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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 allnew 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 doityourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves.This handson book, covering the entire range of forecastingfrom the basics all the way to leadingedge modelswill benefit supply chain practitioners, demand planners, forecasters, and analysts looking to go the extra mile with demand forecasting.
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