The Generative Artificial Intelligence large language product design multi-model framework for manufacturing operations

Leung, EKH ORCID: 0000-0003-2058-0287, Balan, N, Lee, CKH and Xie, S ORCID: 0000-0003-1134-5285 (2025) The Generative Artificial Intelligence large language product design multi-model framework for manufacturing operations Journal of the Operational Research Society, ahead- (ahead-). pp. 1-27. ISSN 0160-5682, 1476-9360

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Abstract

Product conceptual design is paramount in targeting specific customer segments, elevating brand equity, and ultimately boosting revenue sources. In the era of digitalisation, Generative Artificial Intelligence (GAI) is emerging to advance this process. This study introduces a pioneering framework, namely Generative Artificial Intelligence Product Designer (GAI-PD), that harnesses the power of GAI to revolutionise the design generation process, fostering efficiency, creativity, and responsible Artificial Intelligence (AI) adoption. GAI-PD is a novel multi-model framework integrating Generative Adversarial Network (GAN) and Large Language Model (LLM) that empowers designers to leverage GAN’s capabilities for realistic visual output and LLM’s natural language interaction. A comprehensive case example demonstrates the framework’s effectiveness, achieving 90.23% accuracy in generating valid design concepts and showing that hyperparameter tuning can significantly enhance image quality. From a managerial perspective, the findings indicate that adopting the GAI-PD framework can substantially reduce product development cycle times and accelerate time-to-market, offering a distinct competitive advantage. The study also explores avenues for future research, such as using advanced GAI models for high-resolution design generation and automated design evaluation, ultimately paving the way for enhanced design processes in diverse domains and promoting responsible AI adoption.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences, 49 Mathematical Sciences, 35 Commerce, Management, Tourism and Services, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering, 9 Industry, Innovation and Infrastructure
Divisions:	Faculty of Humanities & Social Sciences Faculty of Humanities & Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	01 Oct 2025 15:12
Last Modified:	23 May 2026 10:36
DOI:	10.1080/01605682.2025.2570407
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3194690
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