Demand–Driven Forecasting, Second Edition – A Structured Approach to Forecasting

<p>Foreword xi</p>
<p>Preface xv</p>
<p>Acknowledgments xix</p>
<p>About the Author xx</p>
<p>Chapter 1 Demystifying Forecasting: Myths versus Reality 1</p>
<p>Data Collection, Storage, and Processing Reality 5</p>
<p>Art–of–Forecasting Myth 8</p>
<p>End–Cap Display Dilemma 10</p>
<p>Reality of Judgmental Overrides 11</p>
<p>Oven Cleaner Connection 13</p>
<p>More Is Not Necessarily Better 16</p>
<p>Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans 17</p>
<p>Northeast Regional Sales Composite Forecast 21</p>
<p>Hold–and–Roll Myth 22</p>
<p>The Plan that Was Not Good Enough 23</p>
<p>Package to Order versus Make to Order 25</p>
<p> Do You Want Fries with That? 26</p>
<p>Summary 28</p>
<p>Notes 28</p>
<p>Chapter 2 What Is Demand–Driven Forecasting? 31</p>
<p>Transitioning from Traditional Demand Forecasting 33</p>
<p>What s Wrong with The Demand–Generation Picture? 34</p>
<p>Fundamental Flaw with Traditional Demand Generation 37</p>
<p>Relying Solely on a Supply–Driven Strategy Is Not the Solution 39</p>
<p>What Is Demand–Driven Forecasting? 40</p>
<p>What Is Demand Sensing and Shaping? 41</p>
<p>Changing the Demand Management Process Is Essential 57</p>
<p>Communication Is Key 65</p>
<p>Measuring Demand Management Success 67</p>
<p>Benefits of a Demand–Driven Forecasting Process 68</p>
<p>Key Steps to Improve the Demand</p>
<p>Management Process 70</p>
<p>Why Haven t Companies Embraced the Concept of Demand–Driven? 71</p>
<p>Summary 74</p>
<p>Notes 75</p>
<p>Chapter 3 Overview of Forecasting Methods 77</p>
<p>Underlying Methodology 79</p>
<p>Different Categories of Methods 83</p>
<p>How Predictable Is the Future? 88</p>
<p>Some Causes of Forecast Error 91</p>
<p>Segmenting Your Products to Choose the Appropriate Forecasting Method 94</p>
<p>Summary 101</p>
<p>Note 101</p>
<p>Chapter 4 Measuring Forecast Performance 103</p>
<p> We Overachieved Our Forecast, So Let s Party! 105</p>
<p>Purposes for Measuring Forecasting Performance 106</p>
<p>Standard Statistical Error Terms 107</p>
<p>Specific Measures of Forecast Error 111</p>
<p>Out–of–Sample Measurement 115</p>
<p>Forecast Value Added 118</p>
<p>Summary 122</p>
<p>Notes 123</p>
<p>Chapter 5 Quantitative Forecasting Methods Using Time Series Data 125</p>
<p>Understanding the Model–Fitting Process 127</p>
<p>Introduction to Quantitative Time Series Methods 130</p>
<p>Quantitative Time Series Methods 135</p>
<p>Moving Averaging 136</p>
<p>Exponential Smoothing 142</p>
<p>Single Exponential Smoothing 143</p>
<p>Holt s Two–Parameter Method 147</p>
<p>Holt s–Winters Method 149</p>
<p>Winters Additive Seasonality 151</p>
<p>Summary 156</p>
<p>Notes 158</p>
<p>Chapter 6 Regression Analysis 159</p>
<p>Regression Methods 160</p>
<p>Simple Regression 160</p>
<p>Correlation Coefficient 163</p>
<p>Coefficient of Determination 165</p>
<p>Multiple Regression 166</p>
<p>Data Visualization Using Scatter Plots and Line Graphs 170</p>
<p>Correlation Matrix 173</p>
<p>Multicollinearity 175</p>
<p>Analysis of Variance 178</p>
<p>F–test 178</p>
<p>Adjusted R2 180</p>
<p>Parameter Coefficients 181</p>
<p>t–test 184</p>
<p>P–values 185</p>
<p>Variance Inflation Factor 186</p>
<p>Durbin–Watson Statistic 187</p>
<p>Intervention Variables (or Dummy Variables) 191</p>
<p>Regression Model Results 197</p>
<p>Key Activities in Building a Multiple Regression Model 199</p>
<p>Cautions about Regression Models 201</p>
<p>Summary 201</p>
<p>Notes 202</p>
<p>Chapter 7 ARIMA Models 203</p>
<p>Phase 1: Identifying the Tentative Model 204</p>
<p>Phase 2: Estimating and Diagnosing the Model Parameter Coefficients 213</p>
<p>Phase 3: Creating a Forecast 216</p>
<p>Seasonal ARIMA Models 216</p>
<p>Box–Jenkins Overview 225</p>
<p>Extending ARIMA Models to Include Explanatory Variables 226</p>
<p>Transfer Functions 229</p>
<p>Numerators and Denominators 229</p>
<p>Rational Transfer Functions 230</p>
<p>ARIMA Model Results 234</p>
<p>Summary 235</p>
<p>Notes 237</p>
<p>Chapter 8 Weighted Combined Forecasting Methods 239</p>
<p>What Is Weighted Combined Forecasting? 242</p>
<p>Developing a Variance Weighted Combined Forecast 245</p>
<p>Guidelines for the Use of Weighted Combined Forecasts 248</p>
<p>Summary 250</p>
<p>Notes 251</p>
<p>Chapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA 253</p>
<p>Linking Demand to Supply Using Multi–Tiered Causal Analysis 256</p>
<p>Case Study: The Carbonated Soft Drink Story 259</p>
<p>Summary 276</p>
<p>Appendix 9A Consumer Packaged Goods Terminology 277</p>
<p>Appendix 9B Adstock Transformations for Advertising GRP/TRPs 279</p>
<p>Notes 282</p>
<p>Chapter 10 New Product Forecasting: Using Structured Judgment 283</p>
<p>Differences between Evolutionary and Revolutionary New Products 284</p>
<p>General Feeling about New Product Forecasting 286</p>
<p>New Product Forecasting Overview 288</p>
<p>What Is a Candidate Product? 292</p>
<p>New Product Forecasting Process 293</p>
<p>Structured Judgment Analysis 294</p>
<p>Structured Process Steps 296</p>
<p>Statistical Filter Step 303</p>
<p>Model Step 305</p>
<p>Forecast Step 308</p>
<p>Summary 313</p>
<p>Notes 316</p>
<p>Chapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process 317</p>
<p>Strategic Value Assessment Framework 319</p>
<p>Strategic Value Assessment Process 321</p>
<p>SVA Case Study: XYZ Company 323</p>
<p>Summary 351</p>
<p>Suggested Reading 352</p>
<p>Notes 352</p>
<p>Index 355</p>