06083nam 2200541 450 991055484250332120220413123232.01-119-80987-81-119-80989-41-119-80988-6(CKB)4100000011991596(MiAaPQ)EBC6686364(Au-PeEL)EBL6686364(OCoLC)1263025982(EXLCZ)99410000001199159620220413d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierConsumption-based forecasting and planning predicting changing demand patterns in the new digital economy /Charles W. ChaseHoboken, New Jersey :John Wiley & Sons, Inc.,[2021]©20211 online resource (270 pages)Wiley and SAS Business Ser.Includes index.1-119-80986-X Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- About the Author -- Chapter 1 The Digital Economy and Unexpected Disruptions -- Disruptions Driving Complex Consumer Dynamics -- Impact of the Digital Economy -- What Does All This Mean? -- Shifting to a Consumer-Centric Approach -- The Analytics Gap -- Why Predictive and Anticipatory Analytics? -- Difference Between Predictive and Anticipatory Analytics -- The Data Gap -- The Impact of the COVID-19 Crisis on Demand Planning -- Closing Thoughts -- Notes -- Chapter 2 A Wake-up Call for Demand Management -- Demand Uncertainty Is Driving Change -- Challenges Created by Demand Uncertainty -- Ongoing "Bullwhip" Effect -- When Will We Learn from Our Past Mistakes? -- Why Are Companies Still Cleansing Historical Demand? -- Consumer Goods Company Case Study -- Primary Obstacles to Achieving Planning Goals -- Why Do Companies Continue to Dismiss the Value of Demand Management? -- Six Steps to Predicting Shifting Consumer Demand Patterns -- Closing Thoughts -- Notes -- Chapter 3 Why Data and Analytics Are Important -- Analytics Maturity -- Collecting and Storing Consumer Data -- Why Is the Data Ecosystem Important? -- Why Data and Analytics? -- Building Trust in the Data -- AI/Machine Learning Creates Trust Challenges -- Pursuit of Explainability -- Engage with Domain Experts and Business Specialists -- Why Is Downstream Data Important? -- Demand Management Data Challenges -- How Much Data Should Be Used? -- Demand-Signal Repositories -- What Are Demand Signal Repositories? -- Benefits of a Demand Signal Repository -- What Are Users Looking to Gain? -- Why Is It Important? -- What Is Consumption-Based Analytics? -- Closing Thoughts -- Notes -- Chapter 4 Consumption-Based Forecasting and Planning -- A Change of Mindset Is Required.Why Consumption-Based Forecasting and Planning? -- What Is Consumption-Based Forecasting and Planning? -- Consumption-Based Forecasting and Planning Case Study -- Consumption-Based Forecasting and Planning Six-Step Process -- Understanding the Relationship Between Demand and Supply -- Why Move Demand Planning Downstream Closer to the Consumer? -- The Integrated Business Planning Connection -- Demand Management Champion -- Closing Thoughts -- Notes -- Chapter 5 AI/Machine Learning Is Disrupting Demand Forecasting -- Straight Talk About Forecasting and Machine Learning -- What Is the Difference Between Expert Systems and Machine Learning? -- Do Machine Learning Algorithms Outperform Traditional Forecasting Methods? -- M4 Competition -- M5 Competition -- Basic Knowledge Regarding Neural Networks -- Why Combine ML Models? -- Challenges Using Machine Learning Models -- Data Challenges and Considerations -- Black Box Effects -- Interpretation of the ML Model Output -- Case Study 1 -- Using Machine Learning to Enhance Short-Term Demand Sensing -- A Practical Application of Demand Sensing Using Machine Learning -- Converting Weekly Forecasts to Daily Forecasts -- Overall Results -- Weekly Forecast Results -- Daily Forecast Results -- Conclusions -- Case Study 2: Using Advanced Analytics to Adapt to Changing Consumer Demand Patterns -- Situation -- Approach to Short-Term Demand Sensing -- Data Investigation -- Analytics Approach -- Results -- Delivering Real-Time Results -- Closing Thoughts -- Notes -- Chapter 6 Intelligent Automation Is Disrupting Demand Planning -- What Is "Intelligent Automation"? -- How Can Intelligent Automation Enhance Existing Processes? -- What Is Forecast Value Add? -- Do Manual Overrides Add Value? -- Case Study: Using Intelligent Automation to Improve Demand Planners' FVA -- A New IA Approach Called "Assisted Demand Planning".Process Approach -- Process Steps -- Results -- Closing Thoughts -- Notes -- Chapter 7 The Future Is Cloud Analytics and Analytics at the Edge -- Why Cloud Analytics? -- What Are the Differences Between Containers and Virtual Machines? -- Why Cloud Analytics? -- Predictive Analytics Are Creating IT Disruptions -- Data Is Influencing Software Development -- Why Cloud-native Solutions? -- Why Does All This Matter? -- Cloud-Native Forecasting and Planning Solutions -- Why Move to a Cloud-Native Demand Planning Platform? -- Why "Analytics at the Edge"? -- Edge Analytics Benefits -- Edge Analytics Limitations -- Forecasting at the Edge -- Cloud Analytics Versus Edge Analytics -- Closing Thoughts -- Notes -- Index -- EULA.Wiley and SAS business series.Demand (Economic theory)Business logisticsBusiness forecastingElectronic books.Demand (Economic theory)Business logistics.Business forecasting.658.40355Chase Charles967720MiAaPQMiAaPQMiAaPQBOOK9910554842503321Consumption-based forecasting and planning2834067UNINA