In general, RCM is a process through which you determine what you must do over time to ensure a physical asset fulfills its intended functions, considering the context it is operating within. The maintenance you are performing is focused on preserving the reliability of the equipment to deliver its functions. This infers that you are making decisions. Decisions require insights. Insights require data collection.

 

Ugh… data collection.

Sorry to say, but every great idea needs data to support it. Otherwise, how will you know it was a great idea? Data, or rather the insights you get from it, tells you if you need to do something, and when you do, whether the something was effective or not. Unlike in the movies, we do not have the Force to guide us as we make decisions on equipment maintenance.

The refinement of your RCM activities will require that you are collecting data on them over a long period. As such, you not only need to have a system where that data is collected, but you must also define specifically what that data is. Nothing worse than having a great system to put data in, but the data is incomplete or missing when you try to use it a year later for analysis.

 

Insightful, indeed!

So, you have a place for the data and the field teams are skipping along each day entering valuable data points with ease and zero impact on their day job. Your data sets are growing quickly, and the level of excitement is becoming palpable for the much-awaited insights into how your RCM program is progressing.

“What is it again we are trying to do?” is not what you want to hear in a meeting with upper management. The insights you are looking for (in general) need to be defined well in advance. This should happen when you form the data needs early in the process. Back to data collection: If you are not collecting data that supports the insights you want to get, then you end up with wide error bars and uncertainty.

Given well-defined insights you are looking for will then define the data you need. However, keep an open space for discovering those insights that the data may expose that you didn’t think of… or want. Data can surprise you! Perhaps too, over time, the insights you thought you would get from the data are not panning out, yet other results are becoming obvious or consistent. Now you have some real gems to dig into.

 

So many decisions to make!

All this equipment history that you have collected and analyzed means nothing and was a waste of time if you do not make decisions based on them. More bad news: If a decision cannot point back to a specific insight from the data, then most likely it was a bad decision. (Ok, occasionally, we make a good guess as to which door to open and win the car!)

It can be proven (with data) that most equipment failures are not due to the age of the equipment. Given a clear definition of the failures your equipment can experience (based upon your initial RCM analysis) this statement becomes true. Generally, forces outside of the equipment cause the most grief. Are you capturing data and deriving insights about these outside forces so that 1) you are aware of them and 2) can make decisions about how to eliminate or greatly reduce them?

Maybe that outside contractor performing maintenance on a specific set of equipment is beginning to have an impact on the reliability of the equipment. Their excuse of, “It’s just getting old,” might not hold water if you have data to the contrary. Why does this pump string always go off-line the day after Ricky Bobby Pumps and Racing Supplies comes and does their maintenance cycle?

 

Centering your Maintenance on Reliability

Years ago, I was a Reliability Engineer with my main source of equipment data being from a Maximo instance. Our RCM studies were centered around the decisions we were able to make based on the insights we gleaned from our collected data. While there were many sources of data, they all (for the most part) coalesced into our Maximo. As we made decisions and made course corrections, we were able to adjust Maximo, often unknown to the field teams, to refine the data we needed.

Nothing has changed really, just the volume of data and the tools we have now to perform analyses. The determination of whether a piece of equipment is reliable enough for our purposes is still the same set of calculations. Modern tools like Maximo come with features and functions that have replaced the weeks of pawing through equipment data looking for insights. Business Intelligence and Machine Learning tools have really upped the RCM game.

 

Wrap up

In theory, your maintenance and manufacturing teams are doing real work all day to the benefit of the equipment. The question is, “Are they executing tasks that support your RCM goals?” And, perhaps more importantly, are they sources of good data for you to make decisions with? TRM/IDCON have many years of experience across industries guiding clients through processes related to RCM and leading towards tangible changes to the bottom line. Make contact to find out more.

 

Article by John Q. Todd, Sr. Business Consultant / Product Researcher at TRM. Reach out to us at AskTRM@trmnet.com if you have any questions or would like to discuss deploying MAS 8 or Maximo AAM for condition-based maintenance/monitoring.

 

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