“What gets measured gets managed.” This principle, often attributed to management consultant Peter Drucker, has never been more relevant than in today’s data-driven maintenance landscape.
Modern condition-based maintenance (CBM) systems generate vast amounts of data, such as vibration readings, temperature shifts, and pressure changes, giving organizations an unparalleled view of equipment health. But having this data is just the first step; true value comes from how well it’s put to use.
The big question for reliability and maintenance professionals is: Do you have the tools to extract the insights that make key performance indicators (KPIs) meaningful for your CBM program? Without the ability to leverage that data, even the best maintenance strategy will fall short.
In this article, we’ll explore how the right tools can help your team transform that mountain of CBM data into actionable insights, turning measurements into meaningful actions that keep your operations efficient and profitable.
Using KPIs to Gauge Success
First, a quick refresher on KPIs. You’re likely familiar with how they are used as the go-to method for tracking progress in most CBM programs. In the simplest terms, KPIs are metrics that help track how close a team is to hitting their maintenance goals. For example, how quickly do repairs happen after they are requested? Is downtime kept to a minimum?
KPIs reveal patterns in efficiency, effectiveness, quality, compliance, and even cost savings, giving you a clear picture of where your program delivers and where it may fall short. Tracking compliance, response times, efficiency, failure rate, and ROI, for instance, are all good gauges of program success.
Tracking KPIs can feel manageable in a small plant with 50 to 100 machines. But as your plant scales to 2,000 or 3,000 machines, each generating multiple data sets per month, the complexity grows. Tracking metrics to make sense of all that data becomes a much bigger challenge, given that every data point needs to be analyzed, filtered, and linked to a specific KPI.
The influx of data — from subtle shifts in equipment performance to operational irregularities — can make it difficult to extract meaningful insights. Important patterns can easily slip through the cracks, leaving your team with too much data and not enough direction.
Advanced Tools for a Complex CBM Landscape
Reliability professionals need sophisticated solutions that can extract signals from the noise of constant data collection. AI-powered software like Azima DLI can do just that by sifting through heaps of data and extracting insights to show teams exactly how to drive KPI improvements.
Simplifying Data Collection
Manual data collection can quickly become a logistical nightmare, especially as CBM programs grow in scale. Back in the day, teams spent hours collecting data on-site, followed by just as much time analyzing it. For every hour spent in the field, another hour was required to process the results.
This workflow was inefficient and prone to gaps — overdue machines would slip through the cracks, missed warning signs would go unnoticed, and results had to be tracked manually through spreadsheets or PDF reports. The challenge wasn’t just data collection, but making sure the right data reached the right people at the right time.
Automated Azima tools streamline this process. Every data point is tracked automatically via wireless sensors. Even entry-level personnel can handle data collection using user-friendly Azima devices such as the TRIO® triaxial sensor or Accel 310™ wireless vibration sensors. These tools significantly reduce the workforce hours needed to keep data collection running efficiently.
Using the customizable Azima portal, facility technicians can specify whether a machine needs data collected monthly, quarterly, or semi-annually. The system automatically starts a clock for each machine, sending alerts when it’s time for data collection. For assets that are only used seasonally, the platform includes a seasonal scheduler that allows users to temporarily pause data collection and avoid being marked as out of compliance when machines aren’t in operation.
Tracking Maintenance With Ease
The customizable Azima portal makes it easy for users to check and track machine status and compliance. Users can create machine hierarchies to determine which assets are most critical.
Additionally, fault severity can trigger automated email alerts when repair actions need to be taken – which we’ll explore more in detail in the next section. Whether you’re in the office or on-the-go, alerts follow you, notifying you of any serious conditions in real time. You can look at your phone and see when a machine is in serious condition right away.
Assigning Severity Ratings For Efficient Maintenance
Five severity levels — green, slight, moderate, serious, and extreme — help prioritize maintenance efforts. Machines marked as serious or extreme are flagged for immediate attention, while lower-priority assets remain on watch.
For example, if a pump that has been green for many months suddenly switches to serious, then the system will mark it as a top priority. And while a fan imbalance marked as serious might already be scheduled for repair during a planned outage, the system ensures the issue remains top of mind until it’s fully addressed.
In this way, Azima helps your team catch issues early, prioritize urgent fixes, and extend asset lifespan, directly affecting maintenance KPIs like mean time between failures (MTBF) and overall equipment effectiveness (OEE).
While this proactive flagging helps you stay ahead of immediate issues, addressing deeper, recurring problems requires a closer look at fault rates. Azima can help here as well.
Tracking Fault Rates to Reduce Reactive Maintenance
High fault rates can indicate that your maintenance approach is more reactive than proactive, resulting in too much downtime. The first step to improving fault rates is tracking when faults occur.
Azima can help your team recognize patterns in fault data and work to implement strategies that prevent equipment failures before they happen. This enables a shift to proactive maintenance and earlier intervention. Over time, reduced fault rates become a key KPI, showing that your CBM program is driving real, measurable improvement.
One customer experienced this change firsthand. When Azima first started working with them, 175 machines out of 1,200 were in serious or extreme fault, leading to a 15% fault rate — an unusually high figure. Over four years, the customer worked to repair these critical assets and, with the help of Azima, successfully reduced their fault rate by 95%. This led to estimated cost savings of $8.2 million over the same period.
This outcome provides a clear picture of measurable impact.
Setting Benchmarks for Long-Term Performance Success
For a more precise assessment of gains achieved, Azima gathers and analyzes KPI data, offering insights through comparison with industry averages.
This allows teams to track their performance year after year and identify areas for improvement. If your organization has multiple plants, you can compare plant A to plant B to see where they align or differ, helping standardize your processes. If applied effectively, this benchmarking process highlights not only areas for improvement but also instances where proactive maintenance has prevented larger issues.
Highlighting Key Saves
One of the most significant measures of a CBM program’s success is its ability to identify and address potential failures before they escalate into critical issues — what Azima refers to as key saves. A key save occurs when a high-priority fault is identified and fixed, preventing costly downtime or damage.
Consider a fan that had been flagged as “serious” for months due to an imbalance. After scheduling a repair during a planned outage, the fan is balanced, and by the next data collection, it’s back to normal. That’s a key save — capturing the repair that prevented a more serious failure down the line.
However, not all issues qualify as key saves, particularly when dealing with bad actors. Bad actors are assets that repeatedly return to fault conditions despite multiple repairs. For example, if a fan goes out of balance every few months after being repaired, it would be classified as a bad actor. These recurring issues often stem from deeper structural problems, such as a flimsy base or an elevated motor causing excessive vibration.
Azima helps identify these patterns and prevents bad actors from being misclassified as key saves. Instead of counting the issue multiple times, the system focuses on resolving the underlying cause, ensuring that long-term solutions are applied rather than temporary fixes.
By tracking key saves and bad actors, Azima provides a clear picture of your CBM program’s impact on critical KPIs. This also directly ties into cost avoidance, showing how proactive maintenance reduces expenses by addressing problems before they escalate. With Azima, you can collect data that serves as a straightforward indicator of maintenance impact.
Turning Data into Real Results
The power of any CBM program lies in how well it turns data into actionable results. When KPIs are properly aligned with business goals, they become more than just metrics — they also become drivers of success. With Azima, companies can shift from reactive to proactive maintenance, catching potential failures before they escalate and making data a cornerstone of decision-making.
Rather than just monitoring machines, Azima helps teams focus on what matters most, whether it’s maintaining compliance, lowering fault rates, or prioritizing critical repairs. This approach improves asset reliability and directly impacts your bottom line. By leveraging data to drive smarter, faster actions, companies see a measurable improvement in ROI, ensuring that every machine is contributing to long-term performance and efficiency gains.
Author bio: Jeff Langford is a senior analyst and professional services team manager at Fluke Reliability, with over 20 years of expertise in predictive maintenance. He holds a CAT III vibration analysis certification and is infrared certified. Jeff currently leads a global team of nine analysts, helping organizations optimize their maintenance strategies and improve asset reliability.