## 4 Simulation Study and Results
### Results of the Simulation Study  
The decision model must be run through to carry out the individual simulation runs, and then the output is transferred to the corresponding structure of the simulation model. Then, the simulations can be conducted. The underlying data for the assembly operations and the results of the simulation study can be accessed via the link provided in the Supplementary Materials. The first two investigation steps selected a complex production programme with a high \(C_{pp}\) value as the reference point. The runs were executed with a step-by-step increase of the observed control variable, and various target variables were evaluated. In particular, the target variables of throughput times and flow rate as a ratio of execution time to throughput time were considered. The results of the individual investigation steps are explained below. **Substudy 1:** This step examined the influence of the hybrid value \(\mathit{PUD}_{hy}\). In each run, the production programme and the scaling factor \(k_{\mathit{flex}}\) remained the same. Figure 4 shows the flow rate for the different hybrid values \(\mathit{PUD}_{hy}\). When the hybrid value is 0, all assembly operations are included in the line. If the hybrid value is 1, all assembly operations are included in the flex zone so that it is a pure job shop. In this case, the high flow rate of 100% in the job shop does not correspond to reality due to the assumptions made in the simulation (Section 4.1). The assembly operations have different \(\mathit{PUD}\) values depending on the production programme. The \(\mathit{PUD}_{hy}\) value determines which assembly operations are assigned to the flex zone and which are assigned to the line due to their high occurrence. A hybrid value 0.6 means that all assembly operations with a higher \(\mathit{PUD}\) than 0.6 are sorted into the line and those with a lower \(\mathit{PUD}\) into the flex zone. However, this means that assembly operations that do not occur in every product variant are scheduled into the line, so the line must have a longer cycle time. Accordingly, the flow rate here is also relatively low. With increasing hybrid value, the flow rate also increases since only the assembly operations that occur in most product variants and, therefore, have a high \(\mathit{PUD}\) value are scheduled into the line. The hybrid values \(\mathit{PUD}_{hy}\) 0.8 and 0.9 have achieved a flow rate higher than 80% (blue box). The assembly operations and \(\mathit{PUD}\) values were analysed to select a suitable hybrid value. A hybrid value of \(\mathit{PUD}_{hy}\) 0.8 was chosen because, at \(\mathit{PUD}_{hy}\) 0.9, frequent assembly operations would be assigned to the flex zone. In summary, it can be concluded that a hybrid organisation structure can achieve a higher flow rate than a production line if the production programme is very complex with product variants in different quantities. **Substudy 2:** The influence of the scaling factor \(k_{\mathit{flex}}\) for the duration of assembly operations in the flex shop was examined in the next step. For this, 0.8 was used as a constant for the hybrid value \(\mathit{PUD}_{hy}\). Since the throughput time \(\mathit{TTP}\) also increases with an increasing scaling factor \(k_{\mathit{flex}}\), Figure 5 shows the throughput time \(\mathit{TTP}\) as the target variables. The scaling factor \(k_{\mathit{flex}}\) was successively increased in the simulation runs, and  
Figure 4: Flow rate \(\mathrm{F}_{r}\) of different hybrid values \(\mathit{PUD}_{hy}\). accordingly, the throughput time \(TTP\) increased. The scaling factor \(k_{flex}\) has already been entered into the decision model, which outputs the number of required workstations. If the scaling factor \(k_{flex}\) is higher, the number of workstations in the flex zone has to increase accordingly. The results show that a further workstation is required from a markup of 75% and that eight workstations are required accordingly.