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 traditional statistical forecasting models, Part II on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both (demand) forecasting 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, demand planners, forecasters, and analysts looking to go the extra mile with demand forecasting.

About the Author

Nicolas Vandeput is a supply chain data scientist specializing in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016, delivering models and training courses worldwide. He co-founded SKU Science—a demand forecasting platform—in 2018. Passionate about education, Nicolas is an avid learner enjoying teaching at universities. He currently teaches forecasting and inventory optimization to master students in CentraleSupelec, Paris, France.

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