Application of a predictive model to reduce unplanned downtime in automotive industry production processes : a sustainability perspective

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorPontifical Catholic University of Paraná, Industrial and Systems Engineering Program-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorPontifical Catholic University of Paraná, Industrial and Systems Engineering Program-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.contributorUniversity of Brasilia, Industrial Engineering Department-
Autor(es): dc.creatorOjeda, Juan Cristian Oliveira-
Autor(es): dc.creatorMoraes, João Gonçalves Borsato de-
Autor(es): dc.creatorSousa Filho, Cezer Vicente de-
Autor(es): dc.creatorPereira, Matheus de Sousa-
Autor(es): dc.creatorPereira, João Victor de Queiroz-
Autor(es): dc.creatorDias, Izamara Cristina Palheta-
Autor(es): dc.creatorSilva, Eugênia Cornils Monteiro da-
Autor(es): dc.creatorPeixoto, Maria Gabriela Mendonça-
Autor(es): dc.creatorGonçalves, Marcelo Carneiro-
Data de aceite: dc.date.accessioned2026-08-11T11:06:38Z-
Data de disponibilização: dc.date.available2026-08-11T11:06:38Z-
Data de envio: dc.date.issued2026-02-24-
Data de envio: dc.date.issued2026-02-24-
Data de envio: dc.date.issued2025-04-27-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/54100-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/su17093926-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0001-5202-0586-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0005-6604-8183-
Fonte completa do material: dc.identifierhttps://orcid.org/0009-0008-0310-192X-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0001-5413-0423-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-1359-3250-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0003-1238-2301-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-4957-6057-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1187128-
Descrição: dc.descriptionThe automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.-
Descrição: dc.descriptionFaculdade de Tecnologia (FT)-
Descrição: dc.descriptionDepartamento de Engenharia de Produção (FT EPR)-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).-
Palavras-chave: dc.subjectAprendizado de máquina-
Palavras-chave: dc.subjectManutenção preditiva-
Palavras-chave: dc.subjectIndústria automotiva-
Palavras-chave: dc.subjectIndústria 4.0-
Palavras-chave: dc.subjectSustentabilidade-
Título: dc.titleApplication of a predictive model to reduce unplanned downtime in automotive industry production processes : a sustainability perspective-
Tipo de arquivo: dc.typelivro digital-
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