1. Improving Equipment Life
While a mix of quantitative and qualitative metrics have been traditionally used to forecast problems to reduce downtime, predictive maintenance can allow companies to successfully maximize equipment life. Real-time equipment and production-line data generated by IIoT sensors can provide companies with a comprehensive view of equipment health to effectively prioritize and schedule repairs, as well as optimize them for better performance. By predicting potential failures before they occur, teams can address any issue before it reaches the point of no return and can keep machinery and devices running at peak efficiency for longer timeframes.
2. Enhancing Quality of Production
According to a recent study, the industrial sector highlighted how predictive maintenance implementations yielded a positive ROI in 83% of the cases and that 45% of those surveyed reported amortization in less than a year. This is due to the ability of predictive maintenance systems to detect errors in real-time. When issues can be dealt with proactively, factors that affect overall productivity such as downtime, costs, and safety issues will no longer hamper businesses as much as before. Maintenance teams can achieve peak efficiency by preemptively scheduling repairs, instead of being reallocated to malfunctioning machinery. With IoT systems providing constant data and changes to processes on a regular and timely basis, problems can be dealt with while at the same time improving overall production quality.
3. Reducing Downtime and Maintenance Costs
In this regard, the evolving state of IoT-based predictive maintenance will allow operators to be more proactive when it comes to identifying equipment faults. For equipment-driven industries, in particular, historical data gathered from a variety of IoT devices and sensors can provide key metrics on machine health, usage, and risk areas. In the absence of complete historical data, predictive AI-powered models can help create frameworks that fill in the gaps by simulating real-world use cases. When action is planned around the data gathered, it can cut down on costs greatly while also heavily reducing downtime.