  In this study, we introduce Generative Manufacturing Systems (GMS) as a novel
approach to effectively manage and coordinate autonomous manufacturing assets,
thereby enhancing their responsiveness and flexibility to address a wide array
of production objectives and human preferences. Deviating from traditional
explicit modeling, GMS employs generative AI, including diffusion models and
ChatGPT, for implicit learning from envisioned futures, marking a shift from a
model-optimum to a training-sampling decision-making. Through the integration
of generative AI, GMS enables complex decision-making through interactive
dialogue with humans, allowing manufacturing assets to generate multiple
high-quality global decisions that can be iteratively refined based on human
feedback. Empirical findings showcase GMS's substantial improvement in system
resilience and responsiveness to uncertainties, with decision times reduced
from seconds to milliseconds. The study underscores the inherent creativity and
diversity in the generated solutions, facilitating human-centric
decision-making through seamless and continuous human-machine interactions.
