Supply chain mapping through retrieval-augmented generation: applications to the electronics industry
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TJOR_A_2608868_O.pdf
Size
1.69 MB
Format
Adobe PDF
Checksum (MD5)
6a057a6ff5c5f28cde90e3f62f380488
Author(s)
Jackson, Ilya
Saénz, Maria Jesus
Ivanov, Dmitry
Ma, Benedict Jun
Date Issued
December 19, 2025
Journal
Journal of the Operational Research Society
Publisher
Taylor & Francis
Citation
Jackson, I., Jesús Saénz, M., Ivanov, D., & Ma, B. J. (2026). Supply chain mapping through retrieval-augmented generation: applications to the electronics industry. Journal of the Operational Research Society, 1–21.
Version
Final published version
Abstract
This paper presents a novel methodology for automated multi-tier supply chain mapping, leveraging Retrieval-Augmented Generation (RAG) and network science techniques. We developed an RAG-based approach that extracts supplier-customer relationships from unstructured public data sources, including SEC 10-K filings and earnings calls. The extracted entities are structured into a directed supply chain graph and analysed using network science metrics such as centrality, modularity, and path length. The case study focuses on three of the largest contract manufacturers in the electronics industry: Hon Hai Precision Industry (Foxconn), Flex Ltd., and Jabil Inc. Our findings demonstrate that Generative AI (GAI), specifically LLMs enhanced with RAG, can construct scalable and comprehensive supply chain graphs. The proof of concept is successful, as evidenced by the construction of a directed supply chain graph encompassing 4,644 nodes and 8,341 edges, covering three of the largest contract manufacturers in the electronics industry.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/01605682.2025.2608868
