Harnessing Large Language Models for Cognitive Assistants in Factories
S. Kernan Freire (TU Delft - Internet of Things)
Mina Foosherian (University of Bremen)
C.W. Wang (TU Delft - Human-Centred Artificial Intelligence)
Evangelos Niforatos (TU Delft - Internet of Things)
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Abstract
As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, real-time data integration, security, privacy, and ethical concerns, LLM-powered CAs can become valuable assets in manufacturing settings and other industries.