When critical equipment fails without notice, it adds a multiplier to the maintenance costs. But the damage doesn’t stop there. On the Operations side, production costs skyrocket, safety risks increase, emissions events occur, and — of arguably the greatest importance — order deliveries are delayed.

Predictive analytics are changing all that!

We know emergency maintenance is expensive, costing up to five times more than planned maintenance. But it’s the operational losses that really impact the business. Those go beyond revenue losses from unplanned downtime and extend to the relationship with the customer, and that’s where the real opportunity lies. 

Right now, predictive analytics are providing alerts weeks or months in advance of equipment failures. Leading companies are using that increase in notification time to change how they respond to downtime. By getting Maintenance and Operations working together, we can take the information from these earlier alerts and use it to quickly reschedule production. That’s all possible because with more notice, we now have choices for when we take the unit down for maintenance. 

This isn’t just some theoretical exercise. It’s actually being done today in low-density polyethylene (LDPE) production, where a critical piece of equipment (a hyper compressor) frequently fails, resulting in the losses described above. With predictive analytics, companies can literally gain weeks of advanced notification — and importantly, the technology can be implemented in just a few weeks.

Slaying the dragon takes teamwork. The smartest companies are already using predictive analytics to transform their business processes (like in this example), integrating scheduling workflows with asset performance management (APM) to help them respond to impending downtime in a business-optimal way. 

Learn more about the power of predictive analytics in my new white paper, Ramp up Reliability With Low-Touch Machine Learning for Hyper Compressor Monitoring.