Application of a Predictive Model to Reduce Unplanned Downtime in Automotive Industry Production Processes: A Sustainability Perspective

Juan Cristian Oliveira Ojeda, João Gonçalves Borsato de Moraes, Cezer Vicente de Sousa Filho, Matheus de Sousa Pereira, João Victor de Queiroz Pereira, Izamara Cristina Palheta Dias, Eugênia Cornils Monteiro da Silva, Maria Gabriela Mendonça Peixoto and Marcelo Carneiro Gonçalves ()
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Juan Cristian Oliveira Ojeda: Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná (PUCPR), Curitiba 80215-901, Brazil
João Gonçalves Borsato de Moraes: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
Cezer Vicente de Sousa Filho: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
Matheus de Sousa Pereira: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
João Victor de Queiroz Pereira: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
Izamara Cristina Palheta Dias: Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná (PUCPR), Curitiba 80215-901, Brazil
Eugênia Cornils Monteiro da Silva: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
Maria Gabriela Mendonça Peixoto: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil
Marcelo Carneiro Gonçalves: Industrial Engineering Department, University of Brasilia (UNB), Brasilia 70910-900, Brazil

Sustainability, 2025, vol. 17, issue 9, 1-29

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.

Keywords: machine learning; predictive maintenance; automotive industry; sustainability; unplanned downtime; Industry 4.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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