Mean Time Between Robot Failures (MTBRF) KPI

Mean Time Between Robot Failures (MTBRF) is a critical metric for assessing operational efficiency in automated environments.

It directly influences business outcomes such as production uptime and maintenance costs.

A higher MTBRF indicates fewer disruptions, leading to improved ROI metrics and enhanced financial health.

Conversely, a lower MTBRF may signal underlying issues that could escalate operational costs and impact strategic alignment.

Organizations that leverage MTBRF effectively can make data-driven decisions to optimize their robotics investments and improve overall performance indicators.

How Mean Time Between Robot Failures (MTBRF) Connects to Your Strategy

Mean Time Between Robot Failures (MTBRF) belongs to the ISO 10218 KPI group, a set of one hundred thirty-three members organized around robotics safety rather than pure uptime. The group leads with Robot Safety Incidents Rate first, then Safety Incident Rate for Robotic Operations, then Robot Safety Standard Adherence Rate and Robot Compliance with ISO 10218, followed by Robotics Safety Compliance Ratio, Functional Safety Certification Rate, Safety Training Recurrence Interval, and Emergency Stop Activation Frequency. This KPI ranks fifty-sixth of one hundred thirty-three, so it is a reliability metric living well below the incident and compliance measures the group prioritizes. Its balanced scorecard perspective is internal, and it plays a leading role: reliability erodes before it surfaces as an incident, so a falling MTBRF is an early warning ahead of the lagging safety counts near the top of the group. The clearest tension is with Emergency Stop Activation Frequency, the eighth-ranked co-metric. A robot can post a long mean time between failures precisely because operators trip the emergency stop often, halting the machine before a failure is recorded, which suppresses the failure count while raising activation frequency. Read the two together: a healthy MTBRF that rides on frequent emergency stops is masking risk, not managing it.

Measuring Mean Time Between Robot Failures (MTBRF) in Practice

The canonical formula is total operational hours divided by number of robot failures, and both terms hide definitional forks that decide what the metric even means. Operational hours can be powered on time, commanded motion time, or scheduled production time, and each yields a different reliability picture; idle-but-energized hours in the numerator will inflate the result without any change in how the robot performs. The denominator is harder still: a failure has to be defined before it can be counted. Decide whether a failure is any stop, only an unplanned stop, only a stop requiring maintenance, or only one that breaches a safety function, because under ISO 10218 a safety-relevant fault and a nuisance trip are not the same event and should not share a bucket.

The source data usually spans three systems that were never designed to agree. Runtime lives in the robot controller or a manufacturing execution layer; failure events live in a maintenance or work order system; safety-function faults live in the safety controller and its logs. Join them on machine identifier and timestamp, and reconcile the clocks, since a maintenance ticket opened hours after the controller logged the stop will misalign the failure with the operational window it belongs to. Confirm every system covers the same fleet and the same period, or the numerator and denominator will describe different robots.

Segment before averaging. A cell of collaborative robots working alongside people fails differently from caged industrial arms, and blending them produces a fleet number that describes neither. Split by robot class, duty cycle, and application, and separate infant mortality on newly commissioned units from steady state operation, because early commissioning failures will drag a young fleet's figure down for reasons unrelated to design reliability. The instrumentation pitfall specific to this metric is failure definition drift: as teams reclassify what counts as a failure, or as operators absorb faults through manual intervention that never reaches the log, the failure count changes while the machines do not, and the trend line moves for reasons that have nothing to do with reliability.

Common Pitfalls

Many organizations underestimate the importance of MTBRF, leading to reactive rather than proactive maintenance strategies.

• Neglecting routine maintenance schedules can cause unexpected failures. Regular checks and updates are essential to ensure robots operate at peak performance and avoid costly downtime.
• Failing to analyze failure data prevents organizations from identifying patterns. Without this analytical insight, companies miss opportunities to implement preventive measures and improve MTBRF.
• Overlooking operator training can lead to misuse of robotic systems. Proper training ensures that staff can effectively manage and troubleshoot robots, reducing the likelihood of failures.
• Ignoring environmental factors, such as temperature and humidity, can impact robot performance. Maintaining optimal conditions is crucial for maximizing the lifespan and reliability of robotic systems.

Improvement Levers

Enhancing MTBRF requires a multifaceted approach that focuses on both technology and human factors.

• Implement predictive maintenance technologies to anticipate failures before they occur. Utilizing data analytics can help identify potential issues, allowing for timely interventions that enhance MTBRF.
• Invest in high-quality components and materials for robotic systems. Superior parts can significantly reduce failure rates and extend the operational lifespan of robots.
• Regularly train staff on best practices for operating and maintaining robotic systems. Well-trained operators are more adept at identifying early signs of trouble, which can prevent failures.
• Establish a robust feedback loop for continuous improvement. Encouraging operators to report issues and suggest enhancements fosters a culture of accountability and proactive problem-solving.

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OKRs That Use Mean Time Between Robot Failures (MTBRF)

Within the ISO 10218 KPI group, the objective to enhance the overall safety compliance level across robotic operations gives Mean Time Between Robot Failures (MTBRF) a supporting role rather than a headline one. The compliance key results the group lists, such as improving Robot Compliance with ISO 10218 and raising the Robotics Safety Audit Pass Rate, describe adherence, while reliability underwrites it: equipment that fails less often is easier to keep in a compliant state. A team can add a directional key result to extend mean time between failures over the period as evidence that the compliance gains rest on genuinely more reliable machines and not on paperwork alone. Frame any figure as a goal the team sets and describe the direction of improvement rather than lifting specific numbers.

A second framing ladders to the group objective of strengthening real-time safety controls to mitigate collision and operational hazards. That objective already tracks Emergency Stop Activation Frequency, and MTBRF pairs with it directly: a team can commit to raising reliability while lowering emergency stop activations together, so that a longer mean time between failures reflects fewer real faults rather than more manual halts. Holding both in the same objective keeps reliability honest and ties it to the hazard-reduction outcome the group actually names.

See OKR Examples for ISO 10218

KPI Categories

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