# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 2 Theoretical Foundations and Research Gap
The LLM components extract material data and link it to LCI databases. Through testing on a real building project, it was shown that this method can significantly reduce the manual work needed to combine data from different sources. These studies demonstrate that LLMs can greatly simplify LCA data preparation by automatically processing text-based information that was previously time-consuming to handle manually. [PERSON] et al. (2025) [27] created the CECA method to automatically calculate embodied carbon in construction projects. Their system uses an LLM to read building specifications and automatically match materials and equipment to the correct emission factors from databases, eliminating manual lookups. The LLM-based approach achieved 84% matching accuracy and calculated carbon footprints with only 13% error compared to traditional LCA methods, while being 216 times faster than manual processes. This shows how LLMs can efficiently process unstructured data like material lists and BIM data and connect them to LCA databases, essentially automating the data compilation process. The GDB framework by [PERSON] et al. (2024) [28] integrates \"synthetic data generated through language models\" into an LCA-KG to close data gaps. In their application, the LLM was used to generate plausible missing data for a 3D printing LCA use case. This hybrid approach improved the coverage of early-stage LCA models and highlighted relationships between engineering decisions and environmental impacts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **LCA** & **SCM** & **Methodology** & **Technology** & **Application Domain** \\ \hline [PERSON] et al. (2023) [22] & X & & Semantic graph & \multirow{2}{*}{GDB} & LCI \\  & & & for LCI & & management \\ \hline [PERSON] et al. (2024) [23] & X & X & KG-based & \multirow{2}{*}{GDB} & Carbon emission \\  & & & carbon flow analysis & & \\ \hline [PERSON] et al. (2025) [24] & X & X & KG with & \multirow{2}{*}{GDB} & Supply chain \\  & & & multicriteria decision & & reconfiguration \\ \hline [PERSON] et al. (2024) [25] & X & X & Automated LCA & \multirow{2}{*}{GDB} & Product LCA \\  & & & with GDB & & (electronics) \\ \hline [PERSON] et al. (2024) [26] & X & & LLM for BIM parsing and & \multirow{2}{*}{LLM} & Building LCA (architecture/engineering) \\  & & & LCA automation & & \\ \hline [PERSON] et al. (2025) [27] & X & & LLM for BIM parsing & \multirow{2}{*}{LLM} & Embodied carbon in \\  & & & and LCA automation & & construction \\ \hline [PERSON] et al. (2024) [28] & X & & KG-based LCA & \multirow{2}{*}{GDB, \multirow{2}{*}{LLM} & Product LCA \\  & & & with AI enrichment & & LLM & (3D printing case) \\ \hline [PERSON] \& [PERSON] (2023) [29] & X & X & NLP-driven & GDB, \multirow{2}{*}{LLM} & Supply chain carbon \\  & & & emissions graph & LLM & footprint tracking \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review overview: integration of GDBs and LLMs in SCM and/or LCAs. [PERSON] & [PERSON] (2023) [29] presented a KG (E-liability) for supply chain carbon accounting. Their approach uses AI to turn unstructured corporate reports and shipping records into a dynamic emissions graph. Their system uses GPT to automatically identify supply chain connections from text documents, creating and updating a comprehensive database for emissions tracking. The review reveals two dominant paradigms: (1) GDBs for supply chain emissions data structuring (e.g., [PERSON]'s LCI [22], [PERSON]'s CarbonKG [23]), and (2) LLMs automating unstructured data (e.g., [PERSON]'s BIM parsing [26], [PERSON]'s CECA [27]). Notably, [PERSON] et al. [28] and [PERSON] & [PERSON] [29] pioneered hybrid approaches, using LLMs to populate GDBs, but neither addresses dynamic route optimization. While these approaches address key challenges in LCA and supply chain modeling, none of them offer a unified, dynamic solution for multimodal transport emissions analysis.