On the shop floor, two worlds often coexist without speaking to each other: that of production, which monitors OEE and key performance indicators, and that of maintenance, which controls machine reliability with MTBF and MTTR. The result? Maintenance indicators that operate in a vacuum, meetings where each department points the finger at the other, and above all, unplanned stoppages that continue to plague industrial performance.
Yet these indicators are not in competition with each other: they complement each other. One measures the visible impact of losses, the other identifies the underlying causes. Understanding how to cross-reference them gives you the means to act in the right place, at the right time, and to sustainably improve results, rather than just observing them.
Why OEE and MTBF/MTTR are often misunderstood
The first misunderstanding stems from a confusion of roles. Many manufacturers use OEE as if it were a diagnosis, whereas it’s really a thermometer – a point developed in our analysis ofOEE as a KPI or genuine management tool.
It reveals a fever (65% availability, 78% performance, 92% quality), but says nothing about the infection causing it.
Conversely, MTBF and MTTR give an indication of equipment reliability, but do not directly explain its operational impact.
In the international manufacturing industry, this confusion is amplified: we talk aboutavailability andefficiency without linking them to the actual losses described in the article on why measure OEE.
The silo indicator trap
Another frequent confusion is that these indicators are calculated in silos.
Production reports an OEE of 68% and asks “why so low?
Maintenance replies that the MTBF is “within the norm”.
Without a link between the two, it’s impossible to know whether the drop in OEE is due to long stoppages, repetitive micro-stoppages or quality drift – a problem also addressed in our comparison of OEE vs FEE vs ERR.
This situation is critical when looking at the number of failures.
A “correct” MTBF can mask chronic instability, while a high MTTR on few failures can have a devastating impact.
The problem of a posteriori calculation
Finally, these indicators are too often calculated a posteriori, on spreadsheets, several days after the event. As a result, we react instead of anticipating, and field teams lose confidence in figures they don’t recognize. Manual data collection generates errors and omissions, and the organization cannot make decisions in real time.
When analyzing the total working time of a piece of equipment over a month, discrepancies between field data and manual calculations can reach 15-20%. This inaccuracy makes it impossible to accurately analyze the causes of losses, and to effectively prioritize corrective actions.
The fundamental differences between these indicators
Before crossing them, it’s important to understand what each one measures. These three key performance indicators have distinct but complementary roles in the manufacturing industry.
Overall Equipment Effectiveness: the operational performance indicator
OEE (Overall Equipment Effectiveness) is an operational performance indicator. It is calculated by multiplying three rates: Availability, which represents actual operating time divided by planned operating time (excluding planned stoppages), Efficiency, which compares actual output to theoretical output, and Quality, which measures the ratio of compliant parts to parts produced.
An OEE of 60% means that out of 100 units of theoretically productive time, only 60 are actually transformed into compliant products. This is a synthetic, global indicator, which reveals losses without explaining their origin. It takes into account all losses on planned production time (excluding planned stoppages such as preventive maintenance or weekends), and enables you to assess the competitiveness of your production facilities.
The OEE has become one of the most widely used key performance indicators in the global manufacturing industry, offering a global and comparable view across production sites. However, without analysis of the underlying causes, OEE remains an observation rather than a lever for improvement.
Mean Time Between Failures: measuring reliability and mean time between failures
MTBF (Mean Time Between Failures) measures the reliability of your production assets. The higher the MTBF, the less the equipment breaks down. It is the mean time before failure for repairable equipment, expressed in operating hours, and represents the average life between successive failures.
It helps anticipate failures and plan preventive maintenance. A high MTBF indicates optimum operating time between interventions, and helps keep equipment in good working order.
MTBF is calculated on the basis of the total effective operating time divided by the recorded number of failures. This measure of reliability is essential for comparing similar equipment and identifying those requiring special attention. In some industries, MTBF is even included in maintenance contracts with equipment suppliers.
Failure rate: a complementary indicator of MTBF
The failure rate, which is the inverse of the MTBF, also quantifies the frequency of failures per unit of time. This value is particularly useful for comparing the reliability of equipment of the same type in different industrial fields. The lower the failure rate, the better the equipment’s reliability.
By analyzing the failure rate over several periods, we can detect trends: a progressively increasing rate indicates equipment degradation and the need for preventive action. This temporal analysis of the failure rate is just as important as its absolute value at a given moment.
Mean Time To Repair: measuring reparability
MTTR (Mean Time To Repair) measures the reparability of your equipment. The lower the MTTR, the faster the repairs. It’s the average repair time required to bring equipment back into service after a breakdown.
A high MTTR indicates intervention difficulties: lack of spare parts, insufficient skills, access problems, or repair complexity. MTTR takes into account diagnosis, repair, testing and reinstallation.
In modern industry, reducing MTTR has become a strategic issue. Every minute saved on an intervention is multiplied by the annual number of failures to calculate the total gain. This is why team training, spare parts availability and equipment accessibility are critical performance factors.
MTTF: a complementary indicator for maintenance indicators
MTTF (Mean Time To Failure) is another average service life indicator used for non-repairable equipment. Unlike MTBF, which measures the mean time between successive failures of repairable equipment, MTTF measures the mean time to final failure of non-repairable equipment.
In the manufacturing industry, MTTF is particularly useful for system design and sizing, enabling the complete replacement of a component to be anticipated rather than its repair. For example, an LED bulb has an MTTF (it is not repaired), while an electric motor has an MTBF (it is repaired).
The distinction between MTTF and MTBF is essential for calculating maintenance costs and optimizing spare parts inventory management. A component with a short MTTF requires replacement stock, while equipment with a low MTBF requires spare parts and repair skills.
Complementarity of the three indicators
In short, OEE measures where we’re losing out, while MTBF and MTTR help us understand why we’re losing out, at least in terms of availability. Combining them gives us a complete picture of industrial performance.
This complementarity is all the more powerful as it allows us to move away from the sterile debate between production and maintenance. The two departments work on the same data, with different but converging angles of analysis: maximizing productive time and minimizing losses.
How to cross-reference OEE and MTBF/MTTR to identify the real causes of losses
The real power of these maintenance indicators lies in analyzing them together, by machine, and in real time. This approach improves the competitiveness of your production facilities.
Mapping availability losses with failure rates
Start by breaking down the availability rate of your OEE. If you see a rate of 70%, that means 30% losses. But are these losses due to a few long stoppages (major breakdowns), numerous repetitive micro-stoppages (blockages, jams), or poorly optimized series changes?
This is where MTBF and MTTR become invaluable. A low MTBF (e.g. 80 hours) with a high MTTR (e.g. 3 hours) indicates equipment that is unreliable AND takes a long time to repair. It’s a critical machine that’s a drain on your availability. Conversely, a correct MTBF (200 hours) but a degraded availability rate may reveal micro-stoppages not counted in the “official” breakdowns.
Cross-analysis of failure rate and efficiency (performance) also enables us to identify equipment that is operating in degraded mode. These situations, invisible in MTBF calculations alone, have a major impact on overall OEE and require specific preventive maintenance or recalibration actions.
Analyze the number of failures and their impact on total uptime
The number of failures over a given period is a key indicator for assessing the health of your equipment. By cross-referencing this number of failures with the MTBF and MTTR, you obtain a precise view of the criticality of each machine.
For example, a piece of equipment with 15 failures per month and an average MTTR of 2 hours represents 30 hours of downtime, while another piece of equipment of the same type with 5 failures and an MTTR of 4 hours totals 20 hours. The former therefore requires priority intervention despite a lower MTTR.
This analysis must also take into account the total working time of each piece of equipment. Equipment operating 24 hours a day will naturally have a higher number of failures than equipment operating 8 hours a day, even with the same MTBF. This is why indicators should always be reduced to a comparable basis (e.g., per 1000 hours of operation).
Prioritize maintenance actions according to key indicators
By cross-referencing OEE and MTBF/MTTR, you can segment your equipment according to its criticality. Critical machines (low OEE + low MTBF + high MTTR) require absolute priority: reinforced preventive maintenance plan, failure mode analysis, and available spare parts stock.
Fragile machines (low MTBF but moderate OEE impact) require increased monitoring and optimization of repair times. Poorly parameterized machines (correct MTBF but low OEE) require more attention to slowdowns and adjustments. This matrix makes it possible to get out of “fire brigade” mode and allocate maintenance resources where the impact is greatest, according to the total uptime and total operating time of each piece of equipment.
Move from observation to action with real-time data collection
The problem with manual or weekly calculations is that they freeze the analysis. With a real-time monitoring system like TeepTrak, stop data is collected automatically, time-stamped and qualified. MTBF and MTTR are calculated naturally from the historical data, and can be instantly cross-referenced with the OEE of each line. The sensor network ensures continuous, reliable data collection.
In concrete terms, this enables us to detect deviations before they become critical (MTBF gradually decreasing), to identify recurring stoppages due to the same root cause, even if they are short, and to measure the real impact of a maintenance action. This approach transforms key performance indicators into concrete levers for action.
Setting up such a system requires an initial investment, but the return on investment is rapid. On average, manufacturers who switch from manual monitoring to an automated system see a 10-15% improvement in their OEE in less than a year, simply thanks to better visibility and responsiveness.
MTBF and MTTR calculation: methods and examples
Understanding how to calculate these indicators is essential to using them effectively. The formula is simple, but rigorous data collection makes all the difference.
MTBF calculation: formula and practical application
MTBF is calculated using the following formula: MTBF = Total running time / Number of failures. For example, if a machine has been running for 720 hours in one month, and has had 4 failures, the MTBF will be 720/4 = 180 hours.
This example calculation shows that, on average, the equipment operates 180 hours between breakdowns. The MTBF can be used to anticipate preventive interventions and plan shutdowns.
It’s important to note that MTBF should only include actual operating time, not total calendar working time. If a piece of equipment is shut down at the weekend, these hours do not count in the calculation. This distinction is essential to obtain a reliable and comparable indicator.
Average repair time formula and interpretation
The calculation of average repair time follows a similar logic: MTTR = Total repair time / Number of repairs. If these 4 failures required 2h, 3h, 1h30 and 2h30 repair time respectively, the total repair time is 9 hours.
The MTTR will therefore be 9/4 = 2.25 hours per intervention, i.e. approximately 2 hours and 15 minutes. This average repair time is a direct indicator of the efficiency of your maintenance department.
A high MTTR can have several causes: lack of skills, unavailable spare parts, difficulties in accessing equipment, or the intrinsic complexity of the breakdown. By analyzing these causes, we can implement targeted actions to reduce MTTR: training, inventory management, improved technical documentation, or ergonomic modifications.
Example of a complete calculation: a real-life industrial case study
Let’s take an example of a complete calculation on a hydraulic press monitored for 3 months. The total monitoring time was 2,160 hours (90 days × 24h), with a total effective operating time of 1,850 hours (excluding planned shutdowns). The equipment experienced 12 major failures requiring a cumulative 48 hours of repair. The number of repairs therefore corresponds to the number of failures.
MTBF calculation: 1,850 / 12 = 154 hours. MTTR calculation: 48 / 12 = 4 hours. These results show that, on average, the press operates for 154 hours between two breakdowns, requiring 4 hours of repair each time. If the industrial target for this type of equipment is an MTBF > 200h and an MTTR < 3h, this machine clearly requires corrective action.
Refine analysis by failure type
Mean time between failures can be calculated globally or by failure type. This granularity enables you to identify recurring failures. For example, if 8 out of 12 failures are related to the hydraulic system, you know where to focus your efforts. This approach improves competitiveness by targeting the most profitable interventions and optimizing asset life.
In this article, we systematically recommend categorizing failures by subsystem (mechanical, hydraulic, electrical, pneumatic) and by type (wear, adjustment, random fault). This classification makes it possible to calculate specific MTBFs by category, and to identify equipment weak points with precision.
Case in point: recurring stops on a traffic jam line
Let’s take a typical case from the food industry. A bottling line has an OEE of 62%, well below the expected 75%. The production manager points the finger at “too many machine stoppages”, while maintenance retorts that the MTBF is 180 hours, and therefore “within industry standards”.
Diagnostics: identifying hidden losses
Digging deeper with a detailed monitoring system, we discover that the availability rate is only 72% (and not 85% as estimated). Long stoppages (>10 min) account for only 40% of availability losses, while the remaining 60% are due to micro-stoppages (2-5 minutes) linked to recurrent jamming on the outfeed conveyor.
These micro-stops, too short to be recorded as “failures” by maintenance, do not appear in the MTBF calculation. Yet they occur 8 to 12 times per shift, or 30 to 45 minutes lost every day. The official number of failures does not reflect the reality on the ground.
Analysis of total working time over a month also reveals that these microstops represent almost 25 hours lost per week, the equivalent of a full shift. This loss, invisible in traditional indicators, explains the gap between acceptable MTBF and degraded OEE.
Action taken: targeted preventive maintenance
After analysis, the problem was a progressive misalignment of the bottle guides. By implementing targeted preventive maintenance (weekly instead of monthly adjustment) and a warning system from the 3rd micro-stop, the team reduced micro-stops by 70%. The team also raised the availability rate to 84% and increased the OEE from 62% to 71% in 6 weeks.
Implementing this solution required close collaboration between production and maintenance, perfectly illustrating the benefits of cross-referencing key performance indicators. Without a detailed analysis of production stoppages, the problem would have remained invisible in the macro indicators.
Numerical results: impact on maintenance indicators
The MTTR for this type of incident has been cut from 4 minutes to 2 minutes, thanks to operator training in rapid adjustment. Average repair time was halved. The line’s overall MTBF improved from 180h to 245h, as mechanical stresses linked to jams were reduced.
The failure rate fell by 35%, and operators regained confidence in their equipment, which is now in good, stable working order. This experience demonstrates the value of an integrated approach combining performance and reliability.
Over and above the figures, this improvement has also had an impact on the line’s performance (efficiency): less operator stress, less raw material wastage during restarts, and more stable product quality. The overall OEE is doubly improved.
Conclusion: measure to understand, understand to act
OEE, MTBF, MTTR: these three indicators are not competitors, but allies. OEE tells you how much you’re losing, MTBF and MTTR help you understand why you’re losing, at least in terms of machine reliability. But it’s by cross-referencing them, in real time, with a fine granularity (by machine, by station, by type of stoppage), that you get an actionable vision.
Too many manufacturers are content to take stock of their industrial performance at the end of the week. The best ones continuously monitor their reliability, identifying drifts before they become critical, and mobilizing production and maintenance around the same field data.
In all industries, this integrated approach is becoming a major competitive advantage. Sites that master the OEE-MTBF-MTTR crossover gain 10 to 20% in production capacity without heavy investment, simply by optimizing existing processes. It is this operational intelligence that makes the difference in increasingly competitive markets.
FAQ : OEE, MTBF and MTTR
What’s the difference between OEE and MTBF?
OEE measures the overall performance of a piece of equipment, combining availability, performance and quality. It reveals how much you’re losing. MTBF measures equipment reliability by calculating the mean time between failures. It explains why you’re losing out (in terms of availability). OEE is a result indicator, MTBF is a root cause indicator. Cross-referencing them enables you to identify critical machines and prioritize maintenance actions.
How do you calculate a machine’s MTBF?
MTBF is calculated according to the formula: MTBF = Total running time / Number of failures. For example, if your machine operates for 500 hours and breaks down 5 times, MTBF = 500/5 = 100 hours. Important: count only actual uptime (excluding planned shutdowns and weekends). Categorize failures by type (mechanical, electrical, hydraulic) to refine the analysis and target preventive actions.
What makes a good MTTR in the industry?
A “good” MTTR depends on the sector and the type of equipment, but here are some benchmarks: MTTR < 2h = excellent (effective reactive maintenance), MTTR between 2-4h = acceptable for complex equipment, MTTR > 4h (high MTTR) = problematic, requires action (training, spare parts, documentation). The objective is not just a low MTTR, but a stable, predictable MTTR. Highly variable mean time to repair indicates a lack of standardization.
MTBF vs MTTF: what’s the difference?
MTBF (Mean Time Between Failures) measures the mean time between failures of repairable equipment (e.g. electric motor, pump). It’s repaired and it’s up and running again. MTTF (Mean Time To Failure) measures the time to final failure of a non-repairable component (e.g. light bulb, fuse, electronic board). It is replaced entirely. MTBF is used to plan preventive maintenance, while MTTF is used to size spare parts stocks.
How to improve your OEE using MTBF and MTTR?
First, analyze your availability rate (a component of OEE). If it’s low, look at the MTBF: if it’s low, step up preventive maintenance to space out breakdowns. Then look at the MTTR: if it’s high, train your teams, optimize spare parts stocks and improve accessibility. Cross-reference these maintenance indicators by machine to identify critical equipment. Prioritize those with low OEE + low MTBF + high MTTR: the impact will be maximal.
Why cross-reference OEE and MTTR?
Because a low OEE can have very different causes. If your availability is at 70% with a correct MTBF but a high MTTR (e.g. 5h), the problem is not the frequency of breakdowns but their repair time. Actions: technician training, parts stock, adapted tools. If, on the other hand, the MTTR is low (30 min) but the number of failures high (20 failures/month), the problem is reliability. Actions: preventive maintenance, failure mode analysis.
What tools are needed to monitor OEE, MTBF and MTTR in real time?
TEEPTRAK is an industrial monitoring solution that automatically collects production downtime data in real time. The platform instantly calculates OEE, MTBF and MTTR per machine, and enables these key performance indicators to be cross-referenced to identify root causes. With a network of sensors and continuous data collection, TEEPTRAK transforms your maintenance indicators into concrete levers for action. Implementation is rapid, with an average ROI of less than 6 months.
FAQ : OEE, MTBF and MTTR
What's the difference between OEE and MTBF?
OEE measures the overall performance of a piece of equipment, combining availability, performance and quality. It reveals how much you’re losing. MTBF measures equipment reliability by calculating the mean time between failures. It explains why you’re losing out (in terms of availability). OEE is a result indicator, MTBF is a root cause indicator. Cross-referencing them enables you to identify critical machines and prioritize maintenance actions.
How do you calculate a machine's MTBF?
MTBF is calculated according to the formula: MTBF = Total running time / Number of failures. For example, if your machine operates for 500 hours and breaks down 5 times, MTBF = 500/5 = 100 hours. Important: count only actual uptime (excluding planned shutdowns and weekends). Categorize failures by type (mechanical, electrical, hydraulic) to refine the analysis and target preventive actions.
What makes a good MTTR in the industry?
A “good” MTTR depends on the sector and the type of equipment, but here are some benchmarks: MTTR < 2h = excellent (effective reactive maintenance), MTTR between 2-4h = acceptable for complex equipment, MTTR > 4h (high MTTR) = problematic, requires action (training, spare parts, documentation). The objective is not just a low MTTR, but a stable, predictable MTTR. Highly variable mean time to repair indicates a lack of standardization.
MTBF vs MTTF: what's the difference?
MTBF (Mean Time Between Failures) measures the mean time between failures of repairable equipment (e.g. electric motor, pump). It’s repaired and it’s up and running again. MTTF (Mean Time To Failure) measures the time to final failure of a non-repairable component (e.g. light bulb, fuse, electronic board). It is replaced entirely. MTBF is used to plan preventive maintenance, while MTTF is used to size spare parts stocks.
Why cross-reference OEE and MTTR?
Because a low OEE can have very different causes. If your availability is at 70% with a correct MTBF but a high MTTR (e.g. 5h), the problem is not the frequency of breakdowns but their repair time. Actions: technician training, parts stock, adapted tools. If, on the other hand, the MTTR is low (30 min) but the number of failures high (20 failures/month), the problem is reliability. Actions: preventive maintenance, failure mode analysis.
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