## 6 Discussion
It is important that the order of importance is determined according to the scenario under investigation. A comparison of failure modes 3 and 16 shows that both the traditional RPN and ITHWD models rank failure mode 3 first and identify it as the most critical. Failure mode 16, however, has higher severity and occurrence values, indicating greater criticality. The reason that failure mode 3 is identified as more critical in these models is the fact that the detectability variable for failure mode 3 is two points higher than for failure mode 16. However, this variable is the one with the lowest weight in the analyzed scenario. Thus, both the RPN and ITHWD models show low sensitivity to the relative contribution of each variable when their respective weights are considered. In contrast, the proposed model (FMEA ROC-CoCoSo) is more sensitive and assigns the first place in the ranking to failure mode 16, which is consistent with its higher criticality. This sensitivity can also be easily observed in the proposed model for other failure modes, namely, failure modes 18 and 11. 3. _Coherence between input and output:_ Another important point to emphasize is the coherence of the proposed model between the input (classifications assigned to FMEA variables) and the output (ranking of failure modes). For example, failure modes 17 and 19 have the same classifications in the severity, occurrence, and detectability variables, i.e., S = 7, O = 4, and D = 4. As the classifications in the FMEA variables are identical, one would expect them to occupy the same position in the ranking. However, in the fuzzy VIKOR and ITHWD models, the rankings of these two failure modes are different. In the fuzzy VIKOR model, failure mode 17 is in 10 th place, while failure mode 19 is in 9 th place. In the ITHWD model, failure mode 17 is ranked 7 th and failure mode 19 is ranked 8 th. This discrepancy in the rankings is not correct, as the values assigned to the FMEA variables are the same for both failure modes. Analyzing the performance of the proposed model in this regard shows that the model has adequate coherence between input and output, as expected. The model assigns the same rank position to failure types 17 and 19, which correctly reflects the identical classifications of the FMEA variables. This coherence can also be observed in the RPN model, which leads to the same result. The hybrid FMEA-ROC-CoCoSo model is characterized by its combination of simplicity, analytical robustness, and sensitivity to FMEA variables. Practical application shows that the model is capable of producing coherent and accurate rankings, taking into account the weighting of each variable according to the industrial context under study. The model's ease of implementation also makes it an effective tool for risk management in industries where accuracy and rapid application of results are crucial. Its ability to overcome the limitations of traditional models such as RPN and its consistency in classifying failure modes with identical values make it a valuable choice for efficiently solving industrial problems tailored to the specifics of each case. Despite its advantages, the model is not without its limitations. One of the limitations of the proposed method lies specifically in the use of the ROC approach in risk scenarios where the order of importance of risk variables is insufficient to fully characterize their weights. The ROC method assigns relative weights following a fixed proportion derived from a specific formulation (Equation (1)), which may not align with the desired distribution of weights for decision variables in other risk scenarios. Nevertheless, for improving FMEA, the ROC method proves effective and adequately captures the dominance of each variable over the others. This balance ensures that the ROC weighting method remains effective in its application to FMEA.