Demand Forecasting Best Practices

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