Data Science for Supply Chain Forecasting
by: Nicolas Vandeput
Publisher finelybook 出版社: De Gruyter; 2nd edition (March 22,2021)
Language 语言: English
Print Length 页数: 280 pages
ISBN-10: 3110671107
ISBN-13: 9783110671100
Book Description
By finelybook
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:
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
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Data Science for Supply Chain Forecasting,2nd Edition 9783110671100.zip
