## 4 Discussion of the Simulation Results  
Statistical analysis was conducted on the simulation results in order to understand the impact that time perturbations (such as _ALCTV_, _FP_, and _PFCTV_) and model mix perturbations (\(SEQ\)) have on the performance of the assembly system (measured by the aforementioned parameters \(W\), \(T\), and \(B\)). Inferential analysis was conducted first, in order to quantify the magnitude of the effects of the input variables on the simulation outputs. Then, an ANOVA analysis was performed, aimed at the determination of the interactions between input and output parameters. With regards to the inferential analysis, a Pareto chart of the standardized effects was drawn for each output parameter (\(T\), \(W\), and \(B\)) and is represented in Figures 3-5 respectively. The interaction plots of the ANOVA analysis are also reported for each considered output parameter (\(T\), \(W\), and \(B\)) and presented in Figures 6-8 respectively. With relation to the total throughput \(T\), Figure 3 shows that the input variables that have a significant effect on the output are mainly \(SEQ\) and \(PFCTV\), followed by the interaction of _FP_ and _PFCTV_, _FP_ and the interactions _PFCTV-SEQ_, _FP-SEQ_, _FP-PFCTV-SEQ_. Focusing on the main influencing factors, the trends shown in Figure 6 indicate that the larger the deviation of both \(SEQ\) and \(PFCTV\) from their nominal values, the larger the reduction of the mean of the total throughput \(T\). With regard to the _FP-PFCTV_ interaction, Figure 6 indicates that the larger _FP_, the bigger the effect of \(PFCTV\) on the reduction of the throughput \(T\), and vice versa. Similar considerations can be drawn for the average station working time \(W\). Figure 4 shows that the input variables that have a significant effect on the output are mostly \(SEQ\) and \(PFCTV\), followed by the interaction \(SEQ\)-\(PFCTV\) and then by _FP-PFCTV_, _FP-SEQ_, and _FP-PFCTV-SEQ_. Again, the trends represented in Figure 7 show that deviating from the nominal values of both \(SEQ\) and \(PFCTV\) ends up reducing the mean of the average station working time \(W\). Finally, for the last output parameter \(B\), the total buffer content, Figure 5 shows that the main influencing variables are _SEQ_, _ALCTV_, and _PECTV_, followed by _FP-SEQ_, _PECTV-SEQ_, _FP-PECTV_, and _FP-PECTV-SEQ_. The trends reported in Figure 8 indicate that a deviation from the nominal value of the three main influencing factors (_SEQ_, _ALCTV_, and _PECTV_) results in an increase of the mean of the total buffer content \(B\). To visualize the relations between the values of the three output parameters across different simulation scenarios, a contour plot is shown in Figure 9: \(W\) and \(B\) are represented on the x- and y-axis, respectively, while different levels of the value of \(T\) are represented in a green-shading scale. Figure 4: Pareto chart of the standardized effects of the inputs on \(W\). Figure 3: Pareto chart of the standardized effects of the inputs on \(T\). Figure 5: Pareto chart of the standardized effects of the inputs on \(B\). Figure 6: Interaction plot of the input parameters _FP_, _ALCTV_, _PFTV_, _SEQ_ for \(T\). Figure 7: Interaction plot of the input parameters _FP_, _ALCTV_, _PFTV_, _SEQ_ for \(W\). Figure 9 shows that there is a set of scenarios where all three main output parameters tend towards their theoretical optimum at the same time: \(T\) and \(W\) are maximized, and \(B\) is minimized. These scenarios are represented in dark green in the bottom-right corner of Figure 9. There are also two other areas, however, where the highest values of \(T\) do not correspond to the minimum values of \(B\) or the maximum values of \(W\): they are the two dark green areas at the center of Figure 9. In these scenarios, the maximum values of throughput can be reached even if the total buffer content and the average station working time are sub-optimal. Figure 8: Interaction plot of the input parameters \(FP\), \(ALCTV\), \(PFCTV\), \(SEQ\) for \(B\).