Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance
This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/854 |
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| author | Leonel Patrício Leonilde Varela Zilda Silveira |
| author_facet | Leonel Patrício Leonilde Varela Zilda Silveira |
| author_sort | Leonel Patrício |
| collection | DOAJ |
| description | This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations. |
| format | Article |
| id | doaj-art-4430842820ab4df881ed5c179120b8ae |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4430842820ab4df881ed5c179120b8ae2025-01-24T13:21:04ZengMDPI AGApplied Sciences2076-34172025-01-0115285410.3390/app15020854Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive MaintenanceLeonel Patrício0Leonilde Varela1Zilda Silveira2Department of Production and Systems, Algoritmi/LASI, University of Minho, 4804-533 Guimarães, PortugalDepartment of Production and Systems, Algoritmi/LASI, University of Minho, 4804-533 Guimarães, PortugalDepartment of Mechanical Engineering, Sao Carlos School of Engineering, University of Sao Paulo, Sao Paulo 13566-590, BrazilThis paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.https://www.mdpi.com/2076-3417/15/2/854RPAmachine learningintegration systemspredictive maintenanceSIRPM |
| spellingShingle | Leonel Patrício Leonilde Varela Zilda Silveira Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance Applied Sciences RPA machine learning integration systems predictive maintenance SIRPM |
| title | Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance |
| title_full | Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance |
| title_fullStr | Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance |
| title_full_unstemmed | Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance |
| title_short | Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance |
| title_sort | proposal for a sustainable model for integrating robotic process automation and machine learning in failure prediction and operational efficiency in predictive maintenance |
| topic | RPA machine learning integration systems predictive maintenance SIRPM |
| url | https://www.mdpi.com/2076-3417/15/2/854 |
| work_keys_str_mv | AT leonelpatricio proposalforasustainablemodelforintegratingroboticprocessautomationandmachinelearninginfailurepredictionandoperationalefficiencyinpredictivemaintenance AT leonildevarela proposalforasustainablemodelforintegratingroboticprocessautomationandmachinelearninginfailurepredictionandoperationalefficiencyinpredictivemaintenance AT zildasilveira proposalforasustainablemodelforintegratingroboticprocessautomationandmachinelearninginfailurepredictionandoperationalefficiencyinpredictivemaintenance |