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Retail analytics : integrated forecasting and inventory management for perishable products in retailing / By Anna-Lena Sachs.

By: Sachs, Anna-LenaMaterial type: TextTextSeries: Lecture Notes in Economics and Mathematical Systems: 680Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st edDescription: xvii,111p. ; PB 23.5 CMContent type: text Media type: computer Carrier type: online resourceISBN: 9783319133058Subject(s): Production management | Operations research | Decision making | Management science | Sales management | Operations Management | Operations Research/Decision Theory | Operations Research, Management Science | Sales/DistributionAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 005.768
Contents:
Introduction -- Literature Review -- Safety Stock Planning under Causal Demand Forecasting -- The Data-Driven Newsvendor with Censored Demand Observations -- Data-Driven Order Policies with Censored Demand and Substitution -- Empirical Newsvendor Decisions under a Service Contract -- Conclusions.
Summary: 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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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Book St Aloysius Institute of Management & Information Technology
MCA 005.768 SACA (Browse shelf) Available MCA17103
Total holds: 0

Introduction -- Literature Review -- Safety Stock Planning under Causal Demand Forecasting -- The Data-Driven Newsvendor with Censored Demand Observations -- Data-Driven Order Policies with Censored Demand and Substitution -- Empirical Newsvendor Decisions under a Service Contract -- Conclusions.

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.

Description based on publisher-supplied MARC data.

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