In the current competitive world, long hours of downtime can come with costly consequences. Unwanted project delays, lost wages, and low ROI, to name a few. In this regard, industries use mean time to repair (MTTR) as one of the most trusted metrics to improve their maintenance operations efficiency and increase asset uptimes. Understanding MTTR meaning and other MTTR KPIs can further help industries improve their overall system reliability and increase asset ROI.
What is MTTR?
In simplest terms, MTTR is defined as the average time it takes to repair a system, which is usually mechanical. This gives an estimated timeline for the repair maintenance cycle in the event of a system failure.
Let’s take a closer look at this through an example.
If an IC chip in your system fails and it takes you four hours to purchase and receive the new IC chip and one hour to replace it in the system, the MTTR would be five hours.
Now let’s learn how to calculate MTTR for a company with several unplanned maintenance jobs going on over the span of a year.
MTTR Formula and Calculation
The MTTR score is calculated based on assumptions that all maintenance work orders for an asset are carried out one after another with learned professionals on board.
Let’s say the company maintenance log has recorded breakdown maintenance on a milling machine over the last year as:
|Start Date and Time||End Date and Time||Total Hours|
|01-03-2021, 15:20 hrs||01-03-2021, 16:10 hrs||50 mins|
|04-05-2021, 10:05 hrs||05-05-2021, 05:05 hrs||19 hrs|
|28-07-2021, 19:20 hrs||28-07-2021, 20:30 hrs||1 hr 10 mins|
Here, MTTR = (Total maintenance time) / Total number of failures
= (50 min + 19 hrs + 1 hr 10 min) / 3
= 21 hrs/3
= 7 hrs/ failure
This indicates that it takes 7 hrs on average for the company to repair and restore the milling machine. That’s okay, but what is an ideal MTTR score?
What Is a Good MTTR?
Time is money.
Long hours of breakdown maintenance can cause business disruptions, leading to loss of revenue and customer dissatisfaction. That given, the shorter the time it takes to address system breakdowns, the better. In this regard, the MTTR score is a powerful predictor of system outage time frames in the event of a breakdown. Thus, a low MTTR score is desirable.
In many industries, an ideal MTTR score should be less than five hours. In order to attain a world-class MTTR score of under five hours, you have to devise maintenance plans that are comprehensive and keep all factors in mind that could have a potential impact on the repair cycle, such as the type of assets, their age, and the degree of failure they are prone to, and what resources are needed given all of these factors.
How Is MTTR Used?
Conducting an MTTR analysis helps industries take the following actions with ease.
Repair or Replace an Asset
An upward trend in the MTTR score is inevitable as the machine ages. When the score starts to escalate, it's time to make a smart replace-or-repair decision. You can decide whether to throw away money on lost production or to bear a big one-time expense to save resources on costly breakdowns in the long run.
Improve Work Order Efficiency
If the work order process takes more time than a learned man’s guess, there is a weak link somewhere between the time when notifications are generated and when the actual procedure starts. It could be slow responding sensors, unavailability of repair resources, or lack of trained operators for a particular asset. Working on the root cause can dramatically improve the MTTR score.
Good Inventory Management
Good maintenance practices start with keeping your resources ready for an emergency. Spending time finding the right tools can increase the MTTR score despite having a team of experts on board. Thus, a high MTTR might indicate improper inventory management, and a more streamlined system might be the answer to keeping downtime to a minimum.
Despite all these benefits, the MTTR score has some limitations in helping you improve your machine reliability.
Limitations of MTTR
Though the MTTR score is a good indicator of how to improve asset uptime, it does not account for overall asset reliability factors that contribute to initial breakdown events. The score may create an alarm to look into your internal processes, but it can’t directly point toward the root cause.
Other factors that impact MTTR and overall asset uptime could lie within your alert system or team expertise. In this regard, other incident metrics can use the MTTR score to help you identify the weak links and predict the reliability of a manufacturing plant more accurately.
Incident Metrics: MTTR, MTBF, MTTF, MTTA
Three other metrics tie into MTTR that will provide data to help run your facility at peak performance. Below we will discuss:
- Mean time to acknowledge
- Mean time to fail
- Mean time between failure
Mean Time to Acknowledge (MTTA)
This metric represents the alertness of your team. MTTA is the average time required for the operators to respond to a task after the alert has been generated.
For instance, if a machine has ten breakdowns in total and the time between alert and acknowledgment for each breakdown is added together, the total comes to 60 minutes.
Now, The MTTA is 60/10 = 6 minutes per breakdown.
That means, the operators take 6 minutes on average to respond to an alert.
Mean Time to Fail (MTTF)
MTTF represents the average time between two consecutive failures of a non-repairable product.
Suppose you have a light bulb that has an average lifetime of three years. We can say, its MTTF is three years.
Now, if you consider four industrial bulbs with an average life of 21 hrs, 20 hrs, 18 hrs, and 28 hrs, respectively, the MTTF would be:
MTTF = (21 + 20 + 18 + 28) / 4 = 21.75 hours
But, what about systems that can be repaired or restored back to action?
This is where MTBF comes into the picture.
MTBF Mean Time between Failure (MTBF)
This reliability metric represents the average time between two consecutive failures of a repairable product during its normal life.
It’s not that you cannot calculate MTBF for a non-repairable product, but it will be the same as the MTTF value.
To further simplify, MTBF is a key reliability metric for systems that can be repaired or restored and MTTF is the expected time to failure of a system.
To calculate MTBF, you need MTTF and MTTR values.
Let’s look at an example.
Suppose you have a xerox machine with an average failure rate of one year (MTTF =1 year), and the breakdown maintenance takes two hrs on average.
MTBF = MTTF + MTTR
= 1 year (8760 hrs) + 2 hrs
= 8762 hrs
In short, a second failure is probable after 8762 hours from the first repair. You can plan preventive maintenance to extend this MTBF value to its maximum limit.
Mean Time to Respond Vs. Mean Time to Recovery Vs. Mean Time to Repair
Until now, we have acknowledged MTTR as mean time to repair. However, it can also be acknowledged as the short abbreviation for mean time to recovery and mean time to respond as well. Though they may all sound the same, they represent different states of a machine's reliability.
Usually when an outage occurs, the system generates an alert. After the alert chimes in, the operator takes time to start the breakdown maintenance procedure. Finally, depending on the degree of work required, it takes operators a certain amount of time to bring the system back to order.
Based on this assumption, here is how we can define these MTTRs.
Mean time to respond: The average time required for operators to recover a system from the time alerts are generated.
Mean time to recovery: The time required to recover a system back to order starting from its point of failure.
Mean time to resolve: The average time spent on completely addressing the root cause of a machine failure so that it won’t happen again in the future.
The timeline diagram below will help visualize the concept.
How LLumin Improves MTTR
LLumin’s AI-powered CMMS system monitors the critical parameters of an asset through integrated sensors and generates auto notifications for prompt actions.
First, the system sends notifications to the team of technicians through auto-generated emails. If there is no response within a preset time period, it will escalate to the maintenance supervisors. But as the conditions are getting urgent now, supervisors are notified via text message to ensure prompt actions are taken.
Furthermore, it generates work orders with detailed procedures to be followed along with the list of parts required to complete the job.
As a result, operators spend less time on preparation and increase their efficiency. Overall this improves the MTTR in the long run.
Cutting-Edge CMMS+ Software to Improve MTTR
Our web-based CMMS+ application uses machine-level data to catch problems at their source and generate proactive actions to eliminate them before it gets too late. Moreover, it can reduce unplanned downtime up to 40% within a year and MTTR by 20% within 24 months of going live.
We assign a dedicated manager for faster deployment following our honed implementation process. LLumin’s CMMS+ software cost varies according to the plan of your operation. We offer three well-tailored packages—Professional, Premium, and Enterprise to benefit small, medium, and large enterprises with room for ample customization.
Getting Started With LLumin
LLumin develops innovative software to manage and track assets in industrial factories, municipalities, and universities.To get started, we encourage you to schedule a free demo or Contact the experts at LLumin to see how our CMMS software can help you reach your efficiency and cost-cutting goals.