Using Analytics to Enhance a Food Retailer's Shelf-Space Management

Detalhes bibliográficos
Autor(a) principal: Bianchi Aguiar,T
Data de Publicação: 2016
Outros Autores: Elsa Marília Silva, Luís Guimarães, Maria Antónia Carravilla, José Fernando Oliveira, Amaral,JG, Liz,J, Lapela,S
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5702
http://dx.doi.org/10.1287/inte.2016.0859
Resumo: This paper describes the results of our collaboration with the leading Portuguese food retailer to address the shelf-space planning problem of allocating products to shop-floor shelves. Our challenge was to introduce analytical methods into the shelf-space planning process to improve the return on space and automate a process heavily dependent on the experience of the retailer's space managers. This led to the creation of GAP, a decision support system that the company's space-management team uses daily. We developed a modular operations research approach that systematically applies mathematical programming models and heuristics to determine the best layout of products on the shelves. GAP combines its analytical strength with an ability to incorporate different types of merchandising rules to balance the tradeoff between optimization and customization.
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spelling Using Analytics to Enhance a Food Retailer's Shelf-Space ManagementThis paper describes the results of our collaboration with the leading Portuguese food retailer to address the shelf-space planning problem of allocating products to shop-floor shelves. Our challenge was to introduce analytical methods into the shelf-space planning process to improve the return on space and automate a process heavily dependent on the experience of the retailer's space managers. This led to the creation of GAP, a decision support system that the company's space-management team uses daily. We developed a modular operations research approach that systematically applies mathematical programming models and heuristics to determine the best layout of products on the shelves. GAP combines its analytical strength with an ability to incorporate different types of merchandising rules to balance the tradeoff between optimization and customization.2018-01-08T09:52:18Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5702http://dx.doi.org/10.1287/inte.2016.0859engBianchi Aguiar,TElsa Marília SilvaLuís GuimarãesMaria Antónia CarravillaJosé Fernando OliveiraAmaral,JGLiz,JLapela,Sinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:24Zoai:repositorio.inesctec.pt:123456789/5702Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:03.592440Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Using Analytics to Enhance a Food Retailer's Shelf-Space Management
title Using Analytics to Enhance a Food Retailer's Shelf-Space Management
spellingShingle Using Analytics to Enhance a Food Retailer's Shelf-Space Management
Bianchi Aguiar,T
title_short Using Analytics to Enhance a Food Retailer's Shelf-Space Management
title_full Using Analytics to Enhance a Food Retailer's Shelf-Space Management
title_fullStr Using Analytics to Enhance a Food Retailer's Shelf-Space Management
title_full_unstemmed Using Analytics to Enhance a Food Retailer's Shelf-Space Management
title_sort Using Analytics to Enhance a Food Retailer's Shelf-Space Management
author Bianchi Aguiar,T
author_facet Bianchi Aguiar,T
Elsa Marília Silva
Luís Guimarães
Maria Antónia Carravilla
José Fernando Oliveira
Amaral,JG
Liz,J
Lapela,S
author_role author
author2 Elsa Marília Silva
Luís Guimarães
Maria Antónia Carravilla
José Fernando Oliveira
Amaral,JG
Liz,J
Lapela,S
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Bianchi Aguiar,T
Elsa Marília Silva
Luís Guimarães
Maria Antónia Carravilla
José Fernando Oliveira
Amaral,JG
Liz,J
Lapela,S
description This paper describes the results of our collaboration with the leading Portuguese food retailer to address the shelf-space planning problem of allocating products to shop-floor shelves. Our challenge was to introduce analytical methods into the shelf-space planning process to improve the return on space and automate a process heavily dependent on the experience of the retailer's space managers. This led to the creation of GAP, a decision support system that the company's space-management team uses daily. We developed a modular operations research approach that systematically applies mathematical programming models and heuristics to determine the best layout of products on the shelves. GAP combines its analytical strength with an ability to incorporate different types of merchandising rules to balance the tradeoff between optimization and customization.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2018-01-08T09:52:18Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5702
http://dx.doi.org/10.1287/inte.2016.0859
url http://repositorio.inesctec.pt/handle/123456789/5702
http://dx.doi.org/10.1287/inte.2016.0859
dc.language.iso.fl_str_mv eng
language eng
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