## 6 Discussion  
In fact, the application of the proposed hybrid model proved to be quite simple and fast. Compared to the four models presented in Table 9, only the traditional RPN method was slightly easier and faster to apply. However, it is important to emphasize that the FMEA ROC-CoCoSo model was specifically designed to combine ease of use with a more robust and comprehensive analysis, making it particularly suitable for complex industrial contexts. This has achieved one of the main objectives that motivated the development of this hybrid model: to create a practical and efficient tool for industrial environments where quick and easy implementation is required without compromising the accuracy of the results. Validation through practical application confirms the suitability of the model for efficiently solving industrial problems, further increasing its usefulness in production and risk management scenarios. Regarding the performance of the model, we can observe the following:  
1. _Classification of failure modes:_ comparing the results obtained with the proposed model for failure modes 11 and 12, we note that the model can assign different classifications to these failure modes, unlike the traditional RPN method which assigns the same classification to them. This limitation of the traditional RPN is also overcome by \begin{table}
\begin{tabular}{c c c} \hline \hline
**FM** & **k** & **Rank** \\ \hline
3 & 4.894953 & 4 \\ \hline
16 & 5.556488 & 1 \\ \hline
18 & 5.25047 & 2 \\ \hline
2 & 4.230755 & 6 \\ \hline
1 & 3.670658 & 9 \\ \hline
17 & 4.189717 & 7 \\ \hline
19 & 4.189717 & 7 \\ \hline
4 & 3.398767 & 10 \\ \hline
9 & 2.602686 & 11 \\ \hline
11 & 5.030212 & 3 \\ \hline
12 & 4.333838 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final ranking of alternatives. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**FM**} & **S** & **O** & **D** & **RPN** & **Rank** & **Rank** & **Rank** & **Ranking** \\ \cline{3-10}  & & & & **RPN** & **Fuzzy VIKOR** & **ITHWD** & **Cocoso S \textgreater{} O \textgreater{} D \\ \hline
3 & 7 & 5 & 7 & 245 & 1 & 2 & 1 & 4 \\ \hline
16 & 8 & 6 & 5 & 240 & 2 & 5 & 5 & 1 \\ \hline
18 & 8 & 4 & 7 & 224 & 3 & 3 & 2 & 2 \\ \hline
2 & 6 &  
the other three methods presented in Table 9. In addition, the critical failure modes identified by the proposed model are consistent with the expectations from the case study, especially failure modes 16 and 18, which are ranked first and second, respectively. These failure modes have the highest degree of criticality, which confirms that the model is able to prioritize the risks in accordance with the real operational hazards. The ranking is consistent with the weighting of the severity, occurrence, and detectability variables, which were considered most important for this industrial context. This correspondence between the results of the model and the expected prioritization strengthens the reliability and robustness of the FMEA ROC-CoCoSo model, as it accurately reflects the critical failure modes and ensures that the most severe risks are identified and effectively addressed. Furthermore, this result demonstrates the model's ability to produce a clear and actionable risk hierarchy, which is essential for decision making in an industrial environment. By classifying failure modes in a way that is consistent with practical expectations and operational reality, the model provides a level of reliability and precision that is critical for preventing system failures and optimizing risk mitigation strategies. 2. _Sensitivity to the weighting of FMEA variables:_ The proposed model shows a good sensitivity in the assignment of weights for each FMEA variable. The ranking determined with the ROC-CoCoSo model clearly reflects the weighting of each variable, in the order S > O > D (severity > occurrence > detectability). This order was chosen because it is appropriate for the case study under investigation, in which severity and probability of occurrence have a greater weight than detectability.