{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:26:55Z","timestamp":1775752015924,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Foundation for Science and Technology","award":["UIDB\/00319\/2020"],"award-info":[{"award-number":["UIDB\/00319\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/app15020854","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T12:27:54Z","timestamp":1737030474000},"page":"854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-5996","authenticated-orcid":false,"given":"Leonel","family":"Patr\u00edcio","sequence":"first","affiliation":[{"name":"Department of Production and Systems, Algoritmi\/LASI, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2299-1859","authenticated-orcid":false,"given":"Leonilde","family":"Varela","sequence":"additional","affiliation":[{"name":"Department of Production and Systems, Algoritmi\/LASI, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-0403","authenticated-orcid":false,"given":"Zilda","family":"Silveira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sao Carlos School of Engineering, University of Sao Paulo, Sao Paulo 13566-590, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1471","DOI":"10.1002\/widm.1471","article-title":"Data mining in predictive maintenance systems: A taxonomy and systematic review","volume":"12","author":"Esteban","year":"2022","journal-title":"Wiley Interdiscip. 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