A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systems
This addresses the problem of integrating AI assistants into manufacturing quality systems for industry practitioners, but it is incremental as it focuses on architectural design rather than novel AI methods.
The study tackled the lack of a human-centered software architecture for integrating Large Language Models-Cognitive Assistants into Quality Management Systems in manufacturing by designing a component-based architecture, which was validated through expert focus groups to ensure flexibility, scalability, modularity, and work augmentation.
Large Language Models-Cognitive Assistants (LLM-CAs) can enhance Quality Management Systems (QMS) in manufacturing, fostering continuous process improvement and knowledge management. However, there is no human-centred software architecture focused on QMS that enables the integration of LLM-CAs into manufacturing in the current literature. This study addresses this gap by designing a component-based architecture considering requirement analysis and software development process. Validation was conducted via iterative expert focus groups. The proposed architecture ensures flexibility, scalability, modularity, and work augmentation within QMS. Moreover, it paves the way for its operationalization with industrial partners, showcasing its potential for advancing manufacturing processes.