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Record Nr. |
UNINA9910830942503321 |
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Autore |
Chase Charles |
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Titolo |
Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase |
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Pubbl/distr/stampa |
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Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
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©2021 |
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ISBN |
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1-119-80987-8 |
1-119-80989-4 |
1-119-80988-6 |
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Descrizione fisica |
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1 online resource (270 pages) |
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Collana |
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Disciplina |
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Soggetti |
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Demand (Economic theory) |
Business logistics |
Business forecasting |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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 |
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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. |
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Sommario/riassunto |
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"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 |
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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"-- |
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