LLM-Powered Cognitive Assistants for Knowledge Sharing among Factory Operators
Samuel Kernan Freire (TU Delft - Internet of Things)
A. Bozzon – Promotor (TU Delft - Sustainable Design Engineering)
Evangelos Niforatos – Copromotor (TU Delft - Internet of Things)
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Abstract
Modern factories showlittle resemblance to the assembly lines froma century ago. Nowadays, a single human might be responsible for setting up, operating, and maintaining a complex chain of machines. This shift has made the work of factory operators more cognitively demanding, requiring constant monitoring and problem-solving. Key to tackling these challenges is the effective collaboration of human operators in exchanging ideas and knowledge, from high-level problem-solving strategies to solutions for emerging issues. Yet, the heightened productivity requirements and small shifts mean fewer opportunities to share this knowledge face-to-face and reporting practices are deprioritized. Thus, the manufacturing industry faces a knowledge management crisis.
This dissertation investigates the integration of conversational AI assistants into manufacturing settings to facilitate knowledge sharing among factory operators. Capitalizing on recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), this research investigates the designing and evaluation of conversational AI tools that efficiently capture and share human knowledge on the factory floor while addressing operator needs and concerns. The introduction of conversational AI assistants for knowledge sharing—referred to as cognitive assistants (CA) in this work—in factory environments promises significant benefits but comes with numerous challenges....