Reporting 69Forecasting Software Selection 74Editorial Comment on Forecasting Practices 76Notes 78Chapter 4 Forecast Value Added Analysis 81What Is Forecast Value Added? 82The Naïve Forecast 83Why Is FVA Important? 90FVA Analysis: Step-by-Step 92Further Application of FVA Analysis 101Case Studies 102Summary: The Lean Approach to Forecasting 107Notes 108Chapter 5 Forecasting without History.111Typical New Product Forecasting Situations 111New Product Forecasting by Structured Analogy 114Organizational Realignment 120Summary 131Notes 132Chapter 6 Alternative Approaches to the Problems of Business Forecasting.133Statistical Approach 134Collaborative Approach 136Supply Chain Engineering Approach 142Pruning Approach 145Summary 149Notes 150Chapter 7 Implementing a Forecasting Solution 151Why Do Forecasting Implementations Fail? 151Preproject Assessment 153Requesting Information or Proposals 154Evaluating Software Vendors 155Warning Signs of Failure 157Notes 159Chapter 8 Practical First Steps 161Step 1: Recognize the Volatility versus Accuracy Relationship 161Step 2: Determine Inherent and Artifi cial Volatility 165Step 3: Understand What Accuracy Is Reasonable to Expect 166Step 4: Use Forecast Value Added Analysis to Eliminate Wasted Efforts 167Step 5: Utilize Meaningful Performance Metrics and Reporting 168Step 6: Eliminate Worst Practices 169Step 7: Consult Forecasting Resources 170Notes 173Chapter 9 What Management Must Know About Forecasting 175Aphorism 1: Forecasting Is a Huge Waste of Management Time 175Aphorism 2: Accuracy Is Determined More by the Nature of the Behavior Being Forecast than by the Specific Method Being Used to Forecast It 177Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness 179Aphorism 4: You May Not Control the Accuracy Achieved, But You Can Control the Process Used and the Resources You Invest 180Aphorism 5: The Surest Way to Get a Better Forecast Is to Make the Demand Forecastable 182Aphorism 6: Minimize the Organization’s Reliance on Forecasting 183Aphorism 7: Before Investing in a New System or Process, Put It to the Test 184Notes 185Epilogue 187Glossary 189Appendix Forecasting FAQs 193Accuracy Expectations 193Performance Benchmarks 196Performance Measurement and Reporting 198The Naïve Forecast 208Forecast Value Added Analysis 211Forecast Modeling 220Politics and Practices of Forecasting 224Demand Volatility 227Forecasting Process 230Judgment 237Forecasting Organization 238Low Volume/Intermittent Demand 239New Product Forecasting 241Forecasting Hierarchy 242Software Selection 245Index 247