Warehouses and distribution centers depend on maintenance to run effectively and efficiently. If dock doors fail, conveyors stop, or HVAC issues put inventory at risk, the business feels it fast. Downtime goes up. Orders get delayed. Labor gets wasted. Audits are failed.

That’s why you need a solid set of maintenance KPIs that can guide your strategy. The right metrics tell you whether your team is preventing problems, responding efficiently, and building enough control to support the wider operation. Ultimately, numbers on a dashboard are just the start—the real value of maintenance KPIs for facilities leaders is the action they drive.

In this guide, you will find the maintenance KPIs that matter most for industrial facilities like warehouses and distribution centers, how to calculate them, what they tell you, benchmarks, and how to improve them.

Key takeaways

• Track a balanced set of KPIs, not just one or two. PM compliance without reactive work, backlog, and downtime context can hide real problems.
• For facilities maintenance, the most useful core KPIs are PM compliance, planned vs. reactive work, schedule compliance, backlog, MTTR, asset availability, maintenance costs, found failure rate, and mean time between failures.
• If your data is weak, start by standardizing work orders, priorities, labor hours, asset hierarchies, and failure codes. Better KPI reporting starts with better execution data.

Nine maintenance KPIs for facilities

1. PM compliance

What does it measure?: If scheduled preventive maintenance tasks were completed on time.

How to calculate PM compliance: PM compliance = (PM work orders completed on time / PM work orders scheduled) x 100

For example, let’s say you scheduled 120 PM work orders in March and completed 108 by their due date. In this situation, your PM compliance is 90%

Why it matters: For warehouses and distribution centers, PM compliance is one of the clearest signals of whether your team is staying ahead of failures. If inspections and routine preventive maintenance tasks slip, you usually see the impact later in downtime, emergency work, or compliance risk.

Benchmark: World-class organizations usually achieve 90% PM compliance or higher with 80% being the standard benchmark for mature maintenance teams.

How to improve it: Start by reducing PM overload. Many teams miss PM targets because the schedule is unrealistic, not because technicians are underperforming. Review frequencies, remove low-value tasks, and separate critical PMs from low-risk routines. Then improve weekly scheduling, parts readiness, and access coordination with operations.

2. Planned maintenance percentage

What does it measure?: How much of your maintenance labor goes to planned work versus reactive work. That doesn’t necessarily mean all of that work is preventive maintenance. Run to failure repairs can also be planned and scheduled.

How to calculate planned maintenance percentage: Planned maintenance % = (Planned maintenance labor hours / Total maintenance labor hours) x 100

Let’s say your team logged 1,600 maintenance labor hours in a month. Of those, 12,000 were part of planned work. In this scenario, your planned maintenance percentage (PMP) is 75%.

Why it matters: A high reactive percentage usually means more disruption, overtime, and rushed decisions, with less time for root cause work. Planned maintenance should be measured using labor hours, not just work order counts, because counts can understate the true operational burden.

Benchmark: World-class PMP is generally considered to be 90% or higher, with over 80% being the mark of a good facility maintenance team. Anything between 65% and 80% is typical for maintenance teams. If your PMP dips below 65%, you likely are doing too much reactive maintenance.

How to improve it: Tighten priority definitions first. Many teams label too much work as urgent. Then review top sources of reactive work by asset and failure mode. In warehouses, the repeat offenders are often material handling equipment, dock systems, HVAC, compressors, lighting, and charging infrastructure.

3. Schedule compliance

What does it measure?: Whether the work you committed to for the week actually got done.

How to calculate schedule compliance: Schedule compliance = (Scheduled work completed / Total scheduled work) x 100

For example, you may have scheduled 320 labor hours for last week and completed 288 of those hours as scheduled, which makes your schedule compliance 90%.

Why it matters: A team with a low schedule compliance percentage is typically facing too many disruptions to complete their work on time. Finding the root causes of these disruptions means that your team can act more efficiently and maintain equipment to reduce downtime.

Benchmark: A good directional benchmark is 90% or more. Below that, you should ask whether the issue is poor planning, too much break-in work, missing parts, or weak coordination with operations.

How to improve it: Build a realistic weekly schedule, not an aspirational one. Protect capacity for break-in work. Make sure planned jobs are kitted, permitted, and ready before they hit the schedule.

4. Maintenance backlog

What does it measure?: The amount of identified work waiting to be completed, usually in crew weeks.

How to calculate maintenance backlog: Backlog in weeks = Estimated labor hours in backlog / Available weekly labor hours

For instance, you could have 900 estimated labor hours in your backlog maintenance tasks and your team has 180 available labor hours per week. That means your backlog is 5 weeks.

Why it matters: Backlog is not automatically bad. A healthy backlog gives planners time to prepare jobs, stage materials, and schedule work efficiently. Too little backlog can mean poor work identification. Too much backlog usually means your team is overloaded or your prioritization is weak.

Benchmark: Use five to seven crew weeks as a practical benchmark for ready, planned work. For facilities teams, you may also track separate backlogs for corrective work, compliance work, and deferred low-priority repairs.

How to improve it: First, sort backlogged tasks by risk, compliance impact, business impact, and readiness. Then separate true backlog from stale requests, duplicates, and work that should be capital or vendor-managed.

5. Mean time to repair (MTTR)

What does it measure?: The average time needed to restore an asset after failure.

How to calculate MTTR: MTTR = Total repair time / Number of repair events

For example, a conveyor broke down three times in the last month. The repairs took a total of 12 hours to complete and get the conveyor back online. That means the MTTR for the conveyor is four hours.

Why it matters: MTTR affects throughput, dock velocity, labor efficiency, and service levels. It is especially useful for critical systems where even short disruptions create ripple effects.

Benchmark: There is no single universal MTTR benchmark because asset complexity varies too much. For facilities, the better benchmark is internal—get a benchmark for each asset or failure mode, then try to improve over time.

How to improve it: Focus on diagnostics, standard job plans, spare parts availability, technician access to asset history, and clear escalation paths. If MTTR stays high, the issue is often planning, parts, or troubleshooting quality, not technician effort alone.

6. Asset availability

What does it measure?: The percentage of time a critical asset or system is able to perform its intended function.

How to calculate asset availability: Availability % = (Total scheduled time - Downtime) / Total scheduled time x 100

Let’s say that your facility has a sortation conveyor that’s scheduled to run 300 hours in a month. It experiences nine hours of downtime. In this case, the availability of the conveyor is 97%.

Why it matters: For warehouses and distribution centers, uptime is often the most important KPI operations. It translates maintenance performance into the results that business leaders care about most, like on-time shipments, labor efficiency, and revenue per unit.

Benchmark: Organizations with asset availability of 95% or above for operational critical equipment are the gold standard. However, your target should reflect asset criticality. A backup exhaust fan and a main conveyor line should not share the same uptime goal.

How to improve it: Pair availability with failure frequency and repair speed. High uptime can hide recurring short failures. Improve PM quality, critical spares, root cause analysis, and response coordination with operations.

7. Mean time between failures (MTBF)

What does it measure?: The average operating time between one failure and the next for a specific asset or asset class.

How to calculate MTBF: MTBF = Total operating time / Number of failures

For example, a conveyor system ran for 720 hours in a month and failed 6 times. In this scenario, the MTBF would be 120 hours.

Why it matters: MTBF helps you understand reliability. That’s especially important for conveyors, dock doors, compressors, sortation equipment, and battery charging systems. A team can have a decent MTTR and still be stuck in a cycle of recurring failures. MTBF helps show whether assets are actually becoming more stable over time.

Benchmark: There is no universal MTBF benchmark because it depends heavily on the asset type, operating hours, environment, and criticality. A dock door in a high-traffic facility should not be compared to a rooftop unit that cycles less frequently. For this KPI, the most useful benchmark is usually internal with a goal of increasing MTBF month over month or quarter over quarter.

How to improve it: Start by identifying the assets with the lowest MTBF and the biggest operational impact. Then review repeat failure modes, improve PM task quality, tighten inspection routines, and standardize corrective repairs. If MTBF is flat or getting worse, the problem is often weak root cause follow-through, poor repair quality, or PMs being completed without actually reducing failure risk.

8. Maintenance costs

What does it measure? The total cost of maintaining assets, tracked overall or segmented by asset, asset class, site, or work type.

How to calculate maintenance costs: Total maintenance cost = Labor cost + Parts and materials cost + Contractor/vendor cost + Other maintenance-related expenses

You can also break this into more useful views, such as:

• Maintenance cost per asset
• Maintenance cost by asset class
• Reactive maintenance cost vs. planned maintenance cost

For example, let’s say a facility spend the following in a single quarter:

• $85,000 on internal labor
• $30,000 on parts and materials
• $20,000 on contractors

The total maintenance cost is $135,000. If 40% of that came from reactive work, reactive maintenance costs were $54,000.

Why it matters: Cost is one of the clearest ways to connect maintenance performance to business outcomes. Facilities leaders care about staying on budget, controlling contractor use, and understanding where spend is rising. Maintenance cost also helps reveal whether the team is paying a premium for reactive work, repeat failures, or poor asset decisions. On its own, cost does not tell the full story, but paired with uptime, reactive work, and backlog, it gives leadership a much clearer view of maintenance performance.

Benchmark: Public cost benchmarks are harder to use cleanly because labor rates, asset usage, and facility conditions vary so much. That makes internal benchmarking more practical than broad external comparisons. A good way to benchmark costs at your facility includes segmenting costs by time period, asset/asset group, maintenance type (preventive vs. reactive), and/or cost type (parts, labor, etc.). Take context into account as well. For example, one month may see more intensive production than others, meaning more reactive maintenance. Make a plan to target one area and improve it every month or quarter.

How to improve it: Break maintenance costs into meaningful buckets first. If you only track total spend, you will not know what is actually driving it. Review which assets consume the most labor, parts, and contractor support. Then look for patterns: repeat failures, overtime, emergency callouts, poor PM execution, or excessive parts usage. The goal is to shift spend away from disruption and toward planned, lower-risk work.

9. Found failure rate and corrective compliance rate

What does it measure?: How often PMs uncover issues and whether the corrective work from those findings is completed on time.

How to calculate found failure rate and corrective compliance rate: Found failure rate = (Number of PMs that identified a failure or actionable defect / Total PMs completed) x 100

Corrective compliance rate = (Corrective work orders from PM findings completed by due date / Total corrective work orders created from PM findings) x 100

For instance, your team completed 200 PMs and 40 of those PMs found an issue that required follow-up work. Your found failure rate is 20%. Those 40 PM findings generated 30 corrective work orders with 24 being completed by their due date, making your corrective compliance rate 80%.

Why it matters: PM compliance alone does not tell you whether preventive maintenance is effective. A team can close PMs on time and still miss developing problems or fail to act on what they find. These two KPIs make PM performance much more meaningful. Found failure rate shows whether inspections are actually surfacing issues. Corrective compliance rate shows whether the organization follows through before those issues turn into failures, downtime, or safety risk.

Benchmark: There is no universal public benchmark for either KPI because results vary based on PM maturity, asset condition, and inspection quality. For found failure rate, a very low number may mean inspections are too superficial, unnecessary, or technicians are not documenting issues consistently. A very high number can mean PMs are effective at catching issues, or that assets are deteriorating and the team is finding too much, too late.

For corrective compliance rate, critical corrective work from PM findings should be completed at a very high rate and on time. Lower-priority findings can be managed through backlog, but they should still be tracked In practice, both metrics are best benchmarked internally by asset class and criticality.

How to improve it: Start by making PMs more inspection-driven and less checkbox-driven. Make it easy for technicians to record defects, attach photos, and trigger follow-up work directly from the PM. Then prioritize corrective work based on risk and asset criticality so findings do not disappear into the backlog. If found failure rate is suspiciously low, review inspection quality and PM frequencies. If corrective compliance is weak, the issue is usually prioritization, scheduling, or lack of ownership after the PM is closed.

How to use maintenance KPIs together

Looking at KPIs in isolation often doesn’t tell you the full story about the health and impact of your maintenance program. Without connecting metrics together and spotting trends across your dashboard, you’ll be unable to make good decisions or any decision at all for improving maintenance.

A practical way to combine KPIs is to group them into three categories that represent higher-level elements of your maintenance program:

• Control metrics: Like PM compliance, schedule compliance, and backlog
• Disruption metrics: Like planned maintenance percentage, MTTR, and repeat failures
• Outcome metrics: Like availability, MTBF, and maintenance costs

Each group answers a different question.

• Control metrics tell you if your plan is working. Are PMs getting done? Is the weekly schedule realistic and protected? Is there enough ready work in the pipeline?
• Disruption metrics tell you how much instability the team is dealing with, both with its internal processes and external influences. Are parts unavailable when needed? Are repairs taking too long? Are the same assets failing again and again?
• Outcome metrics tell you whether the operation is actually improving. Are critical assets becoming more reliable? Is uptime more consistent? Are costs more predictable?

The value comes from reading the relationships between them. For example:

• High PM compliance and low found failure rate can mean either that PMs are being scheduled too frequently and draining your resources or that the PMs aren’t being completed thoroughly enough because of vague or incomplete procedures.
• High backlog and low schedule compliance often means planning is weak or break-in work is too high.
• Low MTTR and MTBF can mean the team is responding quickly, but not fixing root causes.
• Stable costs and worsening uptime can mean that maintenance isn’t getting enough time or access to equipment to maintain it properly and prevent downtime.
• High corrective compliance and rising MTBF is usually a strong sign that preventive work is actually reducing failures.

A good KPI program should help you interpret cause and effect and take action based on the insights. If your KPIs cannot answer the questions you have or don’t lead to any material change in your maintenance strategy, you probably have too many metrics or the wrong ones.

How to segment your KPIs

Segmenting your KPIs lets you dig into why the numbers are what they are so you can make informed and targeted decisions that improve maintenance and reliability. It uncovers recurring failures, bad actor assets, and problems with your processes, whether it’s response procedures, parts purchasing, or something else.

Here are a few ways to segment your metrics:

• Asset criticality: See whether the team is protecting the assets that matter most. A missed PM on a low-risk exhaust fan is not the same as a missed PM on a critical conveyor.
• Asset class: Find where reliability problems are concentrated. If MTBF is improving for HVAC but worsening for dock doors, you know where to focus your improvement efforts.
• Site: compare how different facilities operate. This matters for regional leaders trying to standardize processes and understand why one site is performing better or worse.
• Shift: Expose standardization and training gaps. For example, repeat failures may happen more on the overnight shift or data quality problems trace back to documentation on one shift versus another.
• Labor source: See whether contractors are being used strategically or as a patch for weak internal capacity, missing skills, or recurring breakdowns.
• Priority: Identify whether the organization is truly in control of urgency or simply labeling too much work as urgent.
• Maintenance type: Spot trends in planned vs. reactive maintenance to understand the value of maintenance, the effectiveness of your PM program, and what assets are causing the most reactive work.

How to get the maintenance data you need for your KPIs

Teams often assume they have a reporting issue when they really have an execution issue. 

The dashboard looks messy because the work is being logged inconsistently. Labor hours are missing. Priority codes mean different things to different people. PMs are closed without useful findings. Corrective work gets buried in a generic work order bucket. In that environment, even a good KPI can point you in the wrong direction.

Good metrics depend on a few basic building blocks.

1. A consistent asset hierarchy: If one conveyor is listed three different ways in the system, or critical assets are not clearly identified, your failure and cost trends will be unreliable.
2. Clear work order types: PM, corrective, reactive, emergency, inspection, and compliance work should be tagged clearly to distinguish planned work from disruption.
3. Labor and cost tracking. Work order counts are useful, but they do not show how much time or money the team is actually spending.
4. Priority definitions that reflect business impact. Emergency should mean immediate risk to safety, compliance, or operations, not that someone is asking loudly.
5. Basic failure coding and follow-up discipline. You need a way to tell the difference between failures or repeat issues will stay invisible.

A practical starting point looks like this:

• Standardize work order categories (ex. PM, corrective, reactive, etc.)
• Require labor hours on every closed work order
• Define priority codes
• Build a clean asset register for critical systems first
• Add simple failure codes for repeat issues
• Make PM findings able to trigger corrective work directly

It also helps to build in a few simple governance habits:

• Review a sample of closed work orders each week for data quality
• Correct bad priority usage quickly
• Make supervisors accountable for missing labor hours or vague closeout notes
• Audit PM completion for quality
• Keep definitions visible so every technician logs work the same way

Most teams do not need more KPIs. They need better inputs. That is why it is smarter to start small. Build trust in a focused set of well-defined metrics first. Once your work order data is consistent and your asset structure is clean, you can always add more detail later.

The value of your maintenance KPIs is in the action they drive

Maintenance teams in warehouses and distribution centers do not need a giant dashboard. They need a focused scorecard that guides their decision-making and drives action on a daily basis. That is how KPIs become useful. Not as reporting theater, but as a way to make better maintenance decisions before the next disruption hits.