Integration of Data Analytics and Data Mining for Machine Failure Mitigation and Decision Support in Metal–Mechanical Industry

Sidnei Alves de Araujo, Silas Luiz Bomfim, Dimitria T. Boukouvalas, Sergio Ricardo Lourenço, Ugo Ibusuki and Geraldo Cardoso de Oliveira Neto ()
Additional contact information
Sidnei Alves de Araujo: Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street, 235/249—Liberdade, São Paulo 01504-001, Brazil
Silas Luiz Bomfim: Industrial Engineering Post Graduation Program, Federal University of ABC, São Bernardo do Campo, São Paulo 09606-045, Brazil
Dimitria T. Boukouvalas: Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street, 235/249—Liberdade, São Paulo 01504-001, Brazil
Sergio Ricardo Lourenço: Industrial Engineering Post Graduation Program, Federal University of ABC, São Bernardo do Campo, São Paulo 09606-045, Brazil
Ugo Ibusuki: Industrial Engineering Post Graduation Program, Federal University of ABC, São Bernardo do Campo, São Paulo 09606-045, Brazil
Geraldo Cardoso de Oliveira Neto: Industrial Engineering Post Graduation Program, Federal University of ABC, São Bernardo do Campo, São Paulo 09606-045, Brazil

Logistics, 2025, vol. 9, issue 3, 1-16

Abstract: Background : The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results : The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry.

Keywords: predictive maintenance; data analytics; data mining; metal–mechanical industry; machine failure; production logistics (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2305-6290/9/3/109/pdf (application/pdf)
https://www.mdpi.com/2305-6290/9/3/109/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:9:y:2025:i:3:p:109-:d:1719768

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().