A Generative AI-Driven Cloud Framework for Predictive Optimization in Smart Manufacturing Ecosystems

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dc.contributor.authorVaz, Sonia
dc.date.accessioned2026-05-05T10:51:11Z
dc.date.available2026-05-05T10:51:11Z
dc.date.issued2026
dc.description.abstractThe ecosystems of smart manufacturing produce huge quantities of heterogeneous data through sensors, machines, and manufacturing processes that provide the prospects of predictive optimization and present the challenges associated with data scarcity and uncertainty. Conventional predictive models tend to use very few historical failure data and deterministic forecasts and therefore become less effective in dynamic industrial processes. The paper suggests a cloud-based architecture based on the generative AI model that combines generative modelling, predictive analytics, and scenario-based optimization to improve decision-making in smart manufacturing. Generative AI is used to simulate more real operational conditions in the future, such as infrequent failure cases, whereas deep learning models predict the health of the machine, its production rate, and quality. These projections are converted into prescriptive operations with the help of a cloud-based stochastic optimization engine that considers the limits of operations and uncertainty. Empirical test on an industrial testbed shows that the framework proposed is able to drastically decrease downtime and unplanned failures and increase throughput and energy efficiency when compared to rule-based systems and predictive only systems. The findings shape how effective is the integration of generative intelligence and cloud optimization of manufacturing performance enhancement due to its robust and scalable nature. The experimental results accelerate the productivity of nearly 22 percent, reduce energy consumption by 18 percent and result in a 28 percent decrease in unplanned downtime when compared to conventional optimization approaches. Furthermore, the results indicate that the framework is robust and adaptable to dynamic workload fluctuations and does not suffer from the impact of concept drift; thus, it is suitable for scalable and adaptive smart manufacturing environments.
dc.identifier.citationRedouane, K., Veernapu, K., Konduru, S. C., Surendranath, N., Fikri, Y., & Vaz, S. (2026, March). A Generative AI-Driven Cloud Framework for Predictive Optimization in Smart Manufacturing Ecosystems. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.
dc.identifier.urihttp://rcca.ndl.gov.in/handle/123456789/558
dc.language.isoen
dc.publisherInnovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6)
dc.titleA Generative AI-Driven Cloud Framework for Predictive Optimization in Smart Manufacturing Ecosystems
dc.typeArticle
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