A new fuzzy group MCDM approach for failure mode and effects analysis

Failure mode and effects analysis (FMEA) is one of the well-known techniques of quality management that is used for continuous improvements and has become increasingly important in new product development, manufacture or engineering applications. In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O) and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. Due to the vagueness and uncertainty existing in the evaluating process, crisp numbers representing RPNs in the traditional FMEA might be improper or insufficient in contrast to fuzzy numbers. Currently, the fuzzy methods and linear programming method have been proposed as an effective solution for the calculations of fuzzy RPNs. In this study, a new fuzzy group decision making (FGDM) model based on α-level sets, is proposed to generate more accurate fuzzy RPNs and ensure to be robust against the uncertainty. This model allows decision makers (DMs) to evaluate FMEA risk factors using linguistic terms such as Certain, Very High, High, Slightly High, Medium, Slightly Low, Low, Very Low or None rather than precise numerical values, allows them to express their opinions independently. A case study is investigated using the proposed model to illustrate its applications in RPN assessment. It is shown that the FGDM model offers a flexible, practical and effective way of RPN modelling.

Keywords:

Fuzzy decision making , Failure mode and effects analysis , Multiple criteria decision making