Key Points
- KPIs for condition-based maintenance can be organized beyond their metric category. One method is to organize by who needs to see them and what decision each number supports.
- KPIs only become actionable when they're connected to the workflows where maintenance decisions are made.
- Every role in the maintenance chain, from the plant manager justifying the budget to the technician completing the repair, needs different proof that the CBM program is working. Without role-specific visibility, the program produces data without producing confidence.
- Condition-based and predictive approaches can reduce maintenance costs by up to 25% and increase uptime by 10-20%, but only when KPIs are tracked in real time and tied to execution.
Relying on proof rather than hearsay
Three illustrations:
- A maintenance manager knows the CBM sensors have flagged three bearing faults this quarter, but when leadership asks whether the program is actually reducing downtime, the answer turns into a story. Why not just prove it with numbers?
- The reliability engineer suspects that MTBF has improved since the condition monitoring rollout, but proving it means pulling data from two systems and building a spreadsheet that's outdated before it's finished.
- The technician fixed the pump on the first visit because the alert told them exactly what was failing, but nobody tracks first-time fix rate, so that win disappears into the shift log.
These three illustrations all have one thing in common. That is, they represent a major indication that a condition-based maintenance program will likely stall.
The problem isn’t data collection, but visibility. Their sensors work, and the diagnostics are detecting real faults. But the KPIs that prove it, the ones that each role needs so they can trust the program and defend its value, either don't exist or live in disconnected tools that nobody checks until audit season.
The eight KPIs in this article are organized by the four roles that depend on them most. They are plant managers, maintenance managers, reliability engineers, and technicians. Each pair of KPIs prioritizes high-impact visibility that the role needs from a condition-based maintenance program to know, with confidence, whether it's working.
What the Plant Manager Needs to See
Plant managers need KPIs that connect condition-based maintenance performance to production outcomes and budget justification.
Plant managers don't analyze vibration spectra or review diagnostic reports. They answer leadership questions about whether equipment is running and whether maintenance spending is under control. The KPIs that matter at this level are the ones that translate condition data into numbers that justify the program's existence.
Availability
Availability measures the percentage of scheduled time an asset is actually operational and ready to produce. This is sometimes referred to as asset utilization.
Availability = (Operating Time / Planned Production Time) x 100
A facility running at 85% availability on a critical production line is losing roughly 1.5 hours per 10-hour shift to unplanned stops. That's time the plant manager has to account for in production forecasts, delivery commitments, and capacity planning.
When availability is calculated from real-time condition monitoring data rather than end-of-month spreadsheets, it becomes a leading indicator. The plant manager can see trends forming before they become production misses rather than documenting losses after the fact.
World-class operations target 90% or higher for critical assets. The gap between current performance and this benchmark represents the operational opportunity that a condition-based maintenance program should be closing.
Planned maintenance percentage (PMP)
PMP reveals how much of your maintenance effort is proactive versus reactive.
PMP = (Planned Maintenance Hours / Total Maintenance Hours) x 100
A widely accepted benchmark for ‘world-class’ operations is a target of 80-90% PMP. The industry average sits around 55%, which means many teams spend nearly half their time responding to failures they didn't anticipate.
A plant manager presenting a 55% PMP to leadership is presenting a program where almost half the maintenance budget is consumed by work the team couldn't plan for.
That's a number that invites scrutiny rather than confidence. A condition-based program should push PMP upward by converting reactive events into planned interventions, giving the plant manager a trend line that demonstrates the program is shifting the balance.
What the Maintenance Manager Needs to See
Maintenance managers need KPIs that reveal whether condition insights are translating into faster repairs and disciplined execution.
The maintenance manager sits between the reliability engineer's analysis and the technician's wrench. Their concern isn't whether sensors are detecting faults. It's whether those detections are changing how the team works day to day. Are repairs happening faster? Is the schedule being followed? These two KPIs answer those questions directly.
Mean time to repair (MTTR)
MTTR measures the average time required to restore an asset to operational status after a failure.
MTTR = Total Repair Time / Number of Repairs
High MTTR usually points to three problems:
- Parts weren't available when the technician arrived
- The diagnosis wasn't clear enough to guide the repair
- The team had to troubleshoot on-site because the alert didn't provide sufficient context
In a condition-based program, MTTR should decrease over time because condition insights give the maintenance team advance notice of what's failing, how severe it is, and what parts and procedures are needed.
When that information reaches the team before the failure becomes critical, the repair window compresses. The maintenance manager who tracks MTTR alongside condition-based alert volume can see whether diagnostic quality is translating into execution speed.
Preventive maintenance compliance (PM compliance)
PM Compliance measures whether scheduled preventive maintenance tasks are actually completed within their required windows.
PM Compliance = (Completed PMs / Scheduled PMs) x 100
A monthly PM should be completed within plus or minus three days of its target date to count as on-time. Applying the 10% rule provides even more precision to this target. A PM schedule that isn't followed is virtually worthless.
Slipping compliance is often invisible without real-time tracking. A maintenance manager reviewing last month's data may not realize that compliance dropped from 90% to 74% during the third week until the consequences surface as an unplanned failure or an audit finding.
Condition-based programs compound this risk when condition alerts generate additional work that competes with scheduled PMs for the same technician hours. If the maintenance manager can't see compliance in real time, the reactive work triggered by condition alerts may quietly be displacing the preventive work that keeps the broader program healthy.
What the Reliability Engineer Needs to See
Reliability engineers need KPIs that validate whether condition monitoring is catching faults earlier and improving asset performance over time.
The reliability engineer is the person who evaluates whether the condition-based maintenance investment is producing diagnostic value. Their role demands proof not just that sensors are collecting data, but that the data is reducing the time between fault detection and corrective action. These two KPIs provide that proof from different angles.
Mean time between failures (MTBF)
MTBF measures the average operating time between failures, indicating equipment reliability over a given period.
MTBF = Total Operating Time / Number of Failures
For the reliability engineer, the trend matters more than the absolute number. A pump with an MTBF of 500 hours isn't inherently good or bad. That depends on the asset type, its criticality, and its operating context. But if that same pump's MTBF was 400 hours six months ago, the upward trend confirms that condition-based interventions are catching degradation earlier and extending the operating window between failures.
A declining MTBF, on the other hand, signals that something is being missed. Either the monitoring system isn't detecting the relevant failure modes, the diagnostic guidance isn't specific enough to drive effective repairs, or the corrective actions aren't addressing root causes.
MTBF is the reliability engineer's primary validation metric for whether the condition-based program is actually producing earlier detection. Without it, the case for continued investment rests on anecdote rather than evidence.
Overall equipment effectiveness (OEE)
OEE combines availability, performance, and quality into a single metric that reveals how effectively equipment is being used during scheduled production time.
OEE = Availability x Performance x Quality
While it is widely recognized that world-class operations can achieve roughly 85% OEE, the reality is that most manufacturing facilities operate between 60%-65%. That means roughly a third of scheduled production capacity is lost to some combination of downtime, speed reductions, and quality defects.
Where CBM connects to OEE
For reliability engineers, the availability component is the most directly influenced by condition-based maintenance. But performance and quality also carry diagnostic signals. A machine running at reduced speed may be compensating for a developing mechanical issue that vibration analysis should be catching. A rise in defect rate may correlate with asset degradation, which the condition monitoring system could flag before it affects production output.
A facility that tracks availability in isolation but ignores OEE may miss these correlated signals, treating symptoms individually rather than connecting them to a common equipment health picture.
What the Technician Needs to See
Technicians need KPIs that show whether they're arriving at the right asset, making the right diagnosis, and completing the fix on the first visit.
Every upstream KPI discussed in this article, from availability to MTBF to PMP, ultimately depends on what happens when the technician reaches the machine. If the diagnosis is wrong, the parts aren't staged, or the work instructions don't match the actual condition, the repair takes longer, the asset stays down, and the numbers everybody else watches get worse. The technician's KPIs should reflect execution quality, not just volume.
First-time fix rate
First-time fix rate measures the percentage of repairs completed successfully on the first visit without requiring a return trip.
This is the most direct operational proof that condition-based diagnostics are working at the execution level. When a technician arrives knowing what's wrong, how severe it is, and which parts and procedures are needed, the fix is done on the first visit.
When the alert is vague, the diagnosis is incomplete, or the work order lacks context, the technician troubleshoots on arrival, discovers they need a different part, or realizes the actual failure doesn't match what the system reported. That means a second trip, more downtime, and a higher MTTR that cascades upward through every other KPI in the chain.
A condition-based maintenance program that can't improve first-time fix rate isn't delivering diagnostic value to the people who actually do the work.
Work order completion rate
Work order completion rate tracks the percentage of assigned work orders completed within a given period.
For technicians, this KPI reflects whether priorities are clear and whether their shift is spent on productive maintenance rather than chasing false alarms, waiting for parts, or navigating unclear instructions.
When condition-based work orders are generated with diagnostic context attached, technicians can prioritize with confidence. They know which alert is urgent, which can wait, and what the resolution looks like before they walk to the asset.
Without visibility into their own completion rate, technicians have no feedback loop on whether their effort is moving the program forward or just keeping them busy.
How Tractian Delivers KPI Visibility
Tractian's platform surfaces and supports the KPIs each role depends on through integrated condition monitoring, AI-powered diagnostics, maintenance execution workflows that update in real time, and plug-and-play production monitoring that provides OEE visibility for any machine.
The KPIs we’ve outlined aren’t new concepts or math. But, they require visibility that's connected to execution. And it’s that connection that is the difference between a program that tracks these numbers and one that acts on them.
Tractian closes the gap by integrating condition monitoring with a maintenance execution platform, so that every insight, work order, and completed repair automatically feeds into the same KPI framework.
For the plant manager, Tractian's real-time dashboards track availability, PMP, and backlog without manual report compilation. Every work order is categorized as planned or unplanned at creation, so PMP is always current. The plant manager sees live performance data rather than waiting for someone to compile a monthly summary that's already outdated by the time it arrives.
For the maintenance manager, MTTR and PM compliance are calculated automatically as work is completed and logged. Condition-based alerts generate work orders with diagnostic context attached, so the maintenance manager can track whether condition insights are producing faster, better-targeted repairs. The mobile execution platform ensures that data is captured at the point of work, not reconstructed from memory hours later.
For the reliability engineer, Tractian's AI-powered diagnostics detect all major failure modes through continuous monitoring of vibration, ultrasound, temperature, and magnetic fields. MTBF trends are tracked per asset and benchmarked at three levels, against the machine's own historical baseline, against similar assets within the facility, and anonymously against industry data.
Root cause analysis tools and spectral analysis workspaces give reliability engineers the analytical depth to validate every condition-based decision, and automatically calculate KPIs like MTBF, MTTR, availability, and reliability rather than manually.
For OEE specifically, Tractian's plug-and-play production monitoring connects directly to machines through clip-on IoT sensors that capture cycle times, stoppages, and quality events in real time, feeding availability, performance, and quality data into live OEE dashboards without manual collection or operator-dependent logging.
This gives reliability engineers the production-side data they need to correlate equipment health trends from condition monitoring with actual OEE impact on the line.
For the technician, prescriptive alerts arrive with clear guidance on what is wrong, how severe it is, and what to do next. AI-generated SOPs standardize execution so the technician isn't relying on memory or hunting for procedures. The mobile app with QR code access, offline capability, and built-in team communication ensures they have the information they need before they reach the asset, directly improving first-time fix rate and work order completion.
What makes this work as a KPI framework is that every layer feeds the same system.
- The condition sensor detects the fault.
- The production monitoring system captures the performance impact.
- The platform diagnoses it and generates the work order.
- The technician executes the repair and logs the result.
- The KPIs update.
- The reliability engineer validates the trend.
- The plant manager sees the outcome.
No export, no manual calculation, no delay between detection and visibility.
Learn more about Tractian's condition monitoring and KPI tracking to see how high-quality, decision-grade IoT data transforms your program into AI-powered closed-loop maintenance execution workflows.
FAQs about Condition-Based Maintenance KPIs
- How do condition-based maintenance KPIs differ from standard maintenance metrics?
Standard metrics describe what has already happened. Condition-based maintenance KPIs shift toward leading indicators by connecting real-time asset health data to performance trends. Instead of counting failures after the fact, CBM KPIs track whether the program detects faults earlier and converts insights into planned work. - What is a good MTBF for condition-based maintenance?
MTBF varies by asset type, criticality, and operating context. The absolute number matters less than the trend. A rising MTBF confirms that condition-based interventions are catching degradation earlier and extending the operating window between failures. A declining MTBF signals that detection or corrective actions need attention. - How does condition monitoring improve first-time fix rate?
Condition monitoring with prescriptive diagnostics gives technicians a clear picture of what's wrong, how severe it is, and what parts and procedures are needed before they arrive at the asset. That diagnostic clarity reduces troubleshooting time, eliminates return visits, and directly improves first-time fix rate. - How do I prove ROI on a condition-based maintenance program?
Track PMP, availability, and MTTR over time. Improving PMP demonstrates the shift from reactive to planned work. Rising availability shows the production impact. Falling MTTR confirms that condition insights are producing faster, more targeted repairs. Together, these KPIs translate directly into reduced downtime costs, lower emergency spending, and higher output.
