## 3 Results
### Parametric Modelling and Assessing the Employees' Utilisation--Result
1--the distribution of the operations uniformly for individual employees according to the analysed time consumption for the given operations by the MTM UAS methodology. * Variant No. 2--loading the workplaces, the material entering the assembly line in the reach zones up to 1 m, a robot will automatically ensure loading the components. The inter-operational handling at individual workplaces, utilising the gravity and accumulation conveyors. * Variant No. 3--completing two devices (production bottleneck), automated inspection activities--detecting material failures after machining by a camera system. After making any change the system interactively assessed the impact and calculated the resulting parameters of the necessary staff capacities of the designed production system. Table 3 and Figure 9 bring the resulting design parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Person** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **7** \\ \hline \multirow{8}{*}{Condens} & \(S_{o}\) & 3.9 & 2.9 & 0.0 & 2.3 & 6.3 & 6.9 & 6.2 & 0 \\  & \(M_{o}\) & 29.6 & 23.3 & 1.7 & 26.2 & 42.1 & 5.2 & 14.2 & 0 \\  & \(Ac_{R}\) & 31.2 & 0.0 & 41.6 & 100.0 & 20.8 & 27.7 & 66.7 & 0 \\  & \(Ac_{S}\) & 5.0 & 3.4 & 6.5 & 3.4 & 6.7 & 6.5 & 2.8 & 0 \\  & \(Ac_{So}\) & 2.7 & 3.4 & 4.9 & 1.8 & 7.1 & 8.9 & 7.1 & 0 \\  & \(Ac_{Mn}\) & 5.4 & 6.9 & 18.0 & 16.3 & 4.2 & 7.9 & 6.8 & 0 \\  & \(L_{o}\) & 12.5 & 0.6 & 0.5 & 9.3 & 5.1 & 1.5 & 6.1 & 0 \\  & \(Se_{o}\) & 3.7 & 3.7 & 3.7 & 3.7 & 3.7 & 3.7 & 3.7 & 0 \\  & Total & 94.0 & 46.1 & 76.9 & 162.8 & 96.1 & 68.3 & 113.5 & 0 \\ \hline \multirow{8}{*}{Condens} & \(S_{o}\) & 3.9 & 2.9 & 0.0 & 2.4 & 6.3 & 6.9 & 6.2 & 0.0 \\  & \(M_{o}\) & 29.6 & 23.3 & 1.7 & 26.2 & 43.4 & 5.2 & 14.2 & 0.0 \\  & \(Ac_{R}\) & 41.0 & 23.0 & 41.6 & 36.0 & 20.0 & 27.7 & 46.7 & 0.0 \\  & \(Ac_{S}\) & 5.0 & 3.3 & 6.5 & 3.3 & 6.7 & 6.5 & 2.8 & 0.0 \\  & \(Ac_{So}\) & 2.7 & 3.4 & 4.9 & 1.8 & 7.1 & 8.9 & 7.1 & 0.0 \\  & \(Ac_{Mn}\) & 5.4 & 7.9 & 18.0 & 16.2 & 3.0 & 7.9 & 6.8 & 0.0 \\  & \(Lo\) & 3.1 After the result analysis, we can say that we succeeded in adapting the division of activities of all operators in such a way that the data model does not create any operator's overload. The data model achieves for \(Q_{S}=3,600\) the indicator values of \(N_{op}=7.31\) and \(Fz=1.36\). The designed changes brought a 20% increase in manufacturing in the production stage with one more operator. The \(Fz=1.36\) coefficient value is lower and it means that after implementing the deigned changes in production it will be necessary to use a 7.61% lower amount of the human work compared with the current state. We can claim that our software enriched with the designed methodology can mediate information and feedback about the impact of the designed changes in the production layout for the capacity need of the production staff. The case study proved it was possible to reveal a remarkable potential for designing the existing manufacturing systems through this software solution. The practical benefits of this solution include especially the shortening of the necessary time for designing the production layout and its subsequent planning especially in the phase of the variant design. The possibility of verifying a more extensive quantity of solutions when we take into account various limiting conditions is closely connected with this fact. As this designed solution reduces the time necessary in the phase of the variant design, the costs inevitable for creating a new production organisation or expenses for changes in the existing production layout are also decreased.