Demand Forecasting Best Practices

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