Michael Gilliland, Len Tashman, Udo Sglavo

Business Forecasting (eBook, PDF)

The Emerging Role of Artificial Intelligence and Machine Learning

• Format: PDF

Produktbeschreibung

Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field 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. You will find: * Discussions on deep learning in forecasting, including current trends and challenges * Explorations of neural network-based forecasting strategies * A treatment of the future of artificial intelligence in business forecasting * Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 432
• Erscheinungstermin: 29. April 2021
• Englisch
• ISBN-13: 9781119782599
• Artikelnr.: 61925757

Autorenporträt

MICHAEL GILLILAND is Marketing Manager for SAS forecasting software and Associate Editor of Foresight: The International Journal of Applied Forecasting. He is author of The Business Forecasting Deal. LEN TASHMAN is the founding editor of Foresight: The International Journal of Applied Forecasting. He is emeritus professor of business administration at the University of Vermont and Director of the Center for Business Forecasting. UDO SGLAVO is Vice President of Analytics R&D at SAS and holds several patents in the area of advanced analytics. His writings have appeared in Analytics magazine and the book Big Data and Business Analytics.

Inhaltsangabe

Foreword (Spyros Makridakis and Fotios Petropoulos) xi

Preface (Michael Gilliland, Len Tashman, and Udo Sglavo) xv

State of the Art 1

Forecasting in Social Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Fotios Petropoulos) 1

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 F