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. Events around the book Link to a
De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok
supply chain innovator and CEO of Wahupa Spyros Makridakis professor at the University of
Nicosia and director of the Institute For the Future (IFF) and Edouard Thieuleux founder of
AbcSupplyChain discuss the general issues and challenges of demand forecasting and provide
insights into best practices (process models) and discussing how data science and machine
learning impact those forecasts. The event will be moderated by Michael Gilliland marketing
manager for SAS forecasting software: https: youtu.be 1rXjXcabW2s
