Consumption-based forecasting and planning predicting changing demand patterns in the new digital economy

"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 li...

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Detalles Bibliográficos
Otros Autores: Chase, Charles, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc [2021]
Colección:Wiley and SAS business series.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009634691006719
Tabla de Contenidos:
  • 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.