Authors : Sohail Sayed; Nauman Sayed

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/mr3j43km

DOI : https://doi.org/10.38124/ijisrt/26sep297

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Large language models offer unprecedented analytical capability, but their knowledge is frozen at the last training date — rendering them unusable for organizations whose mission depends on emerging, timely information [1]. This paper argues that retrieval-augmented generation is the missing mechanism that converts LLMs from static reasoners into realtime supply chain disruption intelligence systems: retrieval supplies the current evidence, grounding supplies the facts, and agentic orchestration supplies the decision loop. We propose RAGENT-SC, a retrieval-augmented agentic framework coupling (i) a continuously updated multi-format knowledge layer (news streams, contracts, supplier records, operational KPIs, and a supply network knowledge graph), (ii) hybrid vector–graph retrieval with sublinear scalability, (iii) grounded generation with provenance metadata, (iv) agentic orchestration of monitoring, analysis, planning, and audit agents, and (v) a governance layer with hallucination verification and human-in-the-loop escalation.

Keywords : Retrieval-Augmented Generation, Large Language Models, Supply Chain Disruption Intelligence, Knowledge Graphs, Decision Support Systems, Real-Time Analytics, Agentic AI, Supply Chain Risk Management.

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