Data Science for Supply Chain Forecasting

ISBN-13: 9783110671100 / Angielski / Miękka / 2021 / 310 str.

Nicolas Vandeput

Recenzje

"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

Spis treści

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

O autorze

Nicolas Vandeput is a supply chain data scientist specialized in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016 and co-founded SKU Science-a smart online platform for supply chain management-in 2018. He 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: he has taught forecasting and inventory optimization to master students since 2014 in Brussels, Belgium.