Data Science for Supply Chain Forecasting

Nicolas Vandeput

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English

De Gruyter
22 March 2021

Economic forecasting; Management: leadership & motivation; Quality Assurance (QA) & Total Quality Management (TQM); Knowledge management; Sales & marketing management

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

By: Nicolas Vandeput
Imprint: De Gruyter
Country of Publication: Germany
Edition: 2nd ed.
Dimensions: Height: 240mm, Width: 170mm,
Weight: 524g
ISBN: 9783110671100
ISBN 10: 3110671107
Pages: 310
Publication Date: 22 March 2021
Audience: Professional and scholarly , Undergraduate , ELT Advanced
Format: Paperback
Publisher's Status: Active

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

Nicolas Vandeput is a supply chain data scientist specialized in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016 and co-founded SKU Science—a smart online platform for supply chain management—in 2018. He enjoys discussing new quantitative models and how to apply them to business reality. Passionate about education, Nicolas is both an avid learner and enjoys teaching at universities: he has taught forecasting and inventory optimization to master students since 2014 in Brussels, Belgium.

Reviews for Data Science for Supply Chain Forecasting

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