The Future of Manufacturing with AI and Data Analytics

Neel Shah, Sneh Shah, Janvi Bhanushali, Nirav Bhatt, Nikita Bhatt, Hiren Mewada

This chapter explores the potential of applying AI and data analytics to transform manufacturing. It provides an overview of new research trends in smart manufacturing, including the use of IoT, big data, and advanced AI technologies like machine learning and digital twins. The conceptual background of relevant AI approaches is discussed, including deep learning, reinforcement learning, unsupervised learning, and state-of-the-art models. A key focus is examining the role of AI in predictive maintenance through data-driven techniques for remaining useful life estimation, anomaly detection, prognostics, and optimizing maintenance strategies. Challenges and limitations such as noisy data, imbalanced datasets, and high computational requirements are addressed. The opportunities enabled by AI in manufacturing are highlighted, spanning synthetic data generation, real-time prediction, and enhancing asset utilization. The chapter concludes that transformative gains in productivity, sustainability, and resilience will arise from thoughtfully leveraging AI and data to inform decision-making in industrial settings. Adoption remains in the early stages, and realizing the full potential will require interdisciplinary collaboration and purposeful innovation.