However, it's one thing to want a predictive maintenance model, and quite another thing to actually implement it in a manufacturing environment. At times it can seem overwhelming. We have so much data to draw on, and between the rise of Big Data and the Internet of Things (IoT), it can be overwhelming to consider how to achieve true predictive maintenance, above and beyond the preventive maintenance we widely practice.
With that, let's look at four key things we can do as we strive to implement a predictive maintenance approach across our organization, and at the same timein the spirit of our previous messageconsider how these ideas correlate to overall sustainability performance.
1. There is no turnkey solution you can buy
When we look at a lot of other enterprise software solutions, be it Enterprise Quality Management Software (EQMS) or Manufacturing Operations Management (MOM), we have a lot of existing best practices and very defined and widely adopted solutions we can implement and apply to our existing processes. Sure, we often need to make a tweak here, a customization there, but all told, a lot of these solutions speak to long-standing and widely adopted management standards and best practices.
It's not so much the case for predictive maintenance. At this point we can't seek out a vendor, implement a solution, and expect that voila somehow we'll have an effective predictive maintenance model across our enterprise. Every manufacturing organization is so unique from the specifics of individual fixed assets to the complexities of machine-to-machine interactions that there's no template we can simply apply and say, here, this is predictive maintenance.'
That said, it is a very achievable journey to get there. At the core of many such systems we see today is Enterprise Asset Management (EAM) software that serves as the main enterprise application to manage maintenance activities. Then add-on applications such as those provided by companies like Meridium and Ivaraare used to develop the analytics. These are not turnkey solutions, but they are examples of predictive maintenance models and a knowledge base that can be adapted to specific assets over time.
2. Pick the right assets
There are essentially three categories of maintenance in manufacturing: break-fix (reactive), preventive, and predictive. Using the three classes of maintenance mentioned can be a great way to define predictive maintenance priorities. Look around your manufacturing environments and consider which equipment you would treat on a reactive basis, as opposed to a preventive or predictive basis.
We have seen companies come in and try to provide the connective tissue between asset performance and preventive maintenance, but as we say, we are still a long way from any sort of turnkey solution to preventive maintenance. Such solutions may provide a checklist that you can map against your own specific requirements, but you really have to start from ground zero, especially if you have no predictive maintenance program in place.
So, start by categorizing your assets according to the three categories mentioned. Begin with the assets that would benefit best from a predictive maintenance approach. These are the ones you will want to start with, especially if you are trying to demonstrate quick wins with senior management, from whom you may require endorsement for a more comprehensive predictive maintenance program.
In the next post, we will cover points three and four. Stay tuned....
This post was first seen on MFRTECH weekly newsletter at www.mfrtech.com. To learn more about MFRTECH, please visit the website provided. To learn more about LEAN and its many applications, please feel free to contact the LEAN Accountants of McKonly & Asbury.