Five steps to reach smart predictive maintenance
Although technology can transform your maintenance program, best-in-class maintenance programs aren’t generally built in a day. Here’s how you can enable smart predictive maintenance by following the steps outlined below:
Step 1: Start small with a pilot
A pilot should generally take about three to four weeks on one or two critical assets. This initial effort will include sensor implementation and data streaming connections, as well as initial performance visualization dashboards.
Step 2: Asset health monitoring
It takes time to collect performance data, so patience is key. You’ll want this information, as well as any asset failure data in order to generate better predictions.
Step 3: Optimize failure thresholds
Once data can be reliably connected remotely and an asset has provided enough failure data, the failure thresholds can be optimized.
Step 4: Leverage data science
Then, a data scientist can create predictive models, along with machine learning technology to update algorithms—increasing predictive capabilities with each failure until unplanned downtime can be avoided.
Step 5: Reaching smart predictive maintenance
For those with long-term vision, achieving steps 1-4 can lead you to smart predictive maintenance, which can help your business maintain a competitive edge.
No matter where you are on your maintenance journey, smart predictive maintenance can accelerate your digital transformation. Want to learn more? Our partners at Deloitte Digital can help. Read more from Chris Coleman and Ryan Manes.