# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 2 Theoretical Foundations and Research Gap  
This section presents a literature review to identify current research about LCAs, SCM based on the technologies of KGs, graph databases (GDBs), and LLMs. The research aimed to study the current applications of these technologies for enhancing transport data quality and sustainability assessments, and advanced supply chain analytics. The Scopus database search used a keyword-based approach with the following search string: (TITLE-ABS-KEY(\"Supply Chain\" AND \"Knowledge Graph\")) OR (TITLE-ABS KEY((\"LCA\" OR \"Life Cycle Assessment\") AND \"Knowledge Graph\")) OR (TITLE-ABS-KEY(\"Supply Chain\" AND \"LLM\")) OR (TITLE-ABS-KEY(\"Graph Database\" AND \"Supply Chain\")). The search was limited to English peer-reviewed journal articles and excluded review papers. This selection resulted in a total number of 74 documents. Analysis of the existing literature revealed a predominant focus on supply chain risk assessment and financial modeling, particularly within pharmaceutical and high-risk industrial domains (e.g., [PERSON] et al. 2023 [20], [PERSON] & [PERSON] 2022, [21]). To narrow down these areas, the final review focused on contributions that deal with the following:  
* The use of KGs or GDBs to model transport chains, product flows, LCAs, or the LCI
* The application of LLMs to automate route generation, enrich incomplete SCM or LCA data  
An overview of the final selected studies with their technological approach, methodology, and application domain is listed in Table 1.  
[PERSON] et al. (2023) [22] developed a semantic GDB for the LCI, enabling a \"queryable, scalable, intelligible, and exchangeable\" data format that supports \"expressive and instantaneous queries in real-time over a considerable amount of LCI data\". This graph-based LCI model represents products, processes, and flows as connected data points, which makes it easier to combine different LCA datasets and enables real-time recommendations. [PERSON] et al. (2024) [23] addressed the necessity for effective carbon emission tracking in manufacturing industries. The authors proposed KG modeling methods to quantify and predict carbon emissions in complex manufacturing supply chains. Their framework (CarbonKG) integrates intelligent technologies (e.g., process mining) to analyze the carbon footprint across the entire life cycle of a product. This approach is especially relevant given the complexities associated with the long supply chains typically encountered in manufacturing environments. By facilitating carbon traceability, the framework aids manufacturers in understanding their emissions pathways to promote more informed decision-making. [PERSON] et al. (2025) [24] developed a KG model to enable sustainable reconfiguration of supply chains 5.0. Their system formalizes supply chain entities, activities, resources, and constraints while embedding sustainability indicators (e.g., costs, emissions) directly into the decision logic. The graph structure supports the evaluation of alternative configurations based on environmental, economic, and social performance criteria. This graph-based approach provides transparency and promotes trade-offs between efficiency and environmental impact. [PERSON] et al. (2024) [25] introduced a KG method to automate LCA modeling. Their system recommends background processes and flow datasets to build automatic LCA models. In a case study, the approach achieved a top-10 flow recommendation precision of ~79.5% (4\(\times\) higher than conventional search) and 2.45\(\times\) faster retrieval. The KG also ranks processes by geography to help define system boundaries and functional units. This information is finally fed into the software OpenLCA for calculation. [PERSON] et al. (2024) [26] described a novel workflow that combines LLMs with a computational geometry engine to automate building LCAs. Their system uses the cognitive capabilities of LLMs for processing unstructured data (e.g., material names, quantities from text) together with a parametric design framework called COMPAS.