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Data Science for Supply Chain Forecasting

Autor Nicolas Vandeput
en Limba Engleză Paperback – 22 mar 2021
Open source statistical toolkits have progressed tremendously over the last decade. In this book Nicolas Vandeput demonstrates that these toolkits are more than enough to address real-world forecasting challenges as found in supply chains.
Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting contends that a true scientific method that includes experimentation, observation and constant questioning must be applied to supply chain as well. The first part of the book is focused on statistical "traditional" models and the second on machine learning. The various chapters are focused either on forecast models or on new concepts (overfit, underfit, kpi, outliers). The book is full of python examples to show the reader how to apply these models him/herself.
This is a book for practitioners focusing on data science and machine learning and demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. Through its hands-on approach, it is accessible to a large audience of supply chain practitioners.
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Specificații

ISBN-13: 9783110671100
ISBN-10: 3110671107
Pagini: 282
Ilustrații: 105 b/w ill., 55 b/w tbl.
Dimensiuni: 167 x 237 x 18 mm
Greutate: 0.53 kg
Editura: WALTER DE GRUYTER INC

Notă biografică

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.

Cuprins

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