Business Forecasting : The Emerging Role of Artificial Intelligence and Machine Learning.
- 1st ed.
- 1 online resource (435 pages)
- Wiley and SAS Business Series .
- Wiley and SAS Business Series .
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- State of the Art -- Forecasting in Social Settings: The State of the Art* -- I. The Facts -- A Brief History of Forecasting -- When Predictions Go Wrong -- Improving Forecasting Accuracy over Time -- The Importance of Being Uncertain -- II. What We Know -- On Explaining the Past versus Predicting the Future -- On the (Non)existence of a Best Model -- On the Performance of Machine Learning -- III. What We Are Not Sure About -- On the Prediction of Recessions/Booms/Non-stable Environments -- On the Performances of Humans versus Models -- On the Value of Explanatory Variables -- IV. What We Don't Know -- On Thin/Fat Tails and Black Swans -- On Causality -- On Luck (and Other Factors) versus Skills -- V. Conclusions -- Notes -- References -- Chapter 1 Artificial Intelligence and Machine Learning in Forecasting -- 1.1 Deep Learning for Forecasting* -- Introduction -- What Is a Neural Network? -- How Do We Forecast with Neural Nets? -- Examples of Neural Forecasting Models -- References -- 1.2 Deep Learning for Forecasting: Current Trends and Challenges* -- Applying Neural Nets as Global Forecasting Models -- Pros and Cons of Neural Forecasting -- Current Trends and Challenges -- DL Software for Forecasting -- References -- 1.3 Neural Network-Based Forecasting Strategies* -- Introduction -- Neural Network Modeling in SAS Visual Forecasting -- Modeling Strategies -- Case Study: Ozone Prediction -- Case Study: Solar Energy Forecasting -- Best Practices and Other Tips -- Conclusion -- Acknowledgments -- References -- 1.4 Will Deep and Machine Learning Solve Our Forecasting Problems?* -- Introduction -- The Good and the Bad -- The Problems -- What about the M4 Competition? -- Conclusion -- References. 1.5 Forecasting the Impact of Artificial Intelligence: The Emerging and Long-Term Future* -- Introduction -- The 10 Major Emerging Trends -- Supercomputers, High-Speed Networks, and the Cloud -- Medicine and Genomics -- Renewable Energy and Energy Storage -- AVs and Drones -- Greater Wealth and More Comfort -- The "Science Fiction" Type of Technological Developments -- Conclusions -- References -- 1.6 Forecasting the Impact of Artificial Intelligence: Another Voice* -- Forecasting AI's Computational Power -- AI's Impact on Employment -- Blockchain, IA, and Forecasting -- Intelligence Augmentation -- The Long-Term Future -- AI for Forecasting -- Summary -- 1.7 Smarter Supply Chains through AI* -- Introduction -- Supply-Chain Challenges -- Control-System Theory for Supply-Chain Management -- Applying AI/ML -- Lessons Learned -- References -- 1.8 Continual Learning: The Next Generation of Artificial Intelligence* -- Introduction -- Eliminating Our Own Complexities -- AutoML -- Continual Learning -- Continual Learning Augmentation -- Conclusion -- Acknowledgment -- References -- 1.9 Assisted Demand Planning Using Machine Learning* -- The Life of a Demand Planner -- What Is Forecast Value Added? -- Using Intelligent Automation to Improve a Demand Planner's FVA -- Conclusion -- References -- 1.10 Maximizing Forecast Value Add through Machine Learning and Behavioral Economics* -- Introduction -- Predicting Forecast Value Add -- Override Size -- Forecastability of Demand -- Override Classification Techniques -- Analysis -- Behavioral Economics: How to Influence Forecaster Behavior -- Summary -- References -- 1.11 The M4 Forecasting Competition - Takeaways for the Practitioner* -- M4 Background -- M4 Results -- Takeaways for Forecasting Practitioners -- Criticism of the M4 -- Looking Ahead to the M5 -- References -- Chapter 2 Big Data in Forecasting. 2.1 Is Big Data the Silver Bullet for Supply-Chain Forecasting?* -- The "Big Data" Bubble -- Forecasting by Item or Consumer -- Big Data and Causal Forecasting -- Conclusion -- References -- 2.2 How Big Data Could Challenge Planning Processes across the Supply Chain* -- Introduction -- "Big Data" Sources and the Potential They Bring -- The Challenge for Aggregate and Detailed Planning -- Conclusions -- References -- Chapter 3 Forecasting Methods: Modeling, Selection, and Monitoring -- 3.1 Know Your Time Series* -- Data Availability -- Stationarity -- Forecastability and Scale -- Key Takeaways -- Note -- References -- 3.2 A Classification of Business Forecasting Problems* -- Introduction -- Dimensions of the Classification -- Strategic Forecasting -- Tactical Forecasting -- Operational Forecasting -- Publicly Available Data Sets -- Consequences: People, Skills, Methods, and Software -- References -- 3.3 Judgmental Model Selection* -- Forecasting with Judgment -- Exploring the Performance of Judgmental Model Selection -- The Behavioral Experiment -- Why Model-Build Works Better -- Implications for Software -- Final Comments -- References -- Commentary: A Surprisingly Useful Role for Judgment -- References -- Commentary: Algorithmic Aversion and Judgmental Wisdom -- References -- Commentary: Model Selection in Forecasting Software -- Reference -- Commentary: Exploit Information from the M4 Competition -- Reference -- 3.4 A Judgment on Judgment* -- Patterns That Aren't There -- Stories Trump Numbers -- The Perils of Imagination and Memory -- Chained to an Anchor -- A Pleasant Pension Surprise? -- Judging Lots of Possibilities -- References -- 3.5 Could These Recent Findings Improve Your Judgmental Forecasts?* -- Surprises -- Competition -- Combination -- Conclusions -- References -- 3.6 A Primer on Probabilistic Demand Planning*. Moving to a Probabilistic Perspective of the Future -- The Differences between Statistical and Probabilistic Forecasts -- Planning with Probabilistic Forecasts -- 3.7 Benefits and Challenges of Corporate Prediction Markets* -- Introduction: Corporate Prediction Markets -- Forecast Accuracy -- Factors Influencing Accuracy -- Big Data - A Competitive Approach -- Concluding Thought -- References -- 3.8 Get Your CoV On . . .* -- Getting Your CoV On . . . -- Discussion on Demand -- A Side Discussion on MAPE and WMAPE -- 3.9 Standard Deviation Is Not the Way to Measure Volatility* -- Takeout -- 3.10 Monitoring Forecast Models Using Control Charts* -- Introduction -- Background -- Residual Analysis Methodology -- Illustrative Examples -- Summary -- References -- 3.11 Forecasting the Future of Retail Forecasting* -- Introduction -- Your Next Shopping Trip -- Digital Technologies and Trends -- Implications for the Retail Industry -- Implications for Retail Forecasting -- Conclusion -- References -- Commentary -- References -- Chapter 4 Forecasting Performance -- 4.1 Using Error Analysis to Improve Forecast Performance* -- Preview and Key Points from the Author -- Key Concepts -- Error Analysis -- Drivers of Forecast Quality -- Lessons Learned -- Conclusion -- References -- 4.2 Guidelines for Selecting a Forecast Metric* -- Idiosyncracies about the Measurement of Forecast Error -- Forecast Error Is a Funny Thing -- Lies, Damn Lies, and Statistics -- 4.3 The Quest for a Better Forecast Error Metric: Measuring More Than the Average Error* -- Introduction -- Point Forecasts vs. Probabilistic Forecasts -- Uncertainty and Inventory -- Percentile Error Metrics -- Conclusions -- Appendixes -- References -- 4.4 Beware of Standard Prediction Intervals from Causal Models* -- Introduction: Standard Prediction Intervals for a Regression Model Forecast. Sources of Error in the Standard Prediction Interval -- Sources of Error Not Accounted For -- A Case Study -- The Derivation of the Inflation Factors -- Simulation -- Conclusions -- Extensions -- References -- Chapter 5 Forecasting Process: Communication, Accountability, and S& -- OP -- 5.1 Not Storytellers But Reporters* -- News and Evidence -- Not Storytellers But Reporters -- The Duty of Clarity -- Reference -- 5.2 Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast?* -- Responsibility versus Accountability -- Are You Forecasting or Sales Planning? -- Are Responsibilities and Overall Accountability Well Defined? -- Appropriate Metrics for Sales Planning Process Monitoring -- Changes to the Game -- Who's "On First" in Your Company? The Checklist -- Conclusion -- 5.3 Communicating the Forecast: Providing Decision Makers with Insights* -- Asking Decision Makers What They Need -- Storyboard Structure -- Risk Management -- Conclusion -- References -- 5.4 An S& -- OP Communication Plan: The Final Step in Support of Company Strategy* -- Introduction -- Communications to Support Business Strategy -- S& -- OP and Strategy Execution -- The S& -- OP Communication Plan -- Summary -- References -- 5.5 Communicating Forecasts to the C-Suite: A Six-Step Survival Guide* -- Six Tips for Explaining the Forecast to Execs -- Introduction -- Articulate What the CFO Needs to Believe to Use the Forecast -- Accountants and Statisticians Think Differently about Data -- Don't Talk about Complex Diagnostic Statistics -- Expect a Skew in Consensus Forecasts -- Explain Sensitivities to Changes in Independent Variables as Thumb Rules, Not Coefficients -- Your Forecast Will Be Wrong - Be Ready to Explain If Error Looks More Likely to the Upside or Downside -- References -- 5.6 How to Identify and Communicate Downturns in Your Business*. Part 1: Forecasting Heroes Catch Turning Points.
9781119782599
Business forecasting. Artificial intelligence. Machine learning.