Reducing Downtime in Boiler Manufacturing: A Predictive Maintenance Approach Using AI-Integrated CRM Systems

In today's highly competitive manufacturing environment, unplanned downtime represents a significant operational challenge, especially in heavy industries such as boiler manufacturing. This study proposes AI-integrated Customer Relationship Management (CRM) system forpredictive maintenance, aiming to reduce equipment downtime and enhance overall productivity. By leveraging machine learning algorithms, historical maintenance data, and real-time sensor inputs, the integrated system can predict potential equipment failures before they occur. The study demonstrates that predictive insights, when aligned with customer-centric service data through a CRM platform, can lead to proactive maintenance scheduling, optimal resource allocation, and enhanced customer satisfaction. Experimental evaluation in a mid-sized boiler manufacturing unit shows a 35% reduction in unplanned downtime and a 25% improvement in maintenance response time.