Data Science for Supply Chain Forecast is the ultimate book for supply practitioners to learn how to use data science and machine learning to forecast demand. The book is full of examples, code extracts, ideas and step-by-step how-to to show you how you can do it. You don’t need a math PhD nor to be an IT genius, to start using machine learning today.



Learn what is underfitting & overfitting and how you can avoid both. Create your own models to spot demand outliers and correct them automatically. Understand what are the differences between all the forecast KPI (RMSE, MAE & MAPE) and which is best suited to your dataset.

Discover what is machine learning and start to experiment with one of the 6 models explained in the book. Create one of the most advanced forecast models and let your computer classify the different products into categories. 

Statistical models will allow you to understand the underlying structures behind the demand of any products. It will also be a great addition to any machine learning model. Data Science for Supply Chain Forecast will cover 6 different statistical model to get you up-to speed.

The book contains a short introduction to python and multiple code extracts as well as practical how-to for YOU to experiment with the different algorithms. You don’t need to be a IT person to start to apply your machine learning models. 


Nicolas Vandeput


Data Science for

Supply Chain Forecast