Any malfunctioning of critical assets could adversely affect plant or business operations. In other words, the quality of the asset management can make or break an organisation. The traditional time-based maintenance ensures reliability, but the costs are significant. It is a balancing act between asset over maintenance or oversight failure. But what if beyond monitoring you could also predict failure based on usage and wear?

Predictive algorithms typically apply machine learning to condition monitoring data to predict failure. They prescribe actions based on predictive scoring, identifies factors that influence asset health, and delivers a detailed comparison of historical factors affecting the asset performance. Aligning maintenance to the demands of equipment optimizes spend and relevance. Work planning is optimized, creating savings beyond core maintenance and mitigating operational risks to workers.

Effective predictive maintenance harnesses the convergence of data from instrumentation and IoT with advanced analytics and AI technologies through digitized systems.

According to an A. T. Kearney survey, where 558 companies that used computerized maintenance management systems, showed an average 28.3 percent increase in the productivity of maintenance, 20.1 percent reduction in equipment downtime,19.4 percent savings in the cost of materials, 17.8 percent decrease in inventory maintenance and repair.

So, are you in a reactive mode? If you prefer to get proactive, join The Economic Times Live Webinar, APM with Predictive Maintenance, powered by IBM, how to shift the gears up to a predictive asset maintenance road-map!