Business forecasting : the emerging role of artificial intelligence and machine learning

Business forecasting : the emerging role of artificial intelligence and machine learning /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Gilliland, Michael (Editor), Tashman, Len, 1942- (Editor), Sglavo, Udo, 1968- (Editor)
Format: Electronic eBook
Language:English
Published: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021]
Series:Wiley and SAS business series.
Subjects:
Business forecasting.
Artificial intelligence.
Machine learning.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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245 0 0 |a Business forecasting :  |b the emerging role of artificial intelligence and machine learning /  |c edited by Michael Gilliland, Len Tashman, Udo Sglavo. 
264 1 |a Hoboken, New Jersey :  |b John Wiley & Sons, Inc.,  |c [2021] 
300 |a 1 online resource ( xvii, 414 pages) :  |b illustrations (some color). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Wiley and SAS business series 
504 |a Includes bibliographical references and index. 
505 0 0 |a Machine generated contents note:   |t Forecasting in Social Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Fotios Petropoulos) --   |g ch. 1   |t Artificial intelligence and Machine Learning in Forecasting --   |g 1.1.  |t Deep Learning for Forecasting (Tim Januschowski and colleagues) --   |g 1.2.  |t Deep Learning for Forecasting: Current Trends and Challenges (Tim Januschowski and Colleagues) --   |g 1.3.  |t Neural Network-Based Forecasting Strategies (Steven Mills and Susan Kahler) --   |g 1.4.  |t Will Deep and Machine Learning Solve Our Forecasting Problems? (Stephan Kolassa) --   |g 1.5.  |t Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future (Spyros Makridakis) --   |t Commentary: Spyros Makridakis's Article "Forecasting The Impact Of Artificial Intelligence" (Owen Davies) --   |g 1.6.  |t Forecasting the Impact of Artificial Intelligence: Another Voice (Lawrence Vanston) --   |t Commentary: Response to Lawrence Vanston (Spyros Makridakis) --   |g 1.7.  |t Smarter Supply Chains through AI (Duncan Klett) --   |g 1.8.  |t Continual Learning: The Next Generation of Artificial Intelligence (Daniel Philps) --   |g 1.9.  |t Assisted Demand Planning Using Machine Learning (Charles Chase) --   |g 1.10.  |t Maximizing Forecast Value Add through Machine Learning and Behavioral Economics (Jeff Baker) --   |g 1.11.  |t M4 Forecasting Competition - Takeaways for the Practitioner (Michael Gilliland) --   |t Commentary -The M4 Competition and a Look to the Future (Fotios Petropoulos) --   |g ch. 2   |t Big Data in Forecasting --   |g 2.1.  |t Is Big Data the Silver Bullet for Supply-Chain Forecasting? (Shaun Snapp) --   |t Commentary: Becoming Responsible Consumers of Big Data (Chris Gray) --   |t Commentary: Customer versus Item Forecasting (Michael Gilliland) --   |t Commentary: Big Data or Big Hype? (Stephan Kolassa) --   |t Commentary: Big Data, a Big Decision (Niels van Hove) --   |t Commentary: Big Data and the Internet of Things (Peter Catt) --   |g 2.2.  |t How Big Data Could Challenge Planning Processes across the Supply Chain (Tonya Boone, Ram Ganeshan, and Nada Sanders) --   |g ch. 3   |t Forecasting Methods: Modeling, Selection, and Monitoring --   |g 3.1.  |t Know Your Time Series (Stephan Kolassa and Enno Siemsen) --   |g 3.2.  |t Classification of Business Forecasting Problems (Tim Januschowski and Stephan Kolassa) --   |g 3.3.  |t Judgmental Model Selection (Fotios Petropoulos) --   |t Commentary: A Surprisingly Useful Role for Judgment (Paul Goodwin) --   |t Commentary: Algorithmic Aversion and Judgmental Wisdom (Nigel Harvey) --   |t Commentary: Model Selection in Forecasting Software (Eric Stellwagen) --   |t Commentary: Exploit Information from the M4 Competition (Spyros Makridakis) --   |g 3.4.  |t Judgment on Judgment (Paul Goodwin) --   |g 3.5.  |t Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) --   |g 3.6.  |t Primer on Probabilistic Demand Planning (Stefan de Kok) --   |g 3.7.  |t Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) --   |g 3.8.  |t Get Your CoV On (Lora Cecere) --   |g 3.9.  |t Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) --   |g 3.10.  |t Monitoring Forecast Models Using Control Charts (Joe Katz) --   |g 3.11.  |t Forecasting the Future of Retail Forecasting (Stephan Kolassa) --   |t Commentary (Brian Seaman) --   |g ch. 4   |t Forecasting Performance --   |g 4.1.  |t Using Error Analysis to Improve Forecast Performance (Steve Morlidge) --   |g 4.2.  |t Guidelines for Selecting a Forecast Metric (Patrick Bower) --   |g 4.3.  |t Quest for a Better Forecast Error Metric: Measuring More Than the Average Error (Stefan de Kok) --   |g 4.4.  |t Beware of Standard Prediction Intervals from Causal Models (Len Tashman) --   |g ch. 5   |t Forecasting Process: Communication, Accountability, and S&OP --   |g 5.1.  |t Not Storytellers But Reporters (Steve Morlidge) --   |g 5.2.  |t Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? (Chris Gray) --   |g 5.3.  |t Communicating the Forecast: Providing Decision Makers with Insights (Alec Finney) --   |g 5.4.  |t S&OP Communication Plan: The Final Step in Support of Company Strategy (Niels van Hove) --   |g 5.5.  |t Communicating Forecasts to the C-Suite: A Six-Step Survival Guide (Todd Tomalak) --   |g 5.6.  |t How to Identify and Communicate Downturns in Your Business (Larry Lapide) --   |g 5.7.  |t Common S&OP Change Management Pitfalls to Avoid (Patrick Bower) --   |g 5.8.  |t Five Steps to Lean Demand Planning (John Hellriegel) --   |g 5.9.  |t Move to Defensive Business Forecasting (Michael Gilliland) --   |t Afterwords: Essays on Topics in Business Forecasting --   |t Observations from a Career Practitioner: Keys tp Forecasting Success (Carolyn Allmon) --   |t Demand Planning as a Career (Jason Breault) --   |t How Did We Get Demand Planning So Wrong? (Lora Cecere) --   |t Business Forecasting: Issues, Current State, and Future Direction (Simon Clarke) --   |t Statistical Algorithms, Judgment and Forecasting Software Systems (Robert Fildes) --   |t Easy Button for Forecasting (Igor Gusakov) --   |t Future of Forecasting Is Artificial Intelligence Combined with Human Forecasters (Jim Hoover) --   |t Quantile Forecasting with Ensembles and Combinations (Rob J. Hyndman --   |t Managing Demand for New Products (Chaman L. Jain) --   |t Solving for the Irrational: Why Behavioral Economics Is the Next Big Idea in Demand Planning (Jonathon Karelse) --   |t Business Forecasting in Developing Countries (Bahman Rostami-Tabar) --   |t Do the Principles of Analytics Apply to Forecasting? (Udo Sglavo) --   |t Groupthink on the Topic of AI/ML for Forecasting (Shaun Snapp) --   |t Taking Demand Planning Skills to the Next Level (Nicolas Vandeput) --   |t Unlock the Potential of Business Forecasting (Eric Wilson) --   |t Building a Demand Plan Story for S&OP: The Business Value of Analytics (Dr. Davis Wu). 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 |a Description based on online resource; title from digital title page (viewed on May 26, 2021). 
650 0 |a Business forecasting. 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
700 1 |a Gilliland, Michael,  |e editor. 
700 1 |a Tashman, Len,  |d 1942-  |e editor. 
700 1 |a Sglavo, Udo,  |d 1968-  |e editor. 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |t Business forecasting  |d Hoboken, New Jersey : Wiley, 2021.  |z 9781119782476  |w (DLC) 2021002141 
830 0 |a Wiley and SAS business series. 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=6579254  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
907 |a .b38260980  |b 211018  |c 211018 
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919 |a .ulebk  |b 2020-07-09 
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