book

Demand Forecasting Best Practices

Name: Demand Forecasting Best Practices
Author: Nicolas Vandeput
ISBN: 9781633438095

by Nicolas Vandeput

July 2023

Intermediate to advanced

216 pages

5h 34m

English

Manning Publications

Audiobook available

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inside front cover
Full quotes from reviewers of Demand Forecasting Best Practices:
Demand Forecasting Best Practices
Copyright
contents
front matter
prefaceacknowledgmentsabout this bookHow this book is organized: a roadmapliveBook discussion forumabout the authorabout 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 excellence1.2.1 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 Definitions2.2.1 Demand, sales, and supply2.2.2 Supply plan, financial budget, and sales targetsSummary
3 Capturing unconstrained demand (and not sales)
3.1 Order collection and management3.2 Shortage-Censoring and Uncollected Orders3.2.1 Using demand drivers to forecast historical demand3.3 Substitution and cannibalizationSummary

4 Collaboration: data sharing and planning alignment
4.1 How supply chains distort demand information4.2 Bullwhip effect4.2.1 Order forecasting4.2.2 Order batching4.2.3 Price fluctuation and promotions4.2.4 Shortage gaming4.2.5 Lead time variations4.3 Collaborative planning4.3.1 Internal collaboration4.3.2 External collaboration4.3.3 Collaborating with your suppliersSummary
5 Forecasting hierarchies
5.1 The three forecasting dimensions5.2 Zooming in or out of forecasts5.3 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 periods6.2 Reconciling demand forecasting and supply planning6.3 Looking further ahead6.3.1 Optimal service level and risks6.3.2 Collaboration with suppliers6.4 Going further: Lost sales vs. backorders6.4.1 Lost sales6.4.2 Backorders6.4.3 HybridSummary
7 Should we reconcile forecasts to align supply chains?
7.1 Forecasting granularities requirements7.2 One number forecast7.3 Different hierarchies . . . different optimal forecasts7.3.1 Spot sales and stock clearances7.3.2 Product life-cycles7.3.3 Example: top-down vs. bottom up7.4 One number mindsetSummary
Part 2. Measuring forecasting quality
8 Forecasting metrics
8.1 Accuracy and bias8.2 Forecast error and bias8.2.1 Interpreting and scaling the bias8.2.2 Do it yourself8.2.3 Insights8.3 Mean Absolute Error (MAE)8.3.1 Scaling the Mean Absolute Error8.3.2 Do it yourself8.3.3 Insights8.4 Mean Absolute Percentage Error (MAPE)8.4.1 Do it yourself8.4.2 Insights8.5 Root Mean Square Error (RMSE)8.5.1 Scaling RMSE8.5.2 Do it yourself8.5.3 Insights8.6 Case study – Part 1Summary
9 Choosing the best forecasting KPI
9.1 Extreme demand patterns9.2 Intermittent demand9.3 The best forecasting KPI9.4 Case study – Part 2Summary
10 What is a good forecast error?
10.1 Benchmarking10.1.1 Naïve forecasts10.1.2 Moving average10.1.3 Seasonal benchmarks10.2 Why tracking demand coefficient of variation is not recommended10.2.1 COV and simple demand patterns10.2.2 COV and realistic demand patternsSummary
11 Measuring forecasting accuracy on a product portfolio
11.1 Forecasting metrics and product portfolios11.2 Value-weighted KPIsSummary
Part 3. Data-driven forecasting process
12 Forecast value added
12.1 Comparing your process to a benchmark12.1.1 Internal benchmarks12.1.2 Industry (external) benchmarks12.2 Tracking Forecast Value Added12.2.1 Process efficacy12.2.2 Process efficiency12.2.3 Best practices12.2.4 How do you get started?Summary
13 What do you review? ABC XYZ segmentations and other methods
13.1 ABC XYZ segmentations13.1.1 ABC analysis13.1.2 ABC XYZ analysis13.2 Using ABC XYZ for demand forecasting13.2.1 Products’ importance13.2.2 Products’ forecastability13.2.3 ABC XYZ limitations13.3 Beyond ABC XYZ: Smart multi-criteria classificationSummary
Part 4. Forecasting methods
14 Statistical forecasting
14.1 Time series forecasting14.1.1 Demand components: Level, trend, and seasonality14.1.2 Setting up time series models14.2 Predictive analytics and demand drivers14.2.1 Demand drivers14.2.2 Challenges14.3 Times series forecasting vs. predictive analytics14.4 How to select a model14.4.1 The 5-step framework14.4.2 4-step model creation frameworkSummary
15 Machine learning
15.1 What is machine learning?15.1.1 How does the machine learn?15.1.2 Black boxes versus white boxes15.2 Main types of learning algorithms15.2.1 Short history of machine-learning models15.2.2 Tree-based models15.2.3 Neural networks15.3 What should you expect from ML-driven demand forecasting?15.3.1 Forecasting competitions15.3.2 Improving the baseline15.4 How to launch a machine-learning initiativeSummary
16 Judgmental forecasting
16.1 When to use judgmental forecasts?16.2 Judgmental biases16.2.1 Cognitive biases16.2.2 Misalignment of incentives (intentional biases)16.2.3 Biased forecasting process16.3 Group forecasts16.3.1 Wisdom of the crowds16.3.2 Assumption-based discussionsSummary
17 Now it’s your turn!
Closing words
references
index
inside back cover

Content preview from Demand Forecasting Best Practices

16 Judgmental forecasting

In chapters 14 and 15, we discussed how forecasting models work. Forecasting engines will populate a forecast baseline, but this is not the end of your forecasting process. As illustrated in figure 16.1, different teams can still enrich the forecast using various insights and sources of information. Because they rely on human judgment, these adjustments are called judgmental forecasts.

Figure 16.1 Example of a demand planning process

As we will discuss in this chapter, judgmental forecasts come with risks and pitfalls. ...

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Demand Forecasting Best Practices

by Nicolas Vandeput

16 Judgmental forecasting

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