In this article
Familiarity with key abbreviations for incident management KPIs (Key Performance Indicators) is essential for effective performance analysis. In this article, we’ll explore calculating metrics like MTTR and MTBF, compare different metrics, and consider the role of software tools, such as CMMS and EAM systems, in managing and improving metrics like MTBF and MTTR.
Key takeaways
Definitions of reliability metrics
What is MTTF?
MTTF stands for mean time to failure. It is the average lifespan of a given device. The mean time to failure is calculated by adding up the lifespans of all the devices and dividing it by their count.
MTTF = total lifespan across devices / # of devices
MTTF is specific to non-repairable devices, like a spinning disk drive; the manufacturer would talk about its lifespan in terms of MTTF.
For example, consider three dead drives pulled out of a storage array. S.M.A.R.T. indicates that they lasted for 2.1, 2.7, and 2.3 years, respectively.
(2.1 + 2.7 + 2.3) / 3 = ~2.37 years MTTF
We should probably buy some different drives in the future.
MTTF alternatively stands for mean time to fix, but it seems that “failure” is the more common meaning.
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What is MTBF?
MTBF stands for the mean time between failures. MTBF is used to identify the average time between failures of something that can be repaired.
The mean time between failures is calculated by adding up all the lifespans of devices and dividing by the number of failures:
MTBF = total lifespan across devices / # of failures
The total lifespan does not include the time it takes to repair the device after a failure.
An example of MTBF would be how long, on average, an operating system stays up between random crashes.
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What is MTTR?
MTTR stands for mean time to repair, mean time to recovery, mean time to resolution, mean time to resolve, mean time to restore, or mean time to respond. Mean time to repair and mean time to recovery seem to be the most common.
The mean time to repair (and restore) is the average time it takes to repair a system once the failure is discovered. It is calculated by adding the total time spent repairing and dividing that by the number of repairs.
MTTR (repair) = total time spent repairing / # of repairs
For example, let’s say three drives we pulled out of an array, two of which took 5 minutes to walk over and swap out a drive. The third one took 6 minutes because the drive sled was a bit jammed. So:
(5 + 5 + 6) / 3 = 5.3 minutes MTTR
The mean time to repair assumes that the failed system is capable of restoration and does not require replacement. It is synonymous with the mean time to fix.
Mean time to recovery, resolution, and resolve is the time it takes from when something goes down to when it is back and at full functionality. This includes everything from finding the problem, fixing it, and using technology (like CMMS and EAM systems) to analyze historical data and current assets to develop your maintenance strategy. In DevOps and ITOps, keeping MTTR to an absolute minimum is crucial.
MTTR (recovery) = total time spent discovery & repairing / # of repairs
The mean time to respond is the most basic of the bunch. The mean time to respond is the average time it takes to respond to a failure.
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Each reliability metric—MTTR, MTBF, MTTF—serves a unique purpose in reducing downtime and improving performance.
What is MTRS?
MTRS stands for the mean time to restore service. It is the average time it takes from when something that has failed is detected to the time that it is back and at full functionality. MTRS is synonymous with mean time to recovery and is used to differentiate mean time to recovery from mean time to repair. MTRS is the preferred term for mean time to recovery, as it’s more accurate and less confusing, per ITIL v4.
MTRS = total downtime / # of failures
Let’s take an example of an organization that suffered from four outages. The downtime for each failure is the following:
- Outage 1: 3 hours
- Outage 2: 2 hours
- Outage 3: 4 hours
- Outage 4: 1 hour
First, calculate the total downtime experience: 3 + 2 + 4 + 1 = 10 hours
After that, divide the total downtime by the number of outages: 10 / 4 = 2.5 hours
That gives you an MTRS of 2.5 hours, which may need improvement depending on how vital your services are.
For example, if the service going down is a payment system, whether online payments or in-store payments with a POS, you don’t want those systems down for several hours at a time.
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What is MTBSI?
MTBSI stands for mean time between service incidents and is used to measure reliability. MTBSI is calculated by adding MTBF and MTRS together.
MTBSI = MTBF + MTRS
Here’s an example of an enterprise’s database server. Over a span of several weeks, you collect the following information:
- MTBF: 300 hours
- MTRS: 4 hours
To calculate your MTBSI, just add those numbers: 300 + 4 = 304 hours
This means your database server will experience an incident, on average, every 304 hours. This metric will help your maintenance team assess your server’s reliability and look for opportunities to improve uptime.
After all, you don’t want critical applications going down too often when your team relies on them being online.
What is MTTD?
MTTD stands for mean time to detect. This is the average time it takes you, or more likely a system, to realize that something has failed. MTTD can be calculated by adding up all the times between failure and detection and dividing them by the number of system failures.
MTTD = total time between failure & detection / # of failures
MTTD can be reduced with a monitoring platform capable of checking everything in an environment. With a monitoring platform like LogicMonitor, MTTD can be reduced to a minute or less by automatically checking everything in your environment for you.
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What is MTTI?
MTTI stands for mean time to identify. Mean time to identify is the average time it takes for you or a system to identify an issue. You can calculate the MTTI by adding the total time from discovering an issue to identifying the solution and dividing that number by the total number of occurrences.
MTTI = total time from issue occurrence to identification / number of issues
For example, let’s look at an example where your organization is responsible for maintaining a web application. Over the course of a month, you identify four instances of poor performance:
- Occurrence 1: issue identified in 35 minutes
- Occurrence 2: issue identified in 20 minutes
- Occurrence 3: issue identified in 10 minutes
- Occurrence 4: issue identified in 15 minutes
Start by calculating the total time to identify your web issues: 35 + 20 + 10 + 15 = 80 minutes
Then divide by the number of issues (80 / 4 = 20) to get 20 minutes as the MTTI to identify issues. For critical applications, you may want to reduce this by adding real-time monitoring to gather data about your IT infrastructure, creating alerts to notify your team about issues that may contribute to an occurrence, and training your team to interpret monitoring data.
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What is MTTK?
MTTK stands for mean time to know. MTTK is the time between when an issue is detected and when the cause of that issue is discovered. In other words, MTTK is the time it takes to figure out why an issue happened. To calculate this, determine the amount of time it takes your team to identify the root cause of problems and divide it by the number of problems encountered.
MTTK = total time from issue detection to root cause identification / number of issues
For example, imagine that your organization maintains critical infrastructure for customers (such as a SaaS service) that they rely on to function. Any downtime will lead to dissatisfaction and a potential loss of revenue.
You measure your MTTK to determine how quickly your team gets services back online. Your team has the following identification times over the course of a month:
- Issue 1: 1.5 hours
- Issue 2: 1.75 hours
- Issue 3: 1 hour
You can calculate your MTTK with the following: 1.5 hours + 1.75 hours + 1 hour / 3 incidents = 1.42 MTTK
Knowing this number will help you determine how effective the process your team uses to diagnose processes is. You can then look for areas to optimize to reduce your MTTK.
What is MDT?
MDT stands for mean downtime. It is simply the average period that a system or device is not working. MDT includes scheduled downtime and unscheduled downtime. In some sense, this is the ultimate KPI. The goal is 0. Improving your mean time to recovery will ultimately improve your MDT.
MDT = total downtime / number of events
Let’s take an example of a critical application your IT team supports. Over the course of a month, you experience the following downtimes:
- Instance 1: 2 hours
- Instance 2: 30 minutes
- Instance 3: 1 hour
- Instance 4: 25 minutes
Calculate the MDT by adding the total time and the number of instances: (120 + 30 + 60 + 25) / 4 = 58.75 minutes
Depending on when those downtimes occur, your team may need to look for optimizations to reduce them—or if they are planned downtime, make sure they occur during off hours when demand is reduced.
What is MTTA?
MTTA stands for mean time to acknowledge. It is the average time from when a failure is detected to when work begins on the issue.
MTTA = total time to acknowledge detected failures / # of failures
Imagine the 100-meter dash. The starting horn sounds; you detect it a few milliseconds later. After a few more milliseconds, your brain has acknowledged the horn by making your legs start running. Measure that 100 times, divide by 100, voila, MTTA.
This KPI is particularly important for on-call DevOps engineers and anyone in a support role. DevOps engineers need to keep MTTA low to keep MTTR low and to avoid needless escalations. Support staff needs to keep MTTA low to keep customers happy. Even if you’re still working toward a resolution, customers want to know their issues are acknowledged and worked on promptly.
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What is MTTV?
MTTV stands for the mean time to verify. Mean time to verify is typically the last step in mean time to restore services, with the average time from when a fix is implemented to having that fix verified that it is working and has solved the issue.
MTTV = total time to verify resolution / # of resolved failures
You can improve this KPI in your organization by automating verification through unit tests at the code level or with your monitoring platform at the infrastructure, application, or service level.
From MTTR to MTTF, knowing what each metric measures is key to effective maintenance and reliability management.
Metric comparisons
MTTR vs MTBF
MTBF (Mean Time Between Failures) measures the time a system operates before failure, indicating its reliability and helping plan maintenance schedules. MTTR (Mean Time to Repair) measures the time it takes to repair a system after failure, focusing on minimizing downtime and repair costs. Simply put, MTBF evaluates reliability, while MTTR measures repair efficiency.
Calculating MTTR and MTBF
Let’s say an IT team manages several servers with a total number of 10 assets. During that time:
- Total operational time: 720 hours (24 hours x 30 days) for each server, for 7,200 total hours
- Number of failures: 5 server failures
- Total repair time: 15 hours for repairs
Starting with MTBF, start with the total number of operational hours and divide it by the number of failures: 7,200 / 5 = 1,400 hours
This means you have an average of 1,400 hours of uptime before you experience a server failure that leads to unscheduled downtime.
Calculating MTTR, on the other hand, tells you how well your team handles repairs and how quickly they get the servers back online.
To calculate this, take the total repair time and divide it by the number of repairs: 15 hours / 5 repairs = 3 hours
These calculations will help you understand how often downtime occurs, how long it takes to bring services online, and how often you can expect it to happen each month.
Improving MTTR and MTBF
These calculations will also help your team improve maintenance schedules to address these problems, reducing total downtime and the number of incidents. Predictive and preventative maintenance strategies can be implemented to catch potential issues before they become major problems, increasing MTBF and decreasing MTTR.
Implementing redundancies and fault tolerance measures can also greatly improve both MTBF and MTTR. By having backup systems in place, downtime due to hardware failures can be minimized or even eliminated.
MTTF vs MTBF
The main difference between MTTF and MTBF is how each is resolved, depending on what failure happened. In MTTF, what is broken is replaced, and in MTBF, what is broken is repaired.
MTTF and MTBF even follow the wording naturally. “To failure” implies it ends there, while “between failures” implies there can be more than one.
In many practical situations, you can use MTTF and MTBF interchangeably. Lots of other people do.
The remedy for hardware failures is generally replacement. Even if you’re repairing a problematic switch, you’re likely replacing a failed part. Something like an operating system crash still requires something that could be considered a “repair” instead of a “replacement.”
MTTF and MTBF are largely the concerns of vendors and manufacturers. You can’t change the MTTF on a drive, but you can run them in a RAID and drive down MTTR for issues within your infrastructure.
You generally can’t directly change your hardware’s MTTF or MTBF. Still, you can use quality components, best practices, and redundancy to reduce the impact of failures and increase the overall service’s MTBF.
MTTD vs MTTI
The mean time to detect and the mean time to identify are mostly interchangeable, depending on your company and the context.
MTTD vs MTTA
Detecting and acknowledging incidents and failures are similar but often differentiate themselves in the human element. MTTD is most often a computed metric that platforms should tell you.
For instance, in the case of LogicMonitor, MTTD would be the average time from when a failure happened to when the LogicMonitor platform identified the failure.
MTTA takes this and adds a human layer, taking MTTD and having a human acknowledge that something has failed.
MTTA is important because while the algorithms that detect anomalies and issues are incredibly accurate, they are still the result of a machine-learned algorithm. A human should make sure that the detected issue is indeed an issue.
MTTF (failure) vs MTTR: Mean time to failure vs Mean time to repair
Mean time to failure typically measures the time in relation to a failure. Mean time to repair measures how long it takes to get a system back up and running. This makes for an unfair comparison, as what is measured is very different.
Let’s take cars as an example. Let’s say your 2006 Honda CR-V gets into an accident. MTTF could be calculated as the time from when the accident occurred to the time you got a new car. MTTR would be the time from when the accident occurred to when the car was repaired.
MTTF (fix) vs MTTR: Mean time to fix vs mean time to repair
Mean time to fix and mean time to repair can be used interchangeably. The preferred term in most environments is mean time to repair.
MTRS vs MTTR: Mean time to restore service vs mean time to repair
The mean time to restore service is similar to the mean time to repair service, but instead of using the time from failure to resolution, it only covers the time from when the repairs start to when full functionality is restored.
In general, MTTR as a KPI is only so useful. It will tell you about your repair process and its efficiency, but it won’t tell you how much your users might be suffering. If it takes 3 months to find the broken drives, and they are slowing down the system for your users, 5.3 minutes MTTR is not useful or impressive.
Typically, customers care about the total time devices are down much more than the repair time. They want to be down as little as possible. For the sake of completeness, let’s calculate this one too:
((5 + 5 + 6) + ( 3 + 3 + 3) ) / 3 = 8.3 minutes MTTR
In general, the MTTR KPIs will be more useful to you as an IT operator.
The role of CMMS and EAM systems in managing reliability metrics
Computerized Maintenance Management Systems (CMMS) and Enterprise Asset Management (EAM) software are essential tools available to your team that will help them track reliability and failure metrics. They offer many features that help, including:
- Maintenance scheduling: Automate preventative maintenance tasks to reduce unexpected breakdowns
- Asset performance monitoring: Track your company’s assets in real-time to detect issues early
- Data analysis and reporting: See insights from historical data to make informed decisions and predict future performance
These tools will help your organization move from a reactive approach to a proactive one, where you stay ahead of problems and minimize downtime.
From ambiguity to action: Defining KPIs for better outcomes
When an incident occurs, time is of the essence. These KPIs, like MTTF, MTTD, MTTR, and MTBF, can help you gain better insight into your remediation processes and find areas to optimize.
Unfortunately, because each KPI has subtle similarities, many meanings differ from company to company. For example, MTTF and MTBF both tell you how long you can expect a device to stay online before failing, but MTTF is often used to identify how long a device takes to break (instead of offline for repair).
If these initialisms come up in a meeting, I suggest clarifying the meaning with the speaker—eventually solidifying these definitions in your organizations to avoid confusion. Otherwise, you might be DOA.
Mike Rodrigues is a tech leader with 15+ years in IT. He's passionate about helping organizations streamline their IT ecosystems to achieve mission-driven success, using observability tools that deliver predictive insights and actionable data. His expertise spans across network management, cloud services, and automation, making him a trusted advisor for staying ahead in IT.
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