Demand Forecasting Best Practices

Table of contents

1. inside front cover
2. Full quotes from reviewers of Demand Forecasting Best Practices:
3. Demand Forecasting Best Practices
4. Copyright
5. contents
6. front matter
   1. preface
   2. acknowledgments
   3. about this book
      1. How this book is organized: a roadmap
      2. liveBook discussion forum
   4. about the author
   5. about the cover illustration
7. Part 1. Forecasting demand
8. 1 Demand forecasting excellence
   1. 1.1 Why do we forecast demand?
   2. 1.2 Five steps to demand planning excellence
      1. 1.2.1 Objective. What do you need to forecast?
      2. 1.2.2 Data. What data do you need to support your forecasting model and process?
      3. 1.2.3 Metrics. How do you evaluate forecasting quality?
      4. 1.2.4 Baseline model. How do you create an accurate, automated forecast baseline?
      5. 1.2.5 Review Process. How to review the baseline forecast, and who should do it?
   3. Summary
9. 2 Introduction to demand forecasting
   1. 2.1 Why do we forecast demand?
   2. 2.2 Definitions
      1. 2.2.1 Demand, sales, and supply
      2. 2.2.2 Supply plan, financial budget, and sales targets
   3. Summary
10. 3 Capturing unconstrained demand (and not sales)
    1. 3.1 Order collection and management
    2. 3.2 Shortage-Censoring and Uncollected Orders
       1. 3.2.1 Using demand drivers to forecast historical demand
    3. 3.3 Substitution and cannibalization
    4. Summary
11. 4 Collaboration: data sharing and planning alignment
    1. 4.1 How supply chains distort demand information
    2. 4.2 Bullwhip effect
       1. 4.2.1 Order forecasting
       2. 4.2.2 Order batching
       3. 4.2.3 Price fluctuation and promotions
       4. 4.2.4 Shortage gaming
       5. 4.2.5 Lead time variations
    3. 4.3 Collaborative planning
       1. 4.3.1 Internal collaboration
       2. 4.3.2 External collaboration
       3. 4.3.3 Collaborating with your suppliers
    4. Summary
12. 5 Forecasting hierarchies
    1. 5.1 The three forecasting dimensions
    2. 5.2 Zooming in or out of forecasts
    3. 5.3 How do you select the most appropriate aggregation level?
       1. 5.3.1 Which aggregation level should you focus on?
       2. 5.3.2 What granularity level should you use to create your forecast?
    4. Summary
13. 6 How long should the forecasting horizon be?
    1. 6.1 Theory: Inventory optimization, lead times, and review periods
    2. 6.2 Reconciling demand forecasting and supply planning
    3. 6.3 Looking further ahead
       1. 6.3.1 Optimal service level and risks
       2. 6.3.2 Collaboration with suppliers
    4. 6.4 Going further: Lost sales vs. backorders
       1. 6.4.1 Lost sales
       2. 6.4.2 Backorders
       3. 6.4.3 Hybrid
    5. Summary
14. 7 Should we reconcile forecasts to align supply chains?
    1. 7.1 Forecasting granularities requirements
    2. 7.2 One number forecast
    3. 7.3 Different hierarchies . . . different optimal forecasts
       1. 7.3.1 Spot sales and stock clearances
       2. 7.3.2 Product life-cycles
       3. 7.3.3 Example: top-down vs. bottom up
    4. 7.4 One number mindset
    5. Summary
15. Part 2. Measuring forecasting quality
16. 8 Forecasting metrics
    1. 8.1 Accuracy and bias
    2. 8.2 Forecast error and bias
       1. 8.2.1 Interpreting and scaling the bias
       2. 8.2.2 Do it yourself
       3. 8.2.3 Insights
    3. 8.3 Mean Absolute Error (MAE)
       1. 8.3.1 Scaling the Mean Absolute Error
       2. 8.3.2 Do it yourself
       3. 8.3.3 Insights
    4. 8.4 Mean Absolute Percentage Error (MAPE)
       1. 8.4.1 Do it yourself
       2. 8.4.2 Insights
    5. 8.5 Root Mean Square Error (RMSE)
       1. 8.5.1 Scaling RMSE
       2. 8.5.2 Do it yourself
       3. 8.5.3 Insights
    6. 8.6 Case study – Part 1
    7. Summary
17. 9 Choosing the best forecasting KPI
    1. 9.1 Extreme demand patterns
    2. 9.2 Intermittent demand
    3. 9.3 The best forecasting KPI
    4. 9.4 Case study – Part 2
    5. Summary
18. 10 What is a good forecast error?
    1. 10.1 Benchmarking
       1. 10.1.1 Naïve forecasts
       2. 10.1.2 Moving average
       3. 10.1.3 Seasonal benchmarks
    2. 10.2 Why tracking demand coefficient of variation is not recommended
       1. 10.2.1 COV and simple demand patterns
       2. 10.2.2 COV and realistic demand patterns
    3. Summary
19. 11 Measuring forecasting accuracy on a product portfolio
    1. 11.1 Forecasting metrics and product portfolios
    2. 11.2 Value-weighted KPIs
    3. Summary
20. Part 3. Data-driven forecasting process
21. 12 Forecast value added
    1. 12.1 Comparing your process to a benchmark
       1. 12.1.1 Internal benchmarks
       2. 12.1.2 Industry (external) benchmarks
    2. 12.2 Tracking Forecast Value Added
       1. 12.2.1 Process efficacy
       2. 12.2.2 Process efficiency
       3. 12.2.3 Best practices
       4. 12.2.4 How do you get started?
    3. Summary
22. 13 What do you review? ABC XYZ segmentations and other methods
    1. 13.1 ABC XYZ segmentations
       1. 13.1.1 ABC analysis
       2. 13.1.2 ABC XYZ analysis
    2. 13.2 Using ABC XYZ for demand forecasting
       1. 13.2.1 Products’ importance
       2. 13.2.2 Products’ forecastability
       3. 13.2.3 ABC XYZ limitations
    3. 13.3 Beyond ABC XYZ: Smart multi-criteria classification
    4. Summary
23. Part 4. Forecasting methods
24. 14 Statistical forecasting
    1. 14.1 Time series forecasting
       1. 14.1.1 Demand components: Level, trend, and seasonality
       2. 14.1.2 Setting up time series models
    2. 14.2 Predictive analytics and demand drivers
       1. 14.2.1 Demand drivers
       2. 14.2.2 Challenges
    3. 14.3 Times series forecasting vs. predictive analytics
    4. 14.4 How to select a model
       1. 14.4.1 The 5-step framework
       2. 14.4.2 4-step model creation framework
    5. Summary
25. 15 Machine learning
    1. 15.1 What is machine learning?
       1. 15.1.1 How does the machine learn?
       2. 15.1.2 Black boxes versus white boxes
    2. 15.2 Main types of learning algorithms
       1. 15.2.1 Short history of machine-learning models
       2. 15.2.2 Tree-based models
       3. 15.2.3 Neural networks
    3. 15.3 What should you expect from ML-driven demand forecasting?
       1. 15.3.1 Forecasting competitions
       2. 15.3.2 Improving the baseline
    4. 15.4 How to launch a machine-learning initiative
    5. Summary
26. 16 Judgmental forecasting
    1. 16.1 When to use judgmental forecasts?
    2. 16.2 Judgmental biases
       1. 16.2.1 Cognitive biases
       2. 16.2.2 Misalignment of incentives (intentional biases)
       3. 16.2.3 Biased forecasting process
    3. 16.3 Group forecasts
       1. 16.3.1 Wisdom of the crowds
       2. 16.3.2 Assumption-based discussions
    4. Summary
27. 17 Now it’s your turn!
    1. Closing words
28. references
29. index
30. inside back cover