## 4 Results
### Quantitative Scenarios Based on Key Performance Indicator Construction  
In this part of the method, different quantitative scenarios were simulated in the model; thus, a sensitivity analysis was performed to determine which variables were more sensitive to different parameters. Three scenarios were considered; the first one represented a less favorable situation for the organization, the second one represented the normal distribution process conditions and the third one represented a favorable situation for the organization. The following data (Table 4) show the normal scenario as an example of how the parameters were defined. To define indicators, the data in Table 4 were considered to generate the total result shown in Table 5. To explain this idea, two types of equations must be created: one summative (profit, distribution cost) and the other average (vehicle usage capacity, transportation cost/Kg, and cost-profit ratio). According to the information in Table 4, the variable total profit ($) shown in Table 5 is the result from each profit according to the transit vehicle and C2 truck generated with the following formula  
\[TP\ (\$)=\sum_{i=1,\ 2,\ 3,\ 4}Ti \tag{8}\]  
where \(TP\ (\$)= total profit; \(Ti\) = Transit vehicle; \(i\) = routes--Ciudad Obregon, Etchojoa, Bacum, and Campo 5. The final parameter for \(TP\ (\$)\) was 73,627.4 + 32,741.3 + 21,180.5 + 3994.8 = 131,544. The average vehicle usage capacity (AUVC) is defined by Equation (9):  
\[AUVC=\sum_{i=1,\ 2,\ 3,\ 4}ATi \tag{9}\]  
where \(AUVC\) = average vehicle usage capacity; \(ATi\) = average of each Transit vehicle; \(i\) = routes--Ciudad Obregon, Etchojoa, Bacum, and Campo 5. The average vehicle usage capacity parameter in Table 5 was obtained according to an AUVC of \((39.06+56.25+34.38+39.06)/4=41.21\). The seven key performance indicators selected to investigate these scenarios were considered with two requirements: (1) the indicator showed its impact on the results obtained, and (2) the organization \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Routes (Clients)**} \\ \hline \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Ciudad Obregon**} & \multicolumn{2}{c}{**Etchojoa**} & \multicolumn{2}{c}{**Bicum**} & \multicolumn{2}{c}{**Campo 5**} \\ \cline{2-9}  & **Transit** & **C2** & **Transit** & **C2** & **Transit** & **C2** & **Transit** & **C2** \\ \hline Total profit \$ & 73,627.4 & 73,455.5 & 32,741.3 & 28,967.8 & 21,180.5 & 20,059.8 & 3994.8 & 3337.6 \\ Total distribution cost \$ & 439.5 & 610.51 & 2580.3 & 6352.7 & 928.5 & 2048.3 & 561.9 & 1218 \\ Vehicle average usage capacity & 39.06 & 5.98 & 56.25 & 8.61 & 34.38 & 5.26 & 39.06 & 5.98 \\ Transportation average cost/kg (\$/kg) & 47.15 & 7.66 & 67.29 & 10.93 & 42 & 6.83 & 8.4 & 1.37 \\ Cost–profit ratio & 0.59 & 0.82 & 7.31 & 17.99 & 4.20 & 9.27 & 12.33 & 26.74 \\ \hline \multicolumn{9}{l}{The indicators generate the final parameter for each scenario related to each route; the final parameters are shown in} \\ \end{tabular}
\end{table} 
Table 4: Normal scenario sensitivity analysis for wholesale demand per month. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Pessimist Scenario**} & \multicolumn{2}{c}{**Normal Scenario**} & \multicolumn{2}{c}{**Optimist Scenario**} \\ \cline{2-5}  & **Transit** & **C2** & **Transit** & **C2** & **Transit** \\ \hline Total Profit ($) & 103,549 & 97,131.2 & 131,544 & 125,820.6 & 190,754.2 \\ Total Distribution Costs ($) & 5016.48 & 9445.15 & 4510.2 & 8743.11 & 577 \\ Vehicle average usage capacity & 34.34 & 5.58 & 41.21 & 6.69 & 93.7 \\ Transportation average cost per kg (\$/kg) & 0.80 & 2.02 & 0.9 & 1.55 & 0.29 \\ Cost–profit ratio & 7.87 & 20.47 & 6.10 & 13.70 & 2.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wholesale demand summary of simulated scenarios. had not considered the indicator within their analysis.