“ If all maintenance does is make downtime organized and predictable, it can make a huge difference. ”
- Stuart Fergusson, Senior Manager of Sales Engineering, Fiix
There is one thing that has always stood in the way of predictive maintenance becoming routine: Data maturity.
Data maturity is a fancy way of saying your operation has three things: Lots of accurate data, the resources to make decisions from that data, and the processes to put that plan into action.
60% of manufacturing companies in a 2019 survey said they plan to use predictive maintenance, which is an increase from 49 percent in 2017.1
The first and last parts of that equation aren’t usually the problem. Between maintenance logs, inventory records, and machine sensors, there’s no shortage of stuff to analyze. And most teams have more than enough skill to execute a maintenance plan. The missing link is the ability to find insights in the haystack of numbers.
James Kovacevic talking about the evolution of predictive maintenance.
That’s no surprise. It’s a huge task to sort the good data from the bad, find patterns, and connect what has happened to what should happen. But more accessible technology is enabling the industry to break through this data maturity wall.
Artificial intelligence tools can now be baked into the software you use every day, like a CMMS. These AI tools operate in the background, sprinting through the data, and delivering crystal clear directions.
Not only does this help teams catch failure early, order the right number of parts, or pinpoint their most important work orders, it also allows them to do something about it.
There’s no quick fix for the data maturity problem. But new AI tools have turned the issue from a trek up Mount Everest to a hike up a steep hill. What once looked impossible is now very much within reach.