Business forecasting: the emerging role of artificial intelligence and machine learning
Gespeichert in:
| Weitere Verfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
[2021]
|
| Schriftenreihe: | Wiley and SAS business series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 414 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119782476 |
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Datensatz im Suchindex
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| adam_text | Contents Foreword (Spyros Makridakis and Fotios petropoulos) xi Preface (Michael Gilliland, Len Tashman, and Udo Sglavo) xv State of the Art 1 Forecasting in Sodai Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Folios Petropoulos) Chapter 1 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 InteUigence: Another Voice (Lawrence Vanston) 84 Commentary: Response to Lawrence Vanston (Spyros Makridakis) 1.7 Smarter Supply Chains through AI (Duncan Klett) 92 94 1.8 Continual Learning: The Next Generation of Artifidal Intelligence (Daniel Philps) ЮЗ 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 2.1 Big Data in Forecasting 135 Is Big Data the
Silver Builet for Supply-Chain Forecasting? (Shaun Snapp) 136 Commentary: Becoming Responsible Consumers of Big Data (Chris Gray) Commentar) : Customer versus Item Forecasting (Michael Gilliland) 146 142
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) 2.2 152 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) 3.5 Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) 207 198 3.6 A Primer on Probabilistic Demand Planning (Stefan de Kok) 3.7 Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) 215 3.8 Get Your CoV On . . . (Lora Ceccre) 3.9 Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) 230 211 225 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 4.1 259 Using Error Analysis to Improve Forecast Performance (Steve Morlidge) 260 4.2 Guidelines
for Selecting a Forecast Metric (Patrick Bower) 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 5.1 271 Forecasting Process: Communication, Accountability, and S OP Not Storytellers But Reporters (Steve Moriidge) 298 297
CONTENTS 5.2 Why Is И 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 С-Suite: A Six-Step Surviva! Guide (Todd Tornaiak) 325 5.6 How to Identify and Communicate Downturns in Your Business (Larry Lapide) 331 5.7 Common S OP Change Management Pitfalis to Avoid (Patrick Bower) 5.8 Five Steps to Lean Demand Planning {John Hellriegc!) 342 5.9 The Move to Defensive Business Forecasting (Michael Gilliland) 338 346 Afterwords: Essays on Topics in Business Forecasting 351 Observations from a Career Practitioner: Keys to Forecasting Success (Carolyn Alimon) 351 Demand Planning as a Career (Jason Breault) 354 How Did We Get Demand Planning So Wrong? (Lora Cecere) 357 Business Forecasting: Issues, Current State, and Future Direction (Simon Clarke) 358 Statistical Algorithms, Judgment and Forecasting Software Systems (Robert Fildes) 361 The «Easy Button» for Forecasting (Igor Gusakov) 364 The Future of Forecasting Is Artificial Intelligence Combined with Human Forecasters (Jim Hoover) 367 Quantile Forecasting with Ensembles and Combinations (Rob J. Hyndman) 371 Managing Demand for New Products (Chaman L. Jain) 376 Solving for the Irrational: Why Behavioral Economics Is the Next Big Idea in Demand Planning (Jonathon Kareise) 380 Business Forecasting in Developing Countries (Bahman Rostami-Tabar) 382 Do the Principles of Analytics Apply to
Forecasting? (Lido Sglavo) 387 Groupthink on the Topic of AI/ML for Forecasting (Shaun Snapp) 390 Taking Demand Planning Skills to the Next Level (Nicolas Vandcput) Unlock the Potential of Business Forecasting (Eric Wilson) 392 394 Building a Demand Plan Story for S OP: The Business Value of Analytics (Dr. Davis Wu) 396 About the Editors 401 Index 403
|
| adam_txt |
Contents Foreword (Spyros Makridakis and Fotios petropoulos) xi Preface (Michael Gilliland, Len Tashman, and Udo Sglavo) xv State of the Art 1 Forecasting in Sodai Settings: The State of the Art (Spyros Makridakis, Rob J. Hyndman, and Folios Petropoulos) Chapter 1 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 InteUigence: Another Voice (Lawrence Vanston) 84 Commentary: Response to Lawrence Vanston (Spyros Makridakis) 1.7 Smarter Supply Chains through AI (Duncan Klett) 92 94 1.8 Continual Learning: The Next Generation of Artifidal Intelligence (Daniel Philps) ЮЗ 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 2.1 Big Data in Forecasting 135 Is Big Data the
Silver Builet for Supply-Chain Forecasting? (Shaun Snapp) 136 Commentary: Becoming Responsible Consumers of Big Data (Chris Gray) Commentar)': Customer versus Item Forecasting (Michael Gilliland) 146 142
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) 2.2 152 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) 3.5 Could These Recent Findings Improve Your Judgmental Forecasts? (Paul Goodwin) 207 198 3.6 A Primer on Probabilistic Demand Planning (Stefan de Kok) 3.7 Benefits and Challenges of Corporate Prediction Markets (Thomas Wolfram) 215 3.8 Get Your CoV On . . . (Lora Ceccre) 3.9 Standard Deviation Is Not the Way to Measure Volatility (Steve Morlidge) 230 211 225 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 4.1 259 Using Error Analysis to Improve Forecast Performance (Steve Morlidge) 260 4.2 Guidelines
for Selecting a Forecast Metric (Patrick Bower) 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 5.1 271 Forecasting Process: Communication, Accountability, and S OP Not Storytellers But Reporters (Steve Moriidge) 298 297
CONTENTS " 5.2 Why Is И 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 С-Suite: A Six-Step Surviva! Guide (Todd Tornaiak) 325 5.6 How to Identify and Communicate Downturns in Your Business (Larry Lapide) 331 5.7 Common S OP Change Management Pitfalis to Avoid (Patrick Bower) 5.8 Five Steps to Lean Demand Planning {John Hellriegc!) 342 5.9 The Move to Defensive Business Forecasting (Michael Gilliland) 338 346 Afterwords: Essays on Topics in Business Forecasting 351 Observations from a Career Practitioner: Keys to Forecasting Success (Carolyn Alimon) 351 Demand Planning as a Career (Jason Breault) 354 How Did We Get Demand Planning So Wrong? (Lora Cecere) 357 Business Forecasting: Issues, Current State, and Future Direction (Simon Clarke) 358 Statistical Algorithms, Judgment and Forecasting Software Systems (Robert Fildes) 361 The «Easy Button» for Forecasting (Igor Gusakov) 364 The Future of Forecasting Is Artificial Intelligence Combined with Human Forecasters (Jim Hoover) 367 Quantile Forecasting with Ensembles and Combinations (Rob J. Hyndman) 371 Managing Demand for New Products (Chaman L. Jain) 376 Solving for the Irrational: Why Behavioral Economics Is the Next Big Idea in Demand Planning (Jonathon Kareise) 380 Business Forecasting in Developing Countries (Bahman Rostami-Tabar) 382 Do the Principles of Analytics Apply to
Forecasting? (Lido Sglavo) 387 Groupthink on the Topic of AI/ML for Forecasting (Shaun Snapp) 390 Taking Demand Planning Skills to the Next Level (Nicolas Vandcput) Unlock the Potential of Business Forecasting (Eric Wilson) 392 394 Building a Demand Plan Story for S OP: The Business Value of Analytics (Dr. Davis Wu) 396 About the Editors 401 Index 403 |
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| spelling | Business forecasting the emerging role of artificial intelligence and machine learning edited by Michael Gilliland, Len Tashman, Udo Sglavo Hoboken, New Jersey Wiley [2021] © 2021 xvii, 414 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Wiley and SAS business series Unternehmensentwicklung (DE-588)4125011-4 gnd rswk-swf Strategische Planung (DE-588)4309237-8 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Unternehmensentwicklung (DE-588)4125011-4 s Prognoseverfahren (DE-588)4358095-6 s DE-604 Strategische Planung (DE-588)4309237-8 s Zeitreihenanalyse (DE-588)4067486-1 s Gilliland, Michael (DE-588)141866969 edt Tashman, Len 1942- (DE-588)1120731755 edt Sglavo, Udo 1968- (DE-588)1120731410 edt Erscheint auch als Online-Ausgabe, epub 978-1-119-78258-2 Erscheint auch als Online-Ausgabe, PDF 978-1-119-78259-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032769724&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Business forecasting the emerging role of artificial intelligence and machine learning Unternehmensentwicklung (DE-588)4125011-4 gnd Strategische Planung (DE-588)4309237-8 gnd Prognoseverfahren (DE-588)4358095-6 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4125011-4 (DE-588)4309237-8 (DE-588)4358095-6 (DE-588)4067486-1 (DE-588)4143413-4 |
| title | Business forecasting the emerging role of artificial intelligence and machine learning |
| title_auth | Business forecasting the emerging role of artificial intelligence and machine learning |
| title_exact_search | Business forecasting the emerging role of artificial intelligence and machine learning |
| title_exact_search_txtP | Business forecasting the emerging role of artificial intelligence and machine learning |
| title_full | Business forecasting the emerging role of artificial intelligence and machine learning edited by Michael Gilliland, Len Tashman, Udo Sglavo |
| title_fullStr | Business forecasting the emerging role of artificial intelligence and machine learning edited by Michael Gilliland, Len Tashman, Udo Sglavo |
| title_full_unstemmed | Business forecasting the emerging role of artificial intelligence and machine learning edited by Michael Gilliland, Len Tashman, Udo Sglavo |
| title_short | Business forecasting |
| title_sort | business forecasting the emerging role of artificial intelligence and machine learning |
| title_sub | the emerging role of artificial intelligence and machine learning |
| topic | Unternehmensentwicklung (DE-588)4125011-4 gnd Strategische Planung (DE-588)4309237-8 gnd Prognoseverfahren (DE-588)4358095-6 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Unternehmensentwicklung Strategische Planung Prognoseverfahren Zeitreihenanalyse Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032769724&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gillilandmichael businessforecastingtheemergingroleofartificialintelligenceandmachinelearning AT tashmanlen businessforecastingtheemergingroleofartificialintelligenceandmachinelearning AT sglavoudo businessforecastingtheemergingroleofartificialintelligenceandmachinelearning |