# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 3 Methodology
### Conceptual Framework: Combining Generative AI with Graph-Based Optimization  
Traditional supply chain applications in SMEs typically store transport routes as isolated, disconnected records within spreadsheets or relational database tables. This fragmentation creates a fundamental limitation: the absence of contextual connectivity supporting network-level analysis and the optimization of transport flows. The IAESaddresses this challenge through a paradigm shift from linear (relational) representations toward a network-based property graph model, where all logistical entities and their interactions are represented as nodes and relationships.  
This transformation enables each transport relation (e.g., Supplier A to Plant B) to become a part of a connected and queryable supply chain KG. This KG provides three important capabilities: (i) the inference of plausible intermediate steps, allowing the system to deduce typical cargo hubs or multimodal terminals based on network topology and connectivity patterns, (ii) the reuse of shared waypoints, where common infrastructure elements (ports, terminals) are utilized across multiple routes, and (iii) global optimization, enabling advanced pathfinding algorithms to identify optimal transport solutions based on customizable criteria.  
To operationalize this conceptual model, the IAES utilizes a hybrid architecture that combines the generative reasoning capabilities of LLMs with the structured analytical power of GDBs. Specifically, the application integrates OpenAI GPT-4 with Neo4j Desktop version 5.26.5 to achieve the complementary functionalities mentioned before. The LLM is employed for the semantic interpretation of unstructured logistics information, generating plausible routes and inferring missing waypoints. The property KG serves as the structured repository for all relevant supply chain elements, where nodes represent logistics actors (plants, suppliers, and terminals) and relationships capture transport routes. Each route and node is enriched with essential data properties such as the transport mode, the vehicle type, emissions, or costs. [PERSON] et al. (2018) underlines that using this AI contextual knowledge enhances decision-making in supply chain management. Empirical research also highlights that graph databases demonstrate superior performance compared to traditional relational databases in scenarios requiring relationship identification, traversal operations, or pattern detection ([PERSON], 2018). Neo4j was therefore selected due to its flexible data storage capabilities, built-in graph algorithms, efficient Cypher querying language, and integrated visualization features. The included functionalities support powerful graph traversal and path-matching capabilities, which are necessary for multimodal planning ([PERSON], 2018). Furthermore, the Neo4 js graph data science library, with algorithms such as Dijkstra, is used to detect optimization potential and identify the best routes. From the conceptual model and requirements analysis, the core tasks and functionalities of the IAES were defined.  
The tasks were systematically organized into a modular workflow, which performs the transformation of the raw data into actionable insight. Table 2 provides an overview of this principle, which forms the foundation of the technical realization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tasks** & **Functionality** \\ \hline \multirow{2}{*}{Get} & Capturing raw logistics data from ERP, MES, e.g., systems, like suppliers, plants, customers, and needed or known routes \\ \hline \multirow{2}{*}{Analyze} & Performing network analysis to detect gaps, incomplete paths, and optimization potential \\ \hline \multirow{2}{*}{Coordinate} & Prioritizes missing routes and orchestrates agent-based enrichment workflows \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main tasks and functionalities of the IAES.