## 5 Discussion of Findings
From a methodological perspective, it can be observed that, for the second group of measures (WM measures), WM\({}_{2}\), WM\({}_{3}\) and WM\({}_{5}\) have been sorted to class A through the optimistic procedure (OP), while the pessimistic procedure (PP) suggests a medium priority for implementation. This discrepancy suggests the usefulness of prioritizing measures aimed at optimizing the route-optimization strategy. As such, we strongly recommend the company to invest in implementing RO\({}_{1}\), RO\({}_{2}\) and/or RO\({}_{8}\) to maximize effectiveness in logistic operations. By restricting our recommendation to these specific measures, the company can make more informed decisions on how to allocate resources efficiently while achieving tangible benefits, building momentum for future adoption of WM measures and other measures assigned to the B class. This step-by-step implementation process aims to ensure a strategic and cost-effective transition to data-driven practices, enabling the company to derive maximum value from its solar energy assets while effectively managing budget constraints. The results of the proposed decision-making approach prioritize measures, leaving the final implementation to be tailored by the company based on its specific budgetary and operational considerations. We further specify that the focus of this research lies in providing recommendations for the implementation of strategic measures at a managerial level rather than quantifying their outcomes upon their practical implementation. To narrow down our analysis even more, we now proceed by evaluating the robustness of results related to the route-optimization strategy by implementing a sensitivity analysis on criteria weights. The objective is two-fold: first, to identify if certain measures consistently dominate across different scenarios, indicating their significance relative to others. Second, we aim to discern if adjustments in the weight of a particular criterion prompt recommendations for prioritizing different measures from the ones previously discussed and recommended. By comprehensively analyzing these factors, we aim to gain a deeper understanding of the stability and adaptability of measures for the route-optimization strategy in various contexts. In addition to our baseline scenario (BS) considering the weights originally calculated via DEMATEL, we have developed ten additional scenarios, labeled as S\({}_{1}\), S\({}_{2}\),..., S\({}_{10}\) (see Table 10). Each scenario was developed incrementally, increasing the weight of a specific criterion, and numbered in correspondence with that criterion (B\({}_{1}\), B\({}_{2}\),..., B\({}_{10}\); see Table 1). For example, scenario S\({}_{1}\) emphasizes the weight originally attributed to criterion B\({}_{1}\), scenario S\({}_{2}\) emphasizes the weight originally attributed to criterion B\({}_{2}\), and so on, until scenario S\({}_{10}\), which emphasizes the weight originally attributed to criterion B\({}_{10}\). This means that as we move from S\({}_{1}\) to S\({}_{10}\), each successive scenario incrementally focuses on attributing more importance to the corresponding criterion B\({}_{1}\) to B\({}_{10}\). We specify that the weights of the remaining nine criteria are proportionally decreased to maintain a total weight of 1 in each scenario. Specifically, we have introduced an incremental increase of 30% to the weight of the corresponding criterion from its original value in each scenario. It is important to note that this is an additional increase in the criterion's original weight, effectively augmenting its importance in the decision-making process. The elaborated scenarios provide a comprehensive understanding of the interplay between different criteria and their influence on the robustness of our findings.