Título:	Application of a predictive model to reduce unplanned downtime in automotive industry production processes : a sustainability perspective
Autor(es):	Ojeda, Juan Cristian Oliveira Moraes, João Gonçalves Borsato de Sousa Filho, Cezer Vicente de Pereira, Matheus de Sousa Pereira, João Victor de Queiroz Dias, Izamara Cristina Palheta Silva, Eugênia Cornils Monteiro da Peixoto, Maria Gabriela Mendonça Gonçalves, Marcelo Carneiro
ORCID:	https://orcid.org/0009-0001-5202-0586 https://orcid.org/0009-0005-6604-8183 https://orcid.org/0009-0008-0310-192X https://orcid.org/0000-0001-5413-0423 https://orcid.org/0000-0002-1359-3250 https://orcid.org/0000-0003-1238-2301 https://orcid.org/0000-0002-4957-6057
Afiliação do autor:	Pontifical Catholic University of Paraná, Industrial and Systems Engineering Program University of Brasilia, Industrial Engineering Department University of Brasilia, Industrial Engineering Department University of Brasilia, Industrial Engineering Department University of Brasilia, Industrial Engineering Department Pontifical Catholic University of Paraná, Industrial and Systems Engineering Program University of Brasilia, Industrial Engineering Department University of Brasilia, Industrial Engineering Department University of Brasilia, Industrial Engineering Department
Assunto:	Aprendizado de máquina Manutenção preditiva Indústria automotiva Indústria 4.0 Sustentabilidade
Data de publicação:	27-abr-2025
Editora:	MDPI
Referência:	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.
Abstract:	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.
Unidade Acadêmica:	Faculdade de Tecnologia (FT) Departamento de Engenharia de Produção (FT EPR)
Licença:	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/).
DOI:	https://doi.org/10.3390/su17093926
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