A Multicriteria Customer Classification Method in Supply Chain Management

[EN] Since Kraljic's strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix met...

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Autores: Barrera-Jimenez, Ivan Felipe, Segura, Marina, Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
Tipo de recurso: artículo
Fecha de publicación:2024
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:10251/213521
Acceso en línea:https://riunet.upv.es/handle/10251/213521
Access Level:acceso abierto
Palabra clave:Multiple criteria analysis
Matrix classification algorithm
Supply chain management
Customer segmentation
RFM dimension
MCDM
GLNF
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling A Multicriteria Customer Classification Method in Supply Chain ManagementBarrera-Jimenez, Ivan FelipeSegura, MarinaMaroto Álvarez, Mª Concepción|||0000-0001-8512-3197Multiple criteria analysisMatrix classification algorithmSupply chain managementCustomer segmentationRFM dimensionMCDMGLNFPROMETHEEESTADISTICA E INVESTIGACION OPERATIVA[EN] Since Kraljic's strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix method and validate it in real-world scenarios with a robustness analysis. This method assigns alternatives to one of four classes defined by critical dimensions that integrate several evaluation criteria. Initially, a global search pre-classifies the alternatives using the PROMETHEE net flows. Then, two local searches are carried out that make use of the discriminant properties of the net flow signs to improve the quality of the assignments. This approach is specifically applied to pre-classified alternatives near the boundary between two or more categories. The method has been validated by segmenting thousands of customers. Four customer segments were identified: strategic, collaborative, transactional, and non-preferred. A comparison was made between the results and those derived from an alternative method. Through an extensive sensitivity analysis, the proposed method was shown to be robust to parameter variation, highlighting its reliability in real dynamic contexts. The method provides valuable, easily interpretable information, which constitutes the basis for developing personalised strategies to enhance customer relationship management.MDPI AGFacultad de Administración y Dirección de EmpresasDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadCentro de Gestión de la Calidad y del CambioRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-10-31journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/213521reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2135212026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Multicriteria Customer Classification Method in Supply Chain Management
title A Multicriteria Customer Classification Method in Supply Chain Management
spellingShingle A Multicriteria Customer Classification Method in Supply Chain Management
Barrera-Jimenez, Ivan Felipe
Multiple criteria analysis
Matrix classification algorithm
Supply chain management
Customer segmentation
RFM dimension
MCDM
GLNF
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
title_short A Multicriteria Customer Classification Method in Supply Chain Management
title_full A Multicriteria Customer Classification Method in Supply Chain Management
title_fullStr A Multicriteria Customer Classification Method in Supply Chain Management
title_full_unstemmed A Multicriteria Customer Classification Method in Supply Chain Management
title_sort A Multicriteria Customer Classification Method in Supply Chain Management
dc.creator.none.fl_str_mv Barrera-Jimenez, Ivan Felipe
Segura, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author Barrera-Jimenez, Ivan Felipe
author_facet Barrera-Jimenez, Ivan Felipe
Segura, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author_role author
author2 Segura, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author2_role author
author
dc.contributor.none.fl_str_mv Facultad de Administración y Dirección de Empresas
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Centro de Gestión de la Calidad y del Cambio
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Multiple criteria analysis
Matrix classification algorithm
Supply chain management
Customer segmentation
RFM dimension
MCDM
GLNF
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
topic Multiple criteria analysis
Matrix classification algorithm
Supply chain management
Customer segmentation
RFM dimension
MCDM
GLNF
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] Since Kraljic's strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix method and validate it in real-world scenarios with a robustness analysis. This method assigns alternatives to one of four classes defined by critical dimensions that integrate several evaluation criteria. Initially, a global search pre-classifies the alternatives using the PROMETHEE net flows. Then, two local searches are carried out that make use of the discriminant properties of the net flow signs to improve the quality of the assignments. This approach is specifically applied to pre-classified alternatives near the boundary between two or more categories. The method has been validated by segmenting thousands of customers. Four customer segments were identified: strategic, collaborative, transactional, and non-preferred. A comparison was made between the results and those derived from an alternative method. Through an extensive sensitivity analysis, the proposed method was shown to be robust to parameter variation, highlighting its reliability in real dynamic contexts. The method provides valuable, easily interpretable information, which constitutes the basis for developing personalised strategies to enhance customer relationship management.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-10-31
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/10251/213521
url https://riunet.upv.es/handle/10251/213521
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
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 MDPI AG
publisher.none.fl_str_mv MDPI AG
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
_version_ 1869420620364644352
score 15,812429

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