## 5 Numerical Results  
This section delves into the primary simulation results to underscore the key findings. Figure 1 illustrates the throughput rate differences across the eight combinations, presented as a percentage relative to the no reconfiguration model. The results indicate minor variances, with cases 6 and 8 showcasing a 2% improvement when employing the continuous reconfiguration model with two multi-spindle machines. These cases exhibited higher average utilization and included the introduction of failures. Notably, the introduction of a single multi-spindle machine resulted in reduced performance for case 8. Figure 2 displays the average time in the system for the tested models. Reconfiguration activities showed a noticeable advantage, particularly when failures occurred within the manufacturing system (cases 5-8). Interestingly, the introduction of a single multi-spindle machine may pose challenges in management, as it did not yield improvements in the simulated cases. Conversely, introducing two balanced multi-spindle machines consistently resulted in performance enhancements, particularly when coupled with continuous reconfiguration policies in cases involving failures. Figure 1: Throughput rate. Figure 2: Average time in manufacturing system. Figure 3 depicts the standard deviation of the average time in the system. Interestingly, introducing just one multi-spindle machine tended to increase the instability of the average time in the systems across various tested conditions. Conversely, the other models contributed to enhancing the stability of the average time in the system. Notably, the introduction of two multi-spindle machines, particularly in scenarios involving failures, substantially reduced the fluctuations by over 50%. Figure 4 illustrates the average delay per part. Intriguingly, introducing only one multi-spindle machine resulted in poorer performance in this aspect. On the other hand, reconfiguration without multi-spindle machines or with two multi-spindle machines significantly enhanced this performance metric. Notably, this aspect shows the potential for greater improvement compared with other measures. Figure 4: Average delay for unit of product. Figure 3: Standard deviation of average time in manufacturing system. Similar improvements were observed for the work in process, as depicted in Figure 5. However, the benefits in this regard were slightly lower compared with the average delay for a unit of product. This suggests that while reconfiguration strategies contribute to reducing the work in process, their impact may not be as pronounced as in other studied performance measures. The effects of the inter-arrival time, processing time, and failures were evaluated using Design of Experiment computation to assess their impact on the performance measures. Figure 6 illustrates the main and interaction effects for each model tested. Failures emerged as the most critical factor, followed by the inter-arrival time and the interaction between the inter-arrival time and failures. Other factors exhibited comparatively lower importance. Notably, reconfiguration policies (both periodic and continuous) and the introduction of two multi-spindle machines demonstrated a robustness that mitigated the effects of these factors on the manufacturing system. Based on the analysis of the numerical results, several key observations emerged:  
* Introducing only one multi-spindle machine resulted in an unbalanced reconfiguration of manufacturing systems, complicating reconfiguration activities. Conversely, employing two multi-spindle machines yielded more substantial improvements in the performance measures, indicating a more favorable system behavior. * The continuous reconfiguration policy demonstrated superior performance improvement compared with periodic reconfiguration. Design of Experiment (DoE) analysis underscored the significant impact of introducing failures and exponential inter-arrival times. The reconfigurable manufacturing system exhibited greater resilience to variations in processing time.