An improved approach for failure mode and effect analysis involving large group of experts: An application to the healthcare field

Failure mode and effect analysis (FMEA) is a team-based technique for prospectively identifying and prioritizing failure modes of products, processes, and services. Given its simplicity and visibility, FMEA has been widely used in different industries for quality and reliability planning. However, various shortcomings are inherent to the traditional FMEA method, particularly in assessing failure modes, weighting risk factors, and ranking failure modes, which greatly reduce the accuracy of FMEA. Additionally, the classical FMEA focuses on the risk analysis problems in which a small number of experts participate. Nowadays, with the increasing complexity of products and processes, an FMEA may require the participation of larger number of experts from distributed departments or organizations. Therefore, in this article, we present a novel risk priority approach using cluster analysis and prospect theory for FMEA when involving a large group of experts. Furthermore, an entropy-based method is proposed to derive the weights of risk factors objectively by utilizing the risk-evaluation information. Finally, we take an empirical healthcare risk analysis case to illustrate the proposed large group FMEA (LGFMEA) approach, and conduct a comparative study to evaluate its validity and practicability.