## 4 Conclusions  
This paper presented a novel approach combining Discrete Event Simulation and machine learning to enhance decision-making in logistics. A systematic three-step methodology was employed. The initial phase involved conducting on-site visits to the cooperative to gather relevant data. Subsequently, a simulation model was developed to generate data encompassing various scenarios. Finally, in the third step, a machine learning approach was applied to predict both the queue time of trucks and the lead time. This comprehensive methodology enabled a structured and analytical approach to tackle the problem at hand. This study aimed to understand the logistical impact of selecting different scenarios to support decision-makers in determining the best configuration for the process. Therefore, in the real case of a Brazilian cooperative, the best number of workers to be hired and machines to be allocated to the Soybean Seeds Classification Process was calculated. The results confirm the expected strong connection between the queue time and the lead time, i.e., the higher the lead time, the higher the queue time. In addition, the queue time is affected mainly by the system capacity (number of workers), while the lead time is affected by machine capacity. With the integration of the new machine into the system, the process gains accuracy, ensuring that the results of \"Injury Classification\" are truthful, mitigating the possibility \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{3}{c}{**Lead Time**} & \multicolumn{3}{c}{**Queue Time**} \\ \cline{2-7}  & **Avg** & **Max** & **Std** & **Avg** & **Max** & **Std** & **Scale** \\ \hline N\({}^{\text{o}}\) Workers & \(-0.05\) & \(0.25\) & \(0.29\) & \(-0.45\) & \(-0.59\) & \(-0.56\) & \(-100\) \\ \hline N\({}^{\text{o}}\) Machines & \(-0.43\) & \(-0.54\) & \(-0.26\) & \(-0.29\) & \(-0.35\) & \(-0.33\) & \(-0.50\) \\ \hline Worker Wait & \(-0.24\) & \(-0.10\) & \(0.04\) & \(-0.14\) & \(-0.13\) & \(-0.14\) & \(0.00\) \\ \hline Worker Time & \(0.19\) & \(0.12\) & \(0.00\) & \(0.11\) & \(0.09\) & \(0.10\) & \(0.50\) \\ \hline Machine Time & \(0.82\) & \(0.35\) & \(0.28\) & \(0.11\) & \(0.12\) & \(0.10\) & \(1.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correlation map. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Machine Time** & **Workers** & **Machines** & **Avg. Queue Time** & **Avg. Lead Time** \\ \hline
2 & 5 & 5 & 1.27 & 7.11 \\ \cline{2-5}  & 5 & 3 & 1.31 & 7.12 \\ \cline{2-5}  & 3 & 3 & 55.47 & 7.11 \\ \hline
1 & 5 & 3 & 0.66 & 6.12 \\ \cline{2-5}  & 5 & 5 & 0.67 & 6.10 \\ \cline{2-5}  & 5 & 1 & 1.58 & 6.35 \\ \cline{2-5}  & 3 & 3 & 17.59 & 6.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best alternatives. of fraud and bribery, thereby generating greater reliability for both the company and its clients. Additionally, with this suggested configuration, the machine's implementation should not result in significant queue time, which would render its utilization impractical. As a result, the process becomes more efficient and effective. The utilization of machine learning presents significant potential by facilitating decision-making. This data-driven approach empowers decision-makers to provide informed recommendations for achieving the optimal configuration of the process, thereby enhancing efficiency and productivity. Moreover, the combination of discrete event simulation with machine learning highlights the capacity to leverage diverse techniques in addressing real-world challenges. For theoretical implications, this is the first article, to the best of the authors' knowledge, to employ a combined approach of simulation and machine learning to generate data that aids decision-making regarding investments in process improvements. Regarding the contributions inherent in this work, the first relates directly to the field of agriculture.