For businesses that rely on industrial machinery, tracking maintenance metrics is essential to run lean and stay competitive. These data points give teams insights into where operations can improve and which assets need service before they experience suboptimal performance. Worse, they create unsafe conditions or break down.

With the increasing prevalence of AI in industrial sectors, site managers and other leadership might be preoccupied with modernizing their dashboards with metrics like alert-to-action time and AI model confidence that address that prevalence.

While it might be helpful to track such metrics, maintenance teams should still prioritize core Key Performance Indicators (KPIs) such as Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), preventive maintenance (PM) compliance, and backlog size. This classic mix of lagging and leading maintenance KPIs still provides a wealth of critical insights to teams operating in today’s increasingly challenging landscape.

In this article, we’ll review each of those core KPIs, how expectations surrounding them have changed in the context of modern maintenance operations, and more.

A new interpretation: How to evaluate classic maintenance metrics for today’s operations

Maintenance best practices have changed significantly since the birth of industrial operations in the 18th century — even since the emergence of the concepts that evolved into KPIs in the early 20th century. Where plants and facilities once tracked key performance data via simple logs and manual calculations, now, sites have embraced Industry 4.0 practices; large-scale, automatic data collection and AI-powered analysis and dashboard generation are the norm.

These modern practices and massive data pools can help crews gain more insights faster than ever before and, in theory, should lead to more streamlined operations. But more data doesn’t automatically translate to better operations. Leadership expects maintenance teams to interpret this data more critically, with greater attention to execution quality, data trust, and context than in the past. And as we mentioned before, while it might be tempting to turn to more nascent KPIs to deliver on these expectations, it’s possible to still gain significant, reliable insights from the classic pillars of maintenance performance.

Let’s review each of these KPIs through a modern lens.

1. Mean Time to Repair (MTTR)

MTTR centers on calculating the average amount of time it takes for teams to fix failed equipment or assets. It’s a baseline to help managers and other leadership get a general sense of where the team stands in terms of responsiveness and technical proficiency — it’s not an absolute indication of these facets of performance.

The modern view of MTTR and how to interpret it: Given the increased complexity of modern maintenance operations — especially the continuous struggle of trying to maintain quality with fewer resources — MTTR isn’t just about speed of repair anymore. In fact, MTTR rates that are too low aren’t necessarily a good thing. Abnormally low MTTR rates might indicate that staff feel pressured to implement quick, stopgap repairs that don’t address root malfunctions. As such, the risk of future downtime increases.

To ensure this risk doesn’t remain hidden, team leads and managers should view MTTR rates within the context of other metrics, such as First-Time Fix Rate (FTFR) or rework rates, to ensure technicians are performing proper repairs at the outset of underperformance. For instance, if MTTR rates are low, but FTFR figures are also low while rework rates are high, your low MTTR rates don’t mean much, because technicians aren’t making the correct repair diagnoses.

2. Mean Time Between Failure (MTBF)

MTBF measures the time that elapses between periods of unplanned failure for a piece of equipment, quantifying the availability of that asset and providing an indication of equipment reliability.

The modern view of MTBF and how to interpret it: In the past, teams may have used MTBF to simply provide a sense of asset availability. But with preventive maintenance becoming a higher priority for more maintenance teams, managers and other team leads should use MTBF as an indicator of which pieces of the asset portfolio need a root cause analysis (RCA); outliers that have a particularly low MTBF are likely good candidates for such analysis. Armed with the results of the RCA, teams can adjust PM tasks accordingly to head off issues that caused past failures.

3. PM compliance rate

Preventive maintenance (PM) compliance tracks the percentage of scheduled PM tasks that a maintenance department completes on time. Ideally, organizations want this number to stay at 90% or more of the total PM task load, while ensuring tasks are completed no later than 10% of the length of time of the PM schedule. For example, if one of your PM tasks needs to be performed every two weeks, then technicians should aim to complete that task no later than 1.4 days after the scheduled time.

The modern view of PM compliance and how to interpret it: In the past, many maintenance techs pencil whipped their PM tasks, completing them quickly (and perhaps without much attention) and moving on to wrap up their checklist. While this approach translates to high PM compliance rates, it can leave the facility vulnerable to significant risks, particularly when lax inspections lead to underperformance that can result in equipment failure.

Now, teams must view PM compliance through a more nuanced lens. Other metrics, such as FTFR/rework rates and MTBF, can contextualize the effectiveness of PM work; they help teams get a better sense of whether staff are effectively complying with the necessary PM tasks or if they’re simply ticking off boxes. For instance, if PM compliance rates are high while FTFR is low and MTBF is short, your compliance rates are likely untrustworthy. Additionally, if PM compliance is high but FTFR, MTBF and other metrics that can indicate reliability are problematic, you may need to determine if you’re suffering from PM bloat.

4. Schedule adherence rate

Schedule adherence rates illustrate maintenance technicians’ ability to follow the necessary cadence of maintenance tasks that they’re responsible for. This metric is the bridge between planning and execution.

The modern view of schedule adherence and how to interpret it: Historically, managers created time-based or usage-based maintenance schedules largely based on manufacturers’ recommendations. This made enforcing and maintaining schedule adherence more straightforward.

Now, leaders expect teams to stay agile and carry out condition-based maintenance. This shift means maintenance schedules are less rigid, and tasks can shift based on emerging needs from real-time conditions that IIoT-capable sensors identify. (A robust CMMS is key to centralizing and analyzing this data.) Many organizations configure sensor alarms to trigger when overall vibration levels or temperatures reach a specific limit; otherwise, they establish narrowband thresholds that sound an alarm when signatures match indicators of common faults (imbalance, misalignment, bearing faults, etc.)

Unless condition monitoring sensors indicate otherwise, teams should aim to have a schedule adherence rate of 90% or higher. However, if sensor data shows that an asset needs service, managers may have to reshuffle priorities on the schedule.

5. Backlog size and aging

The size of a facility’s maintenance backlog is the total volume of maintenance tasks that technicians have not yet completed; this volume is measured in a set unit of time, usually in weeks.

The modern view of backlog size and age, and how to interpret it: Managers used to aim to have as small a backlog as possible. But today, a too-small backlog can indicate that your facility is overstaffed or that technicians aren’t detecting necessary work — or possibly both. Though backlog size varies by industry, generally, benchmarks indicate sites should aim for a two- to four-week backlog. This gives teams a continuous stream of tasks when work is slow, but it’s manageable enough that critical items shouldn’t sit long enough to develop into problems that can cause unplanned downtime.

However, it’s not enough to simply strive for that two- to four-week total volume benchmark. Maintenance managers must also consider backlog aging and the mix of tasks that are in the backlog. Critical to-dos should receive a higher prioritization to ensure they don’t sit too long and evolve into worse issues. Additionally, if tasks sit in the backlog for longer than six weeks, your team is falling behind; managers should analyze operations to determine the root cause of this delay.

Common mistakes and pitfalls in tracking maintenance KPIs

Even with advanced tools, industrial teams often fall into these common traps:

  • Data bloat that distracts teams from critical tasks: Particularly for teams focused on PM, it’s easy to fall into a “track everything” mentality. But if data inputs become too numerous, teams may resort to pencil whipping, which degrades the quality of data collection and task tracking. Conduct regular reviews of what you’re tracking and assess how and to what degree it’s helping your team achieve strategic goals.
  • A crowded, misaligned dashboard: Speaking of data bloat, PM isn’t the only facet of maintenance data where more isn’t necessarily better. Many teams track too many KPIs, get pulled in too many directions, and thus spread their resources too thin. It’s better to force-rank which KPIs best align with your goals at a departmental, site, and organizational level. Choose only the most relevant KPIs to track and include on your dashboard. This level of focus gives teams meaningful direction to improve the most valuable, critical operations that have a notable ROI.
  • Measuring KPIs before establishing baseline performance: In order to measure improvements, you must first know where your current performance stands. Gather two to three months’ worth of data to establish a baseline of performance that will better contextualize your KPI data.
  • Reporting without an action plan: Reports mean little if there’s no action plan attached to them. Ensure that insights from regular reports have corresponding action items — complete with deadlines and assignees — to ensure that any problems or concerns the reports uncover don’t fall through the cracks.

Make the most of your KPIs with a CMMS

In today’s world of maintenance operations, it seems daunting to refine your KPIs and provide an achievable, focused roadmap to impactful improvements. That kind of overwhelm is understandable. The sheer volume of maintenance data creates enough noise to leave teams feeling unsure of data quality and uncertain if they’re using data to its full potential. That’s why it’s critical to implement a robust, nimble CMMS that can handle all of the information you and your teams need to accurately track KPIs and hit performance benchmarks.

Cloud-based CMMS eMaint allows users to track all of their assets, even across multiple sites and countries. From work orders and maintenance records to condition monitoring and customized performance reporting, eMaint simplifies analytics to help organizations prevent downtime and improve ROI.

Want to learn more about how eMaint CMMS can help your team establish maintenance and reliability data trust, create more actionable reports, and improve maintenance operations? Take a quick tour of our software or schedule a demo today.