## 6 Discussion  
In Cases 1 and 2, all failure modes had a risk value (RPI r3) that was within the risk limit defined by performance at 1% and 100%, and the upper and lower risk limits were calculated considering the \(QMI\) weight of 30% in the risk value. This result was consistent with the hypothesis that the \(QMI\) performance model could increase or decrease the risk level compared with the results obtained using only the RPI model, i.e., risk assessment without considering performance. This feature can be seen in the overlap of the square and circular lines in Cases 1 and 2. For example, in failure mode 3, the RPI r3 result was higher than the Erisk result, which meant that the performance model reduced the risk level in this failure mode, due to the performance capabilities. In failure mode 4, on the other hand, the opposite was true. When the performance score in the equation was included, the risk level for this failure mode increased, due to the low performance capabilities. Figure 10: Case 3, risk results considering the weights \(QMI\) 0.2, Severity 0.1, Occurrence 0.4, Detectability 0.3. (**a**) project 1 results, (**b**) project 2 results, (**c**) project 3 results, (**d**) effective risk for projects 1, 2, and 3 considering the weights of Case 3. However, in Case 3, without the risk-mitigating performance capabilities, the risk value for some failure modes was higher than the 1% upper bound defined by the risk-mitigating performance capabilities. This result was due to an inconsistency between the risk scenarios. The weighting of RPI r3 was unchanged in all three cases; the weighting was 40% for severity, 30% for occurrence, and 30% for detectability. However, the weighting for the assessment of actual risk in Case 3 was 20% for the _QMI_, 10% for severity, 40% for occurrence, and 30% for detectability. Therefore, the reported weights modulated two different risk scenarios, leading to some inconsistencies. Moreover, it was difficult to achieve full compatibility between risk scenarios with different risk dimensions because the distribution of weights among risk variables could not be done in the same way, since the number of risk variables changed with the dimension of the risk scenario. In the case of RPI r3, only three variables were considered, so the risk scenario had three weights, and in the case of Erisk, four variables were considered, resulting in a risk scenario with four weights. Therefore, it was necessary to choose the right weights to compare risk scenarios with different risk dimensions. In order to perform such a comparison, the risk scenario paradigm had to be maintained. The correlation between Cases 1 and 2 showed a slight increase in the effective risk of Case 2, due to a 10% increase in severity. Due to the scale used, this is not very clear in the graphs, but one can easily come to this conclusion when analyzing the data. In addition, the risk level in Case 3 generally decreased for all failure modes except failure mode number 5, which was the result of the lower weighting given to this case in terms of severity and _QMI_, as well as the lower score given to the risk variables in this project. Thus, the results of the effective risk model were sensitive to the variation of the inputs, and their variations had a significance that was in line with expectations. Table 10 shows the summary of the results for Projects 1 to 3 and Cases 1 to 3. The second column shows the Erisk of the projects considering the 10 failure modes; these results can be seen in the subframes (d) of Figures 8-10. In the third column, the projects with a lower Erisk were found by evaluating the area below the lines defined by the diamond marking in the subframes (a), (b), and (c) of Figures 8-10. Finally, in the fourth column, the difference was found between the actual Erisk results and the minimum possible Erisk results. In all cases, Project 1 was the one with the highest Erisk, followed by Project 2, and the least risky was Project 3.