Data Science for Supply Chain Forecasting

Data science is more than just coding skills; it requires a scientific mindset to solve problems. This second edition of Data Science for Supply Chain Forecasting adds 45% extra content, including an introduction to neural networks and the forecast value-added framework. It covers forecast models, metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers, with do-it-yourself sections in Python and Excel.

Format: Paperback / softback
Length: 310 pages
Publication date: 22 March 2021
Publisher: De Gruyter

Data science is a powerful tool that can be used to solve complex problems, but it requires a scientific mindset and a range of skills beyond just coding. In the field of supply chain forecasting, data science is essential for achieving excellence in demand forecasting.

According to Data Science for Supply Chain Forecasting, Second Edition, a true scientific method that involves experimentation, observation, and constant questioning must be applied to supply chains to achieve optimal demand forecasting. This second edition offers over 45 percent extra content, including four new chapters on neural networks and the forecast value-added framework. The book is divided into three parts: Part I focuses on statistical traditional models, Part II on machine learning, and Part III on demand forecasting process management. Each chapter explores both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers.

The book is designed to be practical, with numerous do-it-yourself sections that provide implementations in Python (and Excel for the statistical models) to demonstrate how readers can apply these models themselves. Whether you are a supply chain practitioner, forecaster, or analyst looking to take your demand forecasting to the next level, this hands-on book will provide you with the knowledge and tools you need.

In addition to its practical content, Data Science for Supply Chain Forecasting, Second Edition is accompanied by a series of events that explore the general issues and challenges of demand forecasting and provide insights into best practices and the impact of data science and machine learning on those forecasts. These events feature experts in the field, including the author Nicolas Vandeput, Stefan de Kok, Spyros Makridakis, and Edouard Thieuleux, who will discuss the latest trends and developments in demand forecasting and share their experiences and expertise.

Overall, Data Science for Supply Chain Forecasting, Second Edition is a comprehensive and practical guide that will help you leverage data science to improve your supply chain forecasting and drive business success. Whether you are a beginner or an experienced practitioner, this book will provide you with the knowledge and skills you need to succeed in the field of supply chain forecasting.

Weight: 522g
Dimension: 172 x 242 x 24 (mm)
ISBN-13: 9783110671100
Edition number: 2nd ed.