Mean time between failures (MTBF) is a common metric used to assess equipment reliability. It represents the average time, in hours, that an asset, component, or system will operate before a breakdown.
Mean time between failures is a crucial metric for any industry where equipment failure can lead to costly downtime, compliance issues, or safety concerns. In this article, we’ll discuss the meaning of MTBF, how it can improve maintenance practices, and how to calculate it through a real-world example.
What is Mean Time Between Failures (MTBF)?
Mean time between failures measures the expected productive life of a system, asset, or component. It estimates the average length of time that equipment operates without interruption between failures.
MTBF is a crucial metric for helping businesses track equipment availability. It’s also a useful way to assess a plant or facility’s overall reliability.
Tracking MTBF helps maintenance managers plan and schedule maintenance tasks more effectively. Used correctly, MTBF can also help teams predict when an asset will need maintenance.
That means that assets promptly get the repairs they need to stay up and running, resulting in much less downtime. MTBF is a key metric in industrial settings, where downtime can derail production and increase costs. MTBF can also reduce maintenance costs by helping set maintenance priorities.
However, it’s also important to recognize the limitations of MTBF. In general, the metric is highly accurate and actionable, but mean time between failures should never be used as a guarantee of reliability. Even an asset with a very high MTBF may have a sudden, unexpected failure.
It’s also wise to keep in mind that mean time between failures can change based on operating times, environmental factors, and usage conditions. Like other metrics, MTBF needs high-quality, up-to-date data in order to be effective.
MTBF Calculation: What is the Formula for MTBF?
The formula for mean time between failures is straightforward, making MTBF calculations easy enough to do in-house. Don’t rely on a manufacturer’s estimate to find an asset’s MTBF — it’s better to use data from the actual machine to determine this metric.
Mean time between failures is highly variable and depends on the asset’s specific operating conditions, utilization, and other factors. That’s why it’s essential to calculate MTBF based on the data obtained directly from your assets.
To calculate MTBF, you’ll need to know the total number of hours a machine or component has been in operation. You’ll also need to know the number of times the asset has failed over that time period.
Once you have collected these necessary measurements, you can apply the MTBF formula. The formula works by dividing the asset’s total operational hours by the number of times it failed in that period.
The formula used to calculate Mean Time Between Failure (MTBF) is:
Hours of operation / Number of failures = MTBF
Step-by-Step Guide to Calculating Mean Time Between Failures
There are just three steps involved in calculating MTBF.
Step one: Determine the total operational hours of the asset in question. Your computerized maintenance management system (CMMS) should already be tracking usage hours, so gathering this data should be easy. Otherwise, you can use asset usage records and work orders to calculate machine uptime.
Step two: Find out the number of failures that occurred to the asset during the operational time. If you’re not certain of the failure rate, use your CMMS’ reporting function. Work order history and maintenance schedules will supply enough data to find out the total number of failures.
Step three: Calculate MTBF using the formula. Take the total number of operational hours and divide that by the number of failures to get the average number of operational hours between failures.
Here’s what the MTBF calculation looks like in practice:
Calculating MTBF: A Real-World Example
Imagine that a pump runs for 1,000 hours and breaks down four times. Using the formula, you would calculate the asset’s MTBF as:
1000 hours of operational time / 4 failures = 250
The mean time between failures for this pump would be 250 hours.
The MTBF formula is simple, but it requires plenty of accurate data. That’s why CMMS is such a game-changer for tracking MTBF and other metrics. CMMS programs act as a centralized repository for all of a plant’s data, such as operational hours and the number of failures. They store all critical information in one easy-to-reach location.
A CMMS also makes it easy to access the data you need remotely. It also automatically tracks many metrics so that you can quickly see trends over time, making it easier to manage asset lifecycles and inventory.
Why Is it Important to Calculate MTBF?
MTBF is an important metric for maintenance teams to track as they work to reduce downtime and extend asset lifespans. Knowing the mean time between failures gives maintenance managers insights into asset health, helping them make informed predictions about future maintenance needs.
The cost of unplanned downtime can be devastating. But the more you know about MTBF, the more easily you can avoid breakdowns.
Tracking MTBF helps teams:
- Pinpoint areas of risk and plan strategies to protect critical assets
- Assess the effectiveness of maintenance strategies and workflows
- Make informed decisions about replacing or repairing equipment
- Improve inventory and spare parts management
Common Uses of MTBF
Mean time between failures is one of the most commonly used key performance indicators (KPIs) in asset management. This metric is widely valued because it provides insight into an asset’s useful lifespan.
Any organization that depends on complex machinery in order to meet its goals will benefit from calculating mean time between failures. MTBF is used across many sectors, but it is especially important in industries like:
- Automotive
- Food and beverage
- Life sciences
- Oil and gas
- Mining
Maintenance, repair, and operations (MRO) teams use MTBF to:
- Evaluate the performance of assets
- Assess the strengths and weaknesses of the MRO team and measure the efficacy of new MRO strategies
- Plan preventive maintenance schedules so that maintenance teams perform repairs when needed, instead of on an arbitrary schedule
- Improve spare parts inventory management to ensure that crews always have the parts they need on-hand
- Make data-driven decisions about when to repair or replace assets, based on the frequency and cost of repairs
- Identify equipment and processes that contribute to revenue loss
Here are a few examples of how MTBF is used in various sectors:
In the automotive industry, organizations use MTBF to assess the reliability of engines, transmissions, and component parts.
In the food and beverage industry, MRO teams use MTBF to help keep food safety equipment up and running. This metric is also useful for improving energy efficiency by ensuring that energy-intensive equipment like refrigerators stays in good condition.
In the life sciences industry, teams use MTBF to increase reliability and keep equipment performing in optimal condition. Slowdowns caused by equipment malfunction can damage product batches or cause expensive delays. Optimizing asset performance helps prevent those slowdowns and ensure uniform production.
Advantages and Disadvantages of Using MTBF
Mean time between failures is a popular metric because it is time-tested: MRO teams trust it to deliver results. At the same time, every KPI has built-in disadvantages. Here are some of the pros and cons of using MTBF.
Advantages of Using MTBF for Maintenance and Reliability
Calculating an asset’s mean time between failures can dramatically reduce downtime. MTBF gives you a good sense of when a machine will likely experience issues, so that your crew can make repairs early and stay ahead of failures.
Similarly, MTBF helps MRO teams plan effective preventive maintenance (PM) schedules. MTBF lets you know roughly how long a component will last before it needs to be replaced and how long a machine can run before it needs routine maintenance. This can also reduce your maintenance costs since your crews won’t perform maintenance tasks until they are truly necessary.
MTBF improves inventory management by helping teams plan exactly when to have spare parts on hand.
In the long run, MTBF extends asset lifespan and improves productivity. Well-maintained assets have a longer useful life and perform at their peak. This improves production speed and can boost product quality as well.
Finally, MTBF can increase safety, both in your facility and in your products. Data-driven maintenance leads to standardized and predictable processes, which produce safer conditions in the workplace because there is less room for human error. In sectors like life sciences and food and beverage, well-maintained assets can mean the difference between safe, high-quality products and inconsistent output that must be discarded.
Disadvantages of Using MTBF as a Performance Metric
Depending on the circumstances, MTBF can be challenging to calculate. Even though the formula itself is straightforward, it relies on data that not every team can access. If you don’t have accurate information about an asset’s operating time or number of failures, it’s impossible to correctly determine its mean time between failures.
By the same token, if your data only covers a short time frame, your MTBF results probably won’t accurately represent the asset’s true condition. Using a CMMS ensures that you’ll always have access to reliable and accurate data.
It’s also important to remember that MTBF is only one metric: used in isolation, it doesn’t provide enough information to build an effective maintenance strategy. For example, calculating the mean time between failures doesn’t tell you the root cause of each failure. It also doesn’t account for the severity of each failure or the time it takes to repair the asset. That’s why it’s so important to calculate MTBF alongside other maintenance KPIs.
Related Maintenance KPIs
In order to understand the full picture of an asset’s condition, it’s a good practice to track several KPIs at once. Here are some of the maintenance metrics that are closely related to MTBF.
Mean Time to Repair (MTTR)
Mean time to repair (MTTR) measures how long it takes to repair or restore an asset after failure. This metric is used to gauge the overall condition of an asset, since aging equipment often takes longer to fix. MTTR is also a great way to assess your maintenance program and identify areas that need improvement.
Mean Time to Failure (MTTF)
Mean time to failure (MTTF) is often confused with MTBF. To be clear, MTTF is the average time that a non-repairable asset will stay up and running before a breakdown. This metric measures the entire useful life of an asset before it needs to be replaced.
MTBF, in contrast, measures the length of time before an asset needs to be repaired.
Uptime
Uptime tracks the percentage of time that an asset is operational. It’s calculated by dividing the total number of minutes of machine availability by the total amount of minutes in the workday (or the week, month, or year). Uptime is a useful metric because it provides a fast, big-picture view of an asset’s availability.
Production Efficiency
Production efficiency measures how efficiently an organization uses its available resources. This metric is useful for providing insights into manufacturing practices, workflow management, and waste. It can also provide a strong incentive for teams to make improvements – and it can encourage organizations to take a closer look at other metrics, like MTBF.
How to Improve MTBF
There are a few key ways to increase an asset’s MTBF.
- First, ensure that your data is accurate by using a trustworthy system such as a CMMS. You need plenty of precise and reliable data to ensure the most up-to-date metrics.
- Next, put your metrics to work. Use the calculated MTBF to create a tailored preventive maintenance plan for each asset. You now have a good idea of when your equipment should fail, so plan necessary maintenance actions before that time.
- When equipment fails, it’s best practice to perform root cause analysis (RCA). Done right, root cause analysis can help you understand the underlying problem causing your asset to fail. This will help you pinpoint exactly what maintenance tasks your team needs to do to prevent future failures.
- Finally, implementing a predictive maintenance program is one of the best ways to improve MTBF. Predictive maintenance leverages condition monitoring data to identify asset faults at the earliest stages — long before they lead to a breakdown. Predictive maintenance provides an early warning system that protects against machine failure and improves the mean time between failures.
A good CMMS — like eMaint — can help improve MTBF by facilitating data collection, helping set maintenance priorities, and analyzing condition monitoring data. The result is a streamlined predictive maintenance approach that leads to greater efficiency and a sharp reduction in downtime.
Speak with a specialist to learn how a CMMS can help track maintenance KPIs and improve your team’s maintenance practices.
