For any industrial manufacturer that’s managing complex equipment and processes every day, maintenance is crucial for not only running the business efficiently and safely, but also for meeting or exceeding the business’ bottom line. With the onset of the Industrial Internet of Things (IIoT) and digitization in recent years, many industrial organizations have struggled to keep up with the amount of data being generated by their assets, and ultimately, how they can use this data to shift from condition-based maintenance practices to predictive maintenance strategies.
Predictive maintenance strategies can help determine the condition of equipment in order to predict when maintenance should be performed. This approach to maintenance can ultimately lead to cost savings over routine preventive maintenance, because maintenance is only performed when warranted.
For those in manufacturing, you know that when machinery breaks, downtime and costly repairs ensue. By implementing predictive maintenance strategies, you can maintain equipment before it breaks down—saving on downtime and maintenance costs. This enables optimal asset maintenance and further improves a plant’s throughput, efficiency, quality, and safety.
Smart predictive maintenance is a modern maintenance technique that leverages multiple technologies and maintenance approaches, including predictive maintenance. Smart predictive maintenance goes beyond traditional preventive and predictive maintenance in three ways:
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:
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.
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.
Once data can be reliably connected remotely and an asset has provided enough failure data, the failure thresholds can be optimized.
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.
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.