Smart Audit System Empowered by LLM

• URL: http://arxiv.org/abs/2410.07677v1
• Date: Thu, 10 Oct 2024 07:36:15 GMT
• Title: Smart Audit System Empowered by LLM
• Authors: Xu Yao, Xiaoxu Wu, Xi Li, Huan Xu, Chenlei Li, Ping Huang, Si Li, Xiaoning Ma, Jiulong Shan,
• Abstract summary: 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; a manufacturing compliance copilot that enhances data processing, retrieval, and evaluation; and a Re-act framework commonality analysis agent that provides real-time, customized analysis. These enhancements elevate audit efficiency and effectiveness, with testing scenarios demonstrating an improvement of over 24%.
• Score: 25.2545519709246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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