Michael Gilliland (Cary, NC) is Marketing Manager for SAS forecasting software, prior to which he held forecasting positions in the food, consumer electronics, and apparel industries. He is the author of several books and writes The Business Forecasting Deal blog (blogs.sas.com/content/forecasting), is Associate Editor of Foresight: The International Journal of Applied Forecasting, and in 2017 received the Lifetime Achievement Award from the Institute of Business Forecasting. He holds a BA in Philosophy from Michigan State University, and master's degrees in Philosophy and Mathematical Sciences from Johns Hopkins University.

Udo Sglavo (Raleigh, NC) is Director of Forecasting R&D at SAS, where he heads up a team of statisticians and midtier developers working on SAS's award-winning software for large-scale automatic forecasting. Prior to SAS, he spent more than five years providing and consuming advanced analytical content and solutions to enterprises ranging from Fortune 500 companies to Internet startups. He is a member of the practitioner advisory board of Foresight magazine (International Institute of Forecasters).

Foreword ix

Preface xiii

State Of The Art xvii

Forecasting in Social Settings: The State of the Art xvii

Chapter 1 Artificial Intelligence and Machine Learning in Forecasting 1

1.1 Deep Learning for Forecasting 2

1.2 Deep Learning for Forecasting: Current Trends and Challenges 11

1.3 Neural Network–Based Forecasting Strategies 18

1.4 Will Deep and Machine Learning Solve Our Forecasting Problems? 35

1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future 42

1.6 Forecasting the Impact of Artificial Intelligence: Another Voice 54

1.7 Smarter Supply Chains through AI 64

1.8 Continual Learning: The Next Generation of Artificial Intelligence 73

1.9 Assisted Demand Planning Using Machine Learning 80

1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics 85

1.11 The M4 Forecasting Competition – Takeaways for the Practitioner 94

Chapter 2 Big Data in Forecasting 105

2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting? 106

2.2 How Big Data Could Challenge Planning Processes across the Supply Chain 125

Chapter 3 Forecasting Methods: Modeling, Selection, and Monitoring 133

3.1 Know Your Time Series 134

3.2 A Classification of Business Forecasting Problems 141

3.3 Judgmental Model Selection 151

3.4 A Judgment on Judgment 168

3.5 Could These Recent Findings Improve Your Judgmental Forecasts? 177

3.6 A Primer on Probabilistic Demand Planning 181

3.7 Benefits and Challenges of Corporate Prediction Markets 185

3.8 Get Your CoV On . . . 195

3.9 Standard Deviation Is Not the Way to Measure Volatility 200

3.10 Monitoring Forecast Models Using Control Charts 202

3.11 Forecasting the Future of Retail Forecasting 213

Chapter 4 Forecasting Performance 229

4.1 Using Error Analysis to Improve Forecast Performance 230

4.2 Guidelines for Selecting a Forecast Metric 241

4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error 247

4.4 Beware of Standard Prediction Intervals from Causal Models 260

Chapter 5 Forecasting Process: Communication, Accountability, and S&OP 267

5.1 Not Storytellers But Reporters 268

5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? 273

5.3 Communicating the Forecast: Providing Decision Makers with Insights 280

5.4 An S&OP Communication Plan: The Final Step in Support of Company Strategy 287

5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide 295

5.6 How to Identify and Communicate Downturns in Your Business 301

5.7 Common S&OP Change Management Pitfalls to Avoid 308

5.8 Five Steps to Lean Demand Planning 312

5.9 The Move to Defensive Business Forecasting 316

About the Editors 321

Afterwords 323

Index 373

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