5 Most Important Asset Management Performance Metrics

Asset-intensive industries like manufacturing, logistics, oil and gas, retail, and agriculture rely on effective asset management metrics to keep operations running smoothly. As management expert Peter Drucker (apocryphally) noted, "If you can't measure it, you can't manage it," and that rings especially true in maintenance and reliability.

Unplanned equipment failures can cost companies an average of $25,000 an hour. That’s why tracking the right performance metrics is so important; it helps MRO teams spot improvement opportunities, reduce downtime, and demonstrate real ROI.

By focusing on the right asset management KPIs, you can assess how well your asset strategy is working and tie day-to-day maintenance performance to real business outcomes. The right metrics give you valuable insights into operational efficiency and help you get more from every asset.

Key Takeaways

• Effective measurement drives improvement: Tracking performance metrics helps teams understand how their assets are really performing, spot what's not working, and show the impact of their maintenance work. It’s one of the most practical ways to prevent costly failures and downtime.
• Balanced metrics framework: To get the full picture, track a mix of reliability (asset availability, MTBF), efficiency (OEE), financial (maintenance cost), and productivity (work order completion rate) metrics.
• Data quality is non-negotiable: Whether using manual or automated systems, the accuracy, consistency, and timing of your data collection can make or break the value of your metrics. Using tools like a CMMS and IoT sensors will dramatically improve the quality of the data you collect.

Asset availability

Asset availability measures the percentage of time equipment is up and running when it’s needed. Put simply, it shows how much of the time an asset is doing what it’s supposed to do. The basic formula for availability is:

For example, if a machine ran for 200 hours in a month but was down for 10 hours (due to planned maintenance or breakdowns), its availability would be about 95%.

i.e. Availability = 200 / (200+10) = 0.952 = 95.2% 

Availability is one of the clearest indicators of asset reliability. When a critical asset goes down, production stalls, so higher availability usually means fewer delays, better output, and lower overall costs. A facility that maintains high availability can hit its performance targets more consistently and avoid the ripple effects of missed deadlines, idle staff, and rushed repairs.

What counts as “good” availability depends on the asset and the industry, but many teams aim for 90% or higher. For essential equipment, targets often push above 95%. If you’re consistently below 85–90%, that’s a sign something’s off, either you’re dealing with too many breakdowns, or repairs and maintenance are taking longer than they should.

To track availability accurately, you need to track all downtime consistently, whether it’s for unexpected failures or scheduled maintenance. Be clear about what counts as downtime and when the asset is considered available. A CMMS or digital log can help you record each event with start and end times and the reason for the stop. Even small changes like rescheduling preventive maintenance outside of peak hours or making sure spare parts are on hand can make a noticeable difference in uptime.

Mean time between failures (MTBF)

MTBF is a reliability metric that tracks the average amount of time a repairable asset runs before it fails. Essentially, it answers the question: How long can you run this equipment, on average, before it breaks down? 

Mathematically, MTBF is calculated as:

For example, if a machine ran for 1,000 hours over the past year and broke down four times, its MTBF would be 250 hours. i.e. MTBF = (1000/4) = 250 hours

That means, on average, it runs about 250 hours between failures. Another way to think of it: After any given repair process, you could expect about 250 hours of running time before the next unplanned failure (though, in reality, this is an average, not a guarantee).

A higher MTBF generally means better reliability. When you know an asset’s typical MTBF, you can schedule planned maintenance activities ahead of time before things break. For example, if a pump has an MTBF of 250 hours, you might plan an inspection every 200–225 hours to catch issues early and avoid unplanned downtime.

"Good" MTBF values are highly dependent on the asset type and its usage. There isn't a one-size-fits-all benchmark, but we can consider some guidelines. Many industrial companies aim for MTBF on the order of hundreds or even thousands of hours for critical machinery.

A few things to keep in mind when using MTBF:

• MTBF is an average. MTBF doesn’t predict exactly when the next failure will happen; it only predicts the average time between failures.
• MTBF needs enough data. If you’ve only logged one or two failures, your MTBF might not be reliable yet.
• MTBF assumes random failure. In reality, some asset failures happen in clusters due to specific root causes or wear-out patterns as equipment ages.

Overall equipment effectiveness (OEE)

OEE is a high-level metric that shows how well equipment is running compared to its full potential. It combines three key factors: availability, performance, and quality.

Here’s how each part of this equation breaks down:

• Availability = Actual operating time / Planned production time. This accounts for downtime losses.
• Performance = Actual production rate / Ideal production rate. This captures speed losses (slow cycles, minor stops).
• Quality = Good units produced / Total units produced. This addresses losses from scrap and rework.

Multiply those three percentages, and you get your OEE score. An OEE of 100% means the machine is running nonstop, at full speed, with zero defects, something that almost never happens in practice, but it’s the benchmark.

The original “world-class” OEE from Seiichi Nakajima was 85%. But a 2024 study showed that most manufacturers had an OEE closer to 55-60%.

To get accurate OEE data, use automated data collection where possible. Manual logging often misses short stops or undercounts scrap, which skews results. With real-time data, your team can pinpoint exactly where performance is slipping, whether it’s unplanned downtime, bottlenecks, or quality issues, and take action faster.

Improving OEE is one of the fastest ways to improve manufacturing performance and identify cost-saving opportunities.

Maintenance cost per asset

Maintenance cost per asset is a metric that measures how much money is spent on maintenance for each specific asset (or on average per asset) over a given time period. It helps answer: "How much does it cost to keep one asset running?" This can be expressed in a couple of ways:

• Average cost per asset: Total maintenance expenditure divided by the number of assets. For example, if your plant spent $500,000 on maintenance last year for 100 total assets, that's $5,000 per asset on average.
• Cost per specific asset: You can calculate the maintenance cost for each individual critical asset to identify which ones are most expensive to maintain. For instance, you might find one production line costs $50k/year in upkeep while another costs $30k.

In general, the formula for maintenance cost per asset is: 

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To get a clear picture of your actual maintenance cost per asset, make sure you’re including:

• Labor costs: Wages or salaries of maintenance teams, including overtime. If production operators perform some maintenance, you might also allocate a portion of their time cost.
• Materials and spare parts: The cost of replacement parts, lubricants, tools, and supplies used in repairs and preventive maintenance.
• Contracted services: Payments to external vendors for maintenance tasks (e.g., specialized calibration, major overhauls, or OEM service contracts).
• Preventive maintenance costs: Expenses for proactive maintenance strategies, like routine servicing, inspections, condition monitoring devices, etc.
• Unplanned repair costs: Any emergency repair expenses.
• Overhead related to maintenance: Sometimes, overhead like maintenance management software subscriptions, training for maintenance teams, or depreciation of maintenance equipment can be factored in.

When calculating maintenance cost per asset, don’t include capital expenditures, like major upgrades or full asset replacements. Those aren’t part of routine maintenance, though high maintenance costs might help justify future asset investments.

Tracking maintenance costs per asset helps with budgeting, cost control, and long-term planning. If your average cost jumps 10-15% in a year, you’ll want to know why - aging equipment? Rising parts costs? Inefficiencies?

It also helps flag problem assets. If similar machines cost $3,000 a year to maintain but one keeps racking up $10,000, that’s a good candidate for root cause analysis or even replacement. In some cases, if an asset’s maintenance cost hits 10% or more of its replacement value annually, it may be more cost-effective to invest in something new.

Work order completion rate

Work order completion rate tracks the percentage of assigned work orders completed within a given time frame. It’s a simple but effective way to see how well your team is keeping up with the workload and can help you prioritize maintenance activities. 

The calculation is straightforward:

For example, if your team received 150 work orders last month and closed out 135 of them, your completion rate = 135/150 = 90%. This means you closed 90% of your tasks, and 10% were left incomplete or overdue.

This metric helps you spot issues with staffing, scheduling, prioritization, or parts availability. A high completion rate usually means your team has the resources and processes to stay on top of work. A low rate often signals a growing backlog, which can lead to delays, reactive repairs, or missed preventive maintenance. This could be due to understaffing, poor scheduling, waiting on parts, or other inefficiencies that affect overall business performance.

In many organizations, a work order completion rate of around 90% or above is considered good for effective asset management. However, if your completion rate is below 85%, it may signal problems with labor, planning, or too much reactive work.

If your team consistently achieves above 95%, then either you are running an exceptionally tight ship or the volume of assigned work is very manageable. 90% completion is ideal, it shows most work is getting done, yet acknowledges there are always a few jobs that roll over due to valid reasons (parts delay, rescheduling, etc.). 

To make the most of this metric, don’t just look at the percentage, look at what’s not getting done and why. 

Here are some practical ways to improve your work order completion rate:

• Prioritize and plan: If your completion rate is low, recheck your prioritization to make sure the most important work gets scheduled first.
• Resource allocation: A persistent low completion rate could mean there are not enough resources (people or time) for the workload. Try to balance workloads and shift resources if some teams are stretched too thin.
• Parts and tools availability: Incomplete work orders often happen because a needed spare part wasn't in stock, causing a delay.
• Set realistic maintenance schedules: Sometimes, the issue is overly optimistic planning, assigning more work in a period than the team can realistically do.

By improving your completion rate, you reduce your maintenance backlog, meaning equipment issues and preventive maintenance tasks are addressed when they need to be and are less likely to snowball into bigger headaches.

Implementing effective measurement systems

Choosing relevant metrics is only half the battle, you also need a system for collecting accurate data and turning it into action. Metrics like MTBF, OEE, or completion rate only work if the data behind them is reliable.

Here are a few best practices for making sure your measurement system holds up:

• Define your metrics clearly: Everyone should be on the same page about what each metric means and how to track it. For example, decide what counts as a failure for MTBF or when a work order is considered “complete.”
• Train the team: Make sure technicians understand why data collection matters. Logging accurate details isn’t about blame, it’s about improving systems.
• Be consistent and timely: Data should be recorded as close to the event as possible. If an asset fails at 3 AM and is repaired by 5 AM, the details should be logged by the end of that shift.
• Use supportive tools: Digital checklists and templates built into work orders help ensure nothing gets missed.
• Audit your data regularly: Look for outliers, missing entries, or inconsistent logging to keep your data clean.
• Use failure codes: Standard codes help you categorize problems and spot patterns over time.

Historically, maintenance data was recorded manually on paper logs or spreadsheets. The downside is that manual methods are labor-intensive and error-prone. Manual data is also often delayed, meaning issues are harder to spot when they’re happening. 

Accuracy, consistency, and timeliness of data are essential in asset management. It's better to track a few key metrics well than many metrics poorly.

The role of technology in modern asset management

Technology plays a growing role in how maintenance teams track performance and keep assets running. With sensors, IoT devices, and CMMS platforms, data collection is faster, more accurate, and often automatic.

For example, sensors can log runtime, monitor equipment condition, and send alerts when thresholds are crossed. IoT devices stream performance data in real time. And modern CMMS tools let techs close work orders, log downtime, or update checklists right from a mobile device.

This kind of automation reduces human error and gives you a clear, up-to-date picture of what’s happening on the floor. If asset availability drops today, you’ll see it right away, not weeks later when the report comes out.

With the right systems in place, tracking metrics becomes part of the daily workflow, not an extra task. Instead of chasing down data, you’re using it to make faster, more informed decisions.

Metrics only matter if they lead to action. By combining clear processes with the right tools, MRO teams can turn data into something useful, like better reliability, less downtime, and smarter planning.