# Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques
## 11 Results of Improvements  
Table 7 shows the system's performance under three improvement strategies, and the baseline (Expo 30) is compared in the table. There is a trade-off between system modifications and throughput, as evidenced by the output being highest in the baseline (167) and lowest in Improvement #3 (124). Waiting and line times dropped dramatically with improvements, indicating improved flow, particularly in Queues 2 and 5. Resource utilization, especially Utilization 2 and 5, decreased in Improvements #2 and #3, indicating fewer bottlenecks. Utilization 2 was still high in all cases, however, suggesting that this is a crucial issue that needs attention. The confidence intervals suggested consistent output trends across replications. All things considered, Improvement #1 provided a well-rounded strategy with reasonable output, shorter wait times, and better resource allocation without seeing sharp declines in efficiency. Figure 7a-d present 95% confidence intervals derived from 10 Arena simulation runs, illustrating the variability and consistency of key performance metrics. Narrow confidence intervals suggest stable outcomes with minimal variability, thereby confirming the reliability of the model. This bolsters the validity of the conclusions drawn from the simulation data across repeated replications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Results** & **Inter-Arrival Time Expo (30)** & **Improvement \#1** & **Improvement \#2** & **Improvement \#3 Expo (40)** \\ \hline output & 167 & 147 & 148 & 124 \\ \hline Total time of & & & & \\ parts 1, 2, and 3 & 2.87, 3.15, 3.12 & 2.54, 2.29, 2.26 & 2.47, 2.91, 3.0 & 2.31, 2.12, 2.39 \\ \hline Number in & & & & \\ parts 1, 2, and 3 & 52, 57, 65 & 59, 48, 49 & 55, 54, 42 & 44, 45, 39 \\ \hline Number out & & & & \\ parts 1, 2, and 3 & 48, 55, and 64 & 55, 45, 47 & 54, 53, 41 & 42, 44, 38 \\ \hline Queue 1 & 0.603 & 0.42 & 0.63 & 0.29 \\ \hline Queue 2 & 1.778 & 0.0145 & 1.5358 & 0.45 \\ \hline Queue 3 & 0.0013 & 0.01177 & 0.0001 & 0.0 \\ \hline Queue 4 & 0.0 & 0.003 & 0.0 & 0.0004 \\ \hline Queue 5 & 0.6878 & 0.5502 & 0.0 & 0.25 \\ \hline Waiting time 1 & 0.2172 & 0.1596 & 0.2540 & 0.13 \\ \hline Waiting time 2 & 0.4919 & 0.00468 & 0.5035 & 0.17 \\ \hline Waiting time 3 & 0.000898 & 0.009708 & 0.0001 & 0.0 \\ \hline Waiting time 4 & 0.0 & 0.00163 & 0.0 & 0.003 \\ \hline Waiting time 5 & 0.3275 & 0.55 & 0.0 & 0.15 \\ \hline Utilization 1 & 67\% & 60\% & 57\% & 50\% \\ \hline Utilization 2 & 86.54\% & 40\% & 77\% & 65\% \\ \hline Utilization 3 & 39.13\% & 32\% & 31\% & 28\% \\ \hline Utilization 4 & 12.85\% & 12\% & 11\% & 9\% \\ \hline Utilization 5 & 85.27\% & 81\% & 39\% & 67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: System performance improvement. Chart 1 bar chart shows the average wait times for the existing, Improvement 1, Improvement 2, and Improvement 3 scenarios. The existing system had the longest queue at 1.75. Improvement 1 drastically reduced the queue to nearly zero. Improvement 2 lowered it to about 1.55. Improvement 3 moderately reduced it to 0.45. Improvement 1 was the most effective in reducing Queue 2, followed by Improvement 3, while Improvement 2 slightly improved over the existing system. Thus, Improvement 1 is the recommended option. Chart 2, titled \"Utilization 2,\" presents the utilization rates across four scenarios: existing, Improvement 1, Improvement 2, and Improvement 3. The existing system showed the highest utilization at 87%. In contrast, Improvement 1 significantly reduced utilization to 40%, indicating potential underutilization. Improvement 2 maintained a high utilization rate of 78%, comparable to the existing scenario. Improvement 3 achieved a moderate utilization of 67%. While Improvement 1 effectively reduced queue length, it did so at the cost of low resource utilization. In comparison, Improvement 2 offered a more balanced outcome, combining queue reduction with sustained high utilization.