Demand Forecasting Best Practices

inside front cover

Demand planning excellence: efficacy and efficiency

5-step framework to achieve demand planning excellence

Full quotes from reviewers of Demand Forecasting Best Practices:

This new book continues to push the FVA mindset, illustrating practices that drive the efficiency and effectiveness of the business forecasting process.

—Michael Gilliland, Editor-in-Chief, Foresight: Journal of Applied Forecasting

A must-read for any SCM professional, data scientist, or business owner. It’s practical, accessible, and packed with valuable insights.

—Edouard Thieuleux, Founder of AbcSupplyChain

An exceptional resource that covers everything from basic forecasting principles to advanced forecasting techniques using artificial intelligence and machine learning. The writing style is engaging, making complex concepts accessible to both beginners and experts.

—Daniel Stanton, Mr. Supply Chain®

Nicolas did it again! Demand Forecasting Best Practices provides practical and actionable advice for improving the demand planning process.

—Spyros Makridakis, The Makridakis Open Forecasting Center, Institute For the Future (IFF), University of Nicosia

This book is now my companion on all of our planning and forecasting projects. A perfect foundation for implementation and also to recommend process improvements.

—Werner Nindl, Chief Architect – CPM Practice Director, Pivotal Drive

This author understands the nuances of forecasting, and is able to explain them well.

—Burhan Ul Haq, Director of Products, Enablers

Both broader and deeper than I expected.

—Maxim Volgin, Quantitative Marketing Manager, KLM

Great book with actionable insights.

—Simon Tschöke, Head of Research, German Edge Cloud

Demand Forecasting Best Practices

Nicolas Vandeput

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ISBN: 9781633438095

contents

Front matter

preface

acknowledgments

about this book

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

Objective. What do you need to forecast?

Data. What data do you need to support your forecasting model and process?

Metrics. How do you evaluate forecasting quality?

Baseline model. How do you create an accurate, automated forecast baseline?

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

Demand, sales, and supply

Supply plan, financial budget, and sales targets

Summary

  3  Capturing unconstrained demand (and not sales)

  3.1  Order collection and management

  3.2  Shortage-Censoring and Uncollected Orders

Using demand drivers to forecast historical demand

  3.3  Substitution and cannibalization

Summary

  4  Collaboration: data sharing and planning alignment

  4.1  How supply chains distort demand information

  4.2  Bullwhip effect

Order forecasting

Order batching

Price fluctuation and promotions

Shortage gaming

Lead time variations

  4.3  Collaborative planning

Internal collaboration

External collaboration

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?

Which aggregation level should you focus on?

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

Optimal service level and risks

Collaboration with suppliers

  6.4  Going further: Lost sales vs. backorders

Lost sales

Backorders

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

Spot sales and stock clearances

Product life-cycles

Example: top-down vs. bottom up

  7.4  One number mindset

Summary

Part 2.  Measuring forecasting quality  

  8  Forecasting metrics

  8.1  Accuracy and bias

  8.2  Forecast error and bias

Interpreting and scaling the bias

Do it yourself

Insights

  8.3  Mean Absolute Error (MAE)

Scaling the Mean Absolute Error

Do it yourself

Insights

  8.4  Mean Absolute Percentage Error (MAPE)

Do it yourself

Insights

  8.5  Root Mean Square Error (RMSE)

Scaling RMSE

Do it yourself

Insights

  8.6  Case study – Part 1

Summary

  9  Choosing the best forecasting 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

Naïve forecasts

Moving average

Seasonal benchmarks

10.2  Why tracking demand coefficient of variation is not recommended

COV and simple demand patterns

COV and realistic demand patterns

Summary

11  Measuring forecasting accuracy on a product portfolio

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

Internal benchmarks

Industry (external) benchmarks

12.2  Tracking Forecast Value Added

Process efficacy

Process efficiency

Best practices

How do you get started?

13  What do you review? ABC XYZ segmentations and other methods

13.1  ABC XYZ segmentations

BC analysis

ABC XYZ analysis

13.2  Using ABC XYZ for demand forecasting

Products’ importance

Products’ forecastability

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

Demand components: Level, trend, and seasonality

Setting up time series models

14.2  Predictive analytics and demand drivers

Demand drivers

Challenges

14.3  Times series forecasting vs. predictive analytics

14.4  How to select a model

The 5-step framework

4-step model creation framework

Summary

15  Machine learning

15.1  What is machine learning?

How does the machine learn?

Black boxes versus white boxes

15.2  Main types of learning algorithms

Short history of machine-learning models

Tree-based models

Neural networks

15.3  What should you expect from ML-driven demand forecasting?

Forecasting competitions

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

Cognitive biases

Misalignment of incentives (intentional biases)

Biased forecasting process

16.3  Group forecasts

Wisdom of the crowds

Assumption-based discussions

Summary

17  Now it’s your turn!

Closing words

references  

index

front matter

preface

As COVID hit us in early 2020, I started training more companies on demand planning. Everyone realized at once that predicting future demand was critical for businesses. But challenging.

I have always been obsessed with continuous improvement and excellence. So, after each training course delivery or project, I took the opportunity to refine my approach to demand forecasting. Coaching teams across four continents and various industries made me realize that demand planning excellence can be boiled down to a set of best practices. Over time, I improved the methodology itself and how to explain it in a structured, straightforward way.

Pretty soon, I realized that I was starting all my projects with the same questions: Are you tracking forecast value added? How do you capture demand? Do you measure yourself against a benchmark? What is driving your demand? And I followed the same steps: collecting demand, determining the right forecasting granularity and horizon, testing out different models, implementing forecast value added, and so on.

Over time, I organized these questions and steps in a structured way: the five-step framework to demand planning excellence.

This book will guide you through these steps. Presenting you with the best practices to lead your demand planning process to excellence.

Feel free to share how you applied these ideas and techniques. You can reach me at nicolas.vandeput@supchains.com or on LinkedIn.

acknowledgments

For my third book, I could count on the amazing LinkedIn community to support me by reviewing my drafts. My warmest thanks go to the following people for their time and dedicated reviews.

Part 1 and Introduction, Slava Grinkevych, Jeff Carruthers, Jeffrey Connors, Adam Sobolewski, Damon DeWaide, Ryker Frandsen, Cassidy Williams, Zlata Jakubović, William van den Bremer, Joel Martycz, Renaud Lecoeuche, Hugues d’Allest, Inigo Diaz, Marc Jacobs, Asmir Tandirević, Nicole Minskoff, Fabian Kleinschmidt, Chris Mousley, Agustin Peña Camprubi, Obinna Ikpengwa, and Mariano Ayerza.

Part 2, Leo Ducrot, Adam Sobolewski, Jeff Carruthers, Michael Ryan, Koen Cobbaert, Daniel Singer, Thamin Rashid, Lokesh Kamani, Pierre-Olivier Mazoyer, Rafael Vicco, Igor Henrique de Freitas, Emad Atef, Hammad Rafique, Daniel More, Tobias Faiß, Fabio Antonio Mangione, Tatiana Usuga, Mark Lado, Hernán David Pérez, Khem Singh Negi, Vi Duong, Mark Fox, Mohit Goyal, and Filip Nilsson.

Part 3 Michael Gilliland, Adam Sobolewski, Jeff Carruthers, Daniel Singer, Thamin Rashid, Pierre-Olivier Mazoyer, Lokesh Kamani, Mauricio Rendon Franco, and Léo Ducrot.

Part 4 and Conclusion, Koen Cobbaert, Slava Grinkevych, Hugues d’Allest, Cassidy Williams, Ryker Frandsen, Inigo Diaz, Fabian Kleinschmidt, Damon DeWaide, Michael Gilliland, and Agustin Pena.

I would also like to thank all Manning’s reviewers for their insightful feedback and advise: Asif Iqbal, Brian Cocolicchio, Burhan Ul Haq, Gustavo Patino, Igor Dudchenko, Ike Okonkwo, Joaquin Beltran, Madhavan Ramani, Maxim Volgin, Oscar Cassetti, Philip Best, Richard Vaughan, Sanket Sharma, Simon Tschöke, Simone Sguazza, Srinivasan M, Sriram Macharla, and Werner Nindl.

I would especially like to thank Koen Cobbaert, Leo Ducrot, and Michael Gilliland for their long-time support—as always. Their insights proved invaluable.

Moreover, I would like to thank Manning’s people for supporting me and the work we achieved together. Especially Ian Hough for the insightful reviews and as well as Andy Waldron for the opportunity and the support.

Finally, I would like to thank my friends and family for encouraging me with my endless projects.

about this book

This book was written for anyone who wants to improve their demand planning process. In particular, this book will help the following roles: demand planners, S&OP managers, supply chain leaders, and data scientists working on supply chain projects.

As a demand planner, you have many insights about your industry, products, and clients. You know your business. But you might face an inefficient demand planning process. Repetitive tasks—like manually filling up Excel files every month—slow you down and keep you away from more value-adding tasks. Discussions, negotiations, and political alignments between teams (such as sales and finance) might erode your overall forecasting accuracy as it diverts you from focusing on what drives business value.

By reading this book, you will learn:

How to leverage tools and analytics to focus your work where you will have the most impact.

How to use a forecasting model to create an accurate forecast baseline.

How to manage stakeholders (sales, marketing, production, finance) and leverage their inputs.

As an S&OP manager or supply chain leader, you manage a team of professionals working on the demand planning process. You want to be sure that your demand forecast helps the other departments (sales, purchasing, manufacturing, logistics) make the right decisions. You need tools to assess whether the overall forecasting process is efficient and effective. You want your teams to focus on the most critical products (we will segment these in chapter 13). Moreover, you need insightful metrics to track your process quality (and forecast accuracy). In the end, you need to ensure that your forecasting process is done in the most efficient way and adds value to the supply chain.

You will learn:

The appropriate forecasting granularity and horizon to use when forecasting demand.

How to select appropriate forecasting metrics to track the quality of your demand planning process.

How to use benchmarks to assess the efficacy and efficiency of your demand-planning process.

How to segment your products to focus the work of your team where they will add the most value.

When multiple teams review a forecast, how to promote ownership and accountability using the Forecast Value Added framework.

As a data scientist working on forecasting models, you need a dataset, a clear business objective (metrics, granularity, horizon), and a set of metrics to optimize. Unfortunately, data scientists often kickstart projects by jumping into creating models rather than spending time understanding the business requirements. This is what this book is about.

You will learn,

To identify the business requirements when forecasting demand.

Which data to feed to your model.

Which metric(s) to use when assessing the quality of your model.

Which demand drivers you could use in your model.

Note that this book will review the pros and cons of the different models you can use to forecast demand. But it will not cover how to make forecasting models. If you want to create your own models, I advise reading my previous book, Data Science for Supply Chain Forecasting.¹

How this book is organized: a roadmap

Let’s take the time to outline our journey and the various questions we will discuss in this book.

Part 1 introduces us to forecasting demand:

Chapter 1 has us begin our journey by introducing the demand planning excellence framework.

Chapter 2 will address the important question of why we forecast demand and how it supports the overall supply chain.

Chapters 3 and 4 will answer why and how we should forecast unconstrained demand rather than constrained sales.

Chapters 5 and 6 will explore forecasting granularity and horizon.

This part will conclude with a discussion on forecast reconciliations in chapter 7.

Part 2 focuses on how we can measure forecasting quality:

Chapter 8 and 9 will start by introducing different forecasting KPIs (Bias, MAE, MAPE, RMSE) and discussing their pros and cons.

Chapter 10 will answer a central question to demand planning: What is a good level of forecast accuracy? by using benchmarks.

Chapter 11 will finish off this part by extending our KPIs to assess the forecasting quality of a whole product portfolio using value-weighted metrics.

Part 3 will cover the data-driven forecasting process:

Chapter 12 will discuss the forecast value added framework that will allow you to track the added value of your whole forecasting process (leveraging the benchmarks and value-weighted metrics we discussed in part 2).

Chapter 13 will explain how we can focus the work of demand parameters using segmentation techniques (such as ABC XYZ).

Part 4 will bring us to the end of the book, focusing on forecasting methods:

Chapter 14 will cover statistical methods for demand forecasting.

Chapter 15 will then cover advanced machine-learning techniques, comparing both ML and statistical 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.

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