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<p>Foreword xv</p> <p>Preface xix</p> <p><b>Chapter 1 Fundamental Considerations in Business Forecasting 1</b></p> <p>1.1 Getting Real about Uncertainty (Paul Goodwin) 3</p> <p>1.2 What Demand Planners Can Learn from the Stock Market (Charles K. Re Corr) 9</p> <p>1.3 Toward a More Precise Defi nition of Forecastability (John Boylan) 14</p> <p>1.4 Forecastability: A New Method for Benchmarking and Driving Improvement (Sean Schubert) 22</p> <p>1.5 Forecast Errors and Their Avoidability (Steve Morlidge) 36</p> <p>1.6 The Perils of Benchmarking (Michael Gilliland) 46</p> <p>1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy? (Stephan Kolassa) 48</p> <p>1.8 Defi ning “Demand” for Demand Forecasting (Michael Gilliland) 60</p> <p>1.9 Using Forecasting to Steer the Business: Six Principles (Steve Morlidge) 67</p> <p>1.10 The Beauty of Forecasting (David Orrell) 76</p> <p><b>Chapter 2 Methods of Statistical Forecasting 81</b></p> <p>2.1 Confessions of a Pragmatic Forecaster (Chris Chatfi eld) 82</p> <p>2.2 New Evidence on the Value of Combining Forecasts (Paul Goodwin) 92</p> <p>2.3 How to Forecast Data Containing Outliers (Eric Stellwagen) 95</p> <p>2.4 Selecting Your Statistical Forecasting Level (Eric Stellwagen) 98</p> <p>2.5 When Is a Flat-line Forecast Appropriate? (Eric Stellwagen) 102</p> <p>2.6 Forecasting by Time Compression (Udo Sglavo) 104</p> <p>2.7 Data Mining for Forecasting: An Introduction (Chip Wells and Tim Rey) 112</p> <p>2.8 Process and Methods for Data Mining for Forecasting (Chip Wells and Tim Rey) 120</p> <p>2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get? (Roy Batchelor) 126</p> <p>2.10 Good Patterns, Bad Patterns (Roy Batchelor) 135</p> <p><b>Chapter 3 Forecasting Performance Evaluation and Reporting 143</b></p> <p>3.1 Dos and Don’ts of Forecast Accuracy Measurement: A Tutorial (Len Tashman) 144</p> <p>3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement (Jim Hoover) 160</p> <p>3.3 A “Softer” Approach to the Measurement of Forecast Accuracy (John Boylan) 170</p> <p>3.4 Measuring Forecast Accuracy (Rob Hyndman) 177</p> <p>3.5 Should We Defi ne Forecast Error as e = F − A or e = A − F? (Kesten Green and Len Tashman) 184</p> <p>3.6 Percentage Error: What Denominator? (Kesten Green and Len Tashman) 188</p> <p>3.7 Percentage Errors Can Ruin Your Day (Stephan Kolassa and Roland Martin) 195</p> <p>3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand (Rob Hyndman) 204</p> <p>3.9 Advantages of the MAD/Mean Ratio over the MAPE (Stephan Kolassa and Wolfgang Schütz) 211</p> <p>3.10 Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations (Lauge Valentin) 217</p> <p>3.11 An Expanded Prediction-Realization Diagram for Assessing Forecast Errors (Roy Pearson) 228</p> <p>3.12 Forecast Error Measures: Critical Review and Practical Recommendations (Andrey Davydenko and Robert Fildes) 238</p> <p>3.13 Measuring the Quality of Intermittent Demand Forecasts: It’s Worse than We’ve Thought! (Steve Morlidge) 250</p> <p>3.14 Managing Forecasts by Exception (Eric Stellwagen) 259</p> <p>3.15 Using Process Behavior Charts to Improve Forecasting and Decision Making (Martin Joseph and Alec Finney) 262</p> <p>3.16 Can Your Forecast Beat the Naïve Forecast? (Shaun Snapp) 276</p> <p><b>Chapter 4 Process and Politics of Business Forecasting 281</b></p> <p>4.1 FVA: A Reality Check on Forecasting Practices (Michael Gilliland) 282</p> <p>4.2 Where Should the Forecasting Function Reside? (Larry Lapide) 288</p> <p>4.3 Setting Forecasting Performance Objectives (Michael Gilliland) 294</p> <p>4.4 Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain (Steve Morlidge) 297</p> <p>4.5 Why Should I Trust Your Forecasts? (M. Sinan Gönül, Dilek Önkal, and Paul Goodwin) 309</p> <p>4.6 High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem? (Paul Goodwin) 315</p> <p>4.7 Should the Forecasting Process Eliminate Face-to-Face Meetings? (J. Scott Armstrong) 319</p> <p>4.8 The Impact of Sales Forecast Game Playing on Supply Chains (John Mello) 327</p> <p>4.9 Role of the Sales Force in Forecasting (Michael Gilliland) 340</p> <p>4.10 Good and Bad Judgment in Forecasting: Lessons from Four Companies (Robert Fildes and Paul Goodwin) 349</p> <p>4.11 Worst Practices in New Product Forecasting (Michael Gilliland) 358</p> <p>4.12 Sales and Operations Planning in the Retail Industry (Jack Harwell) 363</p> <p>4.13 Sales and Operations Planning: Where Is It Going? (Tom Wallace) 372</p> <p>About the Editors 381</p> <p>Index 383</p>

<p><b>MICHAEL GILLILAND</b> is Marketing Manager for SAS forecasting software, editor of the Forecasting Practice section of <i>Foresight: The International Journal of AppliedForecasting</i>, and author of <i>The Business Forecasting Deal</i>. He has published articles in <i>Supply Chain Management Review, Journal of Business Forecasting, Analytics, Supply Chain Forecasting Digest, APICS Magazine, Swiss Analytics Magazine</i>, and <i>Foresight</i>. 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, <i>The Business Forecasting Deal,</i> at blogs.sas.com/content/forecasting. <p><b>LEN TASHMAN</b> is the founding editor of <i>Foresight: The International Journal of Applied Forecasting</i>, now in its 10th year of publication. He serves on the Board of Directors of the International Institute of Forecasters and is organizer and chair of the Forecasting in Practice Track at the annual International Symposium on Forecasting. Len is an emeritus professor of business administration at the University of Vermont and Director of the Center for Business Forecasting. <p><b>UDO SGLAVO</b> is Senior Director of Predictive Modeling R&D at SAS Institute. His team develops industry-leading and award-winning software for data mining, machine learning, and large scale automatic forecasting. He has published articles in <i>Analytics</i>, and is a contributor to <i>The Business Forecasting Deal</i> blog. Udo has served on the practitioner advisory board of <i>Foresight</i>, and holds a diploma in mathematics from University of Applied Sciences, Darmstadt, Germany.

<p>In <i>Business Forecasting</i>, editors Michael Gilliland, Len Tashan, and Udo Sglavo have assembled in one comprehensive resource the most influential studies and writings in applied forecasting. The expert contributors focus on recent discoveries and reveal new methds and approaches in forecasting as well as touch on many controversial topics. Throughout the book, the authors emphasize the fact that enlightened forecasting management (not just fancy new algorithms) is the best way to improve forecasting practice. This valuable collection gives forecast analysts, demand planners, and anyone involved in the forecasting process a practical and thought-provoking guide for forecast modeling and process improvement. <p>Designed to be practical, <i>Business Forecasting</i> explores the fundamental issues of forecasting and contains approaches for avoiding errors, methods for obtaining valid benchmarks, and shows how to use forecasting to steer business on the right course. The authors include information on methods of statistical forecasting and reveal how to select a statistical forecasting level and enhance forecasting by using the data mining tactic. In addition, the text puts the spotlight on performance evaluation and reporting and offers suggestions for tracking forecast accuracy and applying the use of scaled errors in addition to percentage errors in order to monitor forecasting accuracy. The text also contains a thorough review of the overall forecasting process and information on the inevitable politics involved in business forecasting. <p>To help hone forecasting skills, this vital resource covers a wide range of essential tools and topics such as employing accuracy metrics, applying quality assessments, modeling problem data, and overcoming dysfunctional behaviors. The authors also review typically overlooked issues including uncertainty, randomness, and forecastability, as well as offer an examination of emerging areas like forecast value added analysis. <p><i>Business Forecasting</i> is your hands-on guide to the latest developments in the field and contains an array of tools that can enable better processes and improved results.

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