4 tips for organizations upping their predictive maintenance game

Getting ahead of an equipment failure can be the difference between millions of dollars saved, and disastrous losses. Predictive maintenance, which uses data and analytics to determine the health of an asset to predict the best time to perform maintenance, is now easier than ever if implemented with a smart, deliberate approach.

Predictive maintenance isn’t new — for decades, we have assessed the integrity of machines and systems through measurements taken during periodic inspections. What has changed is that with increased connectivity in our devices and more accurate sensors, applying advanced analytics to larger sets of more accurate data vastly improves predictions about the interventions that will be the most effective.

At the recent IoT Slam, which SAS had the honor of hosting, Jennifer Robinson, Director of Local Government Solutions for SAS, moderated a panel that included representatives from Lockheed Martin and Georgia-Pacific, as well as IoT and analytics experts from SAS.

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