An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment

As one of the powerful systematic reliability management tool, the failure mode and effect analysis (FMEA) method is adopted to investigate and analyze potential failure mode (FM) risks of the cold chain green logistics (CCGL). Since FMEA experts have different professional backgrounds and risk preferences, they may give evaluation from different angle and emphasis; and express different limited rational behavior. Therefore, this study proposes an improved FMEA model which considers the expert group and the bounded rational behavior of experts. Firstly, this paper develops the K-means clustering method to partition FMEA experts. Further, a calculation model of attribute weights that considers different risk attitudes is proposed. Additionally, this study proposes a combination final ranking method based on regret theory (RT) and PROMETHEE II method. A practical case and two simulation experiments are carried out to demonstrate the validity, reliability and superiority performance of the proposed FMEA model. The improved FMEA model can enhance the fairness and reasonableness of the expert evaluation, improve the adaptability and flexibility of FMEA, and enrich the theoretical research of FMEA method. Findings indicated that it is necessary to consider the grouping and risk attitudes of experts in the practical risk assessment.

Automated summary

This study introduces an improved FMEA model that considers expert grouping and bounded rational behavior, including clustering of FMEA experts and a model for calculating attribute weights based on different risk attitudes. The study also proposes a combined final ranking method and demonstrates the validity and superiority of the model through a case study and simulation experiments. The findings highlight the importance of considering expert grouping and risk attitudes in practical risk assessment.