Data science for supply chain forecasting

"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. 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