Description
Book Synopsis 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
Table of Contents 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
