## 4 Case Study
### ELECTRE TRI Prioritizing RO and WM Measures  
In the present subsection, we are going to lead two separate iterations of the ELECTRE TRI procedure to assess various data-driven measures related to the two strategies to be implemented by the company according to [4], which we recall being route optimization and warehouse management. We have first led a literature review to formalize a set of relevant measures that can be implemented for each strategy. Ten measures related to route-optimization strategy (RO\({}_{1}\), RO\({}_{2}\),..., RO\({}_{10}\)) are reported in Table 4, while ten measures related to warehouse-management strategy (WM\({}_{1}\), WM\({}_{2}\),..., WM\({}_{10}\)) are formalized in Table 5.  
The literature review on implementation measures provides a strong foundation for this research. As previously done to identify criteria to be weighted with DEMATEL, this approach ensures that the measures assessed in the next two ELECTRE TRI iterations are comprehensive and informed by established practices and theories, providing the company with reliable recommendations. Figure 3: Prominence histogram showing number of criteria associated with the same weight. Figure 2: Causal relation map. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**ID** & **Measure** & **Description** \\ \hline RO\({}_{1}\) & Geographic Information System (GIS) and Weather-Adaptive Routing & The GIS web service facilitates collaborative workspace for transportation departments, streamlining coordination. It utilizes GIS to plan optimal routes for site assessments and installations, adapting to weather forecasts for efficient planning in weather-dependent industries like the Solar Industry [53]. \\ \hline RO\({}_{2}\) & Dynamic Scheduling & Implementing systems that dynamically adjust schedules and routes based on real-time information, such as weather conditions, which are critical in solar installations [54]. \\ \hline RO\({}_{3}\) & Advanced Mapping for Remote Locations & Utilizing high-resolution maps that provide detailed views of remote or difficult-to-access locations to plan the safest and most efficient access routes [55]. \\ \hline RO\({}_{4}\) & Optimized Material Delivery & Scheduling deliveries of heavy equipment like solar panels and storage batteries in coordination with installation progress to avoid on-site congestion [56]. \\ \hline RO\({}_{5}\) & Real-Time Communication Networks & Establishing robust communication links between the field teams and central control to facilitate immediate updates and rerouting based on site conditions [56]. \\ \hline RO\({}_{6}\) & Modular Route Planning & Creating flexible, modular routing plans that can be easily adjusted as project scopes expand or contract, typical in large-scale solar projects. This measure is focused more on the planning stage [57]. \\ \hline RO\({}_{7}\) & Inventory Management Integration & Linking route plans with inventory levels to ensure that all necessary materials are available on-site when needed, reducing return trips [58]. \\ \hline RO\({}_{8}\) & Multi-Modal Transport Solutions & Utilizing a combination of transport methods (road, rail, air) for equipment delivery, especially to remote or hard-to-reach locations [59]. \\ \hline RO\({}_{9}\) & Risk Assessment & Identifying disruptions like traffic congestion or adverse weather, assessing their likelihood and impact on delivery schedules, which allows for proactive strategies to mitigate risks [60]. \\ \hline RO\({}_{10}\) & AI-Enhanced Optimization Tools & implementing AI tools and methods to continually refine and improve routing strategies based on outcomes, learning from each project to optimize future routing [61]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data-Driven Proactive Measures for Route-Optimization (RO) Strategy.