Mean Time to Repair
What is mean time to repair?
Mean time to repair (MTR) is the average time it takes for equipment to be diagnosed, repaired, and recovered after experiencing a failure.
Mean time to repair or recover (MTR), also abbreviated as MTTR, is a metric that is used to assess maintenance effectiveness.
One of the factors that contributes to a plant’s overall equipment effectiveness is the availability of the equipment to perform its functions. When equipment experiences downtime, especially due to failures, its availability is negatively impacted and therefore reflects poorly on the overall productivity of the system.
By keeping a data-driven mindset and proactively improving the MTR, the manufacturing processes can reduce availability losses due to repairs. The idea is to speed up the equipment’s rate of recovery from failures and breakdowns. Minimizing the MTR values of equipment is a step towards maximizing a plant’s productivity.
How to calculate MTR
The MTR is equal to the average duration of each stoppage due to failure. This is calculated by taking the total downtime that goes into the repair and recovery of equipment, divided by the number of times that the same equipment has stopped its operations.
MTR can be written in the form of the formula:
MTR = Total downtime / # occurrences of stoppages
Suppose that a mechanical mixer, usually operating 10 hours per day, broke down twice in a month. Let’s say that the first breakdown was due to a severely broken part that took 3 hours to replace. The second breakdown was caused by the system overheating and it took 4 hours to repair and recondition the mixer.
In this scenario, the total downtime caused by a total of 2 events is 7 hours. This puts the MTR at 3.5 hours as shown by the calculation below.
MTR = Total downtime / # occurrences of stoppages = (3 hours + 4 hours) / 2 occurrences = 3.5 hours per occurrence
Note that MTR formula in itself, is not dependent on the span of time in which the equipment operates (i.e. uptime). MTR is only quantified by the average time each stoppage lasts, regardless of the period between the instances of downtime.
Another metric used with MTR to characterize availability is the mean time between failure (MTBF). Working on these variables are important to ensuring plant availability.
How to improve MTR
The most effective way to reduce MTR is to get to the root cause of the failure before it even occurs. Closely monitoring operational data of each piece of equipment in the plant enables the early detection of possible causes of failure. In today’s setting, these tasks can be completed more easily and accurately with tools such as CMMS software.
Performing preventive maintenance on equipment addresses most issues that eventually lead to major breakdowns. When equipment is routinely scheduled for inspection and maintenance work, points of failure are identified early and the resulting MTR is relatively short.
Another way to improve MTR and increase availability is to map out and plan ahead of possible breakdown scenarios. Anticipation of possible causes of breakdown can better equip the team with efficient solutions to quickly resolve issues.
Tips when using MTR as a key performance indicator
A study on the maintenance benchmarks in the Chilean mining industry briefly discusses some key challenges that were encountered when comparing operations of companies within the industry. Though diligence in noting reliability and availability data has improved over the years, the method of measuring metrics such as MTR has no clear industry standard.
The study found that some mines would include all stoppages (including preventive maintenance and scheduled maintenance) in their calculation of downtime, while others only consider stoppages due to failures and breakdowns. This requires an extra step of understanding the context of recorded data before effectively comparing two or more operations.
Keeping in mind potential inconsistencies in recording data, it is important to always define the methodology by which variables within the operations are measured. This provides a transparent guide for internal teams and external stakeholders alike.