A generative artificial intelligence-driven smart factory using generative adversarial networks

This study proposes a generative artificial intelligence-driven smart factory framework that integrates advanced digital technologies with data-driven decision support across production and supply chain operations. The smart factory is structured around six core dimensions, which are smart assets, smart working, smart manufacturing, smart supply chain, smart products, and smart solution ecosystems. A smart supply chain configuration consisting of a smart manufacturer, smart retail management, and interconnected fourth-party logistics services is developed to enable real-time coordination and optimization. Generative artificial intelligence is employed to synthesize realistic demand patterns and support adaptive decision-making. The proposed model dynamically adjusts key manufacturing and logistics parameters in response to changing market conditions to enhance operational efficiency and financial performance, through which a global optimum minimum cost of $18,226,000.00 is found. The results demonstrate that the proposed framework is effective in capturing lumpy intermittent demand behaviours and supporting smart production planning.