Improving Asset Performance Management With Data Analytics

Industries that are traditionally asset-heavy, such as utilities, have an extra challenge facing them: performing successful asset performance management on an ongoing basis to ensure those assets are generating revenue, not costing money due to underperformance or an unexpected stoppage. While much is being made of data analytics these days, perhaps one of its most powerful capabilities is its ability to keep your current assets performing at the most productive level possible.

Maximizing Asset Returns Through Data Analytics

Assets owned by a business are not passive by their nature. It may appear that certain assets, such as buildings and installed equipment, exist to serve the needs of the internal operations, but a deeper examination reveals the interconnectedness of all your assets. Following this line of thinking, you soon realize that no asset stands independent of the rest of your operations. Downtime for one piece of equipment often creates a negative ripple effect across the entire operation, whether it is immediately perceived or not.

These facts point to the need for proper and successful asset performance management at every level of the operation. Fortunately, the emergence of the industrial internet of things (IIoT) appears at the most relevant time for asset-heavy and asset-dependent enterprises. Combining IIoT with data analytics can empower your business in ways you did not think existed. With the ability to gain a full and transparent overview of your asset operations, you will also begin to see and understand how all of your assets are interrelated and interdependent. This empowers you to better anticipate interferences and interruptions, which in turn provides you with the ability to intercept and avoid unexpected downtimes and equipment crashes.

Forecasting And Managing Asset Performance Needs

We all know about schedule equipment and asset maintenance: managing preventive maintenance initially appears to be a fairly automated process that can be entered into a calendar, put on a count cycle or based upon timed usage. Interestingly though, this seemingly important process only addresses about 18% of equipment failures, while an astounding 82% of failures are related to random events, which on the surface have no apparent pattern.

So while there is value in performing preventive maintenance, it becomes crystal clear that this process does not anticipate even a quarter of potential asset failures that can haunt a business, keeping it operating below optimal levels of performance. Clearly, there is a demand to cover the majority of asset failures once more data analytics can step to the forefront to predict and prevent unexpected asset failures.

Employing Predictive Maintenance

There is a term for this important process: predictive maintenance. The idea that this can potentially capture and prevent up to 82% of typical asset failures makes it a process whose time has clearly arrived.

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