Enhanced FMEA for Supply Chain Risk Identification

Abstract

Supply chain risk identification is fundamental for supply chain risk management. Its main purpose is to find critical risk factors for further attention. The failure mode effect analysis (FMEA) is well adopted in supply chain risk identification for its simplicity. It relies on domain experts’ opinions in giving rankings to risk factors regarding three decision factors, e.g. occurrence frequency, detectability, and severity equally. However, it may suffer from subjective bias of domain experts and inaccuracy caused by treating three decision factors as equal. In this study, we propose a methodology to improve the traditional FMEA using fuzzy theory and grey system theory. Through fuzzy theory, we design semantic items, which can cover a range of numerical ranking scores assessed by experts. Thus, different scores may actually represent the same semantic item in different degrees determined by membership functions. In this way, the bias of expert judgement can be reduced. Furthermore, in order to build an appropriate membership function, experts are required to think thoroughly to provide three parameters. As the results, they are enabled to give more reliable judgement. Finally, we improve the ranking accuracy by differentiating the relative importance of decision factors. Grey system theory is proposed to find the appropriate weights for those decision factors through identifying the internal relationship among them represented by grey correlation coefficients. The results of the case study show the improved FMEA does produce different rankings from the traditional FMEA. This is meaningful for identifying really critical risk factors for further management.