# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 5 Discussion  
The hybrid IAES prototype demonstrates effectively how generative AI and KG-based modelling and optimization can be combined to create supply chain networks. This hybrid application can dynamically and autonomously construct a transport network from fragmented inputs. The enriched KG is decision-ready, and optimal routes can be selected. Additionally, the application effectively demonstrates the powerful collaboration between  
Figure 9: A screenshot from the Streamlit-based dashboard demonstrating the comparative benchmarking of alternative logistics routes. The IAES prototype evaluates multiple routing scenarios, allowing users to select optimization criteria. The results are generated using semi-realistic data. the methods of AI and Operations Research (OR). From the AI side, LLMs (GPT4) were used to generate plausible intermediate waypoints and routes, which are then stored in a GDB (Neo4j). Finally, KG-based optimization algorithms such as Dijkstra help to identify the best paths according to the chosen criteria (e.g., cost, emissions, or duration). In this configuration, the LLM supplies creative solutions, while OR provides precision, making their combination well-suited for logistics applications. In effect, the application separates \"creation\" from \"selection\": one component generates route options, the other identifies the optimal solution based on the requested context. The results illustrate how IAES enables explicit trade-offs in strategic sustainable routing. In the example dashboard below, the carbon-optimal path (road \(\rightarrow\) sea \(\rightarrow\) sea \(\rightarrow\) road) emitted only 154.9 kgCO\({}_{2}\)/kg, whereas the fastest path took 13.8 h at a much higher cost (2753.69 EUR/kg) and emitted 4131 kgCO\({}_{2}\). These results align with prior studies like [PERSON] et al. [37] and show that the prototype can automatically benchmark and compare emission-, cost-, and time-optimized alternatives. In our automotive case study, processing 200 missing transport demands produced 996 enriched legs connecting 281 graph nodes. The assembled network included 437 road, 226 sea, 190 air, and 143 rail connections (a plausible modal split), and node-centrality analysis confirmed that the 15 most-connected waypoints were major global hubs (large seaports or airports). Through this, the application empowers decision-makers to simulate different logistics scenarios, such as prioritizing low-carbon rail segments, excluding specific countries due to geopolitical risk, or additionally minimizing mode-switching to reduce delays. By quantifying these differences, IAES makes trade-offs visible and actionable for SME's, which helps to increase both sustainability and resilience [32]. In summary, the IAES not only completes missing routes via LLM prompts but also builds them into a coherent network that can be queried under varied constraint sets. Another important finding is that the KG grows organically over repeated use, like assembling a LEGO structure piece by piece. Each request for a new route could contribute new waypoints or new links. As more requests are processed, the supply chain KGs become increasingly more complete, but also more reusable and robust. Additionally, nodes representing new suppliers, customers, or plants can easily be docked onto the existing structure, and previously added routes can be reused or reoptimized. This cumulative growth makes the IAES not only a planning tool but also a knowledge base of transport connections. Even previously unused routes or unused intermediate points, enriched by the LLMs, can become part of a new optimized connection. For example, a seaport that was only part of one route in the beginning may later serve as an intermediate waypoint in completely different transport paths. This validates the conceptual framework: the static ERP-derived nodes are transformed into a dynamic supply chain KG, where each additional connection is akin to adding another LEGO block to the structure.