{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:53:30Z","timestamp":1778867610997,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system\u2019s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.<\/jats:p>","DOI":"10.3390\/systems11070348","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Failure Mode and Effect Analysis with a Fuzzy Logic Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3580-807X","authenticated-orcid":false,"given":"Jos\u00e9 Jovani","family":"Cardiel-Ortega","sequence":"first","affiliation":[{"name":"Centro de Innovaci\u00f3n Aplicada en Tecnolog\u00edas Competitivas CIATEC A.C., Omega 201, Industrial Delta, Le\u00f3n 37545, Guanajuato, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Baeza-Serrato","sequence":"additional","affiliation":[{"name":"Departamento de Estudios Multidisciplinarios, Divisi\u00f3n de Ingenier\u00edas, Campus Irapuato-Salamanca, Universidad de Guanajuato, Yuriria 38944, Guanajuato, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.cie.2018.07.012","article-title":"Risk Analysis of Sequential Processes in Food Industry Integrating Multi-Stage Fuzzy Cognitive Map and Process Failure Mode and Effects Analysis","volume":"123","author":"Rezaee","year":"2018","journal-title":"Comput. 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