Description: Data Science for Supply Chain Forecasting, Second Edition, focuses on data science and machine learning and demonstrates how both are closely interlinked. It contends that a true scientific method that includes experimentation, observation and const
Brief description: Nicolas Vandeput, Founder, SupChains; Co-founder SKU Science, Belgium
Review Quotes:
"I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models."
--Joannes Vermorel, Founder and CEO, Lokad
"The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable 'do-it-yourself' ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the 'do-it-yourself' descriptions provided in the book."
--Prof. Spyros Makridakis, Founder of the Makridakis Open Forecasting Center (MOFC) and organizer of the M competitions Institute For the Future (IFF), University of Nicosia
"In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably."
-- Daniel Stanton, Author, Supply Chain Management for Dummies