Let’s say equipment maintenance takes up an average of 30 minutes each day. That’s a valuable slot wasted by scheduled maintenance tasks that could be better predicted and planned. Utilise that time more effectively and you can optimise your plant and achieve significantly better results.
Here’s a closer look at how predictive analytics can improve asset management or equipment maintenance program and what that might mean for your plant.
The Impact of Predictive Analytics
Predictive analytics uses AI to continuously monitor and learn from asset behaviour in real-time. It provides alerts when the operation differs from the historical norm, giving early warning detection of equipment problems.
Predictive analytics is a more effective way of dealing with your operations. It involves using powerful tools to identify issues that might otherwise be missed.
Consider how AI monitors equipment compared to a typical human worker. This worker might be an incredibly asset to your team, but they do still have a key flaw - they’re human.
There are some issues a human just wouldn’t notice. This is in part because the worker has an extensive list of responsibilities, and they aren’t typically given the time they need to look at something closely enough to spot a problem.
An AI tool, on the other hand, can learn about what is normal for your plant and assets and can be programmed for certain thresholds and parameters. As soon as something starts to act abnormally or outside of those boundaries, an alert is sent to the relevant member of staff. Issues are dealt with quickly and effectively, allowing production to continue unaffected.
This kind of predictive maintenance isn’t just looking at the past 24 hours when keeping an eye on machinery and lines. It can learn from up to the last five years to better understand what’s going on. This advanced analytical approach empowers your staff, letting them spot red flags that indicate something needs their attention.
Early warnings significantly reduce unplanned downtime and the accompanying loss of production that’s so frustrating for everyone involved. Plus, real-time data of this kind allows you to make other important decisions that will optimise production.
For example, machinery can be moved to where it can add the absolute most value, or production might be moved to a different line to reduce risk or increase throughput or quality.
Applying predictions about future ensures you catch a lot of the problems that would normally slip through the cracks and escalate. The ARC Advisory Group found that with typical planned and preventive maintenance, you might only be identifying around 18% of “Failure Patterns” problems.
To catch the other 82%, you need predictive technology providing early warning, giving you peace of mind and making sure production targets are met.
It isn’t just production that sees the benefits of predictive analytics. Customer confidence is vital, although it can be harder to track than other metrics.
Planned and preventive routine maintenance alone might frustrate customers who don’t understand why their deliveries aren’t ready on time or product quality is variable. Your customers want to know that targets will be met, and issues are dealt with. Predictive analytics can help with this and lead to delighted customers.
We’ve helped other organisations to utilise predictive analytics. For example, one leading chemical company wanted to cover their entire value chain with multiple work streams, including predictive maintenance and augmented reality.
Our team suggested solutions for both and AVEVA was chosen as their predictive asset analytics partner.
With the right software, they could take advantage of online predictive monitoring for critical equipment assets. This allowed them to reduce unscheduled downtime and improve asset reliability, availability, and performance.
As we’ve already touched on, predictive analysis and condition management isn’t a ‘dig’ at current staff. We aren't suggesting they’re doing a bad job, only that their task is almost impossible without the right tools.
Predictive analytics helps to equip your staff with the right tools to do their job; by giving them early warning information about assets that need maintenance; helping to organise spare parts and consumables in suitable time; assisting in resource planning; providing better information “in-context” empowering them to make better decisions.
Predictive analytics uses historical and real-time data from all plant production assets, providing the right information to the right people at the right time, driving appropriate actions. This improves overall asset performance and effectively manages corresponding engineering and maintenance activities.
Using AI for early equipment failure detection increases asset availability, reduces costs, and avoids unnecessary maintenance and downtime. It can be part of a customer’s digital transformation, driving a wider shift from reactive to proactive and predictive analytical models for everything from maintenance to operations.
Predictive analytics software ties vital information with required actions for other critical plant systems. This includes integration with all Control/PLC/SCADA and Safety solutions, where access to key data is supported through the operator console.
Down with Maintenance Downtime!
Discover More in Our Latest Webinar
For all things predictive analytics, make sure to view our latest webinar. During the sessions, we'll be joined by AVEVA's very own Justin Thomas, an expert in Asset Performance Management, to discover how AVEVA software can bring all your rich operational and asset data into one single platform, leading to better operational efficiency, asset reliability and productivity.
Ready to Make the Switch to Predictive Analytics?
We can write hundreds of blogs on predictive analytics and other effective methodologies, but to really see the benefits we recommend having a go. Only by dipping your toes can you fully appreciate the difference something like condition management can have on your plant.
To help, we’ve got a free trial that allows you to explore how part of our predictive analytics family, “Non-supervised AI” condition management can work for your plant and how it will optimise production. To get started, click the link below.