## 4 Case Study
### ELECTRE TRI Prioritizing RO and WM Measures
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**ID** & **Measure** & **Description** \\ \hline WM\({}_{1}\) & Real-Time Inventory Tracking & These systems provide real-time data on inventory levels and locations within the warehouse. Help in locating items quickly with fewer errors and improve order fulfillment [62]. \\ \hline WM\({}_{2}\) & Automated Replenishment Systems and Multi-Channel Fulfillment & Use of automated systems in monitoring inventory and automatically reordering products. It ensures a continuous supply when stock levels fall below a fixed threshold level and manages inventories efficiently across all sales platforms, reducing stockouts and overstock situations [63]. \\ \hline WM\({}_{3}\) & Cross-Docking & Implementing cross-docking where received products at a warehouse can be processed immediately rather than storing them. Suitable for products with short shelf life or high demand [64]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data-Driven Proactive Measures for Warehouse-Management (WM) Strategy. We now proceed by formalizing the input data required for leading each of the two ELECTRE TRI iterations, which will be separately carried out for measures related to RO and WM strategies. Input data have been formalized according to the explanations provided in [31] and collected in Tables 6 and 7. Specifically, measures reported in Tables 4 and 5 were numerically assessed with the support of an external technical consultant with proven experience in the logistics field. We asked the decision-maker to provide a numerical evaluation on a 10-point scale by assessing measures under the criteria (Table 1) previously weighted through the DEMATEL application. Quantitative evaluations of measures under criteria are reported in Tables 8 and 9, along with ELECTRE TRI results obtained in each iteration in terms of assignment to three different priority classes (A, B, C) according to the pessimistic (PP) and optimistic (OP) procedures. In particular, class A suggests high priority in implementing measures, class B includes measures associated with medium priority recommended for implementation, while class C groups measures that are revealed to not be particularly urgent and whose implementation can be postponed according to budget availability. Results reported in Tables 8 and 9 have been obtained by fixing the cutting value to \(\lambda=0.8\). As explained before in the methodological section, results derived from the ELECTRE TRI pessimistic procedure should be preferred over the optimistic assignment in order to guarantee robustness and pursue risk mitigation. The company should take this aspect into account when formalizing the final decision on specific data-driven measures to be implemented with priority for adopting route optimization and warehouse-management strategies. The pessimistic procedure ensures indeed that decisions are made considering the worst-case scenarios, leading to more conservative and reliable outcomes. On the contrary, the optimistic procedure may overlook potential risks or uncertainties, leading to less robust decisions that could be more vulnerable to unexpected events.