An integrated approach for failure mode and effect analysis based on uncertain linguistic GRA–TOPSIS method

Abstract

This paper provides a novel risk priority approach for failure mode and effect analysis (FMEA), which can overcome some inherent drawbacks of the traditional risk priority number (RPN) method in imprecise risk evaluation, risk factor weighting and questionable RPN computation. Considering FMEA team members’ vagueness and uncertainty in their evaluations on failure modes, two-dimensional uncertain linguistic variables are advised to describe the risk evaluation result of a failure mode and the reliability of the evaluation result. The grey relation analysis–technique for order preference by similarity to ideal solution (GRA–TOPSIS) is applied for determining the risk ranking of the identified failure modes. In particular, a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way. Via a practical healthcare risk analysis case, the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information. Furthermore, by comparing with existing methods, it is shown that the proposed integrated approach excels in the risk evaluation and prioritization of failure modes in FMEA.