Generative AI in predictive Manufacturing

The integration of Generative Artificial Intelligence (Generative AI) into predictive manufacturing marks a transformative shift in how data-driven insights are used to optimize industrial processes, particularly within the realms of automotive and smart manufacturing systems. By harnessing models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based architectures, manufacturers are now able to generate synthetic datasets, simulate failure modes, and enable real-time predictive diagnostics. This technological evolution enhances the efficiency of digital twins, facilitates predictive maintenance, and improves quality control across complex, automated production lines. Within the automotive sector, GenAI is reshaping everything from component design to supply chain resilience through proactive insights and self-learning systems. However, despite these advancements, challenges persist anging from high computational demands and integration barriers to concerns around data quality, security, and workforce readiness. This article presents a comprehensive review of recent literature and practical applications, evaluates the benefits and limitations of GenAI in predictive manufacturing, and explores future directions, including sustainable production, human-AI collaboration, and the realization of Industry 5.0 objectives.