## 4 Discussion  
In this section, we examine the implications of our findings from assessing the container terminal using PCA and the TOPSIS methodologies. We investigate how the integration of these analytical techniques contributes to a more nuanced understanding of terminal \begin{table}
\begin{tabular}{c c} \hline \hline
**Ranking LAC** &
\begin{tabular}{c} **TOPSIS** \\ **(No Throughput Considered)** \\ \end{tabular} \\ \hline
1 & Santos-Brasil \\
2 & Manzanillo-México \\
3 & Buenos Aires-Argentina \\
4 & Kingston-Jamaica \\
5 & Ballboa-Panama \\
6 & Cartagena-Colombia \\
7 & Manzanillo-Panama \\
8 & Callao-Perú \\
9 & Buenaventura-Colombia \\
10 & Colon-Panama \\
11 & Veracruz-México \\
12 & Lazaro Cardena-México \\
13 & Caucedo-Rep. Dominica \\
14 & Itajai-Brasil \\
15 & San Antonio-Chile \\
16 & Freeport-Bahamas \\
17 & Paranagua-Brasil \\
18 & Montevideo-Uruguay \\
19 & Guayaquil-Ecuador \\
20 & Santa Marta + Barranquilla-Col \\
21 & Puerto Limón-Moin Costa Rica \\
22 & San Juan-Puerto Rico \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results obtained from the application of the TOPSIS methodology, excluding throughput. Figure 7: Ranking of ports using the TOPSIS and PCA-TOPSIS models applied. performance. This discussion aims to bridge the gap between theoretical analysis and real-world application, offering a comprehensive evaluation of the methodologies employed and their impact on enhancing operational efficiency and decision-making processes in container terminal management. The results obtained from PCA (Figures 5 and 6) show that the 11 selected variables have similar relevance in ranking the ports. Considering the weight values in the first and second PCs, which together explain over 70% of the original information, all variables have similar weights. Although in the first PC, which explains 50% of the variance, quay draught and the distance to the Panama Canal have the lowest weights, this is reversed in the second PC, which accounts for the remaining 20% of the original information. However, by definition of PCA, these variables influence the ranking less than those with high weights in the first PC. The results presented in Table 4 and Figure 7 combined with the information provided in Figure 3 and Tables A1 and A2 facilitates the understanding of certain situations that occur around some ports and their container traffic. It has been an interesting exercise to carry out a ranking from the point of view of the port's operational capacity, considering all the evaluated criteria. Operational capacity can be similar to Terminal Efficiency, through other methodologies. The most important results will be established by the Port Authority:  
**Colon:** The Panamanian port of Colon has been the most significant port in LAC for several years. This prominence is not only due to its annual TEU performance, which has exceeded four million TEUs since 2018, but also because it consistently ranks No. 1 in all evaluations, significantly ahead of second place. This success is attributed to a combination of technical criteria and the efficient use of resources relative to the output variables. However, its ranking drops when annual TEU performance is not considered, indicating that despite having fewer operational resources compared to ports such as Santos in Brazil, Colon effectively utilizes its resources to secure the top spot when TOPSIS combined with PCA methodologies is applied, taking these resources into account. **Santos:** As Brazil's main port and the second largest in LAC by annual TEU performance, Santos faces limitations in the rankings. This is due to its large operational criteria dimensions and relatively low TEU performance, similar to Colon. The best-ranking Santos achieved, No. 8, was obtained using TOPSIS without PCA and applying three operational criteria with a 60% weighting. This highlights that merely having the largest criteria dimensions is insufficient, indicating a potential underutilization of resources.