No one can say that poor reliability or high maintenance costs are good things for an organization to experience for too long. There may be periods of time where they do occur and might even be necessary, but if they continue long-term, they drain budgets and cause customer dissatisfaction. Pouring money into unreliable equipment can work out in some instances and perhaps short-term, but not always in the long-term.
What is “poor” and “high?”
Of course, these are subjective words. What might be an acceptable level of reliability for one industry can be unimaginable for another. Same with budgets and the percentage anticipated for both corrective and preventive maintenance activities. In these cases, it is very true that “everyone is different.”
Your first step is to define what these words mean in your context. Don’t worry yet about how or where you will get the data to support your goals, just start with defining those goals. The consequences of failure are a good place to begin. If a piece of equipment fails (is unreliable) what is the impact? Can you quantify the cost to production or customer satisfaction? Do these failures translate into increased maintenance costs? (They might not always!)
By defining what is important you can throw these words out on the conference table and your team can pick out the good ones and the bad ones using your definitions, this is a good place to start. You also now know who cares about the metrics and how they use them for decision-making.
Do you have metrics in place that everyone knows what they are for and a system to help you maintain them?
Now comes the hard part: Capturing the data that is then used to provide information on how things are going towards your goals. If your data is limited, inconsistent, or worse, inaccurate, then all your good intended KPIs and reports are not going to tell you much you do not already know.
Given your goals, you need to specify the data needed to support them. This data may come from equipment telemetry directly, or perhaps is reliant upon humans to remember to enter it on their work orders. Maybe the system being used assists in the collection of this important data, or not. Maybe no one really knows how the collected data is ultimately used (bad) or they have a vested interest in the quality of the data and are very willing to assist (good).
One can have the best formulas and techniques in place to interpret the data, but if the data is garbage… well, you know how that goes.
Do you have a system in place that assists in data collection, a system that makes it seamless for users, providing consistent and reliable data sets?
Make decisions. Get better.
Given good data and good definitions for metrics, you are done, right? Wrong! What matters is what you do with these things once you have them in place. It comes down to decision making and improvement activities.
Let’s say that a particular metric is telling you that a set of equipment is in that dreaded, “poor,” range. You will need to spend some time investigating the reasons why. Yes, it could be technical in nature, but it could be due to the operating environment or an issue with how it is maintained. A perfectly good piece of equipment can become a complete wreck if it is operated outside of its design ranges or is not being cared for.
Once you have a line on the root cause(s) of the trouble, decide to either do something about the situation or not. Can the performance of the equipment be made better or is it just the reality (for now) we must live with? Just, “doing something,” is rarely a good idea. Executing on the important issues means using that good data to its fullest to make good decisions.
Do you have a system in place that assists you in making decisions and then implementing the changes you wish to make?
Improvement means change. Change is usually a result of spending money. (Ha Ha!) To get results you must do something different. The change could be dramatic, but it could be as simple as adjusting a frequency of a preventive maintenance activity or adding a couple extra tasks to a standard job plan. The decisions you make are only as good as the results of those decisions.
No matter the scope of the change, you need to follow it to see if it is having the desired effect. Nothing worse than making a change and forgetting to evaluate its results. Maybe you were spot on, and things improved immediately. Maybe you won’t know for a year. Maybe there was an impact, but it was not as much as you’d hoped.
Even if your change was a total failure at making any improvement, that is good to know! Now you have more data and can make different decisions once you analyze the situation.
Do you have a system that assists you in implementing change and observing its effects?
All in one place?
In our personal lives we have a short list of applications we use to monitor life in general. As we collect applications over time, we wish for more global apps that show us data in one place. We also like to enter things in one place, and any related places that need that information are updated behind the scenes.
Same is true in the business world. Nothing worse than having to hop across multiple and disparate applications to find information to make decisions with. Also, having to pull multiple sources of data together, filter, clean up, and transform that data into something coherent for decision makers can absorb many hours.
Enterprise applications suites such as Maximo/AAM bring all the pieces together into one place yet facilitate the variety of departments to focus on their own work. Taking advantage of this level of tools helps you define what is important, capture the data, execute on the important, and make changes to improve that which is important.
Contact TRM to see how we can help you implement a system that assists you all along your decision-making process.
Article by John Q. Todd, Sr. Business Consultant 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.