Revolutionizing Industrial Manufacturing with Big Data and Generative AI: A Path to Predictive Efficiency

Abstract

The integration of Big Data and Generative AI (GenAI) is transforming the industrial manufacturing landscape, enabling enhanced decision-making, predictive maintenance, and process optimization. This study focuses on the application of Generative Adversarial Networks (GANs) and Big Data analytics to streamline manufacturing operations. Leveraging vast datasets from real-time sensors and production logs, we developed a hybrid model combining GANs with Apache Hadoop to analyze production data and identify patterns for predictive maintenance. This model effectively reduces equipment downtime by forecasting potential failures with an accuracy
rate of 94%, a significant improvement over traditional methods. Additionally, we applied GANs to simulate manufacturing scenarios, offering insights into process optimization and defect reduction, which led to a 25% increase in overall production efficiency. The results indicate that integrating Big Data with GenAI in industrial settings enhances predictive capabilities, ensuring smoother operations and greater cost-effectiveness. The framework developed provides a scalable solution for industries seeking to leverage data-driven insights and AI-based innovations to remain competitive.