Failure mode and effect analysis (FMEA) is a risk assessment method that aims to identify the potential failures related to a system, design, service, or process and take precautions before they occur. In traditional FMEA, each failure mode is evaluated according to risk factors, which are occurrence (O), severity (S), and detection (D), by employing ten-point scales. After the multiplication of these evaluation values, the risk priority numbers (RPNs) are obtained for each failure mode, and the ranking of the RPNs reveals the prioritization of failures. However, this procedure includes various shortcomings in terms of assessment and ranking of failure modes. This paper proposes a new FMEA model based on probabilistic interval-valued hesitant fuzzy sets (PIVHFS) handling twofold uncertainties, which are fuzziness and randomness, to enhance the traditional FMEA. The proposed methodology allows FMEA team members to assess the risk of failure modes using interval values, and the interval values are aggregated with their occurrence probabilities. The developed methodology is implemented in analyzing the risks during general anesthesia so as to demonstrate its applicability. The illustrative example is a medical risk assessment problem in which the FMEA team of a hospital aims to analyze the risks of the general anesthesia process and prioritize potential failure modes. The developed method can be used in other industries besides healthcare industry. ORCID NO: 0000-0003-0091-6177
Anahtar Kelimeler: Failure mode and effect analysis, Probabilistic interval-valued hesitant fuzzy sets, Risk assessment
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