One of the most common questions we get asked by our customers is “what Key Performance Indicators (or KPIs) for condition monitoring and predictive maintenance systems do you recommend that we track?” The reason why it’s such a common question is obvious: to realize the benefits of predictive maintenance and to continuously improve the system over time, organizations need to measure its performance using relevant and objective metrics.

With dozens of potential KPIs to choose from – our internal catalogue includes more than 50(!) different metrics – it’s easy to get overwhelmed. However, not all metrics are created equal. In our experience, three KPIs stand out as the most critical for evaluating the performance and business value of a predictive maintenance system: Overall Equipment Effectiveness (OEE), Predictive Accuracy, and Return on Investment (ROI).

These three KPIs together provide a balanced view of operational performance, technical effectiveness, and financial impact. Let’s explore each in more detail and see why we believe they deserve top priority.

1. Overall Equipment Effectiveness (OEE)

OEE is a cornerstone metric in manufacturing, measuring the percentage of planned production time that is truly productive. It combines three components:

• Availability – Uptime as a percentage of planned operating time
• Performance – Speed of production versus ideal speed
• Quality – Ratio of good units to total units produced

When equipment fails unexpectedly, OEE drops—often dramatically. Predictive maintenance aims to reduce unplanned downtime, which directly improves the Availability component of OEE. But the effects often extend to Performance and Quality as well. For example, machines operating in sub-optimal condition may produce defects or run at reduced speeds.

Why OEE Is A KPI That Matters For PdM

OEE is the ultimate operational outcome. It reflects how well a factory turns time into value. When predictive maintenance is working well, you’ll see OEE trend upward over time, especially in highly automated or high-throughput environments. Measuring OEE before and after PdM implementation offers a clear, plant-wide indicator of effectiveness.

2. Predictive Accuracy

While OEE measures outcomes, Predictive Accuracy focuses on the core promise of predictive maintenance: identifying potential failures before they happen. In this context, Predictive Accuracy has three dimensions:

• True Positives (Detection Rate): How often the system correctly predicts a failure before it occurs.
• False Positives (False Alarm Rate): How often the system flags issues that don’t lead to failure.
• Prediction Lead Time: How much advance warning does the system reliably provide the maintenance team.

Striking the right balance between sensitivity and specificity is key. Too many false alarms waste maintenance resources and erode trust in the system. Too few true positives mean costly failures slip through the cracks.

Why Predictive accuracy is a KPI that Matters for PdM

It’s the most direct KPI for evaluating your PdM technology. Whether you’re using vibration analysis, machine learning models, Novity’s TruPrognostics AI or any other approach, predictive accuracy shows whether your system is truly capable of distinguishing between normal and abnormal behavior. A PdM system that doesn’t reliably predict failures early will not move the needle on OEE, or provide the ROI your organization is looking for.

Predictive Accuracy is also important because it represents the first step in a sequence where each subsequent step must be successful to realize the ultimate value from PdM. If accuracy is high but the end results (ROI and OEE) are nolt improving, there are likely other aspects of the operation that need to be understood, such as maintenance planning and team response times (e.g. Mean Time to Repair, or MTTR).

3. Return on Investment (ROI)

Ultimately, investments are considered successful if they generate a positive financial return. ROI brings it all together, measuring the financial value of the PdM program compared to its costs.

ROI calculations should include:

• Benefits:

• Reduced downtime costs (learn more about how to estimate the true cost of downtime here)
• Avoided emergency repair expenses
• Extended equipment life
• Lower inventory carrying costs for spare parts
• Fewer overtime hours or third-party interventions

• Costs:

• Hardware (sensors, gateways)
• Software subscriptions
• Integration and deployment
• Data science or analysis labor
• Ongoing monitoring and support

A sophisticated ROI calculation needs to also consider the time to value and the costs of capital, often expressed through a discount rate that reduces the value of future benefits relative to immediate benefits.

Why ROI is a KPI That Matters For PdM

Predictive maintenance is often part of a broader digital transformation initiative, and executive sponsors will want to see tangible results. ROI provides a bridge between technical performance and business impact. It also helps justify future investments, such as scaling PdM across more assets, sites, or use cases.

A positive ROI isn’t just a pat on the back—it’s the foundation for building organizational momentum. The sooner you can prove that PdM contributes to profitability, the more support you’ll have from finance, operations, and the C-suite.

Putting It All Together

While many other KPIs—such as Mean Time Between Failures (MTBF), maintenance compliance, or work order response time—have their place, they often reflect maintenance performance more broadly rather than the specific value of predictive maintenance. Focusing on OEE, Predictive Accuracy, and ROI keeps your metrics aligned with the fundamental goals of a PdM program:

• Run equipment better and longer (OEE)
• Detect failures before they cause disruptions (Predictive Accuracy)
• Manage maintenance with maximum profitability (ROI)

Each of these KPIs reinforces the others. High predictive accuracy enables better maintenance decisions, which boosts OEE, which then contributes to a positive ROI. By tracking them consistently and reporting them in an integrated way, manufacturers can create a clear narrative of progress, value, and opportunity.

Which of these KPIs is your organization tracking today? Are you partial to other KPIs? We’d love to hear from you about them – drop us a note at engage@novity.us.