FMEA Decision Making

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Recently process equipment failures (PEFs) are recognized as one of the leading causes of process accidents. FMEA as a risk assessment technique has been widely used in process industries. Conventional FMEA uses three parameters severity (S), occurrence (O), and detection (D) as risk factors to calculate a risk priority number (R.P.N) and rank the failure modes with regard to this number. But several shortcomings associated with the FMEA, have been limited its applicability. This paper addresses a new framework based on multiple criteria decision making (MCDM) approach to handle these limitations. In this work we applied the VIKOR technique to rank and prioritize the failure modes trough judgments extracted from cross-functional team
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In the majority of MCDM problems including those that used to improve applicability of FMEA, the weight of criteria are determined only with respect to the subjective judgments of decision makers, however subjective fixed weight methods could cause deviation of indexes’ weights as a result of subjective factors. To the best of our knowledge, the use of MCDM techniques for solving the last shortcoming is limited.
While subjective methods specify weights exclusively based on the preference or opinions of decision makers, objective methods employ mathematical models (i.e. entropy method or multiple objective programming), automatically devoid of taking into account the decision makers’ preferences. objective weighting approach is particularly appropriate for situations where consistent subjective weights could not be acquired (Deng, Yeh, & Willis,
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Currently FMEA has widely been used in different industries including chemical, mechanical, aerospace, nuclear, automotive, electronics, and medical technologies industries ((Chang & Cheng, 2011; Chin, Wang, Poon, & Yang, 2009b; Sharma, Kumar, & Kumar, 2005).
The main characteristic of FMEA that makes different it from other risk assessment tools is that, the key concern of FMEA is to put emphasis on the prevention of failures, rather than present a solution following the failure occurrence. This feature can help safety professionals to regulate the current programs, utilize the recommended measures to decrease the probability of failures, decline the likelihood of failure rates and keep away from hazardous incidents (Hu-Chen Liu,
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