## 4 Implementation
### Results  
For the implementation case explained in Section 4.1, synthetic data is created to be able to apply the methodology proposed. The datasets regarding each skill are created randomly over 1000 data points and they are shown in Figures 12 and 13. The absolute limits for each skill are shown in Figure 11.  
Figure 12: Time-based datasets used as synthetic data for the implementation case. Figure 13: Material removal-based datasets used as synthetic data for the implementation case. Initially, operator 100 is working at WS A, worker 102 is performing in workstation B and 103 at station C. For workstation A, the highest importance index activity is the drilling. At station B, welding is the most important skill, and in WS C, gluing. This importance index is defined to rank the skills to consider them as shown in Algorithm 3. Starting with the complexity function, the following seven figures present the Gaussian distribution of the synthetic data for each skill per skill level. The colored areas have the same meaning as in Figure 6, but all acceptable areas are marked in the same blue tone. The vertical orange line displays the mean for that skill, and the yellow one identifies the NMV, and the green and blue marked ones show the absolute and relative limits. Figure 14 shows the skill of grasping tools and materials for workstation A. The NMV and mean are slightly different. It is common for this to happen since the first one models the theoretical steps and the second expresses the average value for all workers. The closer these values are, the more it indicates that the model is well done according to the needs of the operators. In the graph corresponding to skill level 4, the mean coincides with the inferior relative limit. Figure 15 represents the drilling skill, where the mean coincides with the NMV. Figure 16 shows the grasping abilities in WS B. Mean and MNV values fall apart, which can be translated as either the NMV is incorrectly calculated or there is a reason that it needs to be analyzed by the experts in the line, which would explain why the workers cannot fulfill the requirements. Figure 14: Gaussian distribution of the grasping in workstation A. Figure 17 introduces the welding skill graphs that fall in the same case as shown in Figure 15.  
Figure 16: Gaussian distribution of the grasping in workstation B.  
Figure 15: Gaussian distribution of drilling. Figure 18 shows the grasping abilities at station C, for which the scenario coincides with the one shown in Figure 16.  
Figure 17: Gaussian distribution of welding. Figure 18: Gaussian distribution of the grasping in workstation C.  
Figure 19 presents the gluing skills of the operators, for the same scenario as seen in Figure 12:0  
Last, Figure 20 presents the visual inspection skills of the operators, which coincides with the type of case as in Figure 12:1 All values for each skill and skill level are summarized in Figure 12:2  
Figure 12:3 Gaussian distribution of gluing. Figure 12:4 Gaussian distribution of the visual inspection. In Section 4.1, it was commented that workstations should be ranked in terms of complexity. After applying the method explained in [52], the most complex workstation is C, followed by B, and finally A. Then, data is classified and structured constantly following the format of Algorithms 2 and 3 until the CEP detects the break. In order to detect the breaks in this use case, a specific logic is developed. In a high level, it has stored the schedule of the factory daily breaks. When the time of the break is close, the machines are expected to stop. This way, an interval of time is defined to all machines to stop working. As soon as