Data Science for Supply Chain Forecasting / / Nicolas Vandeput.

• Superior document: Title is part of eBook package: De Gruyter DG Ebook Package English 2021
• VerfasserIn: Vandeput, Nicolas,
• MitwirkendeR: Makridakis, Spyros,; Ndiaye, Alassane B.,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2021]; ©2021
• Year of Publication: 2021
• Edition: 2nd ed.
• Language: English
• Physical Description: 1 online resource (XXVIII, 282 p.)

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.

Table of Contents

Frontmatter -- Acknowledgments -- About the Author -- Foreword – Second Edition -- Foreword – First Edition -- Contents -- Introduction -- Part I: Statistical Forecasting -- 1 Moving Average -- 2 Forecast KPI -- 3 Exponential Smoothing -- 4 Underfitting -- 5 Double Exponential Smoothing -- 6 Model Optimization -- 7 Double Smoothing with Damped Trend -- 8 Overfitting -- 9 Triple Exponential Smoothing -- 10 Outliers -- 11 Triple Additive Exponential Smoothing -- Part II: Machine Learning -- 12 Machine Learning -- 13 Tree -- 14 Parameter Optimization -- 15 Forest -- 16 Feature Importance -- 17 Extremely Randomized Trees -- 18 Feature Optimization #1 -- 19 Adaptive Boosting -- 20 Demand Drivers and Leading Indicators -- 21 Extreme Gradient Boosting -- 22 Categorical Features -- 23 Clustering -- 24 Feature Optimization #2 -- 25 Neural Networks -- Part III: Data-Driven Forecasting Process Management -- 26 Judgmental Forecasts -- 27 Forecast Value Added -- Now It’s Your Turn! -- A Python -- Bibliography -- Glossary -- Index