Data Science for Supply Chain Forecasting

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting.

This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves.

This hands-on book, covering the entire range of forecasting--from the basics all the way to leading-edge models--will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.

Nicolas is a Supply Chain Data Scientist specialized in Demand Forecasting & Inventory Optimization. He always enjoys discussing new quantitative models and how to apply them to business reality. Passionate about education, Nicolas is both an avid learner and enjoys teaching at universities including the University of Brussels; he teaches forecast and inventory optimization to master students since 2014. He founded SupChains in 2016 and co-founded SKU Science-a smart online platform for supply chain management-in 2018.