When a customer’s equipment goes down, they don’t care how many jobs your team closed this month. They care how long they were without it. Mean time to repair (MTTR) puts a number on that wait, and new benchmark data shows how wide the gap is: top service teams close cases in 2.5 days, while the slowest take 10. With Customer Service Week wrapping up, here is how to measure MTTR consistently, how your numbers compare and where the time actually goes.
Key takeaways
- MTTR is the average time it takes to get a failed asset back into working order.
- The formula is total repair time divided by the number of repairs in the same period.
- Agree where the clock starts and stops before you compare numbers. The “R” can mean repair, recovery, respond or resolve.
- For equipment service, a 2026 benchmark puts MTTR at 4.5 days, with top performers at 2.5 days. Your own trend by asset and job type matters more.
- The biggest levers are diagnosis, parts, skills and asset data, the same things that drive first-time fix.
What is MTTR in field service?
MTTR is the average time between an asset failing and it being back in working order. IBM defines it as the average time it takes to repair a system or piece of equipment after it has failed, including finding, analysing and correcting the fault. In field service, that window usually covers diagnosis, technician travel, waiting for parts, the repair itself and testing.
The acronym causes confusion. As Atlassian points out, the “R” can stand for repair, recovery, respond or resolve, and each covers a slightly different window:
- Mean time to repair: the time to fix the asset, including testing.
- Mean time to recovery (or restore): the whole outage, from failure until the asset is fully back in operation.
- Mean time to respond: measured from the first alert rather than from the failure itself.
- Mean time to resolve: the fix plus the work to find the root cause and stop it happening again.
Pick one definition, write it down and use it consistently in reports, dashboards and SLAs. Most arguments about MTTR are really arguments about where the clock starts.
How to calculate MTTR
MTTR = Total repair time ÷ Number of repairs
Example: In one month, a service team completes 40 corrective repairs on a fleet of packaging machines. The total time from fault logged to machine back in production is 220 hours. MTTR = 220 ÷ 40 = 5.5 hours.
Three rules keep the number honest:
- Decide where the clock starts. Fault logged, ticket created or technician on site? IBM flags this as the main source of inaccurate MTTR figures. For customer-facing reporting, start when the fault is logged.
- Count corrective work only. Leave out preventive maintenance, installations and upgrades, or MTTR will fall for the wrong reasons.
- Include return visits. If a job needs a second trip, the clock keeps running until the asset works. Otherwise MTTR hides a poor first-time fix rate.
MTTR vs first-time fix rate vs response time
MTTR is most useful when you read it next to the two metrics it is most often confused with.
| Metric | What it measures | Formula | What it tells you | Blind spot |
|---|---|---|---|---|
| MTTR | Average time from failure to asset restored | Total repair time ÷ number of repairs | How long customers are without their equipment | An average can hide a long tail of very slow jobs |
| First-time fix rate | Share of jobs resolved on the first visit | Jobs fixed on first visit ÷ total jobs × 100 | How well jobs are diagnosed, staffed and equipped | Says nothing about how long the fix took |
| Response time | Time from service request to technician arrival or first action | Total response time ÷ number of requests | Whether you meet SLA arrival windows | A fast arrival without the right part fixes nothing |
The three move together. A team with fast response times but a low first-time fix rate will still have a high MTTR, because many customers wait for a second visit. For the first-time fix side of the equation, see our guide on how to improve first-time fix rate in 2026.
MTTR benchmarks: what the data says
The most useful recent benchmark comes from Aquant’s 2026 Field Service Benchmark Report, which analysed nearly 30 million service events from 161 service organisations over three years.
| Metric | Top performers | Industry benchmark | Bottom performers |
|---|---|---|---|
| MTTR (resolution time) | 2.5 days | 4.5 days | 10 days |
| First-time fix rate | 88% | 77% | 60% |
Read these figures with two caveats. Aquant reports MTTR as resolution time in days, so the numbers reflect the whole service case rather than hands-on repair time. And the data comes from equipment service, so teams working to hour-based SLAs, such as telecom operators or utilities, will see very different values. Aquant also sells AI software to service organisations.
The same report shows how closely MTTR and first-time fix are linked. Failed visits account for 25% of total service cost at the median, 44% for bottom performers and just 14% for top performers.
The cost of slow repairs is also well documented. Siemens’ The True Cost of Downtime 2024 estimates that unplanned downtime costs the world’s 500 largest companies around $1.4 trillion a year, or 11% of their revenues. An hour of downtime costs around $2.3 million in automotive and around $36,000 in fast-moving consumer goods.
Use external figures as a reference point, not a target. The most useful benchmark is still your own MTTR, split by asset type, job type and region, and tracked month by month.
6 ways to reduce MTTR
1. Diagnose before you dispatch
Every hour spent working out what is wrong on site adds to MTTR. Structured fault questions at intake, remote access to machine logs and a short video call with the customer can narrow the likely cause before anyone gets in a van. The technician then arrives knowing the probable fault and the part it needs.
2. Get the right part there on the first trip
Waiting for parts is often the largest single block of repair time. Link parts usage history to asset models and fault codes, stock vans for the faults you actually see rather than a generic list, and give dispatchers live visibility of where parts are held.
3. Match skills to the job
Sending the nearest technician is not always the fastest fix. If they are not trained or certified on that asset, the job will take longer or end in a second visit. Keep skill and certification data current and make it part of the scheduling decision.
4. Clean up your asset data
Wrong model numbers, missing serials or outdated configurations send technicians out with the wrong parts and the wrong expectations. We looked at this in detail in how poor asset data creates repeat field service visits. Every repeat visit caused by bad data shows up directly in MTTR.
5. Put knowledge in the technician’s hands
Service history, manuals and notes from previous fixes on the same asset should be on the technician’s mobile device, not in a back-office system. For unusual faults, quick access to a remote expert can save hours of trial and error.
6. Segment MTTR and review the outliers
A single average hides the jobs that hurt customers most. Report MTTR by asset type, job type and region, and review the slowest 10% of jobs every week. Tag each one by cause, such as parts, access, skills or diagnosis, and fix the most common cause first.
Frequently asked questions
What is a good MTTR in field service?
It depends on the asset, the SLA and travel distances. For equipment service, Aquant’s 2026 Field Service Benchmark Report puts the industry benchmark at 4.5 days, with top performers at 2.5 days and bottom performers at 10 days. Teams working to hour-based SLAs, such as telecom operators or utilities, should benchmark against their SLA restoration times and their own trend.
What is the difference between MTTR and MTBF?
MTBF (mean time between failures) is the average time an asset runs between breakdowns, so it measures reliability. MTTR measures how quickly you restore the asset once it has failed. Together they determine how available the asset is to the customer.
Does MTTR include travel time?
If you measure from fault logged to asset restored, yes: travel, waiting for parts and the repair itself all count. Customer-facing reporting should use this view, because it matches the downtime the customer experiences. Hands-on repair time can be tracked as a separate metric.
How does first-time fix rate affect MTTR?
Every job that is not fixed on the first visit needs another trip, often after days of waiting for a part or a different technician. That waiting time goes straight into MTTR, so improving first-time fix rate is usually the fastest way to bring MTTR down.
