Why Predictive vs. Condition-Based Maintenance Isn’t a Choice but a Spectrum

If you’ve been researching predictive maintenance (PdM) (opens in new tab) vs. condition-based maintenance (CBM) (opens in new tab), chances are you’ve already seen plenty of content that lists out the textbook differences. CBM uses real-time data to monitor equipment conditions and intervene before a failure. PdM goes further, leveraging advanced analytics and machine learning to anticipate failures before they happen.

But here’s the problem: most of those comparisons oversimplify the issue. They frame PdM and CBM as an either/or decision and force maintenance teams to choose between them. In reality, the decision isn’t binary at all. In this blog we’re going to highlight how most modern operations use a blended strategy, applying each paradigm where it makes the most sense.

The overlap between preventive, condition-based and predictive maintenance

It helps to see CBM as the bridge between traditional preventive maintenance and predictive maintenance:

• Preventive maintenance (PM): (opens in new tab) Calendar or usage-based schedules to prevent failures.
• Condition-based maintenance (CBM): Monitoring equipment health through parameters like vibration, temperature, or oil quality, and servicing only when needed.
• Predictive maintenance (PdM): Using historical and real-time data with AI/ML models to forecast failures before they occur.

“CBM is often classified as a type of preventive maintenance because it still prevents failures but it’s more advanced than a simple time-based schedule,” says Jason Afara, a former maintenance manager in food manufacturing, now a Director at Fiix.

At the same time, it lays the groundwork for predictive strategies by building the habit of condition monitoring and data collection.

That’s why the real takeaway isn’t “CBM vs. PdM.” It’s: Where do each of these approaches fit into your broader maintenance strategy? Thinking this way transforms maintenance into a dynamic capability, not a checklist.

CBM sets the foundation: It brings discipline in monitoring and data capture. From there, you build toward PdM by layering in analytics, history, and forecasting.

Why PdM vs. CBM isn’t a choice

Instead of deciding “which is better,” organizations should think in terms of asset criticality, data maturity, and operational practicality. A mixed strategy maximizes uptime and minimizes costs. Here’s why a blended approach works better in practice:

1. Asset criticality
   High-value, critical assets justify the investment in predictive models. Even a few hours of downtime could mean massive production losses, so anticipating failures is worth the complexity. Lower-cost, non-critical assets don’t need predictive analytics. A condition-monitoring strategy, or even time-based PM, is often the smarter, simpler choice.
2. Data and analytics limitations
   Not all machines have the necessary sensors, connectivity, or historical data to support predictive algorithms. For these assets, CBM offers a practical path to reliability until data maturity improves.
3. Operational practicality
   Some equipment is easier to manage on a simple calendar-based schedule, while others benefit more from real-time monitoring. A hybrid approach respects these practical constraints without over engineering the solution.

Clearing the misconceptions: PdM for small and mid-size teams

Many small and mid-size operations hesitate when they hear “predictive maintenance (PdM).” The usual reaction is that PdM is too expensive or overkill, while condition-based maintenance (CBM) feels more affordable but limited. The reality is more nuanced: PdM can absolutely scale down, and hybrid CBM/PdM strategies are increasingly viable.

“A hybrid model makes more sense now than ever because technology is accessible,” says Jason.

Sensor costs have dropped sharply, which makes PdM technology far more accessible than it was even a few years ago. But lower sensor prices don’t automatically equal low-risk PdM. Total cost of ownership still matters, sensor reliability, data analytics, installation, maintenance, and the expertise to interpret results all factor in.

For small and mid-size facilities exploring a hybrid or entry-level PdM approach, consider these starting points:

• Run a pilot on a single critical asset before attempting a plant-wide rollout.
• Evaluate sensor performance over time, not just the sticker price, to avoid costly misreads and replacements.
• Budget for ongoing costs such as sensor calibration, software subscriptions, and staff training to manage the data.

PdM isn’t just for Fortune 500 companies anymore, but success comes from a thoughtful, phased strategy, one that weighs long-term reliability and analytics capability as much as upfront hardware savings.

The path forward: Smarter integrated maintenance

Framing PdM and CBM as competitors misses the bigger picture. CBM is not the “lesser” option, it’s often the foundation that enables predictive strategies down the line. By starting with condition monitoring, organizations build the data infrastructure and operational discipline needed for predictive success.

According to Jason the future of maintenance isn’t about choosing one paradigm over the other. It’s about:

1. Matching the right approach to the right asset
2. Using cost and criticality as decision criteria
3. Evolving toward an integrated, data-driven maintenance strategy

“Predictive versus CBM isn’t really a choice, it’s a spectrum. And the most effective teams know how to move fluidly along that spectrum,” says Jason.

Ultimately, you don’t choose between CBM and PdM, you choose for today’s need, for tomorrow’s vision.

Interested in learning more? Check out what we have for more context on predictive maintenance and CBM:

• How to make condition-based maintenance more effective For CBM fundamentals and the P-F curve concept.
• Choosing the right condition-based maintenance solution For navigating selection, IT integration, and implementation changes.
• Predictive maintenance isn’t always AI and why that matters To help demystify PdM, emphasizing that it’s not all machine learning and magic, but grounded in data quality and operational integration.
• Learn how to develop a predictive maintenance program For those of you looking for strategy, long-term PdM frame working.