Articles liés à Data Science for Supply Chain Forecasting
Synopsis
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.
Events around the book
Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Spyros Makridakis, professor at the University of Nicosia and director of the Institute For the Future (IFF); and Edouard Thieuleux, founder of AbcSupplyChain, discuss the general issues and challenges of demand forecasting and provide insights into best practices (process, models) and discussing how data science and machine learning impact those forecasts.
The event will be moderated by Michael Gilliland, marketing manager for SAS forecasting software:
https: //youtu.be/1rXjXcabW2s
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Nicolas Vandeput, Founder, SupChains; Co-founder SKU Science, Belgium
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Data Science for Supply Chain Forecasting
Edité par
De Gruyter, 2021
ISBN 10 : 3110671107
ISBN 13 : 9783110671100
Ancien ou d'occasion
Couverture souple
Vendeur : medimops, Berlin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. N° de réf. du vendeur M03110671107-V
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Data Science for Supply Chain Forecasting (Paperback or Softback)
Edité par
de Gruyter 3/22/2021, 2021
ISBN 10 : 3110671107
ISBN 13 : 9783110671100
Neuf
Paperback or Softback
Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback or Softback. Etat : New. Data Science for Supply Chain Forecasting 1.1. Book. N° de réf. du vendeur BBS-9783110671100
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Data Science for Supply Chain Forecasting
Vendeur : California Books, Miami, FL, Etats-Unis
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Etat : New. N° de réf. du vendeur I-9783110671100
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Data Science for Supply Chain Forecasting
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : New. 2nd ed. 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. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Spyros Makridakis, professor at the University of Nicosia and director of the Institute For the Future (IFF); and Edouard Thieuleux, founder of AbcSupplyChain, discuss the general issues and challenges of demand forecasting and provide insights into best practices (process, models) and discussing how data science and machine learning impact those forecasts.The event will be moderated by Michael Gilliland, marketing manager for SAS forecasting software:https://youtu.be/1rXjXcabW2s. N° de réf. du vendeur LU-9783110671100
Quantité disponible : Plus de 20 disponibles
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DATA SCIENCE FOR SUPPLY CHAIN FORECASTIN
Vendeur : Speedyhen, London, Royaume-Uni
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Etat : NEW. N° de réf. du vendeur NW9783110671100
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Data Science for Supply Chain Forecasting
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Etat : New. N° de réf. du vendeur 39274575-n
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Data Science for Supply Chain Forecasting
Vendeur : Rarewaves.com UK, London, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : New. 2nd ed. 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. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Spyros Makridakis, professor at the University of Nicosia and director of the Institute For the Future (IFF); and Edouard Thieuleux, founder of AbcSupplyChain, discuss the general issues and challenges of demand forecasting and provide insights into best practices (process, models) and discussing how data science and machine learning impact those forecasts.The event will be moderated by Michael Gilliland, marketing manager for SAS forecasting software:https://youtu.be/1rXjXcabW2s. N° de réf. du vendeur LU-9783110671100
Quantité disponible : Plus de 20 disponibles
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur C8-9783110671100
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Data Science for Supply Chain Forecasting
Vendeur : moluna, Greven, Allemagne
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Etat : New. 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 sta. N° de réf. du vendeur 319492192
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In. N° de réf. du vendeur ria9783110671100_new
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