A decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Itajubá (UNIFEI)-
Autor(es): dc.creatorAguirre-Rodríguez, Elen Yanina-
Autor(es): dc.creatorRodríguez, Elias Carlos Aguirre-
Autor(es): dc.creatorda Silva, Aneirson Francisco-
Autor(es): dc.creatorRizol, Paloma Maria Silva Rocha-
Autor(es): dc.creatorde Carvalho Miranda, Rafael-
Autor(es): dc.creatorMarins, Fernando Augusto Silva-
Data de aceite: dc.date.accessioned2025-08-21T20:22:06Z-
Data de disponibilização: dc.date.available2025-08-21T20:22:06Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s41870-023-01707-8-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307105-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307105-
Descrição: dc.descriptionOrganizing decision-making processes in companies so that they are well-structured and consistent is very important in the constant search for competitiveness and sustainability in business. A recurring and relevant problem refers to the selection of suppliers for outsourced processes, as is the case of outsourcing transportation. In this context, this manuscript presents a model to help managers select freight companies, based on the assessment of logistics costs, applying Machine Learning techniques. The model is integrated with a Decision Support System and was applied to a real case of a multinational automotive company in Brazil, comparing the results with what occurred in practice. The results showed that the automotive company could have saved approximately 7% of its logistics costs by shipping its products annually, with a confidence level of 95%. The proposed framework showed advantages for the company, such as the possibility of quickly simulating possible scenarios and mitigating the logistics costs involved.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Production São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionProduction Engineering and Management Institute Federal University of Itajubá (UNIFEI), MG-
Descrição: dc.descriptionDepartment of Production São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionCAPES: CAPES - 001-
Descrição: dc.descriptionCNPq: CNPq - 304197/2021-1-
Descrição: dc.descriptionCNPq: CNPq 303090/2021-9-
Formato: dc.format1495-1503-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Information Technology (Singapore)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCost reduction-
Palavras-chave: dc.subjectDecision making-
Palavras-chave: dc.subjectLogistics cost-
Palavras-chave: dc.subjectM5P Model Tree-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectTransportation outsourcing-
Título: dc.titleA decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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