As a product manager, you’re responsible for bringing your product to market, but also for effectively managing it once it launches. In order to do this, you need to be aware of the KPIs that enable you to have a comprehensive understanding of the health and performance of your product. System performance metrics help remove any guesswork that may arise and allow you to make informed decisions moving forward.
Within the category of KPIs, system performance metrics unpack the usability, functionality, reliability, and efficiency of a product. They focus on key operational knowledge that you need to understand your product. Some of the most important are mean time to repair (MTTR), mean time between failures (MTBF), mean time to failure (MTTF) and mean time to acknowledge (MTTA).
In this article, you will learn what MTTR is and how to calculate it, as well as other system performance metrics, through examples and best practices.
Mean time to repair (MTTR) is a business metric that measures the time required to identify a product failure and bring the product to its normal operating status. You measure MTTR from the moment you detect a problem, until everything has been resolved. This includes the time needed to diagnose, repair, and test the product.
MTTR provides you with valuable insights into the efficiency of repair work and gives you a sense of how quickly your team can respond to a problem. You want to have lower MTTR values, so that you can avoid customer complaints.
In addition to MTTR, there are three more system performance metrics that can help you monitor the health of your product:
Mean time between failures helps you predict a product’s failure rate by measuring the average time between system failures. You usually only use MTBF for products that can be repaired and returned to operation. For product managers, MTBF provides you with a sense of your product’s reliability over time.
MTBF can be used to forecast future failures, guide maintenance schedules, and identify components of your product that frequently fail and need improvement.
Mean time to acknowledge (MTTA) tells you how much time passes between when you learn about a problem and when you actually start working on the problem. This metric gives you a sense of how long it takes your team to respond to an issue.
The better your observability is, the higher the probability that you have alerting mechanisms in place which can provide early indications that something might become a problem.
Mean time to failure (MTTF) tells you the average time a product can perform before suffering a non-repairable failure. As a product manager, MTTF allows you to estimate the longevity and reliability of a product that cannot be repaired once it fails.
A higher MTTF indicates a longer lifespan and this information provides you with valuable insights into warranty and lifecycle planning.
Now let’s walk through how to calculate the system performance metrics. In each case, we will need to have enough data available to calculate a statistical mean:
To calculate MTBF, you need to:
To perform a simple sample calculation, let’s say you’re looking at a 1,000-hour period for operational hours, during which there were two failures.
Based on that data, you divide 1,000 (operational hours) by 2 (failures), which gives you a MTBF of 500 hours.
Note: When calculating operational hours, you’ll need to be sure to exclude any planned maintenance windows from the calculation, because MTBF focuses on periods of unexpected down time.
To calculate MTTR, you need to:
To perform a simple sample calculation, let’s say you’re looking at total repair hours of 3, during which there were 6 repairs.
Based on that data, you divide 3 (repair hours) by 6 (repairs), which gives you a MTTR of 0.5 hours.
To calculate MTTA, you need to:
To perform a simple sample calculation, let’s say you’re looking at a total acknowledgement time of 1.5, during which there were 6 repairs.
Based on that data, you divide 1.5 (acknowledge hours) by 6 (repairs), which gives you a MTTA of 0.25 hours.
Note: When considering how to calculate this metric, it’s especially important for you to have the same understanding of what constitutes an alert and what constitutes an acknowledgement of an alert.
To calculate MTTF, you need to:
To perform a simple sample calculation, let’s say you’re looking at total operational hours of 800,000, for which there were 20 entities.
Based on that data, you divide 800,000 (operational hours) by 20 (entities), which gives you a MTTF of 10,000 hours.
Now that you know how to calculate system performance metrics, let’s explore how you might use each of these metrics in practice:
MTBF is particularly helpful when evaluating the reliability and availability of your systems. For example:
During periods of restructuring, which often involves a need to “do more with less,” you may see a negative impact on metrics such as MTTR. Examples of practical application of MTTR include:
The practical application of MTTA data is similar to MTTR data, in that worsening MTTA numbers may point to things like “alert fatigue.” Examples of additional insights that you can gain from MTTA data include:
MTTF data is particularly helpful as a means of assessing the relative likelihood of failures for one type of device or sub-component versus another. For example:
Success with any software product requires that you pay attention to multiple areas. Even if you invest heavily in usability, if you underinvest in other areas, such as the infrastructure and people, you jeopardize your ability to achieve long-term product success.
To summarize, you can:
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