Business Forecasting Michael Gilliland 1st edition

Business Forecasting – Ebook Instant Download/Delivery ISBN(s): 9781119782476,1119782473

Product details:

• ISBN-10 ‏ : ‎ 1119782473
• ISBN-13 ‏ : ‎ 978-1119782476
• Author: MICHAEL GILLILAND

In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You’ll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting.

Table contents:

Chapter 1 Artificial Intelligence and Machine Learning in Forecasting 31

1.1 Deep Learning for Forecasting (Tim Januschowski and colleagues) 32

1.2 Deep Learning for Forecasting: Current Trends and Challenges (Tim Januschowski and Colleagues) 41

1.3 Neural Network–Based Forecasting Strategies (Steven Mills and Susan Kahler) 48

1.4 Will Deep and Machine Learning Solve Our Forecasting Problems? (Stephan Kolassa) 65

1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future (Spyros Makridakis) 72

Commentary: Spyros Makridakis’s Article “Forecasting The Impact Of Artificial Intelligence” (Owen Davies) 80

1.6 Forecasting the Impact of Artificial Intelligence: Another Voice (Lawrence Vanston) 84

Commentary: Response to Lawrence Vanston (Spyros Makridakis) 92

1.7 Smarter Supply Chains through AI (Duncan Klett) 94

1.8 Continual Learning: The Next Generation of Artificial Intelligence (Daniel Philps) 103

1.9 Assisted Demand Planning Using Machine Learning (Charles Chase) 110

1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics (Jeff Baker) 115

1.11 The M4 Forecasting Competition — Takeaways for the Practitioner (Michael Gilliland) 124

Commentary –The M4 Competition and a Look to the Future (Fotios Petropoulos) 132

Chapter 2 Big Data in Forecasting 135

2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting? (Shaun Snapp) 136

Commentary: Becoming Responsible Consumers of Big Data (Chris Gray) 142

Commentary: Customer versus Item Forecasting (Michael Gilliland) 146

Commentary: Big Data or Big Hype? (Stephan Kolassa) 148

Commentary: Big Data, a Big Decision (Niels van Hove) 150

Commentary: Big Data and the Internet of Things (Peter Catt) 152

2.2 How Big Data Could Challenge Planning Processes across the Supply Chain (Tonya Boone, Ram Ganeshan, and Nada Sanders) 155

Chapter 3 Forecasting Methods: Modeling, Selection, and Monitoring 163

3.1 Know Your Time Series (Stephan Kolassa and Enno Siemsen) 164

3.2 A Classification of Business Forecasting Problems (Tim Januschowski and Stephan Kolassa) 171

3.3 Judgmental Model Selection (Fotios Petropoulos) 181

Commentary: A Surprisingly Useful Role for Judgment (Paul Goodwin) 192

Commentary: Algorithmic Aversion and Judgmental Wisdom (Nigel Harvey) 194

Commentary: Model Selection in Forecasting Software (Eric Stellwagen) 195

Commentary: Exploit Information from the M4 Competition (Spyros Makridakis) 197

3.4 A Judgment on Judgment (Paul Goodwin) 198

3.5 Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) 207

3.6 A Primer on Probabilistic Demand Planning (Stefan de Kok) 211

3.7 Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) 215

3.8 Get Your CoV On . . . (Lora Cecere) 225

3.9 Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) 230

3.10 Monitoring Forecast Models Using Control Charts (Joe Katz) 232

3.11 Forecasting the Future of Retail Forecasting (Stephan Kolassa) 243 Commentary (Brian Seaman) 255

Chapter 4 Forecasting Performance 259

4.1 Using Error Analysis to Improve Forecast Performance (Steve Morlidge) 260

4.2 Guidelines for Selecting a Forecast Metric (Patrick Bower) 271

4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error (Stefan de Kok) 277

4.4 Beware of Standard Prediction Intervals from Causal Models (Len Tashman) 290

Chapter 5 Forecasting Process: Communication, Accountability, and S&OP 297

5.1 Not Storytellers But Reporters (Steve Morlidge) 298

5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? (Chris Gray) 303

5.3 Communicating the Forecast: Providing Decision Makers with Insights (Alec Finney) 310

5.4 An S&OP Communication Plan: The Final Step in Support of Company Strategy (Niels van Hove) 317

5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide (Todd Tomalak) 325

5.6 How to Identify and Communicate Downturns in Your Business (Larry Lapide) 331

5.7 Common S&OP Change Management Pitfalls to Avoid (Patrick Bower) 338

5.8 Five Steps to Lean Demand Planning (John Hellriegel) 342

5.9 The Move to Defensive Business Forecasting (Michael Gilliland) 346

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