## 4 Results and Discussion
### Phase 1: Analysis of the current situation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Terminal** & **Reduction of Waiting** & **Increase in Traffic** & **Reduced CO\({}_{2}\)** & **Total Economic** \\  & **Times (min)** & **Flow (\%)** & **Emissions (kg)** & **Savings (EUR)** \\ \hline
**Terminal 1** & 2.84 & 71.88 & 67.17 & 167.21 \\
**Terminal 2** & 1.96 & 85.47 & 42.47 & 105.71 \\
**Terminal 3** & 2.49 & 59.52 & 57.97 & 144.30 \\
**Terminal 4** & 2.43 & 53.68 & 60.99 & 151.81 \\
**Terminal 5** & 3.10 & 78.10 & 60.08 & 149.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of implementing calculation at terminals. of the values used has been verified by cross-referencing results with expected industry benchmarks, ensuring that the estimates are representative of actual operational conditions. The below Table 3 shows the calculated mean and standard deviation of the key variables for the five terminals. Regarding the variability analysis, we can say that the results obtained show that the variations in waiting times, vehicular flow, the reduction of CO\({}_{2}\) emissions, and the economic savings between terminals are reasonable and reflect the inherent differences in infrastructure and operational capacity. Averages and deviations can influence managers' decisions in the following manners:  
* Reduction in times (min): the variability is low, indicating that the terminals have fairly similar reduction times. This is reasonable, given that waiting times depend on the optimization of vehicular flow, which is uniform in all simulated terminals. Thus, strategically speaking, a low deviation indicates consistency in reduction times. Managers could prioritize the implementation of IoV and scheduling algorithms to maximize this efficiency uniformly, with the impact of decreasing congestion at peak times, especially at terminals with higher traffic volumes. * Flow increase (%): although the variability for this metric is a little greater, it is still reasonable. This reflects differences in initial (manual) processing capacity and the improvements that automation can provide, which both depend on the infrastructure of each terminal. Strategically, the greater variability in this metric highlights how factors such as initial infrastructure affect flow. Managers should evaluate specific improvements at terminals with a lower increase. The impact would be an increase in capacity without the need for physical expansion. * CO\({}_{2}\) (kg) reduction: dispersion for this metric is moderate, suggesting that emissions vary depending on the volume of manually processed traffic and the initial wait times of each terminal. This is consistent with the variation in vehicular flow. Strategically speaking, the moderate deviation suggests differences in volumes of traffic processed. Implementing IoV at terminals with lower turndown would help achieve consistent environmental targets, resulting in a reduction in the overall carbon footprint of operations. * Economic savings (EUR): the dispersion in savings is moderate. This reflects reasonable differences in operating costs and in the number of manually processed vehicles at each terminal. It would be strategic for managers to identify terminals with lower savings and evaluate specific causes, such as higher energy costs or lower initial efficiency. In turn, this could justify investments in technology through a measurable return. Standard deviations do not indicate extreme disparities between terminals. Variances are reasonable and expected due to differences in initial capacity, traffic volume, and efficiency of manual operations. The results are consistent and reflect a balanced analysis based on data collected from Ro-Ro terminals. An average increase in vehicular flow of close to 70% may seem high, but it depends on several factors specific to the terminals and the initial conditions. The below analysis assesses whether this is reasonable: Key factors affecting increased flow