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Retail analyticsintegrated forecasti...

Sachs, Anna-Lena.

Retail analyticsintegrated forecasting and inventory management for perishable products in retailing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Retail analyticsby Anna-Lena Sachs.
Reminder of title:	integrated forecasting and inventory management for perishable products in retailing /
Author:	Sachs, Anna-Lena.
Published:	Cham :Springer International Publishing :2015.
Description:	xvii, 111 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Business logisticsManagement.
Online resource:	http://dx.doi.org/10.1007/978-3-319-13305-8
ISBN:	9783319133058 (electronic bk.)

Retail analyticsintegrated forecasting and inventory management for perishable products in retailing /
Sachs, Anna-Lena.

Retail analyticsintegrated forecasting and inventory management for perishable products in retailing /[electronic resource] :by Anna-Lena Sachs. - Cham :Springer International Publishing :2015. - xvii, 111 p. :ill., digital ;24 cm. - Lecture notes in economics and mathematical systems,6800075-8442 ;. - Lecture notes in economics and mathematical systems ;461..

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.

ISBN: 9783319133058 (electronic bk.)

Standard No.: 10.1007/978-3-319-13305-8doiSubjects--Topical Terms:

233497
Business logistics
--Management.

LC Class. No.: HD38.5

Dewey Class. No.: 658.5

Retail analyticsintegrated forecasting and inventory management for perishable products in retailing /
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505 0 $a 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.
520 $a 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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