A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods

The working world is undergoing profound changes, and occupational accidents are always a global concern due to substantial impacts on productivity collapse and workers’ safety. To address this problem, Failure Mode and Effects Analysis (FMEA) has been widely implemented to assess such risks. This, however, fails to provide reliable results because of some shortcomings of the risk priority number score of the FMEA including neglecting the weight of risk factors, having doubtful formulation, and performing poorly in distinguishing risks. This study presents a two-phase approach to identify and prioritize Health, Safety and Environment (HSE) risks to focus on critical risks instead of diverting organizational efforts to non-critical ones and overcoming the shortcomings of the traditional score. In the first phase, potential risks are identified, and after determining the value of risk factors using the FMEA technique, Fuzzy C-means (FCM) algorithm is applied to cluster these risks. Then, the weight of risk factors is calculated based on the Fuzzy Best–Worst Method (FBWM), and following this, clusters are labeled based on weighted Euclidean distance. In the second phase, a hybrid Multi-Criteria Decision-Making (MCDM) method is proposed based on the FBWM and combined compromise solution to prioritize risks belonging to the critical cluster. This is to create a distinct priority for risks and facilitate the implementation of corrective/preventive actions. This approach is applied in the automotive industry, and results are compared with other FMEA-based MCDM methods to validate findings. Eventually, a sensitivity analysis is designed to show the ability and applicability of the proposed approach