  Recent advances in natural language processing enable more intelligent ways
to support knowledge sharing in factories. In manufacturing, operating
production lines has become increasingly knowledge-intensive, putting strain on
a factory's capacity to train and support new operators. This paper introduces
a Large Language Model (LLM)-based system designed to retrieve information from
the extensive knowledge contained in factory documentation and knowledge shared
by expert operators. The system aims to efficiently answer queries from
operators and facilitate the sharing of new knowledge. We conducted a user
study at a factory to assess its potential impact and adoption, eliciting
several perceived benefits, namely, 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 commercial and open-sourced LLMs for this
system. The current state-of-the-art model, GPT-4, consistently outperformed
its counterparts, with open-source models trailing closely, presenting an
attractive option given their data privacy and customization benefits. In
summary, this work offers preliminary insights and a system design for
factories considering using LLM tools for knowledge management.
