Stock control analytics: a data-driven approach to compute the fill rate considering undershoots

[EN] One of the most frequently used inventory policies is the order-point, order-up-to-level (s, S) system. In this system, the inventory is continuously reviewed and a replenishment request is placed whenever the inventory position drops to or below the order point, s. The variable replenishment o...

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Detalles Bibliográficos
Autores: Babiloni, Eugenia|||0000-0002-7949-3703, Guijarro, Ester|||0000-0003-1988-0397, Trapero, Juan R.
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/202465
Acceso en línea:https://riunet.upv.es/handle/10251/202465
Access Level:acceso abierto
Palabra clave:Inventory
Fill rate
Lost sales
Undershoots
State-dependent parameter
ORGANIZACION DE EMPRESAS
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spelling Stock control analytics: a data-driven approach to compute the fill rate considering undershootsBabiloni, Eugenia|||0000-0002-7949-3703Guijarro, Ester|||0000-0003-1988-0397Trapero, Juan R.InventoryFill rateLost salesUndershootsState-dependent parameterORGANIZACION DE EMPRESAS[EN] One of the most frequently used inventory policies is the order-point, order-up-to-level (s, S) system. In this system, the inventory is continuously reviewed and a replenishment request is placed whenever the inventory position drops to or below the order point, s. The variable replenishment order quantity and the variable replenishment cycle characterize the system by the use of complex mathematical computations. Different methodological approaches diminish the mathematical complexity by neglecting the undershoots, i.e., the quantity that the inventory position is below the order point when it is reached. In this paper, we conceptually and empirically analyse the bias that neglecting the undershoots introduces into the estimation of the fill rate. After that, we suggest a new methodology developed under a data-driven perspective that uses a state-dependent parameter algorithm to correct such a bias. As a result, we propose two new methods, one parametric and the other nonparametric, to enhance the fill rate estimate. Both methods, named analytics fill rate methods, remove the bias that neglecting the undershoots introduces and are used to illustrate the practical implications of this hypothesis on the performance and design of the (s, S) system. This research is developed in a lost sales context with simulated stochastic and i.i.d. discrete demands as well as actual sales data.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the European Regional Development Fund and Junta de Comunidades de Castilla-La Mancha (JCCM/FEDER, UE) under the project with reference SBPLY/19/180501/000151 and by the Vicerrectorado de Investigacion y Politica Cientifica from UCLM through the research group fund program (PREDILAB; [2021-GRIN-31210]). Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Springer-VerlagCentro de Investigación en Gestión de Empresas (CEGEA)Departamento de Organización de EmpresasFacultad de Administración y Dirección de EmpresasUniversidad de Castilla-La ManchaEuropean Regional Development FundUniversitat Politècnica de ValènciaJunta de Comunidades de Castilla-La ManchaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/202465reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengJunta de Comunidades de Castilla-La Mancha https://doi.org/10.13039/501100011698 SBPLY%2F19%2F180501%2F000151 Soluciones integrales de inteligencia predictiva aplicadas a grandes bases de datos de series temporalesUniversidad de Castilla-La Mancha https://doi.org/10.13039/501100007480 2021-GRIN-31210open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2024652026-06-13T07:49:27Z
dc.title.none.fl_str_mv Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
title Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
spellingShingle Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
Babiloni, Eugenia|||0000-0002-7949-3703
Inventory
Fill rate
Lost sales
Undershoots
State-dependent parameter
ORGANIZACION DE EMPRESAS
title_short Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
title_full Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
title_fullStr Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
title_full_unstemmed Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
title_sort Stock control analytics: a data-driven approach to compute the fill rate considering undershoots
dc.creator.none.fl_str_mv Babiloni, Eugenia|||0000-0002-7949-3703
Guijarro, Ester|||0000-0003-1988-0397
Trapero, Juan R.
author Babiloni, Eugenia|||0000-0002-7949-3703
author_facet Babiloni, Eugenia|||0000-0002-7949-3703
Guijarro, Ester|||0000-0003-1988-0397
Trapero, Juan R.
author_role author
author2 Guijarro, Ester|||0000-0003-1988-0397
Trapero, Juan R.
author2_role author
author
dc.contributor.none.fl_str_mv Centro de Investigación en Gestión de Empresas (CEGEA)
Departamento de Organización de Empresas
Facultad de Administración y Dirección de Empresas
Universidad de Castilla-La Mancha
European Regional Development Fund
Universitat Politècnica de València
Junta de Comunidades de Castilla-La Mancha
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Inventory
Fill rate
Lost sales
Undershoots
State-dependent parameter
ORGANIZACION DE EMPRESAS
topic Inventory
Fill rate
Lost sales
Undershoots
State-dependent parameter
ORGANIZACION DE EMPRESAS
description [EN] One of the most frequently used inventory policies is the order-point, order-up-to-level (s, S) system. In this system, the inventory is continuously reviewed and a replenishment request is placed whenever the inventory position drops to or below the order point, s. The variable replenishment order quantity and the variable replenishment cycle characterize the system by the use of complex mathematical computations. Different methodological approaches diminish the mathematical complexity by neglecting the undershoots, i.e., the quantity that the inventory position is below the order point when it is reached. In this paper, we conceptually and empirically analyse the bias that neglecting the undershoots introduces into the estimation of the fill rate. After that, we suggest a new methodology developed under a data-driven perspective that uses a state-dependent parameter algorithm to correct such a bias. As a result, we propose two new methods, one parametric and the other nonparametric, to enhance the fill rate estimate. Both methods, named analytics fill rate methods, remove the bias that neglecting the undershoots introduces and are used to illustrate the practical implications of this hypothesis on the performance and design of the (s, S) system. This research is developed in a lost sales context with simulated stochastic and i.i.d. discrete demands as well as actual sales data.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/202465
url https://riunet.upv.es/handle/10251/202465
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Junta de Comunidades de Castilla-La Mancha https://doi.org/10.13039/501100011698 SBPLY%2F19%2F180501%2F000151 Soluciones integrales de inteligencia predictiva aplicadas a grandes bases de datos de series temporales
Universidad de Castilla-La Mancha https://doi.org/10.13039/501100007480 2021-GRIN-31210
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer-Verlag
publisher.none.fl_str_mv Springer-Verlag
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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