Manufacturing in the Era of Generative AI: The GAMI (Generative AI Manufacturing Intelligence) Framework and Manufacturing Intelligence Index Model

Generative Artificial Intelligence (GAI) is becoming a significant force driving manufacturing transformation, following automation, digitalization, and the Industrial Internet. Unlike traditional AI, which primarily focuses on prediction, recognition, and classification, GAI possesses capabilities in content generation, knowledge creation, solution design, and autonomous collaborative decision-making, enabling manufacturing systems to gradually evolve from "automated manufacturing" to "intelligent creative manufacturing." However, theoretical research on the integration of GAI with manufacturing is still in its exploratory stage, lacking a systematic theoretical framework and evaluation model for manufacturing intelligence. To address this, this paper proposes a Generative AI Manufacturing Intelligence (GAMI) theory, constructing a five-layer manufacturing intelligence architecture consisting of a data layer, knowledge layer, generation layer, execution layer, and feedback layer, and establishing a Manufacturing Intelligence Index (MII) model. This model posits that the value of a manufacturing system stems from the combined effects of its generative capabilities, knowledge fusion capabilities, execution collaboration capabilities, and continuous learning capabilities. Research results demonstrate that GAI not only improves product design efficiency and production decision-making quality but also promotes the formation of continuous self-optimization capabilities in manufacturing systems. This paper provides a new theoretical foundation and research direction for the intelligent transformation of manufacturing in the era of GAI.