# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 5 Discussion
Over time, even a modest number of queries built a sizeable multimodal network, enabling complex, emergent routes. The LLM also showed strong capability to deal with unreal inputs. When asked to create a route from a node to itself, it responded appropriately: \"Since the start and destination points are the same, no transport is needed. The cargo is already at its destination.\" This demonstrates that the combination of prompt design and model capability can deliver meaningful context-aware responses and offer a highly flexible and adaptable alternative to hardcoded logics [38]. Nevertheless, several limitations were identified, starting with the LLM's waypoint quality varied by the transport mode. Airport waypoints were perfect (100% existed and were correctly placed), but seaport waypoints often had errors (only 50.7% were accurately geolocated, even though 97.1% corresponded to real ports). Inland hubs were worse: freight-rail suggestions were 80.3% valid but only 11.5% correct in position and truck terminal suggestions were 77.8% valid but 0% correct. This issue not only affects accuracy but also has an impact on the route logic. Incorrectly placed or mismatched terminals can lead to implausible mode sequences (e.g., air freight from an inland village) or also self-loop paths (A \(\rightarrow\) A). It was also observed that the LLM's outputs lacked diversity. Identical prompts often returned very similar answers, which indicates low variability (LLM parameter temperature = 0.3) in the planning step. Increasing the temperature or altering prompts could improve variety, but on the other hand, could also increase the risk for hallucinations and the generation of implausible outputs [39; 40]. Repeated LLM responses can limit the expansion of the network and reduce flexibility in route benchmarking. Prompt engineering or validation could help to increase the variety without sacrificing, even when keeping the same parameters. Another practical limitation is that the prototype only integrates a true routing API for the road mode. Sea, air, and rail segments currently use heuristic calculations, so the values for distances, costs, and time are only approximate. Finally, the current application treats all cargo generically. It does not account for product-specific constraints (e.g., refrigeration, dangerous-goods restrictions) or carrier-specific rules, which would be needed for a fully realistic deployment. The limitations demonstrate that there are clear points for improvement. Integrating authoritative mode-specific routing services would enhance accuracy. For example, a marine route API or airline schedule database could supply real ocean/air distances and emissions. It is also planned to leverage structured integration protocols to guide the LLM. For instance, implementing the open Model Context Protocol (MCP) would let the LLM query direct with databases or geographic information (e.g., waypoint information direct from Google Maps or OSR) in real time, grounding its answers [41; 42]. In the current design, each LLM query is independent, so valuable feedback from previous queries is lost. A feedback loop could incorporate knowledge of the existing KG state into the prompts (or use reinforcement learning from graph rewards) so that the LLM becomes informed by what is already known, reducing redundancy or avoiding known invalid suggestions. On the KG side, additional automatic analytics could identify network gaps or critical nodes: for example, computing connected components or betweenness centrality would reveal dead ends or bottlenecks. Over time, this accelerates the LEGO-like growth but also the quality of the network. The domain model should be enriched by including vehicle types and carrier constraints (fleet capacities, container sizes, shipping-line routes, etc.) so that planned scenarios respect real-world operational limits. Exception handling and error recovery also remain as ongoing priorities.