## 5 Results
### Extended RPI Assessment (Erisk) for Each Project and Three Weighting Scenarios  
Case 1 describes a risk scenario in which severity and _QMI_ have equal weight to occurrence and detectability. This is the case, severity (SxO) has a moderate impact, as do detectability and mitigation. Case 2 describes a risk scenario in which severity plays a major role, followed by _QMI_; these types of risk scenarios are associated with safety issues. Finally, Case 3 describes a risk scenario where quality control is an issue. An example of this is mass production, where the number of rejected items can have a strong impact on production costs. Depending on the risk scenario and the respective criteria, these weightings can be determined using the Analytic Hierarchy Process (AHP) method or another similar method.  
Figures 8-10 show the results for effective risk (Erisk) in cases 1, 2, and 3, as described in Table 9. Subfigures (a), (b), and (c) show the risk score for each failure mode, and sub-figure (d) shows the effective risk for each project.  
In each sub-figure (a), (b), and (c), four risk scenarios are described for the 10 failure modes considered. The first scenario is RPI r3, indicated by a square. In this scenario, no mitigation variables were considered, i.e., it represents the traditional RPI model where risk is assessed in a risk space with 3 variables (S, O, D).  
The second risk scenario considered the Erisk model, where the _QMI_ was taken into account. This scenario is indicated by a circle marker and was evaluated in a risk space with 4 variables (S, O, _QMI_, D).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**FR** & **Project 1** & **Project 2** & **Project 3** \\  & _QMI_ & _QMI_ & _QMI_ \\ \hline
1 & 6.38 & 7.22 & 5.20 \\
2 & 6.67 & 5.45 & 5.41 \\
3 & 5.85 & 5.18 & 6.03 \\
4 & 7.06 & 7.10 & 6.44 \\
5 & 6.44 & 6.00 & 6.44 \\
6 & 6.03 & 6.67 & 5.20 \\
7 & 5.82 & 5.18 & 5.20 \\
8 & 6.23 & 5.18 & 5.61 \\
9 & 7.68 & 8.36 & 5.26 \\
10 & 7.68 & 8.36 & 6.09 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _QMI_ results for the three design projects considered in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Variables** & **Project 1** & **Project 2** & **Project 3** \\ \hline _QMI_ & 0.3 & 0.3 & 0.2 \\ Severity & 0.3 & 0.4 & 0.1 \\ Occurrence & 0.2 & 0.2 & 0.4 \\ Detectability & 0.2 & 0.1 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effective risk evaluation—weights given to the risk variables, Cases 1 to 3.  
Figure 8: Case 1, risk results considering the weights _QMI_ 0.3, Severity 0.3, Occurrence 0.2, Detectability 0.2 (**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 1.  
Figure 9: Case 2, risk results considering the weights _QMI_ 0.30, Severity 0.4, Occurrence 0.2, Detectability 0.1. (**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 2.  
Sub-figure (d) shows the effective risk (Erisk) assessed for each project. The results were obtained by calculating the area under each Erisk result obtained for each failure mode previously ranked in ascending order. This order does not affect the results, but it does improve the comparison between the effective risk of each project. Therefore, the values on the \(x\)-axis of sub-figure (d) have no practical meaning, i.e., they are not associated with a particular failure mode, as is the case for sub-figures (a), (b), and (c).  
The lower and upper bounds, represented by a diamond and a triangle, were obtained by setting the \(QMI\) for the lower bound to 1% and for the upper bound to 100% for each failure mode.