## 4 Analysis of Results
The second process, oriented to the manufacture or replenishment of parts, starts at station (A) and ends at station (D), with a cycle time of approximately 125 s. This significantly reduces order response times. When the FC system is first requested to execute the part verification algorithm during the manufacturing process, the MFC node downloads the images and source code from container image repositories and saves them in its local memory, which takes approximately 5 s, while the coordination of loads, monitoring of resources and collection of results with the FCS nodes takes approximately 15 ms. On the other hand, for the analysis of the FC system performance results, it was essential to first evaluate the percentage consumption of the CPU during the execution of each algorithm. The results obtained showed the following percentages of CPU usage: 33% for the process of identifying edges and contours of parts, 54% for the process of comparison with the reference points of the facial regions previously stored in the dataset and 88% for the mathematical calculation. Based on these values, a conditioning was established by the MFC, which restricts the orchestration for any of the FCS nodes if the CPU consumption exceeds 60%; otherwise, it can orchestrate up to two processes per slave. To validate the correct operation of the FC system under this premise, the facial recognition and mathematical calculation processes were executed when station (C) was activated. This behavior was verified by means of the CPU usage readings of the FCS nodes, as shown in Figure 7.  
According to the results of the load on the processors of the FCS nodes, an interesting orchestration dynamic is identified that distributes the workload between them. Figure 7 shows that there are fluctuations that indicate the execution of one or several processes in each FCS node, and there is also an approximate consumption of 28% in the idle state in certain periods due to the fact that they are in alert mode when attending the MFC requests. At the beginning, both nodes start with moderate CPU usage, ranging between 50% and 60%, corresponding to the independent execution of the computer vision algorithms, in which case no intensive use of resources is required. As time progresses between 20 and 40 s, a saturation close to 75% and 80% is observed, indicating the execution of the facial landmark and part quality control processes. In addition, the load distribution between both nodes is understood through an orchestration scheme coordinated by the MFC. Continuing on the timeline, just before the 40 s second mark, the onset of high CPU \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**System Data Flow**} \\ \hline Upload [Mbps] & 30.62 \\ Download [Mbps] & 69.48 \\ \hline \multicolumn{1}{c}{**System Resilience**} \\ \hline Reconnection: MES/CLOUD CLOUD/MES bidirectional communication & 1.5 [s] \\ \hline \multicolumn{1}{c}{**MPS 4.0 Start-Up**} \\ \hline System Start-up Time & 32 [s] \\ Connection: MES/CLOUD-CLOUD/MES bidirectional communication & 1.7 [s] \\ Connection: MES/ESP Master-ESP Master/MES bidirectional communication & 0.5 [s] \\ Initialization of AI model and segmentation algorithm & 21 [s] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MES assessment: start-up, resilience and data flow. usage cycles by the FCS2 node over 90% is visualized, suggesting that the orchestration system identified processor consumption percentages on the FCS1 node over 60% at the time the mathematical calculation process was requested, there was efficient action by the MFC node to avoid oversaturation on the node and, consequently, a stable temperature regime of operation in the control units of the FCS nodes was achieved, as shown in Figure 8.  
This orchestration pattern visualized in Figure 7 is fundamental to maintaining the stability of the system, especially at times when processing demands rose above the established CPU percentage, which was repeatedly observed between seconds 50 and 70.