## 4 Results
### Scenario 2--Subsequent Simulation Experiment  
In the next scenario, the production of 5 days per week was assumed and 5 subsequent simulations were conducted. The first-day simulation was started from an empty state (WIP = 0) and the WIP size at the end of the shift was used for the initialization of the next simulation. The results are presented in Table 5.  
The increasing trend of WIP size in Queue 3 and Queue 4 can be observed, compared to other queues, which is a consequence of the bottleneck in the next workstations  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**WIP** & **Queue 1** & **Queue 2** & **Queue 3** & **Queue 4** & **Queue 5** \\ \hline
0 & 10 & 2.68 & 10.34 & 9.16 & 0.58 \\ \hline
1 & 10 & 2.72 & 10.47 & 9.71 & 0.57 \\ \hline
2 & 10 & 2.79 & 11.11 & 9.95 & 0.58 \\ \hline
3 & 10 & 3.05 & 12.02 & 10.39 & 0.74 \\ \hline
4 & 10 & 3.42 & 12.96 & 11.46 & 0.83 \\ \hline
5 & 10 & 3.82 & 14.12 & 12.62 & 0.97 \\ \hline
6 & 10 & 4.31 & 15.24 & 13.77 & 1.11 \\ \hline
7 & 10 & 4.82 & 16.65 & 14.63 & 1.29 \\ \hline
8 & 10 & 5.41 & 18.01 & 15.52 & 1.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The values of average WIP in the queues after one shift according to different initial WIP sizes (for 20 simulation runs with a 95% Confidence Interval).  
Figure 11: Replication plot of good quality products (OK) in experiments for different initial WIP scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Day** & **Queue 1** & **Queue 2** & **Queue 3** & **Queue 4** & **Queue 5** & **OK** & **NOK** & **OEE** \\ \hline - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - \\ \hline
1 & 10 & 1 & 46 & 29 & 2 & 161 & 12 & 0.7385 \\ \hline
2 & 10 & 12 & 52 & 56 & 1 & 163 & 9 & 0.7477 \\ \hline
3 & 10 & 1 & 68 & 81 & 2 & 176 & 8 & 0.8073 \\ \hline
4 & 10 & 6 & 113 & 63 & 2 & 171 & 10 & 0.7844 \\ \hline
5 & 10 & 3 & 119 & 77 & 2 & 179 & 5 & 0.8211 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The values of WIP in the queues after one shift for five subsequent simulations.  
(Operations 40 and 50). Also, significant variability in production is observed, which is consistent with the production of the real system and is the result of system instability due to random failures.  
In order to assess the efficiency of the system, the OEE metric can be used. As the model includes the constraints related to Availability, Performance, and Quality, the OEE can be calculated directly from Equation (7) [43].  
\[\text{OEE}=\frac{OK}{TOK}=\frac{Number\ of\ good\ quality\ products}{Total\ number\ of\ products\ in\ ideal\ conditions} \tag{7}\]  
The _number of good quality products_ is given in column \(OK\) and the TOK _Total number of products in ideal conditions_ value was calculated including maximal throughput of the bottleneck 27.27 pieces/hour and 8 h work per shift, which gives 218 pieces.  
The average OEE was equal to 77.98% and was lower than the planned 80% (including A = 0.9375, P = 0.9, Q = 0.95) because of failures and loss of availability and loss of work speed, resulting from insufficient WIP content. The obtained result consists of average Availability = 0.9265, Performance = 0.886, and Quality = 0.95 and reflects well the course of the actual production process.  
There are some possibilities for improvement of the OEE, including the enhancement of the maintenance system or change of the Quality Control System (QCS), which enables the removal of faulty semi-finished products from the process and returning of repaired products into the process, which is related to WIP change and may be taken into account during initialization of the model.