Authored by John Clemons, MESA Marketing Committee Chair, based on an interview with Ananth Seshan, MESA Model Sub-Committee Member
I’ve mentioned before that one of the chapters of the new MESA Smart Manufacturing Model covers the production asset lifecycle and how we can make the production asset lifecycle smarter. Let’s look at a few more of the smart ideas included on this topic in the new MESA Smart Manufacturing model.
Monitoring an asset’s health, avoiding unexpected asset failures, and analyzing the root causes of failures, all go a long way toward getting an asset to perform effectively; however, there’s more to it than just that. Ultimately, you want the asset to always run optimally. To ensure the asset runs optimally all the time requires a lot more than just making sure it does fail unexpectedly.
The best way to optimize the performance of an asset is through a digital twin. A digital twin is a digital representation of a physical asset. There’s lots of variables that affect the optimal performance of an asset -processes, products, workloads, ambient conditions, etc. all affect the optimal performance.
Using a digital twin allows you to analyze the above factors and determine their affect on the asset. The digital twin allows you to try multiple strategies to optimize the performance. A digital twin in real-time allows you to dynamically optimize the performance of the asset, while it’s in operation, in the middle of these dynamic conditions.
It’s been difficult to get accurate data on the actual usage of an asset. Many times, the data is supposed to be manually entered into an EAM or CMMS type of system, which allows for human error.
With Smart Manufacturing and the IIoT, accurate data on asset usage can be recorded directly from the controllers and/or PLCs of the assets using interoperable, open-connectivity standards such as OPC. This data can then be updated directly into the EAM or CMMS system. This means you now have accurate asset usage information in your EAM or CMMS which is right where you need it to support your preventative and predictive RCM regimens.
Under Maintenance and Over Maintenance
Under maintenance occurs when an asset is used more than was planned. It can be very costly because it means that the asset is not getting the maintenance it requires which translate to lower asset performance, more product quality issues, and more unplanned downtime.
Over maintenance occurs when an asset is used less than was planned. It can also be costly because it means you’re spending a lot of money on maintenance that’s not needed.
Getting accurate information on asset usage means that traditional time-based preventative maintenance regimens can be completely replaced with usage-based preventative maintenance.
Smart Manufacturing and the IIoT support true condition-based maintenance.
With Smart Manufacturing and the IIoT you can now monitor the conditions of the asset, in real-time, and decide to perform maintenance on the asset only when the conditions warrant.
This is usually the best approach by far because it pretty much eliminates both over maintenance and under maintenance and allows the right maintenance to be applied at the right time to the right asset, in real-time based on the actual conditions of the asset.
This is just a few of the point in the chapter that covers the production asset lifecycle and how we can make the production asset lifecycle smarter.
Stay tuned for more from the new MESA Start Manufacturing Model.