Data Science for Supply Chain Forecasting

 
 
De Gruyter (Verlag)
  • 1. Auflage
  • |
  • erschienen am 22. März 2021
 
  • Buch
  • |
  • Softcover
  • |
  • XXVIII, 282 Seiten
978-3-11-067110-0 (ISBN)
 

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.

 

 

2. Auflage 2021
  • Englisch
  • Berlin/Boston
  • |
  • Deutschland
  • Data science, machine learning, supply chain practitioners, forecasters and analysts
  • Klappenbroschur
  • 105 s/w Abbildungen, 55 s/w Tabellen
  • |
  • 105 b/w ill., 55 b/w tbl.
  • Höhe: 237 mm
  • |
  • Breite: 167 mm
  • |
  • Dicke: 18 mm
  • 526 gr
978-3-11-067110-0 (9783110671100)
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Nicolas Vandeput, Founder, SupChains; Co-founder SKU Science, Belgium is a Supply Chain Data Scientist specialized in Demand Forecasting & Inventory Optimization. He always 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 including the University of Brussels; he teaches forecast and inventory optimization to master students since 2014. He founded SupChains in 2016 and co-founded SKU Science-a smart online platform for supply chain management-in 2018.

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


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