## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Branch** & **Expert 1** & **Expert 2** & **Expert 3** & **Expert 4** & **Expert 5** & **Fuzzy Number \(S_{kl}\)** \\ \hline
1 & VH & H & H & VH & H & (0.6, 0.8, 0.96) \\
2 & VH & H & H & VH & H & (0.6, 0.8, 0.96) \\
3 & H & VH & H & H & H & (0.6, 0.8, 0.98) \\
4 & VH & VH & VH & VH & VH & (0.7, 0.9, 1) \\
5 & VH & M & M & M & H & (0.5, 0.7, 0.88) \\
6 & VH & VH & VH & VH & VH & (0.7, 0.9, 1) \\
7 & VH & H & M & VH & H & (0.5, 0.7, 0.86) \\
8 & VH & H & H & VH & H & (0.6, 0.8, 0.96) \\
9 & H & H & M & H & L & (0.3, 0.5, 0.7) \\
10 & VH & H & H & VH & H & (0.6, 0.8, 0.96) \\
11 & VH & VH & VH & VH & H & (0.6, 0.8, 0.94) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Estimated impacts of identified risks of agent \(A_{11}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Branch** & \(O_{j}\) & \(S_{kl}\) & \(R_{jk}\) + \(\sum_{mk}\) & \(ARS_{jl}\) \\ \hline P1-P4-S5 & & (0.6, 0.8, 0.96) & (1.8, 2.4, 2.9) & (0.648, 1.536, 2.616) \\ P3-P4-S5 & & (0.6, 0.8, 0.96) & (2.1, 2.7, 3) & (0.756, 1.728, 2.707) \\ P3-P7 & & (0.6, 0.8, 0.98) & (0.8, 1.2, 1.5) & (0.288, 0.768, 1.381) \\ P3-P8-D11-D16 & & (0.7, 0.9, 1) & (2.4, 3.2, 3.8) & (1.008, 2.304, 3.572) \\ P3-P8-S10 & (0.5, 0.7, 0.88) & (1.8, 2.4, 2.8) & (0.54, 1.344, 2.316) \\ P8-D11-D16 & (0.6, 0.8, 0.9) & (0.7, 0.9, 1) & (1.9, 2.5, 3) & (0.798, 1.8, 2.82) \\ P8-S10 & & (0.5, 0.7, 0.86) & (1.3, 1.7, 2) & (0.39, 0.952, 1.616) \\ P4-S5 & (0.6, 0.8, 0.96) & (1.4, 1.8, 2) & (0.504, 1.152, 1.804) \\ P10 & & (0.3, 0.5, 0.7) & (0.4, 0.6, 0.8) & (0.072, 0.24, 0.526) \\ S1-S5 & & (0.6, 0.8, 0.96) & (0.7, 1.1, 1.5) & (0.252, 0.704, 1.353) \\ S5 & & (0.6, 0.8, 0.94) & (0.5, 0.7, 0.9) & (0.18, 0.448, 0.795) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Values of \(ARS_{jl}\) of \(A_{11}\) branches. Table 9 shows the \(ARS\) values calculated for each agent in its analysis by objective. It is necessary to add the values per agent, as a single source of occurrence based on Equation (3) and by objective based on Equation (4) (Section 2.10). The corresponding values are shown in Tables 10 and 11. To defuzzify the numbers, Pareto principle was used (approximately 10% A, 20% B, and 70% C) (Figures 7 and 8 respectively). From this analysis, it was obtained that the agents with higher \(ARS\) are the following (in that order of priority): (1) Non-compliance with working methodologies and procedures (\(A_{2i}\)), (2) lack of commitment of workers (\(A_{17\text{-}8}\)), (3) lack of correspondence of training actions and training needs (\(A_{15\text{-}8}\)). The objective with the highest risk of non-compliance is objective 8. For decision making, the previous analysis was incorporated into the design and control of BSC and KRIs were integrated. The establishment of priorities for this projection according to the \(ARS_{j}\) and \(ARS_{l}\) indicators should be taken into account. As part of the actions to mitigate risks and achieve effective SCM, checkpoints associated with the identified risk events were defined. These checkpoints were incorporated into the design of the processes. The thresholds between the measurements made on the control objects were considered for acceptable evaluation. This will respond to compliance with the procedures that describe the operations, technical instructions, physical parameters, time, and other aspects, thus facilitating the control of the activity. As an example, Table 12 shows 14 checkpoints corresponding to the risk events of the Source process. Finally, Table 13 shows a summary that integrates these elements. The company managers were left in charge of a proactive management tool of the strategy, which has allowed them to keep a constant monitoring and diagnosis. Figure 8: Priority by objective.