Maintenance teams track dozens of metrics, but only a handful carry real weight with plant leadership. The gap between what technicians measure and what executives act on creates a persistent challenge. Valuable work goes unrecognized because the data doesn't connect to outcomes that matter to leadership.
Plant managers evaluate maintenance through a specific lens. They want to know:
- Will the equipment be available when production needs it?
- How much unplanned downtime is costing the operation?
- Is the maintenance program improving or declining over time?
Metrics that answer these questions earn attention. Metrics that document activity without connecting to results are often filed away and forgotten.
This article identifies the asset performance metrics that resonate with plant-level leadership and explains how to present maintenance data in terms that demonstrate value. For maintenance and reliability professionals looking to communicate upward more effectively, the difference often comes down to selecting the right metrics and framing them in business language.
Key Points
- Outcome-focused metrics win attention: Executives prioritize metrics that directly link to production, cost, and risk, rather than activity counts or compliance percentages.
- Translation matters as much as data: The same information can land differently depending on whether it's framed as technical output or business outcome.
- The right metrics support resource decisions: Metrics that help leadership make decisions about budget, headcount, or capital investment earn ongoing attention and demonstrate maintenance value.
Why Executives Prioritize Certain Metrics Over Others
Plant leadership evaluates metrics through a consistent filter of risk, cost, and operational impact.
Understanding this lens helps maintenance professionals select and present data that resonates.
Plant managers operate under pressure to deliver production targets while controlling costs and managing safety. Their attention gravitates toward metrics that help them anticipate problems, allocate resources, and report upward to corporate leadership with confidence. A metric that answers "how does this affect production?" or "what does this cost us?" earns consideration. A metric that answers "how many tasks did we complete?" often doesn't.
This creates a challenge for maintenance teams accustomed to tracking activity. Work order counts, logged labor hours, and completed PM tasks all matter operationally. But they describe effort rather than results.
When these metrics dominate reporting, leadership may struggle to see the connection between maintenance investment and business outcomes. Teams in this position often find it harder to justify budget requests or secure headcount because the data doesn't speak the language executives use to make decisions.
The metrics that matter most share a common trait. That is, they connect maintenance activities to outcomes that leadership already tracks.
- Availability ties to production capacity.
- Downtime ties to lost revenue.
- Repair efficiency ties to recovery speed when problems occur.
Framing maintenance data in these terms positions the function as a business driver rather than a cost center.
The Metrics Plant Leadership Care About
Not all metrics carry equal weight in executive conversations. Some connect directly to business outcomes while others serve internal tracking purposes. Knowing the difference helps maintenance teams focus their reporting on what leadership actually uses.
The metrics below are grouped by executive priority. Outcome metrics come first because they answer the questions plant managers ask most frequently. Leading indicators follow because they predict future performance. Financial measures also round out the picture, but we’ll save those for another time.
Outcome Metrics
Availability and downtime
Availability measures the percentage of time equipment is operational and ready when production needs it.
For plant leadership, this metric translates directly to capacity. An asset running at 95% availability delivers more output than one running at 85%, and the gap compounds across multiple machines and shifts.
Availability = ((Scheduled Time − Downtime) / Scheduled Time) × 100
If a packaging line was scheduled to run 720 hours last month and experienced 36 hours of total downtime, availability would be ((720 − 36) / 720) × 100 = 95%.
Downtime is the inverse of availability, and it comes at a price. When a critical asset goes down unexpectedly, the costs extend beyond the repair itself. Production stops, orders slip, overtime accumulates, and secondary damage may occur if the failure cascades. Quantifying downtime in dollars rather than hours helps executives understand the true impact of reliability gaps.
Tracking downtime by cause adds another layer of value. Not all downtime stems from maintenance issues. Some results from operational decisions, material shortages, or external factors.
When maintenance teams can isolate their portion of downtime and show improvement over time, they build credibility and demonstrate program effectiveness.
Mean time between failures (MTBF)
Mean Time Between Failures measures the average operating time between breakdowns for repairable equipment. A rising MTBF indicates improving reliability. A declining MTBF signals emerging problems that may require attention before they escalate.
For executives, MTBF serves as a predictive metric. It helps answer questions like "how often should we expect this asset to fail?" and "is our maintenance program extending equipment life?" These questions matter for budget forecasting, capital planning, and production scheduling. An asset with an MTBF of 500 hours requires different planning than one averaging 2,000 hours between failures.
MTBF = Total Operating Time / Number of Failures
If a compressor operated for 2,400 hours over a year and failed 4 times, its MTBF would be 2,400 / 4 = 600 hours. This means the plant can expect roughly 600 hours of runtime before the next failure, on average.
But the business value lies in interpretation rather than calculation. A maintenance team that can explain what MTBF trends mean for upcoming quarters, and what actions are improving or threatening those trends, provides leadership with actionable insight rather than raw numbers.
Mean time to repair (MTTR)
Mean Time to Repair measures the average duration from when an asset fails to when it returns to operation. Lower MTTR means shorter downtime events and faster recovery when problems occur.
MTTR = Total Repair Time / Number of Repairs
If a motor required repairs three times last quarter, taking 2, 4, and 3 hours respectively, the MTTR would be (2 + 4 + 3) / 3 = 3 hours.
This metric reflects several factors working together:
- Technician skill
- Parts availability
- Diagnostic speed
- Process efficiency
When MTTR is high, it often indicates bottlenecks in one or more of these areas. Perhaps parts aren't stocked locally. Perhaps diagnostics take too long because failure history isn't accessible. Perhaps procedures aren't standardized across shifts.
For plant leadership, MTTR directly impacts production. A failure that takes two hours to repair costs less than one that takes eight. Teams that demonstrate improved MTTR can quantify the value of recovered production hours, which translates easily into dollars that executives understand.
Leading Indicator Metrics
Planned vs. unplanned maintenance ratio
The ratio of planned to unplanned maintenance reveals whether a program operates proactively or reactively. Industry benchmarks suggest that mature maintenance programs target 80% planned work and 20% unplanned, though the right ratio varies by operation and asset criticality.
Planned Maintenance % = (Planned Work Orders / Total Work Orders) × 100
If a team completed 200 work orders last month, with 160 planned and 40 reactive, the planned maintenance percentage would be (160 / 200) × 100 = 80%, yielding an 80:20 ratio
This metric matters to executives because unplanned work typically costs more. Emergency repairs carry premium prices for expedited parts, overtime labor, and the hidden costs of disrupted schedules. Planned work, by contrast, allows for preparation: parts staged, labor scheduled during optimal windows, and procedures reviewed in advance.
A high percentage of reactive maintenance also signals underlying reliability gaps. Equipment that breaks down frequently hasn't received adequate preventive attention, or the preventive tasks being performed aren't addressing the actual failure modes.
Either way, a reactive posture tends to compound over time. Each emergency consumes resources that might otherwise go toward planned improvements, creating a cycle that becomes harder to break the longer it continues.
Schedule compliance and PM completion
Schedule compliance measures whether planned maintenance tasks get completed on time. It functions as a leading indicator where high compliance today predicts better reliability tomorrow, while low compliance signals that future problems are accumulating.
Schedule Compliance = (Completed Scheduled Tasks / Total Scheduled Tasks) × 100
If 47 of 50 scheduled PM tasks were completed on time last month, schedule compliance would be (47 / 50) × 100 = 94%.
Plant leadership values this metric because it answers a simple question. “Is the maintenance plan actually being executed?”
A sophisticated PM program means little if tasks consistently slip or get deferred. When schedule compliance drops, the gap between intended and actual maintenance widens, and the risks that PMs are designed to prevent begin to materialize.
Tracking PM completion rates also helps identify systemic issues. If compliance drops during certain shifts, on certain equipment, or during high-production periods, those patterns reveal where the program needs adjustment. Perhaps staffing is insufficient, or production pressures override maintenance scheduling, or certain tasks take longer than allocated. These insights help leadership address root causes rather than symptoms.
Backlog management
Maintenance backlog represents work that has been identified and scheduled but not yet completed. Some backlog is normal and healthy. It indicates that work is being captured and prioritized rather than forgotten. But a growing backlog, or backlog that ages without resolution, signals resource gaps or systemic problems.
Backlog (in weeks) = Total Backlog Hours / Weekly Labor Capacity
If a maintenance team has 400 hours of pending work and 100 available labor hours per week, the backlog represents 4 weeks of work. Tracking this figure monthly reveals whether the team is keeping pace or falling behind.
Executives care about backlog because it reveals whether the maintenance team can keep pace with demand. A controlled backlog that turns over regularly suggests adequate staffing and effective prioritization.
A backlog that grows month over month, or one where items routinely age past 90 days, suggests the team is falling behind. Left unaddressed, this pattern leads to deferred maintenance, increased reactive work, and the reliability problems that follow.
Breaking the backlog down by age provides additional insight. Knowing how many work orders are 30, 60, or 90 days old shows whether items are being managed or simply accumulating. This granularity helps leadership understand not just the size of the backlog but its trajectory and urgency.
Translating Metrics into Business Language
The same data can land very differently depending on how it's framed. Technical metrics describe what happened. Business language explains why it matters.
Consider the difference between reporting "MTTR improved by 15% this quarter" versus "repair times dropped from 4.2 hours to 3.6 hours on average, recovering approximately 200 production hours across the quarter."
Both statements describe the same improvement, but the second connects to outcomes leadership tracks. Production hours translate to units produced, which translate to revenue. The metric becomes meaningful because it's anchored to business results.
This translation isn't about spin or exaggeration. It's about context. Executives don't spend their days thinking about Mean Time Between Failures or PM compliance percentages. They think about whether production will hit targets, whether costs are under control, and whether risks are being managed. Metrics presented in those terms fit within existing mental frameworks rather than requiring on-the-fly translation.
The same principle applies to forward-looking statements. Rather than reporting "PM compliance reached 94%," explain what that compliance prevented or enabled. Consistent PM execution on critical compressors, for example, might correlate with zero unplanned failures over the past quarter.
That connection transforms a compliance percentage into evidence that the program is working. Leadership can see the link between maintenance activity and the reliability outcomes they care about.
What Makes Metrics Effective for Executive Reporting
Effective metrics share common characteristics that make them useful for leadership conversations. Understanding these traits helps maintenance teams select and present data that earns attention and supports decisions.
They connect to business outcomes
Metrics that answer "so what?" earn more attention than those that simply report activity. Work order counts, labor hours, and task completions all have internal value, but they describe effort rather than results. Executives want to know what that effort produced: higher availability, lower costs, reduced risk, or improved reliability. Metrics that make these connections explicitly require less interpretation and land more effectively.
They show trends, not just snapshots
A single data point lacks context. Knowing that MTBF is 450 hours this month means little without understanding whether that represents improvement, decline, or stability. Trending data over quarters or years reveals direction and supports forecasting. It also demonstrates program effectiveness in ways that snapshots cannot. Leadership can see whether investments in maintenance are producing returns or whether performance is drifting in the wrong direction.
They enable comparison
Numbers gain meaning through comparison. Benchmarking against previous periods shows improvement or decline. Comparing similar assets can reveal outliers that may require attention. Industry benchmarks, where available, provide external context. Without comparison, metrics float without an anchor. A 92% availability rate sounds reasonable until compared to a target of 97% or a previous quarter's 95%. Context transforms data into insight.
They support resource decisions
The most valuable metrics help executives make decisions. “Should we add headcount? Is this equipment worth repairing or replacing? Do we need to adjust the capital budget?”
Metrics that inform these questions earn ongoing attention because they serve a purpose beyond reporting. Teams that connect their data to pending decisions position maintenance as a strategic function rather than an operational cost.
How Tractian Supports Executive-Ready Metrics
Tractian gives plant leadership a unified asset performance management software that manages maintenance, monitors machines, and tracks the metrics that matter. Real-time dashboards automatically display MTBF, MTTR, availability, reliability, backlog, and maintenance costs, updated as work orders close and condition data flows in. No spreadsheets. No manual calculations. Just the visibility to act fast, plan smarter, and show real results.
However, it doesn’t only display numbers. Tractian AI transforms raw sensor data into actionable business intelligence, surfacing patterns and early warning signs that manual tracking would miss. Criticality-based alerts ensure the right issues get attention at the right time.
For plant managers, this means unified visibility across all monitored assets with clear ROI metrics on downtime prevention and cost avoidance, the kind of data that supports strategic resource allocation and capital planning decisions.
- Multi-site comparison and benchmarking capabilities let teams identify performance gaps, spot outliers, and validate what's working across facilities.
- Condition monitoring insights feed directly into reliability metrics, connecting every sensor alert and completed repair to outcomes leadership tracks: downtime avoided, savings realized, failures prevented.
- Custom reporting and Power BI integration ensure data arrives in the formats executives expect, without additional effort to compile it.
See how Tractian turns maintenance data into executive-ready insights by exploring real-time maintenance dashboard software with KPI indicators.
What industries benefit from executive-focused metrics reporting?
Industries with critical rotating equipment and low tolerance for unplanned downtime benefit most from structured metrics reporting:
- Automotive & Parts: High-speed production lines require tight visibility into availability and repair times to maintain throughput targets.
- Fleet: Shop equipment reliability directly affects turnaround times, making MTBF and backlog management essential for scheduling.
- Manufacturing: Continuous monitoring of motors, pumps, and conveyors requires metrics that link maintenance activities to production outcomes.
- Oil & Gas: Tracking reliability and compliance metrics supports both regulatory requirements and operational continuity.
- Chemicals: Process stability depends on early fault detection, making leading indicators like PM compliance critical for plant leadership.
- Food & Beverage: Production schedules leave little room for unplanned downtime, elevating the importance of availability and schedule compliance metrics.
- Mills & Agriculture: Seasonal processing windows make reliability metrics essential for protecting uptime during critical periods.
- Mining & Metals: Heavy equipment and harsh conditions require consistent tracking of MTBF and maintenance costs to manage risk and budget.
Frequently Asked Questions
What metrics do plant managers care about most?
Availability, downtime costs, MTBF, and the ratio of planned to unplanned maintenance typically carry the most weight because they connect directly to production and cost.
How should I present MTBF to non-technical leadership?
Frame MTBF as "average runtime before a breakdown" and connect it to production hours rather than formulas.
What is a good planned vs. unplanned maintenance ratio?
Industry benchmarks suggest 80% planned to 20% unplanned as a target for mature maintenance programs.
How often should I report metrics to plant leadership?
Monthly reporting works for most operations, with weekly visibility reserved for critical assets or active improvement initiatives.
What makes a metric effective for executive reporting?
Effective metrics connect to business outcomes, show trends over time, and support decisions about resources or priorities.
Why do some metrics fail to resonate with executives?
Metrics that measure activity without connecting to outcomes often fail to demonstrate value or support resource requests.
