A Retrieval-Augmented Generation enhanced expert system framework for supply chain risk management: a healthcare industry application

Information from the abstract

Traditional expert systems struggle to manage high-stakes healthcare supply chain risks, such as drug shortages and counterfeiting, since knowledge bases require manual updating. This paper proposes a framework combining Retrieval-Augmented Generation (RAG), fuzzy inference, and Dempster-Shafer evidential reasoning to extract and structure risk knowledge from unstructured documents. A UK and China hospital case study identifies drug shortage and counterfeiting as the highest-priority risks. A small pilot of the RAG component alone shows grounded extraction outperforming a no-retrieval baseline, with no hallucination flagged. To our knowledge, no earlier framework uses RAG for upstream knowledge acquisition in supply chain risk management.

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Related topics: Supply Chain Resilience and Risk Management · Supply Chain and Inventory Management · Impact of AI and Big Data on Business and Society