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Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing

Research output: Book/Report/Proceedings › Book

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Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing. / Sachs, Anna-Lena.
Springer, 2015. 112 p. (Lecture Notes in Economics and Mathematical Systems; Vol. 680).

Research output: Book/Report/Proceedings › Book

Harvard

Sachs, A-L 2015, Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing. Lecture Notes in Economics and Mathematical Systems, vol. 680, Springer. https://doi.org/10.1007/978-3-319-13305-8

APA

Sachs, A-L. (2015). Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing. (Lecture Notes in Economics and Mathematical Systems; Vol. 680). Springer. https://doi.org/10.1007/978-3-319-13305-8

Vancouver

Sachs A-L. Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing. Springer, 2015. 112 p. (Lecture Notes in Economics and Mathematical Systems). doi: 10.1007/978-3-319-13305-8

Author

Sachs, Anna-Lena. / Retail Analytics : Integrated Forecasting and Inventory Management for Perishable Products in Retailing. Springer, 2015. 112 p. (Lecture Notes in Economics and Mathematical Systems).

Bibtex

@book{6b621cfd83a945fe93bff24fd77fb34c,
title = "Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing",
abstract = "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.",
author = "Anna-Lena Sachs",
year = "2015",
doi = "10.1007/978-3-319-13305-8",
language = "English",
isbn = "9783319133041",
series = "Lecture Notes in Economics and Mathematical Systems",
publisher = "Springer",

}

RIS

TY - BOOK

T1 - Retail Analytics

T2 - Integrated Forecasting and Inventory Management for Perishable Products in Retailing

AU - Sachs, Anna-Lena

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-13305-8

DO - 10.1007/978-3-319-13305-8

M3 - Book

SN - 9783319133041

T3 - Lecture Notes in Economics and Mathematical Systems

BT - Retail Analytics

PB - Springer

ER -