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Author

Nauman Sayed

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3 papers indexed here

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Open access Sep 2026

Knowledge Graph-Augmented Large Language Models for Explainable Supply Chain Risk Intelligence

The quality of large language model responses is affected by the inherent opaqueness of their architectures, weakening trustworthiness and limiting their applicability in domains where every recommendation must be justifiable [1]. In supply chain risk management, this is not a nicety but a requirement: predictive analy...

Sohail Sayed, Nauman Sayed · 0 citations
#large language models Open access Sep 2026

Retrieval-Augmented Large Language Models for Real-Time Supply Chain Disruption Intelligence and Decision Support

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 LLM...

Sohail Sayed, Nauman Sayed · 0 citations
#graph neural networks Open access Aug 2026

Multi-Agent Reinforcement Learning for Dynamic Inventory Rebalancing and Last-Mile Fulfillment Under Supply Chain Disruptions

Experiments show that prediction must be coupled with autonomous optimization to deliver practical resilience, and show that the learned policy sustains service levels, shortens recovery time, and reduces total disruption cost relative to base-stock and single-agent baselines.

Sohail Sayed, Nauman Sayed · 0 citations

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