Retail Analytics

Integrated Forecasting and Inventory Management for Perishable Products in Retailing

Business & Finance, Management & Leadership, Operations Research, Production & Operations Management
Cover of the book Retail Analytics by Anna-Lena Sachs, Springer International Publishing
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Author: Anna-Lena Sachs ISBN: 9783319133058
Publisher: Springer International Publishing Publication: December 10, 2014
Imprint: Springer Language: English
Author: Anna-Lena Sachs
ISBN: 9783319133058
Publisher: Springer International Publishing
Publication: December 10, 2014
Imprint: Springer
Language: English

This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.

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