Data Science for Supply Chain Forecasting (eBook, ePUB)
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Lieferung aus: Vereinigte Staaten von Amerika, plus shipping.
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.

Lieferung aus: Deutschland, Versandkostenfrei.
Data Science for Supply Chain Forecasting: 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. Englisch, Taschenbuch.

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Free shipping.
Von Händler/Antiquariat, Book Depository International [58762574], London, United Kingdom.
Language: English. Brand new Book. Data Science for Supply Chain ForecastData Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of experimentation, of observation and of constant questioning; a true scientific method must be applied to supply chain. In the data science field as well as that of the supply chain, simple questions do not come with simple answers. In order to resolve these questions, one needs to be both a scientist as well as to use the correct tools. In this book, we will discuss both. Is this Book for me?This book has been written for supply chain practitioners, forecasters and analysts who are looking to go the extra mile. You do not need technical IT skills to start using the models of this book. You do not need a dedicated server or expensive software licenses: you solely need your own computer. You do not need a PhD in mathematics: mathematics will only be utilized as a tool to tweak and understand the models. In the majority of the cases - especially when it comes to machine learning - a deep understanding of the mathematical inner workings of a model will not be necessary in order to optimize it and understand its limitations.Reviews"In an age where analytics and machine learning are taking on larger roles in the business forecasting, Nicolas' book is perfect solution for professionals who need to combine practical supply chain experience with the mathematical and technological tools that can help us predict the future more reliably." Daniel Stanton - Author, Supply Chain Management For Dummies"Open source statistical toolkits have progressed tremendously over the last decade. Nicolas demonstrates that these toolkits are more than enough to start addressing real-world forecasting challenges as found in supply chains. Moreover, through its hands-on approach, this book is accessible to a large audience of supply chain practitioners. The supply chain of the 21st century will be data-driven and Nicolas gets it perfectly."Joannes Vermorel - CEO Lokad"This book is unique in its kind. It explains the basics of Python using basic traditional forecasting techniques and shows how machine learning is revolutionizing the forecasting domain. Nicolas has done an outstanding job explaining a technical subject in an easily accessible way. A must-read for any supply chain professional."Professor Bram Desmet - CEO Solventure"This book is before anything a practical and business-oriented "DIY" user manual to help planners move into 21st-century demand planning. The breakthrough comes from several tools and techniques available to all, and which thanks to Nicolas' precise and concrete explanations can now be implemented in real business environments by any "normal" planner. I can confirm that Nicolas' learnings are based on real-life experience and can tremendously help on improving top and bottom lines." Henri-Xavier Benoist - VP Supply Chain Bridegstone EMEA.

Lieferung aus: Deutschland, Erscheint vor. 22.03.21, Versandkostenfrei innerhalb von Deutschland.
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.

Lieferung aus: Vereinigte Staaten von Amerika, Free shipping.
Von Händler/Antiquariat, Save With Sam [65537386], North Miami, FL, U.S.A.
Brand New! This item is printed on demand.

Lieferung aus: Deutschland, Noch nicht erschienen. Versandkostenfrei.
Von Händler/Antiquariat, Amazon.de.
Taschenbuch, Ausgabe: 1, Label: De Gruyter, De Gruyter, Produktgruppe: Book, Publiziert: 2020-01-20, Studio: De Gruyter.

Lieferung aus: Vereinigte Staaten von Amerika, plus shipping, Shipping area: DOM.
Von Händler/Antiquariat, GreatBookPrices, MD, Columbia, [RE:5].
Trade paperback.