Foreword xi
Preface xv
Acknowledgments xix
About the Author xx
Chapter 1 Demystifying Forecasting: Myths versus Reality 1
Data Collection, Storage, and Processing Reality 5
Art-of-Forecasting Myth 8
End-Cap Display Dilemma 10
Reality of Judgmental Overrides 11
Oven Cleaner Connection 13
More Is Not Necessarily Better 16
Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans 17
Northeast Regional Sales Composite Forecast 21
Hold-and-Roll Myth 22
The Plan that Was Not Good Enough 23
Package to Order versus Make to Order 25
“Do You Want Fries with That?” 26
Summary 28
Notes 28
Chapter 2 What Is Demand-Driven Forecasting? 31
Transitioning from Traditional Demand Forecasting 33
What’s Wrong with The Demand-Generation Picture? 34
Fundamental Flaw with Traditional Demand Generation 37
Relying Solely on a Supply-Driven Strategy Is Not the Solution 39
What Is Demand-Driven Forecasting? 40
What Is Demand Sensing and Shaping? 41
Changing the Demand Management Process Is Essential 57
Communication Is Key 65
Measuring Demand Management Success 67
Benefits of a Demand-Driven Forecasting Process 68
Key Steps to Improve the Demand Management Process 70
Why Haven’t Companies Embraced the Concept of Demand-Driven? 71
Summary 74
Notes 75
Chapter 3 Overview of Forecasting Methods 77
Underlying Methodology 79
Different Categories of Methods 83
How Predictable Is the Future? 88
Some Causes of Forecast Error 91
Segmenting Your Products to Choose the Appropriate Forecasting Method 94
Summary 101
Note 101
Chapter 4 Measuring Forecast Performance 103
“We Overachieved Our Forecast, So Let’s Party!” 105
Purposes for Measuring Forecasting Performance 106
Standard Statistical Error Terms 107
Specific Measures of Forecast Error 111
Out-of-Sample Measurement 115
Forecast Value Added 118
Summary 122
Notes 123
Chapter 5 Quantitative Forecasting Methods Using Time Series Data 125
Understanding the Model-Fitting Process 127
Introduction to Quantitative Time Series Methods 130
Quantitative Time Series Methods 135
Moving Averaging 136
Exponential Smoothing 142
Single Exponential Smoothing 143
Holt’s Two-Parameter Method 147
Holt’s-Winters’ Method 149
Winters’ Additive Seasonality 151
Summary 156
Notes 158
Chapter 6 Regression Analysis 159
Regression Methods 160
Simple Regression 160
Correlation Coefficient 163
Coefficient of Determination 165
Multiple Regression 166
Data Visualization Using Scatter Plots and Line Graphs 170
Correlation Matrix 173
Multicollinearity 175
Analysis of Variance 178
F-test 178
Adjusted R 2 180
Parameter Coefficients 181
t-test 184
P-values 185
Variance Inflation Factor 186
Durbin-Watson Statistic 187
Intervention Variables (or Dummy Variables) 191
Regression Model Results 197
Key Activities in Building a Multiple Regression Model 199
Cautions about Regression Models 201
Summary 201
Notes 202
Chapter 7 ARIMA Models 203
Phase 1: Identifying the Tentative Model 204
Phase 2: Estimating and Diagnosing the Model Parameter Coefficients 213
Phase 3: Creating a Forecast 216
Seasonal ARIMA Models 216
Box-Jenkins Overview 225
Extending ARIMA Models to Include Explanatory Variables 226
Transfer Functions 229
Numerators and Denominators 229
Rational Transfer Functions 230
ARIMA Model Results 234
Summary 235
Notes 237
Chapter 8 Weighted Combined Forecasting Methods 239
What Is Weighted Combined Forecasting? 242
Developing a Variance Weighted Combined Forecast 245
Guidelines for the Use of Weighted Combined Forecasts 248
Summary 250
Notes 251
Chapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA 253
Linking Demand to Supply Using Multi-Tiered Causal Analysis 256
Case Study: The Carbonated Soft Drink Story 259
Summary 276
Appendix 9A Consumer Packaged Goods Terminology 277
Appendix 9B Adstock Transformations for Advertising GRP/TRPs 279
Notes 282
Chapter 10 New Product Forecasting: Using Structured Judgment 283
Differences between Evolutionary and Revolutionary New Products 284
General Feeling about New Product Forecasting 286
New Product Forecasting Overview 288
What Is a Candidate Product? 292
New Product Forecasting Process 293
Structured Judgment Analysis 294
Structured Process Steps 296
Statistical Filter Step 303
Model Step 305
Forecast Step 308
Summary 313
Notes 316
Chapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process 317
Strategic Value Assessment Framework 319
Strategic Value Assessment Process 321
SVA Case Study: XYZ Company 323
Summary 351
Suggested Reading 352
Notes 352
Index 355
CHARLES W. CHASE Jr. is the Chief Industry Consultant in SAS's Manufacturing & Supply Chain Global Practice, where he is the principal architect and strategist for delivering demand planning and forecasting solutions to improve SAS customers' supply chain efficiencies. He has more than twenty-six years of experience in the consumer packaged goods industry, and is an expert in sales forecasting, market response modeling, econometrics, and supply chain management.
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