# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 4 Use Case
### Results and Validation  
The IAES prototype was executed in a controlled test scenario simulating the multi-modal transport network of an anonymized automotive supplier. A total of 200 missing transport routes were provided as the input, and the full enrichment pipeline was applied. All required route requests were fulfilled successfully, with no direct routing applied. The prototype, as shown in Figure 7, dynamically generated 996 routes (accounting for both A \(\rightarrow\) B and B \(\rightarrow\) A paths), connecting 281 supply chain nodes. The validation of the generated waypoint nodes was conducted through a manual review using geographic maps and internet research. Each node was evaluated against two criteria:  
* Existence: whether the proposed logistics facility is a real, identifiable location suitable for cargo transport. * Location accuracy: whether the coordinates place the node within a valid proximity (defined as \(\leq\)1 km) of its official location. Figure 7: The resulting transport KG after executing the IAES. The graph contains 281 nodes connected by 996 directed transport edges. Nodes are categorized as 4 plants (orange), 44 suppliers (green), 30 customers (blue), and 200 intermediate waypoints (red). Table 7 summarizes the results across the main waypoint categories: airport, seaport, freight rail hub, and truck terminal. Only nodes generated by the application (excluding fixed supplier, customer, or plant entries) were included in the evaluation. The prototype demonstrated excellent accuracy for airport infrastructure, with all 61 nodes correctly matched both in terms of existence and location. This result reflects the high availability and clarity of airport-related data in public databases and the LLM's strong ability to interpret such structured sources. In contrast, seaport nodes, while also exhibiting a high existence validation rate (97.1%), showed significantly lower spatial accuracy. Only 50.7% of the ports were geolocated within the 1 km threshold. This discrepancy is likely due to the complex geographic boundaries of port areas, which may span wide waterfront zones with multiple terminals. The lowest positional accuracy was observed among freight rail hubs, where only 11.5% of valid entries were located correctly. Although 80.3% of these nodes were confirmed to exist, the high spatial deviation suggests a limitation in the LLM's ability to resolve rail-specific logistics sites--potentially due to inconsistent naming schemes or sparse public datasets on inland rail terminals. Truck terminals presented a mixed outcome: 77.8% of generated nodes corresponded to real locations, yet none were within the desired accuracy threshold. This suggests either imprecise geocoding by the LLM or poor distinguishability of logistics-related truck facilities in urban areas. Despite these limitations, it is noteworthy that all evaluated nodes were placed in the correct city and country, which confirms the semantic reliability of the LLM-enriched routing application. Most errors were related to intra-urban coordinate placement rather than broader geographic misclassification. The IAES prototype generated a diverse set of intermediate waypoints across the globe. An analysis of node centrality (the number of inbound and outbound route connections) reveals a strong concentration around key multimodal hubs. As shown in Figure 8, the top 15 most-connected waypoints acted as major intermodal transit points, often bridging continents or modes. This distribution supports the hypothesis that the LLM-generated network structure adheres to real-world multimodal transport hierarchies. It also validates the IAES application's ability to reuse shared waypoints across routes, thereby reducing redundancy and improving connectivity. As mentioned before, the IAES application generated a total of 996 transport routes. The modal split of the enriched routes illustrates the IAES's capacity to generate plausible multimodal chains.