Managing knowledge efficiently is crucial for organizational success. In manufacturing, operating factories has become increasing knowledge-intensive putting strain on the factory's capacity to train and support new operators. In this paper, we introduce a Large Language Model (LLM)-based system designed to use the extensive knowledge contained in factory documentation. The system aims to efficiently answer queries from operators and facilitate the sharing of new knowledge. To assess its effectiveness, we conducted an evaluation in a factory setting. The results of this evaluation demonstrated the system's benefits; namely, in enabling quicker information retrieval and more efficient resolution of issues. However, the study also highlighted a preference for learning from a human expert when such an option is available. Furthermore, we benchmarked several closed and open-sourced LLMs for this system. GPT-4 consistently outperformed its counterparts, with open-source models like StableBeluga2 trailing closely, presenting an attractive option given its data privacy and customization benefits. Overall, this work offers preliminary insights for factories considering using LLM-tools for knowledge management.

翻译：高效管理知识对组织成功至关重要。在制造业中，工厂运营日益知识密集化，给工厂培训和支援新操作员的能力带来了压力。本文提出一种基于大语言模型（LLM）的系统，旨在利用工厂文档中包含的广泛知识。该系统能够高效回答操作员的问题并促进新知识的共享。为评估其有效性，我们在工厂环境中进行了评估。评估结果表明，该系统在加速信息检索和提升问题解决效率方面具有显著优势。然而，研究也显示，当存在人类专家指导时，操作员更倾向于向人类学习。此外，我们针对该系统对若干闭源和开源LLM进行了基准测试。GPT-4始终优于其他模型，而StableBeluga2等开源模型紧随其后，凭借其数据隐私和可定制化优势成为具有吸引力的选择。总体而言，本研究为考虑使用LLM工具进行知识管理的工厂提供了初步见解。