Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase

• Autore: Chase Charles
• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021]; ©2021
• Descrizione fisica: 1 online resource (270 pages)
• Disciplina: 658.40355
• Soggetto topico: Demand (Economic theory); Business logistics; Business forecasting
• Note generali: Includes index.
• Nota di contenuto: 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.
• Sommario/riassunto: "Helps companies understand the short-term changes in consumer demand patterns as a result of the digital economy, and COVID-19. Also, what is driving those changing consumer demand patterns (price, sales promotions, in-store merchandizing, epidemiological, economic and other related factors like unplanned events related to the pandemic crisis). Provides real case examples using real data, and how to apply advanced analytics and machine learning to solve current business problems. Provides a framework for changing the way demand forecasting and planning are done, as well as the change management requirements for sustainability"--
• Titolo autorizzato: Consumption-based forecasting and planning
• ISBN: 1-119-80987-8; 1-119-80989-4; 1-119-80988-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830942503321
• Lo trovi qui: Univ. Federico II
• Serie: Wiley and SAS business series.