Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics: 14 (Springer Series in Supply Chain Management, 14) - Hardcover

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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.

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From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture.