To address the inconsistency in risk prioritization results caused by heterogeneous information and subjective weighting in traditional Failure Mode and Effects Analysis (FMEA), this study proposes a risk priority assessment method based on a heterogeneous entropy weight framework. According to the intrinsic characteristics of different risk factors in cigarette factory packaging systems, crisp numbers, triangular fuzzy numbers, and cloud models are respectively adopted to represent Maintenance Cost, Occurrence frequency, and qualitative risk factors such as Severity and Detection. The entropy weight method is employed to objectively determine the weights of risk factors, and an improved Risk Priority Number (RPN*) is constructed. A case study of a cigarette factory packaging system demonstrates that the proposed method can effectively handle heterogeneous risk information and produce more rational failure mode rankings. Comparative analysis using the Pearson correlation coefficient shows that the proposed method exhibits higher consistency and reliability than traditional RPN and single entropy weight methods.
Liu et al. (Sun,) studied this question.