Extending the horizons of FMEA: An MCDM approach
Failure Mode Effect Analysis is popularly called as FMEA. FMEA is a pro-active tool to access potential failure modes and also evaluate the possible effects arising due to these failures. Typically, a Risk Priority Number (RPN), is evaluated for the identified failure modes. Traditionally, this RPN is computed as the product of three values namely, Severity, Occurrence, and Detectability. The range of this RPN value is from 1 up to 729. The RPN value helps the decision maker, prioritize the potential failure modes and take appropriate action/s to mitigate the risks.
As RPN considers only severity, occurrence and detectability, there are chances that some significant criteria might be missed out or is not considered for evaluation. In this work we consider more than 21 criteria, which can help evaluate/quantify the potential failure modes. While, we do not claim that these criteria form an exhaustive list for the evaluation of failure modes, but will help provide a near holistic view of the problem at hand. With more than 21 criteria for performance evaluation, the RPN value will range from 1 up to 1.09419 x 1020. This is a very large number and will create computational complexities and hence very difficult to develop priorities of the failure modes. This will further have a cascading effect on developing potential strategies for mitigation of risks.
Here, we propose an approach to evaluate the modified RPN, by using MCDM technique/s. This approach will provide a realistic range of modified RPN, and hence help in proper evaluation of the potential failure modes. Thus, this approach helps the decision maker to develop realistic strategies for mitigating risks. In this work, we not only enlist the different criteria for modified RPN evaluation, showcase the developed approach, but also illustrate the procedure with a hypothetical and real-life example. It can also be seen that, with minor modifications in the proposed procedure, this system FMEA approach can be applied to process FMEA as well. We hope that this work will help the decision makers make better and near holistic evaluation decisions.