What Is MTBF? Mean Time Between Failures Explained in Detail

Time for another installment in the series where we explain in detail yet another important metric for tech organizations. After covering MTTD and MTTF, today we answer the question, “What is MTBF?”

As the post title makes clear, MTBF stands for “Mean time between failures.” The acronym refers—like the others that came before it—to an important DevOps KPI. But what actually is it? What is it good for? How do I implement it? Today’s post features the answer to all of the above questions—and more.

The rest of the post will go deeper into MTBF. First, you’ll learn its definition. After that, you’ll see the justifications behind its use. After taking care of the “what” and “why,” it’s time to cover the “how.” We’ll do that by explaining how to calculate this metric, using two simple yet realistic examples.

Then we continue by mentioning other metrics that are essential for DevOps and are intimately related to our topic. After that we wrap-up with a quick summary of the post along with some final considerations. Let get started!

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What Is MTBF? A Quick Definition

Mean time between failures probably has the most intuitive name of all the metrics we’ve covered so far. It means pretty much what you would’ve guessed by its name: it is the arithmetic mean (average) amount of time between failures of a mechanical or electronic system. The metric usually doesn’t include repair times. MTBF values are usually used to predict how likely a single item is to fail within a given timeframe.

Calculating MTBF should be simple enough. All it takes is to look at a failures report for a given system, get the times between the different failures, and then calculate the average time.

Why is this metric so important? We’ll answer that in more detail in the next section. For now, the short answer is that MTBF is a reliability metric. It allows you to determine how reliable a given piece of equipment or other item is, so you can make informed decisions about them.

Why Should You Care About MTBF

In the previous section, we’ve briefly mentioned some of the reasons why MTBF is a worthwhile metric. But now we’re going to dive a little bit deeper on the issue. So, what’s the deal with this metric? Why should your organization care about it?

As you’ve seen, MTBF is a maintenance metric that indicates how long a system can work until it fails. So, this metric is closely associated with the availability (or uptime) of the given equipment or system. Uptime is also a key indicator for organizations. Through it, organizations can determine the overall effectiveness of a piece of equipment of any other system.

It’s important to keep in mind that MTBF is a metric that only applies to repairable items. For non-repairable items, the metric you should use is a different one: good old Mean Time For Failure (more on that in a minute.)

MTBF is a crucial metric to use when evaluating equipment and other systems before a purchase. Since the metric calculates the average length of time between issues, it is an indicator of reliability. Based on the metric, you can have a really good idea of how long a given system works before it stops working. By tracking the metric, you’ll be able to make educated decisions on a lot of areas like safety, maintenance schedules, and many more.

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People often mix “Mean time to failure” with “Mean time between failures.” And to be fair, people who think these two are the same are almost right.

As you’ve seen, MTBF is the metric you use for repairable items. When it comes to non-repairable items, it doesn’t make sense to say “failures” in the plural. A non-repairable item fails just one time, and then it gets replaced. So, in scenarios like that, one should use “Mean time to failure.”

Calculating MTBF in Practice

Let’s now show you how to calculate MTBF. It’s probably easier than you’d think. You just take the total time the item is running (i.e., availability of uptime) and divide it by the number of failures that happened over that time frame. We’ll now see a quick example of how to calculate this metric.

Suppose you have a mechanical machine created to operate 12 hours per day. Then, let’s say it breaks down after operating for four days. The MTBF, in this case, is 48 hours.

MTBF = (12 hours per day * 4 days) / 1 breakdown = 48 hours

The example above is as simple as it can get. The calculation can and does get more steps. For instance, when you analyze longer amounts of time in which several problems happened, that asks for more steps when calculating MTBF.

Let’s make our example slightly more complex. Suppose the same piece of equipment, running for 12 hours per day, breaks down twice in the span of 8 days. The first problem happened 20 hours from the start time and took 2 hours to repair, which means our first session of uptime lasted for 20 hours.

The second problem occurred 50 hours from the start time and took 4 hours to repair. Since the first issue occurred after 20 hours of uptime and took 2 hours to repair, that means the second problem occurred after 28 hours of uptime (50 – 22 = 28.)

The second problem took 4 hours to repair, which means we’re left with 42 (96 – 54) hours of uptime. Now comes the easy part: to plug the values into the MTBF formula:

MTBF = (20 hours + 28 hours + 42 hours) / 2 breakdowns
MTBF = 90 hours / 2 breakdowns = 45 hours

As you can see, the mean time between failures is equivalent to the total uptime for the given item.

Other Metrics to Be Aware Of

MTBF is a key indicator to determine the reliability of systems. Here are some other important metrics you should check out:

  • MTTF (Mean time to failure): The amount of time a product/device/system is expected to work.
  • MTTR (Mean time to repair): The time it takes to fix an issue after its detected.
  • MTTD (Mean time to detect): The time the organization takes to detect an issue.

Final Considerations

In this post, we’ve answered the question, “What is MTBF?” We’ve started by defining the metric, explaining what it means and why it’s important. Then we’ve cleared a common misconception regarding MTBF and MTTF. After that, we’ve proceeded to show how to calculate MTBF in practice using a real example.

Where do we go now? Well, knowledge is an investment that always pays itself handsomely. So what about studying? Read about DevOps key metrics on this blog and other sources. Read about other topics as well. Also don’t forget to take a look at available tools that can help you.

For instance, consider XpoLog. XpoLog help to optimize MTBF with:

  • Deep text inspection for log data to discover errors
  • Anomaly detection monitoring for performance, exceptions and errors
  • Pattern analysis and new errors discover
  • Log volumes and flow monitoring.

XpoLog is a fully automated log management tool. It collects, parses, and tags logs from many different sources.

Log management is essential for implementing MTBF and other metrics. Logs are the closest we have to an authoritative source of truth to determine the start times of problems in production, which makes them a really powerful asset that needs to be properly handled and managed.

That’s all we have for today. Thank you for reading and until next time.

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