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...
Otros Autores: | |
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Formato: | Libro electrónico |
Idioma: | Inglés |
Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2021]
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Colección: | Wiley and SAS business series.
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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.