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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.16325 |
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| _version_ | 1866912970868523008 |
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| author | Galdino, Marcos Grahl, Johanna Hamann, Tobias Abdelrazeq, Anas Isenhardt, Ingrid |
| author_facet | Galdino, Marcos Grahl, Johanna Hamann, Tobias Abdelrazeq, Anas Isenhardt, Ingrid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16325 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systems Galdino, Marcos Grahl, Johanna Hamann, Tobias Abdelrazeq, Anas Isenhardt, Ingrid Software Engineering Artificial Intelligence 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. |
| title | A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systems |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16325 |