用大模型打造智能质检系统，效率提升超24%
Smart Audit System Empowered by LLM
- 用大模型动态评估风险，优化审计资源分配
- 自动生成知识库，提升数据处理与合规判断能力
- 实时分析供应商问题，助力工程师改进生产
制造质量审计在大规模生产环境中对保障产品标准至关重要。传统审计依赖人工，耗时费力，难以在复杂的全球供应链中保持透明性、可追溯性和持续改进。为此，我们提出一种由大语言模型（LLM）驱动的智能审计系统。该系统包含三项创新：动态风险评估模型，简化审计流程并优化资源配置；制造合规助手，增强数据处理、检索与评估能力，构建可自我演进的制造知识库；以及基于Re-act框架的共性分析代理，提供实时定制化分析，帮助工程师获取供应商改进建议。测试结果显示，系统整体效率提升超过24%。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。