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arXiv · 2410.07677

Smart Audit System Empowered by LLM

Xu Yao·Xiaoxu Wu·Xi Li·Huan Xu·Chenlei Li·Ping Huang·Si Li·Xiaoning Ma·Jiulong Shan
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Abstract

Manufacturing quality audits are pivotal for ensuring high product standards in mass production environments. Traditional auditing processes, however, are labor-intensive and reliant on human expertise, posing challenges in maintaining transparency, accountability, and continuous improvement across complex global supply chains. To address these challenges, we propose a smart audit system empowered by large language models (LLMs). Our approach introduces three innovations: a dynamic risk assessment model that streamlines audit procedures and optimizes resource allocation; a manufacturing compliance copilot that enhances data processing, retrieval, and evaluation for a self-evolving manufacturing knowledge base; and a Re-act framework commonality analysis agent that provides real-time, customized analysis to empower engineers with insights for supplier improvement. These enhancements elevate audit efficiency and effectiveness, with testing scenarios demonstrating an improvement of over 24%.

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Xu Yao, Xiaoxu Wu, Xi Li, Huan Xu, Chenlei Li, Ping Huang, Si Li, Xiaoning Ma, Jiulong Shan. 2024-10-10. Smart Audit System Empowered by LLM. https://arxiv.org/abs/2410.07677

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