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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 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

Data Science for Supply Chain Forecasting

I Statistical Forecast



Moving Average



Forecast Error



Exponential Smoothing



Underfitting



Double Exponential Smoothing



Model Optimization



Double Smoothing with Damped Trend



Overfitting



Triple Exponential Smoothing



Outliers



Triple Additive Exponential smoothing



II Machine Learning



Machine Learning



Tree



Parameter Optimization



Forest



Feature Importance



Extremely Randomized Trees



Feature Optimization



Adaptive Boosting



Exogenous Information & Leading Indicators



Extreme Gradient Boosting



Categories



Clustering



Glossary