Mean Time Between Failure (MTBF): What It Means & Why It’s Important

In today’s technology driven world, system reliability is more critical than ever. Mean Time Between Failures (MTBF) serves as a key metric to evaluate the dependability of systems by measuring the average time a system operates without failure. This concept reinforces critical decisions in reliability engineering, maintenance planning, and service level agreements.

Here’s everything you need to know about the MTBF metric including how you can calculate it and important metrics to consider.

What is Mean Time Between Failures?

Mean Time Between Failure (MTBF) refers to the average duration between two failure incidents. MTBF is an important metric for system reliability and availability calculations because it accounts for all phases of the system performance, during which it remains operational.

MTBF can be interpreted in terms of failure frequency: if a system scores high on the MTBF metric, it will fail less often during its useful operating cycle. It is also a prediction of system dependability characteristics such as uptime or availability, and reliability in system performance over the long term. This can be described mathematically as follows:

MTBF = Total Operating Time / Total Number of Failures

System reliability and availability

Let’s first discuss why system reliability and availability calculations are important, and the role of the MTBF metric.

• Reliability refers to the probability that a system continues to operate as expected during a specific time duration. 
• Availability refers to the probability that the system performs correctly at any given instant. 

In both cases, system parameters must remain within a specified range that is required for optimal performance. A system scores high on dependability metrics if it is available (at present) and can perform reliably (in the future). Since MTBF covers the operational phase of a system performance in its entirety between to consecutive failure incidents (on average), it is also considered to be a useful metric to describe system dependability.

In the enterprise IT segment, availability calculations are historically driven by the rationale that for third-party subscription services (SaaS, IaaS, PaaS), you pay only for the resources consumed. The ability to trade high CapEx with affordable OpEx enables agile startup firms to compete with large enterprises purely on grounds of innovation. SMBs are fully dependent on the third-party services to deliver this innovation to the end-user in the market.

(Related reading: CapEx vs OpEx)

Reasons to measure MTBF

Now consider that an uptime guarantee such as six 9’s (99.9999% available) assume constant availability throughout the year with possible outages that total up to 31.56 seconds of downtime.

For an ecommerce store, outages during peak season can cause a large volume of abandoned shopping carts, leading disgruntled consumers to a competitor. This is where the metric of MTBF plays an important role:

• Dependability measurement: MTBF directly measures the probability of failure frequency and therefore describes system dependability. If you can benchmark this performance, you can specify the targets required to guarantee uptime and reliability.
• Maintenance scheduling: Knowing when a system can fail is important for preventive maintenance activities such as component replacement and service.
• Redundancy and backups: Various redundancy schemes and backup strategies offer different levels of fault tolerance. For example, rapid backup schemes can help reduce the time spent to restore a fully operational state but may not reduce failure frequency. Redundancy significantly improves the Mean Time To Repair (MTTR), which increases the duration for which a system can remain operational, thereby indirectly improving the MTBF metric. 
• Cost management: The goal here is to strategically invest in redundancy, backup, repair and detection technologies. In the real world, a system cannot be 100 percent dependable. The tradeoff between cost and improvements in system dependability is usually a negative exponential (after a certain threshold, spending any more on system redundancy will have MTBF improvements decreasing in magnitude).

When interpreting MTBF as a probability measure of failure frequency, an important consideration is its relation to the failure rate.

Failure is measured as the frequency of component failure, or simply the number of components failing per unit time. The inverse of this failure rate can be described as MTBF.

(Related reading: failure metrics)

Why is this important?

Technology components are typically sold with a measure of expected useful service life. Vendors extensively test their products to determine accurate failure rates. This information is then used to empirically calculate system reliability metrics that go into your SLA agreements.

However, the time duration spent detecting and repairing is highly dependent on external factors such as the operating environment of these components, as well as the capability and resources to repair the system. A well-informed reliability engineering strategy therefore must account for the accumulated failure rates of all components, combined with the expected capacity to detect and repair the failed components.

From a business perspective, this means that while a cloud service may offer a guaranteed service uptime of 99.9999%, you should also account for the MTBF and its impact when an outage occurs. A high failure frequency may suggest that during peak load, the service may be unavailable several times, even for small time instances. This may be sufficient to drive your internet traffic away from your online services during crucial moments of interaction such as during checkout, payment processing and product selection.

Other key metrics for system reliability: MTTR, MTTF, & MTTA

Understanding MTTR, MTTF, and MTTA is crucial for assessing system performance and reliability. These metrics provide valuable insights into operational efficiency, enabling you to make well-informed decisions. Here’s what they are used for:

• MTTR Mean Time to Repair: MTTR measures the average time required to repair a system or component after a failure. It helps evaluate the efficiency of repair processes and plan for minimizing downtime.
• MTTF Mean Time to Failure: MTTF measures the average time before a non-repairable component or system fails, providing insight into a product's lifespan and aiding in replacement planning.
• MTTA Mean Time to Acknowledge: MTTA measures the time taken to acknowledge an incident after it occurs. It helps optimize incident management processes and assess response times.

Wrapping up

The reliability and availability of systems play a vital role in ensuring seamless operations and positive customer experiences. When evaluated using MTBF, we gain essential insights into system dependability. Specifically, MTBF highlights the average time a system operates without experiencing failures.

MTBF is a cornerstone metric for organizations aiming to optimize performance. This applies to a wide range of systems, from IT infrastructures to manufacturing equipment. By addressing challenges related to failure frequency, organizations can improve reliability. As a result, this leads to reduced downtime and enhanced productivity. Furthermore, a strong focus on MTBF fosters trust in services and systems, ultimately contributing to higher user satisfaction and operational success.