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

Read now

Unlock full access

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

Part 4. Forecasting methods

In this last part, we will discuss forecasting methods. We will discuss statistical methods in chapter 14, followed by more advanced machine-learning techniques in chapter 15, where we will compare both approaches regarding complexity and expected results. Finally, we will discuss judgmental forecasts in chapter 16 (when to use them and how to avoid intentional and unintentional biases).

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781633438095Publisher Support Publisher Website Errata Page

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Demand Forecasting Best Practices

by Nicolas Vandeput

Part 4. Forecasting methods

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.