Ineffective maintenance management costs U.S. companies more than $60 billion each year, according to research by ARC Advisory Group. In a new white paper InStep Software discusses how companies can use predictive asset analytics software to identify impending equipment failures days, weeks or months before they happen.
While traditional maintenance techniques are primarily reactive in nature, technological advances call for updated strategies that allow companies to make smarter maintenance decisions. Given the amount of data available in today’s digital age, predictive analytics should be a key component in every maintenance strategy, yet surveys continue to show that most maintenance is reactive. A proactive plan gives maintenance and operations personnel early warning of imminent problems so action can be taken in a timely and focused manner.
“In an environment where safety, reliability and productivity are vital, it is essential that companies understand the importance of implementing the most effective and comprehensive maintenance strategies,” said John Kalanik, president of InStep Software. “Predictive maintenance should be a prominent factor in every maintenance plan because of its significant return on investment and the potential to avoid catastrophic situations that may have otherwise gone unnoticed.”
The goal of a predictive maintenance approach is to provide early warning signs of an issue so that maintenance can be scheduled at the most convenient times, before unplanned and expensive failures occur. Added benefits of a predictive strategy include reduced maintenance costs, reduced unscheduled downtime, increased asset utilization and extended equipment life, among others.
“Twenty, even ten, years ago, companies had to manually analyze collected data to make decisions, and some businesses are still doing that today. But as more and more utilities implement smart grid technologies and as industrial equipment becomes increasingly intelligent, it makes more sense than ever to use predictive asset analytics software to receive the greatest return on the vast amount of available data,” Kalanik added.
In support of predictive maintenance, InStep Software offers online asset health and performance monitoring software. The software technology, called PRiSM, is a self-learning analytic application for monitoring the real-time health of critical assets. PRiSM uses artificial intelligence, pattern recognition and sophisticated data mining techniques to determine when a piece of equipment is performing poorly or is likely to fail. PRiSM also includes an alarm manager to provide near real-time updates of how well an asset is functioning in addition to an advanced application for identifying why an asset is not performing as expected.