## 3 Results and Discussion  
Table 3 presents the results for all the scenarios presented in Table 2. All the times are in minutes, and the table is sorted by queue time, ascending. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **Workers** & **Machines** & \begin{tabular}{c} **Worker** \\ **Wait** \\ \end{tabular} & \begin{tabular}{c} **Machine** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Worker** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Queue** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Lead** \\ **Time** \\ \end{tabular} \\ \hline
1 & 5 & 5 & No & 1 & 3.25 & 0.62 & 6.12 \\
2 & 5 & 3 & No & 1 & 3.25 & 0.64 & 6.12 \\
3 & 5 & 5 & Yes & 1 & 10.25 & 0.64 & 6.11 \\
4 & 5 & 3 & Yes & 1 & 10.25 & 0.65 & 6.12 \\
5 & 5 & 3 & No & 1 & 10.25 & 0.66 & 6.11 \\
6 & 5 & 3 & Yes & 1 & 3.25 & 0.66 & 6.12 \\
7 & 5 & 5 & Yes & 1 & 3.25 & 0.67 & 6.10 \\
8 & 5 & 5 & No & 1 & 10.25 & 0.67 & 6.11 \\
9 & 5 & 5 & Yes & 2 & 3.25 & 1.27 & 7.11 \\
10 & 5 & 5 & No & 2 & 10.25 & 1.28 & 7.11 \\
11 & 5 & 5 & No & 2 & 3.25 & 1.28 & 7.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Forecasts (time in minutes). The results show that the variation in the lead time is considerably lower compared to the queuing time. Its values predominantly varied from 6 to 8 min, with a few exceptions close to 15 min. The queue time, on the other hand, presents a high variation, returning values lower than 1 min or greater than 270 days. Table 4 presents the computed statistics for the predicted queue times, including averages, maximum values, and standard deviations, for each of the scenarios over the projected period. An observed trend reveals that a reduction in the number of machines and workers correlates with decreased system responsiveness during historically high-demand periods. This trend is corroborated by the observed escalation in standard deviation values. Indeed, as the average queue time increases, both maximum values and deviations follow suit. These findings underscore the nuanced relationship between resource allocation and system responsiveness in the context of fluctuating demand scenarios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Scenario** & **Workers** & **Machines** & **Worker Wait** & **Machine Time** & **Worker Time** & **Queue Time** & **Lead Time** \\ \hline
12 & 5 & 3 & No & 2 & 3.25 & 1.30 & 7.12 \\
13 & 5 & 5 & Yes & 2 & 10.25 & 1.31 & 7.12 \\
1  
In contrast, when analyzing the statistics related to lead time in Table 5, a minimal variation can be observed across all values. However, particular attention is drawn to scenarios 43 and 44. In these scenarios, where classifications are conducted without the aid of machines and workers are required to manually split grains in half, a significant increase in total processing time is evident, nearly doubling the results obtained in previous scenarios. Tables 4 and 5 present the results, while Table 6 offers a correlation map between input variables, averages, maximum values, and standard deviations. Concerning lead time, there is a direct proportionality with machine processing time, indicated by a factor of 0.82. Additionally, fewer machines result in longer average and maximum times, highlighting the significant impact of machine quantity on service speed. Other factors did not yield significant results. Regarding queue time, the number of workers has a more substantial impact than machine quantity. Increasing the number of workers leads to shorter waiting times for trucks. Surprisingly, both machine and worker processing times have limited impacts on queue time, contradicting expectations of a more significant influence. These findings indicate that service capacity is a more critical factor in queue time than the speed at which services are performed.