Bryan Christiansen

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Asset performance metrics are essential for any organization with equipment-reliant operations. Only by tracking those items that are prone to failure can an enterprise maximize uptime and keep disruptions to a minimum.

Tracking the reliability of assets is one challenge that engineering and maintenance managers face on a daily basis. While failure metrics can be very useful in this context, to use them effectively, you need to know what meaning hides behind their acronyms, how to distinguish between them, how to calculate them, and what does that tell you about your assets.

That’s why we decided to create a simple to follow guide to failure metrics that will help you avoid costly mistakes and successfully monitor equipment performance.

Even the most efficient maintenance teams experience equipment failures. That’s why it’s critical to plan for them.

But first, what does equipment failure look like?

Failure exists in varying degrees (e.g. partial or total failure) but in the most basic terms, failure simply means that a system, component, or device can no longer produce specific desired results. Even if a piece of manufacturing equipment is still running and producing items, it has failed if it doesn’t deliver the expected quantities.

Managing failure correctly can help you to significantly reduce its negative impact. To help you effectively manage failures, there are a number of critical metrics that should be monitored. Understanding these metrics will eliminate guesswork and empower maintenance managers with the hard data they need to make informed decisions.

Which failure metrics should be tracked? Across industries and applications, we’ve found that those are MTTR, MTBF, and MTTF. We’ll discuss what each of those acronyms means and how you can use them to improve your operations.

But before that, we need to discuss one thing that is often overlooked — the importance of having reliable data behind your failure metrics.

In order to make data-backed improvements in equipment failure, it’s crucial for the right data to be collected and for that data to be accurate.

High-level failure statistics require a significant amount of meaningful data. As we’ll show in the failure metrics calculations below, the following inputs must be collected as part of your maintenance history:

As tedious as recording maintenance figures can be, it’s an essential part of improving operations. This process can be painfully time-consuming when done manually, but it’s made simple with a mobile CMMS like Limble that lets you quickly and easily log reliable data for labor hours and downtime on your phone while you’re performing maintenance tasks. Additionally, Limble runs all the calculations of MTTR and MTBF automatically for you, as seen below.

Collecting inaccurate data can cause a lot of issues. Maintenance technicians might occasionally write down the wrong figure is just one example. A potentially much bigger problem is neglecting to record tasks, which leads to incomplete data.

If data is missing or inaccurate, your failure metrics will be useless in informing decisions on improving operations. Worse still, if you are unaware that the data is unreliable, you might end up making operational decisions that could actually be counterproductive and harmful.

Now that we got that out of the way, let’s focus on the things you actually came for.

Mean Time To Repair (MTTR) refers to the amount of time required to repair a system and restore it to full functionality.

The MTTR clock starts ticking when the repairs start and it goes on until operations are restored. This includes repair time, testing period, and return to the normal operating condition.

How do you calculate MTTR?

To calculate MTTR, divide the total maintenance time by the total number of maintenance actions over a given period of time.

Imagine a pump that fails three times over the span of a workday. The time spent repairing each of those breakdowns totals one hour. In that case, MTTR would be 1 hour / 3 = 20 minutes.

A couple of things to note:

Every efficient maintenance system always needs to look at how to reduce MTTR as much as possible. That can be done in a few different ways.

One approach is through tracking spare parts and inventory levels (thereby saving on downtime while sourcing for parts).

Another way is to implement proactive maintenance strategies like predictive maintenance. Predictive maintenance (PdM) will, among other things, allow you to better monitor the condition of in-service equipment and predict potential failure more accurately by using condition-monitoring sensors mounted directly on those components that are prone to failure.

These sensors can alert them well in advance when to expect failure. At this point, the repair is no longer reactive but predictive, as the manager has enough time to arrange for all the resources needed to execute the job.

Why is MTTR helpful?

Taking too long to repair a system or equipment is not desirable as it can have a highly unpleasant impact on business results. This is especially the case for processes that are particularly sensitive to failure. It often results in production downtime, missed deadlines, loss of revenue and so on.

Understanding MTTR is an important tool for any organization because it tells you how efficiently you can respond to and repair any issues with your assets. Most organizations seek to decrease MTTR with an in-house maintenance team supported with the necessary resources, tools, spare parts, and CMMS software.

Maintenance managers can use MTTR to inform maintenance decisions such as:

Mean Time To Repair vs Mean Time To Recovery

There are several commonly used terms for the acronym “MTTR.” The two most common are “mean time to repair” (discussed above) and “mean time to recovery.”

Mean Time To Recovery is a measure of the time between the point at which the failure is first discovered until the point at which the equipment returns to operation. So, in addition to repair time, testing period, and return to normal operating condition, it captures failure notification time and diagnosis.

Although both terms are often used interchangeably, the need for distinction becomes important in the context of Service Level Agreements (SLAs) and maintenance contracts.

Hence, all parties to such contracts will need to agree on what exactly are they measuring.

The second failure metric we are going to cover is Mean Time Between Failures. MTBF measures the predicted time that passes between one previous failure of a mechanical/electrical system to the next failure during normal operation. Or, the time between one system breakdown and the next.