| Campo DC | Valor | Lengua/Idioma |
|---|
| dc.contributor.author | Ojeda, Juan Cristian Oliveira | - |
| dc.contributor.author | Moraes, João Gonçalves Borsato de | - |
| dc.contributor.author | Sousa Filho, Cezer Vicente de | - |
| dc.contributor.author | Pereira, Matheus de Sousa | - |
| dc.contributor.author | Pereira, João Victor de Queiroz | - |
| dc.contributor.author | Dias, Izamara Cristina Palheta | - |
| dc.contributor.author | Silva, Eugênia Cornils Monteiro da | - |
| dc.contributor.author | Peixoto, Maria Gabriela Mendonça | - |
| dc.contributor.author | Gonçalves, Marcelo Carneiro | - |
| dc.date.accessioned | 2026-02-24T15:45:06Z | - |
| dc.date.available | 2026-02-24T15:45:06Z | - |
| dc.date.issued | 2025-04-27 | - |
| dc.identifier.citation | OJEDA, Juan Cristian Oliveira et al. Application of a predictive model to reduce unplanned downtime in automotive industry production processes: a sustainability perspective. Sustainability, Basel, v. 17, n. 9, e3926, 2025. DOI: https://doi.org/10.3390/su17093926. Disponível em: https://www.mdpi.com/2071-1050/17/9/3926. Acesso em: 12 fev. 2026. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/54100 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | MDPI | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Application of a predictive model to reduce unplanned downtime in automotive industry production processes : a sustainability perspective | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Aprendizado de máquina | pt_BR |
| dc.subject.keyword | Manutenção preditiva | pt_BR |
| dc.subject.keyword | Indústria automotiva | pt_BR |
| dc.subject.keyword | Indústria 4.0 | pt_BR |
| dc.subject.keyword | Sustentabilidade | pt_BR |
| dc.rights.license | This 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/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/su17093926 | pt_BR |
| dc.description.abstract1 | The 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. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0009-0001-5202-0586 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0009-0005-6604-8183 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0009-0008-0310-192X | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-5413-0423 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-1359-3250 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-1238-2301 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-4957-6057 | pt_BR |
| dc.contributor.affiliation | Pontifical Catholic University of Paraná, Industrial and Systems Engineering Program | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | Pontifical Catholic University of Paraná, Industrial and Systems Engineering Program | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Industrial Engineering Department | pt_BR |
| dc.description.unidade | Faculdade de Tecnologia (FT) | pt_BR |
| dc.description.unidade | Departamento de Engenharia de Produção (FT EPR) | pt_BR |
| Aparece en las colecciones: | Artigos publicados em periódicos e afins
|
