AI-Driven Predictive Maintenance in Indian Manufacturing: Enhancing Operational Efficiency

Abstract

This paper explores the application of Artificial Intelligence (AI) in predictive maintenance within the Indian manufacturing sector. Predictive maintenance leverages AI techniques like machine learning (ML) and deep learning (DL) to predict equipment failures, thus reducing downtime and operational costs. In India, where manufacturing plays a critical role in the economy, the adoption of AI-driven predictive maintenance is still in its early stages. The paper discusses various ML models, such as Decision Trees and Recurrent Neural Networks (RNN), used to predict machinery failures based on historical data and real-time monitoring. The research includes case studies from Indian manufacturing firms that have implemented AI-based predictive maintenance systems, showcasing improvements in machine uptime, maintenance scheduling, and cost-efficiency. The study also highlights the challenges of integrating AI into traditional manufacturing processes, such as data quality, infrastructure limitations, and resistance to change. Finally, the paper offers insights into how Indian manufacturers can scale these solutions to achieve long-term benefits and maintain global competitiveness.