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This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.
This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.
Maxime C. Cohen is a Professor of Retail and Operations Management, Co-Director of the Retail Innovation Lab, and a Bensadoun Faculty Scholar at McGill University, Canada. He is also a Scientific Advisor on AI and Data Science at IVADO Labs and a Scientific Director at the non-profit MyOpenCourt.org. His core expertise lies at the intersection of data science and operations research. He holds a Ph.D. in Operations Research from MIT, USA.
Paul-Emile Gras is a data scientist at Virtuo Technologies in Paris, France. His expertise is at the interface of demand forecasting and revenue management. Prior to joining Virtuo, he was a research assistant in operations at McGill University, Canada.
Arthur Pentecoste is a data scientist at the Boston Consulting Group's New York office, USA. His main scope of expertise is in predictive modelling and analytics applied to demand forecasting and predictive maintenance.
Renyu Zhang is an Assistant Professor of Operations Management at New York University Shanghai, China. He is also an economist and tech lead at Kuaishou, one of the world's largest online video-sharing and live-streaming platforms. He is an expert on data science and operations research. He holds a Ph.D. in Operations Management from Washington University in St. Louis, USA.
Covers the entire process of demand prediction for any business setting
Discusses all the steps required in a real-world implementation
Includes additional material to assist the learning experience
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Management |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Seiten: | 176 |
| Reihe: | Springer Series in Supply Chain Management |
| Inhalt: |
xvii
155 S. 4 s/w Illustr. 29 farbige Illustr. 155 p. 33 illus. 29 illus. in color. |
| ISBN-13: | 9783030858544 |
| ISBN-10: | 3030858545 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Cohen, Maxime C.
Zhang, Renyu Pentecoste, Arthur Gras, Paul-Emile |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Springer Series in Supply Chain Management |
| Maße: | 241 x 160 x 16 mm |
| Von/Mit: | Maxime C. Cohen (u. a.) |
| Erscheinungsdatum: | 22.12.2021 |
| Gewicht: | 0,436 kg |
Maxime C. Cohen is a Professor of Retail and Operations Management, Co-Director of the Retail Innovation Lab, and a Bensadoun Faculty Scholar at McGill University, Canada. He is also a Scientific Advisor on AI and Data Science at IVADO Labs and a Scientific Director at the non-profit MyOpenCourt.org. His core expertise lies at the intersection of data science and operations research. He holds a Ph.D. in Operations Research from MIT, USA.
Paul-Emile Gras is a data scientist at Virtuo Technologies in Paris, France. His expertise is at the interface of demand forecasting and revenue management. Prior to joining Virtuo, he was a research assistant in operations at McGill University, Canada.
Arthur Pentecoste is a data scientist at the Boston Consulting Group's New York office, USA. His main scope of expertise is in predictive modelling and analytics applied to demand forecasting and predictive maintenance.
Renyu Zhang is an Assistant Professor of Operations Management at New York University Shanghai, China. He is also an economist and tech lead at Kuaishou, one of the world's largest online video-sharing and live-streaming platforms. He is an expert on data science and operations research. He holds a Ph.D. in Operations Management from Washington University in St. Louis, USA.
Covers the entire process of demand prediction for any business setting
Discusses all the steps required in a real-world implementation
Includes additional material to assist the learning experience
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Management |
| Genre: | Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Seiten: | 176 |
| Reihe: | Springer Series in Supply Chain Management |
| Inhalt: |
xvii
155 S. 4 s/w Illustr. 29 farbige Illustr. 155 p. 33 illus. 29 illus. in color. |
| ISBN-13: | 9783030858544 |
| ISBN-10: | 3030858545 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Cohen, Maxime C.
Zhang, Renyu Pentecoste, Arthur Gras, Paul-Emile |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Springer Series in Supply Chain Management |
| Maße: | 241 x 160 x 16 mm |
| Von/Mit: | Maxime C. Cohen (u. a.) |
| Erscheinungsdatum: | 22.12.2021 |
| Gewicht: | 0,436 kg |
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