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Data Science for Supply Chain Forecasting

Nicolas Vandeput (Autor)

Buch | Softcover

XXVIII, 282 Seiten

2021 | 2nd ed.
De Gruyter (Verlag)
978-3-11-067110-0 (ISBN)

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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.

Nicolas 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

"I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models."

--Joannes Vermorel, Founder and CEO, Lokad

"The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable 'do-it-yourself' ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the 'do-it-yourself' descriptions provided in the book."

--Prof. Spyros Makridakis, Founder of the Makridakis Open Forecasting Center (MOFC) and organizer of the M competitions Institute For the Future (IFF), University of Nicosia

"In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably."

-- Daniel Stanton, Author, Supply Chain Management for Dummies

Erscheinungsdatum	24.03.2021
Zusatzinfo	105 b/w ill., 55 b/w tbl.
Verlagsort	Berlin/Boston
Sprache	englisch
Maße	170 x 240 mm
Gewicht	524 g
Themenwelt	Sachbuch/Ratgeber ► Beruf / Finanzen / Recht / Wirtschaft ► Wirtschaft
Themenwelt	Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion
Schlagworte	Data Science • de Gruyter • Demand Forecasting • Forecasting • inventory optimisation • Inventory optimization • machine learning • multi-echelon optimisation • Multi-Echelon Optimization • Nicolas Vandeput • Overfit • Python • SKU Science • SupChains • Supply Chain • supply chain data science • Supply chain forecasting • Supply Chain Management • Underfit
ISBN-10	3-11-067110-7 / 3110671107
ISBN-13	978-3-11-067110-0 / 9783110671100
Zustand	Neuware