## 6 Discussion  
This study aimed to evaluate the performance of an assembly line by analyzing the current task workflow and rebalancing it, if necessary, to achieve optimal production quantity. The study utilized the MTM-UAS time standard and simulation software Simio to perform a new time analysis and assess the effectiveness of the rebalancing process. The results show that the current assembly line was not optimally balanced and required rebalancing to improve production efficiency.  
The distribution of the results of the time analysis, prepared by an expert engineer, shows that the decision to use MTM-UAS was correct and best suited to the type of assembly line that was observed.  
The results of the new time analysis show that there was a clear need for rebalancing, as there was a 38.37% difference between the bottleneck workstation and the least occupied workstation. After rebalancing, the difference in occupancy between the new bottleneck workstation and the new least occupied workstations was reduced to 8.52%.  
To confirm the idea of rebalancing, a simulation model was created to have as little negative impact as possible on the actual assembly line before we could make sure that the rebalancing decision was correct.  
There is a slight difference between the previous results of the timing analysis due to all the other variables that the simulation model can also include. The results of the simulation scenario before rebalancing showed that there was a 47.23% difference in occupancy between the bottleneck workstation and the least occupied workstation. The results of the simulation scenario after rebalancing showed that the difference in occupancy between these workstations was reduced to 12.89%.  
The simulation scenarios also had an impact on the performance of the assembly line, which was increased by 11.4%. This means that a twin assembly line is still needed, but a reduction from the planned three shifts to two on the twin assembly line can be realized.  
All this was achieved without any disturbances to the observed assembly line that was in serial production, which was the main goal of the study.  
Changes have already been made to test the impact of the new balance on the current assembly line. Work steps were changed between workstations so that the learning curve was rerun. The planned output from the simulation scenario results was achieved, and there was positive feedback from the assembly workers as the differences in workstation occupancy were reduced and clearly visible from their point of view.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Workstations 1–8** & **Workstations 9–10** \\ \hline Minimal occupancy (\%) & 67.92 & 86.11 \\ Maximal occupancy (\%) & 100 & 86.46 \\ Dispersion (\%) & 47.23 & 0.41 \\ \hline \end{tabular}
\end{table}
Table 6: Dispersion of assembly line balance before rebalancing.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Workstations 1–8** & **Workstations 9–10** \\ \hline Minimal occupancy (\%) & 88.58 & 96.10 \\ Maximal occupancy (\%) & 100 & 96.53 \\ Dispersion (\%) & 12.89 & 0.45 \\ \hline \end{tabular}
\end{table}
Table 7: Dispersion of assembly line balance after rebalancing.  
This study showed the optimization of balancing results checked using simulation scenarios, but ALBP was still solved by hand. The next step is to use genetic algorithms to solve ALBPs that outperform existing heuristics [70]. In addition, the ergonomic design of the assembly line is an important component of a stable production process. Therefore, the ergonomic weighting of tasks should also be included in the process of solving an ALBP.