Turnover em obras de montagem eletromecânica: aplicação de Machine Learning na gestão de pessoas

Gabriel Filipe Rebuiti Passos; Jailson dos Santos Silva

doi:10.47456/bjpe.v11i4.50222

Authors

Gabriel Filipe Rebuiti Passos Universidade de São Paulo - USP Author https://orcid.org/0009-0002-7025-8595
Jailson dos Santos Silva Universidade Federal de Santa Catarina - UFSC Author https://orcid.org/0000-0002-2083-2934

DOI:

https://doi.org/10.47456/bjpe.v11i4.50222

Keywords:

turnover, HR, electromechanical assembly

Abstract

This study investigated the factors influencing employee turnover in electromechanical assembly projects, focusing on the application of statistical techniques and machine learning to support people management. The research used a dataset of 7,333 terminations that occurred between January 2023 and July 2025 in a Brazilian company from the sector, covering individual, organizational, and work-related variables. After preprocessing and exploratory data analysis, three classification models were applied: binary logistic regression (with stepwise selection), decision tree, and Random Forest (with grid search calibration). The results showed that factors such as percentage of leave days, tenure, project type, and state of residence had greater relevance for position replacement. The Random Forest model achieved the best predictive performance, with an AUC-ROC of 0.878 and a Gini coefficient of 0.756, while logistic regression stood out for the interpretability of its coefficients. The findings indicate that integrating statistical methods and machine learning contributes to anticipating turnover scenarios, supporting more effective retention and allocation strategies in human resources management for industrial projects.

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Author Biographies

Gabriel Filipe Rebuiti Passos, Universidade de São Paulo - USP

Mechanical Engineer and Specialist in Data Science and Analytics from ESALQ/USP
Jailson dos Santos Silva, Universidade Federal de Santa Catarina - UFSC

He holds a bachelor's degree (URCA), a master's degree (UFPB), and is currently a doctoral candidate (UFSC) in Production Engineering, with a doctoral sandwich program at Universitat Jaume I (Spain). He also has a specialization in Data Science, focusing on Business Intelligence (UNOPAR), and an MBA in Finance and Controlling (USP). He works as a Supervising Professor for MBA courses at ESALQ/USP and as a Researcher at the Logistics Performance Laboratory (LDL/UFSC). Furthermore, he is an ad hoc reviewer for international and national journals. Areas of interest: Finance and Value, Cost Management, Supply Chain Management with a focus on Performance and Business Intelligence.

References

Aver, G., Miri, D. H., Chais, C., Matte, J., Ganzer, P. P., & Olea, P. M. (2020). Fatores de rotatividade em uma empresa do segmento metalomecânico: Rotativity factors in a mechanical metal segment company. Revista Visão: Gestão Organizacional, 9(2), 168-186. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Chiavenato, I. (2014). Gestão de Pessoas: O Novo Papel dos Recursos Humanos nas Organizações. 4 ed. Elsevier Brasil.

Cramér, H. (1999). Mathematical methods of statistics (Vol. 9). Princeton university press.

Dutra, J. S. (2016). Gestão de pessoas: modelo, processos, tendências e perspectivas. 3 ed. Atlas. Elmasri, R., Navathe, S. B., & Pinheiro, M. G. (2005). Sistemas de banco de dados.

Fávero, L. P., & Belfiore, P. (2024). Manual de análise de dados: Estatística e machine learning com Excel®, SPSS®, Stata®, R® e Python®. GEN, LTC.

Hair, J. F. (2009). Multivariate data analysis.

Han, J., Kamber, M., & Pei, J. (2020). Data mining: Concepts and. Techniques, Waltham: Morgan Kaufmann Publishers.

Hastie, T. (2009). The elements of statistical learning: data mining, inference, and prediction.

Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of applied psychology, 102(3), 530.

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons. James, G. (2013). An introduction to statistical learning with applications in R.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research. 12, 2825-2830.

Samuel, F. (2024). Retention Strategies for Project- Based Construction Workers: The Role of Career- Family Balance Initiatives.

Spearman, C. (1961). "General Intelligence" Objectively Determined and Measured.