A guide to essential maintenance KPIs and key metrics

Well-designed maintenance KPIs can give maintenance managers visibility into core maintenance processes and help improve asset management.

As modern maintenance practices evolve away from outdated, reactive practices toward more modern, proactive ones, maintenance KPIs play a crucial role in measuring success. For example, modern predictive maintenance platforms leverage artificial intelligence (AI) and the Internet of Things (IoT) to increase the mean time between failures (MTBF), one of the most closely watched metrics in maintenance organizations.

Tracking maintenance KPIs and maintenance metrics has become central to the success of modern maintenance operations, helping maintenance teams keep critical assets available and predictable. When assets fail unexpectedly, organizations can face a wide range of problems, including unplanned downtime, extended repair time, decreased asset value, lost production time and increased maintenance costs.

In the past, organizations relied almost exclusively on reactive maintenance, a maintenance approach that only repairs assets after they break down. Today, maintenance strategies concentrate on preventive and predictive maintenance tactics. These approaches are often supported by a computerized maintenance management system (CMMS) that enables real-time condition monitoring (CM) to optimize maintenance operations.

Maintenance KPIs help maintenance teams transform raw data into indicators of how a system or piece of equipment is functioning. CMMS software collects data from sources like maintenance work orders, IoT sensors embedded in assets and records of spare parts usage to calculate maintenance metrics like MTBF and mean time to repair (MTTR).

Modern maintenance KPIs depend on several core maintenance activities:

• Data collection and visualization
• Condition monitoring
• Continuous improvement
• Benchmarking

Data collection and visualization form the foundation of maintenance KPIs, enabling maintenance teams to identify and track the metrics most relevant to their assets.

Over time, maintenance teams collect data about the assets they are responsible for, such as equipment failures, number of repairs, spare parts usage, inspections and maintenance schedules. They feed that data into a CMMS—although, increasingly, CMMS tools can automate this process.

The CMMS centralizes the asset data and generates a maintenance dashboard that connects the data to performance metrics. Popular metrics the CMMS software can display include:

• Total maintenance hours spent on repairs
• Types of equipment failure that led to a breakdown
• Average time required to restore operations after a failure
• Total number of failures over a reporting period

Modern maintenance management software tracks these performance metrics—and others—over time, giving maintenance technicians a clearer understanding of how they’re performing through KPIs like maintenance backlogs, asset uptime, schedule compliance and equipment availability.

For most organizations, implementing strong data collection and visualization through a CMMS tool lays the groundwork for real-time condition monitoring (CM), a key aspect of most digital transformation initiatives.

CM is a type of predictive maintenance that uses real-time data collection and analysis to monitor asset health, detect faults and identify problems early before they cause unplanned downtime.

CM leverages IoT sensors and advanced AI analysis to detect changes in vibration, temperature, pressure and fluid levels that can help predict when an asset is likely to fail. When implemented as part of an overall maintenance strategy, CM helps optimize maintenance planning, simplify routine maintenance tasks and enhance overall operational efficiency across a wide range of maintenance activities.

Continuous improvement is an ongoing process that begins with the collection of asset data and runs through the identification and tracking of maintenance KPIs and the improvement of maintenance processes over time. Relying on maintenance KPIs, maintenance teams practice continuous improvement by taking corrective actions that help improve operational efficiency and extend asset lifecycles.

The practice of continuous improvement treats maintenance operations as a constantly evolving practice rather than a static set of repairs in response to breakdowns. Maintenance teams that use maintenance KPIs to inform their maintenance activities can constantly optimize their approaches according to the latest information about asset health and performance.

Benchmarking is the systematic process of comparing asset performance, cost and reliability metrics against industry standards, and it’s only possible once an organization has identified and begun tracking maintenance KPIs.

World-class maintenance organizations often target specific industry benchmarks for critical KPIs like MTBF, MTTR, mean time to failure (MTTF), planned maintenance percentage (PMP) and overall equipment effectiveness (OEE).

Benchmarking is only for mature maintenance organizations, as it requires teams to first establish reliable processes and systems for tracking maintenance KPIs, such as the use of a CMMS, condition monitoring and continuous improvement. Once these practices are in place, benchmarking can help organizations see how they compare with peers.

Organizations vary widely in terms of need, operational environment and industry, making it difficult to choose the right KPIs to measure success.

Here’s a close look at six foundational maintenance KPIs that have helped world-class organizations achieve their maintenance goals for decades.

The gold standard for modern maintenance strategies, mean time between failures (MTBF), captures the fundamental purpose of modern maintenance programs—to keep assets running for as long as possible without failing.

MTBF measures the average time assets are in operation before they fail and require attention. The MTBF metric is widely used across all industries to measure asset reliability and prevent breakdowns.

Like MTBF, mean time to repair (MTTR) is also one of the most widely relied-upon ways to measure a maintenance organization’s success. MTTR measures the average time that it takes to repair a piece of equipment after it has failed.

A lower MTTR suggests a more efficient maintenance strategy with a strong approach to spare parts inventory and work order management.

It’s impossible for a maintenance organization to be successful if the assets it is responsible for are continually not operational due to repeated equipment failures that cause both planned and unplanned downtime.

According to a recent report, unplanned downtime costs the world’s 500 most successful companies USD 1.4 trillion annually. Monitoring machine downtime is one of the easiest ways for an organization to gauge the success of a maintenance program because it is often a sign of underlying maintenance management issues.

Mean time to failure (MTTF) measures the average time non-repairable assets, such as light bulbs, batteries and fuses, can run before they fail.

Modern maintenance organizations rely on MTTF to estimate the expected lifespan of cheap, replaceable assets and use it to inform their overall spare parts strategy.

Overall equipment effectiveness (OEE) is a measurement of how reliably a piece of equipment performs over time.

OEE relies on more operational variables than MTBF and MTTR, so it can be harder to measure and manage. It is considered one of the most comprehensive maintenance KPIs organizations can track.

Planned maintenance percentage (PMP) measures the percentage of planned, scheduled maintenance activities against the number that are performed reactively when a piece of equipment fails.

As modern maintenance strategies shift away from reactive maintenance toward more proactive, predictive maintenance, PMP is becoming more important. A higher PMP can be an indicator of a mature, preventive maintenance program that uses a CMMS and new technologies to reduce its dependence on outdated, reactive practices.

Organizations that consistently monitor maintenance metrics and use the information they gather to improve core maintenance processes often realize a wide range of benefits.

Here are some of the most common:

• Improved asset reliability: Tracking maintenance KPIs over time helps improve asset reliability by providing maintenance teams with real-time access to metrics like MTBF, asset uptime and number of failures. By monitoring this information and taking corrective measures, teams can identify trends in asset maintenance, reduce breakdowns and increase equipment availability through reliability-centered maintenance practices.
• Reduced downtime: Equipment downtime is one of the most expensive problems for organizations to manage as it frequently spreads beyond maintenance operations and affects productivity, supply chain management and customer satisfaction. Strong maintenance metrics help maintenance teams avoid costly unplanned downtime by identifying equipment issues sooner and shrinking response times. For example, tracking MTTR helps teams streamline their repairs and reduce the average time they need to restore an asset to working order.
• More efficient maintenance planning: Gathering and tracking data over time helps organizations optimize maintenance schedules and improve resource allocation. By closely monitoring maintenance backlogs and work order completion rates, teams ensure that they complete their most critical maintenance tasks on time.
• Data-driven decision-making: Organizations that establish and track their maintenance KPIs position stakeholders for data-driven decision-making. Maintenance managers that use a modern CMMS can track and evaluate performance data easily and make operational decisions in real-time. Dashboards help identify trends and prioritize investments. For example, if the total maintenance costs of a particular piece of equipment keep rising while MTBF declines, it might be more cost-effective to replace an asset than to keep repairing it.
• Stronger preventive maintenance: Preventive maintenance—proactively repairing and replacing assets before they break—requires strong maintenance KPI tracking to be effective. Organizations that track maintenance KPIs can use that information to predict when assets are more likely to fail and take corrective action. Also, tracking preventive maintenance tasks in a modern CMMS helps organizations shift away from outdated, reactive maintenance practices toward more modern, predictive ones. According to a recent report, switching to preventive maintenance can cut maintenance costs by 12% and reduce equipment downtime by 30–40%.
• More support for continuous improvement: Continuous improvement is the beating heart of maintenance organizations because it allows teams to improve processes over time. Establishing strong maintenance KPIs and rigorously tracking maintenance metrics allows teams to constantly evaluate their procedures and compare their results with industry benchmarks.

While the benefits are undeniable, organizations seeking to successfully identify and track maintenance KPIs still face a number of obstacles.

From struggling with data readiness to integrating modern tools with legacy infrastructure, here are some of the most common:

• Data quality: Inaccurate or unreadable data, often collected by outdated tools, represents a significant barrier to establishing meaningful maintenance KPIs. For example, incomplete work orders created on Excel spreadsheets or by hand can be hard for modern CMMS to read and translate into meaningful KPIs.

• Integration with legacy systems: Integrating modern metrics tracking tools with outdated, legacy infrastructure poses a significant challenge for many organizations. Maintenance operations often rely on different systems to track work orders, inventory management and repairs, which can limit visibility into maintenance activities. Some CMMS software can help centralize this data, but implementing it effectively requires time and investment.

• Tracking the wrong metrics: Not all maintenance KPIs provide equal strategic value to an organization. Modern CMMS dashboards can overwhelm users with information, and it can be easy to focus on metrics that are easy to measure but provide little insight into systems and equipment. For example, work order completion rate is easy to measure and track but has little to do with preventing equipment failures.

• Resistance to change: Properly measuring and learning from maintenance KPIs in a way that can help an organization often requires change at a fundamental level. Maintenance teams that have practiced breakdown maintenance for years have difficulty embracing modern workflows that favor preventive and predictive maintenance strategies.

• Poor benchmarking: Benchmarking is complex and requires the three other foundational aspects of KPI measurement to be in place before it can be effective. Operational environments differ widely, which can make comparing one organization to another—even in the same industry—difficult. An appropriate target for one organization may be unrealistic for another, depending on equipment age, operating conditions and demands.