{"product_id":"business-forecasting-isbn-9781119782476","title":"Business Forecasting","description":"\u003cp\u003e\u003cb\u003eDiscover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eBusiness Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eYou will find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscussions on deep learning in forecasting, including current trends and challenges\u003c\/li\u003e \u003cli\u003eExplorations of neural network-based forecasting strategies\u003c\/li\u003e \u003cli\u003eA treatment of the future of artificial intelligence in business forecasting\u003c\/li\u003e \u003cli\u003eAnalyses of forecasting methods, including modeling, selection, and monitoring\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIn 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.\u003c\/p\u003e \u003cp\u003ePerfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, \u003ci\u003eBusiness Forecasting\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eForeword (Spyros Makridakis and Fotios Petropoulos) xi\u003c\/p\u003e \u003cp\u003ePreface (Michael Gilliland, Len Tashman, and Udo Sglavo) xv\u003c\/p\u003e \u003cp\u003eState of the Art 1\u003c\/p\u003e \u003cp\u003eForecasting in Social Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Fotios Petropoulos) 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Artificial Intelligence and Machine Learning in Forecasting 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Deep Learning for Forecasting (Tim Januschowski and colleagues) 32\u003c\/p\u003e \u003cp\u003e1.2 Deep Learning for Forecasting: Current Trends and Challenges (Tim Januschowski and Colleagues) 41\u003c\/p\u003e \u003cp\u003e1.3 Neural Network--Based Forecasting Strategies (Steven Mills and Susan Kahler) 48\u003c\/p\u003e \u003cp\u003e1.4 Will Deep and Machine Learning Solve Our Forecasting Problems? (Stephan Kolassa) 65\u003c\/p\u003e \u003cp\u003e1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future (Spyros Makridakis) 72\u003c\/p\u003e \u003cp\u003eCommentary: Spyros Makridakis's Article \"Forecasting The Impact Of Artificial Intelligence\" (Owen Davies) 80\u003c\/p\u003e \u003cp\u003e1.6 Forecasting the Impact of Artificial Intelligence: Another Voice (Lawrence Vanston) 84\u003c\/p\u003e \u003cp\u003eCommentary: Response to Lawrence Vanston (Spyros Makridakis) 92\u003c\/p\u003e \u003cp\u003e1.7 Smarter Supply Chains through AI (Duncan Klett) 94\u003c\/p\u003e \u003cp\u003e1.8 Continual Learning: The Next Generation of Artificial Intelligence (Daniel Philps) 103\u003c\/p\u003e \u003cp\u003e1.9 Assisted Demand Planning Using Machine Learning (Charles Chase) 110\u003c\/p\u003e \u003cp\u003e1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics (Jeff Baker) 115\u003c\/p\u003e \u003cp\u003e1.11 The M4 Forecasting Competition -- Takeaways for the Practitioner (Michael Gilliland) 124\u003c\/p\u003e \u003cp\u003eCommentary --The M4 Competition and a Look to the Future (Fotios Petropoulos) 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Big Data in Forecasting 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting? (Shaun Snapp) 136\u003c\/p\u003e \u003cp\u003eCommentary: Becoming Responsible Consumers of Big Data (Chris Gray) 142\u003c\/p\u003e \u003cp\u003eCommentary: Customer versus Item Forecasting (Michael Gilliland) 146\u003c\/p\u003e \u003cp\u003eCommentary: Big Data or Big Hype? (Stephan Kolassa) 148\u003c\/p\u003e \u003cp\u003eCommentary: Big Data, a Big Decision (Niels van Hove) 150\u003c\/p\u003e \u003cp\u003eCommentary: Big Data and the Internet of Things (Peter Catt) 152\u003c\/p\u003e \u003cp\u003e2.2 How Big Data Could Challenge Planning Processes across the Supply Chain (Tonya Boone, Ram Ganeshan, and Nada Sanders) 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Forecasting Methods: Modeling, Selection, and Monitoring 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Know Your Time Series (Stephan Kolassa and Enno Siemsen) 164\u003c\/p\u003e \u003cp\u003e3.2 A Classification of Business Forecasting Problems (Tim Januschowski and Stephan Kolassa) 171\u003c\/p\u003e \u003cp\u003e3.3 Judgmental Model Selection (Fotios Petropoulos) 181\u003c\/p\u003e \u003cp\u003eCommentary: A Surprisingly Useful Role for Judgment (Paul Goodwin) 192\u003c\/p\u003e \u003cp\u003eCommentary: Algorithmic Aversion and Judgmental Wisdom (Nigel Harvey) 194\u003c\/p\u003e \u003cp\u003eCommentary: Model Selection in Forecasting Software (Eric Stellwagen) 195\u003c\/p\u003e \u003cp\u003eCommentary: Exploit Information from the M4 Competition (Spyros Makridakis) 197\u003c\/p\u003e \u003cp\u003e3.4 A Judgment on Judgment (Paul Goodwin) 198\u003c\/p\u003e \u003cp\u003e3.5 Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) 207\u003c\/p\u003e \u003cp\u003e3.6 A Primer on Probabilistic Demand Planning (Stefan de Kok) 211\u003c\/p\u003e \u003cp\u003e3.7 Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) 215\u003c\/p\u003e \u003cp\u003e3.8 Get Your CoV On . . . (Lora Cecere) 225\u003c\/p\u003e \u003cp\u003e3.9 Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) 230\u003c\/p\u003e \u003cp\u003e3.10 Monitoring Forecast Models Using Control Charts (Joe Katz) 232\u003c\/p\u003e \u003cp\u003e3.11 Forecasting the Future of Retail Forecasting (Stephan Kolassa) 243 Commentary (Brian Seaman) 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Forecasting Performance 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Using Error Analysis to Improve Forecast Performance (Steve Morlidge) 260\u003c\/p\u003e \u003cp\u003e4.2 Guidelines for Selecting a Forecast Metric (Patrick Bower) 271\u003c\/p\u003e \u003cp\u003e4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error (Stefan de Kok) 277\u003c\/p\u003e \u003cp\u003e4.4 Beware of Standard Prediction Intervals from Causal Models (Len Tashman) 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Forecasting Process: Communication, Accountability, and S\u0026amp;OP 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Not Storytellers But Reporters (Steve Morlidge) 298\u003c\/p\u003e \u003cp\u003e5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? (Chris Gray) 303\u003c\/p\u003e \u003cp\u003e5.3 Communicating the Forecast: Providing Decision Makers with Insights (Alec Finney) 310\u003c\/p\u003e \u003cp\u003e5.4 An S\u0026amp;OP Communication Plan: The Final Step in Support of Company Strategy (Niels van Hove) 317\u003c\/p\u003e \u003cp\u003e5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide (Todd Tomalak) 325\u003c\/p\u003e \u003cp\u003e5.6 How to Identify and Communicate Downturns in Your Business (Larry Lapide) 331\u003c\/p\u003e \u003cp\u003e5.7 Common S\u0026amp;OP Change Management Pitfalls to Avoid (Patrick Bower) 338\u003c\/p\u003e \u003cp\u003e5.8 Five Steps to Lean Demand Planning (John Hellriegel) 342\u003c\/p\u003e \u003cp\u003e5.9 The Move to Defensive Business Forecasting (Michael Gilliland) 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAfterwords: Essays on Topics in Business Forecasting 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eObservations from a Career Practitioner: Keys to Forecasting Success (Carolyn Allmon) 351\u003c\/p\u003e \u003cp\u003eDemand Planning as a Career (Jason Breault) 354\u003c\/p\u003e \u003cp\u003eHow Did We Get Demand Planning So Wrong? (Lora Cecere) 357\u003c\/p\u003e \u003cp\u003eBusiness Forecasting: Issues, Current State, and Future Direction (Simon Clarke) 358\u003c\/p\u003e \u003cp\u003eStatistical Algorithms, Judgment and Forecasting Software Systems (Robert Fildes) 361\u003c\/p\u003e \u003cp\u003eThe \u0026lt;\u0026gt; for Forecasting (Igor Gusakov) 364\u003c\/p\u003e \u003cp\u003eThe Future of Forecasting Is Artificial Intelligence Combined with Human Forecasters (Jim Hoover) 367\u003c\/p\u003e \u003cp\u003eQuantile Forecasting with Ensembles and Combinations (Rob J. Hyndman) 371\u003c\/p\u003e \u003cp\u003eManaging Demand for New Products (Chaman L. Jain) 376\u003c\/p\u003e \u003cp\u003eSolving for the Irrational: Why Behavioral Economics Is the Next Big Idea in Demand Planning (Jonathon Karelse) 380\u003c\/p\u003e \u003cp\u003eBusiness Forecasting in Developing Countries (Bahman Rostami-Tabar) 382\u003c\/p\u003e \u003cp\u003eDo the Principles of Analytics Apply to Forecasting? (Udo Sglavo) 387\u003c\/p\u003e \u003cp\u003eGroupthink on the Topic of AI\/ML for Forecasting (Shaun Snapp) 390\u003c\/p\u003e \u003cp\u003eTaking Demand Planning Skills to the Next Level (Nicolas Vandeput) 392\u003c\/p\u003e \u003cp\u003eUnlock the Potential of Business Forecasting (Eric Wilson) 394\u003c\/p\u003e \u003cp\u003eBuilding a Demand Plan Story for S\u0026amp;OP: The Business Value of Analytics (Dr. Davis Wu) 396\u003c\/p\u003e \u003cp\u003eAbout the Editors 401\u003c\/p\u003e \u003cp\u003eIndex 403\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMICHAEL GILLILAND\u003c\/b\u003e is Marketing Manager for SAS forecasting software and Associate Editor of \u003ci\u003eForesight: The International Journal of Applied Forecasting\u003c\/i\u003e. He is author of \u003ci\u003eThe Business Forecasting Deal\u003c\/i\u003e.\u003cbr\u003e\u003cb\u003e\u003cbr\u003eLEN TASHMAN\u003c\/b\u003e is the founding editor of \u003ci\u003eForesight: The International Journal of Applied Forecasting\u003c\/i\u003e. He is emeritus professor of business administration at the University of Vermont and Director of the Center for Business Forecasting.\u003cbr\u003e\u003cb\u003e\u003cbr\u003eUDO SGLAVO\u003c\/b\u003e is Vice President of Analytics R\u0026amp;D at SAS and holds several patents in the area of advanced analytics. His writings have appeared in \u003ci\u003eAnalytics\u003c\/i\u003e magazine and the book \u003ci\u003eBig Data and Business Analytics\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eOver the last six decades, business forecasting has demonstrated a remarkable ability to keep executives and managers informed of likely trends in a huge variety of industries and businesses around the world. And while most forecasters didn’t see the unfolding COVID-19 pandemic or its effects coming, more typical and mundane scenarios provide plenty of opportunity for forecasters to show their talent.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eBusiness Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning\u003c\/i\u003e delivers a curated collection of insightful papers designed to shed light on the growing role of artificial intelligence (AI) and machine learning (ML) in the crucial field of business forecasting. The book discusses new and varied techniques and technologies, like cross learning, big data, judgmental model selection, and error analysis, to provide an illuminating view of the rapidly evolving world of forecasting.\u003c\/p\u003e \u003cp\u003eThe resources included contain academic research, case studies, and thoughtful discussions of important business forecasting topics. The articles are influential, informative, and provocative, eliciting new considerations and ideas, as well as practical insights and strategies.\u003c\/p\u003e \u003cp\u003eReaders will also enjoy a collection of original “Afterwords” from recognized forecasting thought leaders in academics, industry, and consulting. These Afterwords—written specifically for this book—cover a variety of topics relating to the issues, current state, and future direction of business forecasting.\u003c\/p\u003e \u003cp\u003ePerfect for financial controllers, chief financial officers, business analysts, and forecast analysts and managers, \u003ci\u003eBusiness Forecasting\u003c\/i\u003e is also an invaluable resource for demand planners and other professionals whose job relies on predicting future states of variables. It will also be of interest to machine learning and artificial intelligence professionals who seek a one-stop and up-to-date resource on the application of those technologies to business and finance.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePrepare for the ongoing and future impact of AI and machine learning on business forecasting with dozens of insightful resources\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhile modern business forecasting is only 60 years old, its impact has been enormous and wide-ranging. From keeping supermarket shelves stocked to informing the decisions of business executives across the world, business forecasting is the foundation for much of the global economy.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eBusiness Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning\u003c\/i\u003e offers readers a collection of over 60 of the most impactful, insightful, and provocative articles and commentaries on the growing impact of AI and machine learning on the practice of business forecasting.\u003c\/p\u003e \u003cp\u003eRanging in subjects from the role of neural networks in forecasting strategies to big data in supply-chain forecasting, judgmental forecast model selection, and the use of forecast error metrics, the book dives deep into topics of central importance to modern forecasting and machine learning.\u003c\/p\u003e \u003cp\u003eIdeal for financial controllers and other executives engaging with forecasts for decision making in companies of all sizes and across a wide range of industries, \u003ci\u003eBusiness Forecasting\u003c\/i\u003e will also earn a place in the libraries of business analysts, forecast managers and directors, and demand planners who hope to stay on the cutting-edge of what business forecasting has to offer.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988871364837,"sku":"NP9781119782476","price":60.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119782476.jpg?v=1761781856","url":"https:\/\/k12savings.com\/es\/products\/business-forecasting-isbn-9781119782476","provider":"K12savings","version":"1.0","type":"link"}