Maintenance teams don’t have the luxury of treating every asset the same, and they shouldn’t. Some pieces of equipment can fail without further consequence. However, others shut down entire lines, causing breaches in compliance or compromising safety in ways that ripple throughout the operation.
Because of this, the industry deems asset criticality analysis as a core process for informed decision-making. It helps you identify what truly matters inside your plant and focus your maintenance strategies where they’ll have the most impact.
Done right, asset criticality analysis becomes the foundation for smarter maintenance planning, resource allocation, and risk mitigation. And when you embed it into your CMMS, you operationalize the analysis with real-time demands to deliver a more robust and productive maintenance operation.
In this article, we’ll walk through the real-world steps for performing an asset criticality analysis inside your CMMS, showing how to prioritize assets based on their potential risks, failure modes, and business impact.
What Is Asset Criticality Analysis?
Asset criticality analysis is a systematic method of assessing the importance of each asset based on the impact its failure would have on operations, safety, and business objectives.
It helps maintenance teams classify and prioritize equipment by calculating a criticality score, typically using criteria such as likelihood of failure, severity of consequences, and detectability.
By assigning a criticality rating to each asset, maintenance leaders can develop more effective maintenance schedules, deploy resources more efficiently, and target high-risk areas with the right strategy.
This establishes a clear framework for supporting risk assessment and maintenance planning while allowing teams to align their efforts with production needs, health and safety requirements, and the overall asset management strategy.
Why Perform a Criticality Analysis?
Performing a criticality analysis is a baseline requirement for maintenance teams aiming to move beyond reactive work and into a structured, risk-informed operation.
It clarifies where to focus efforts, which assets carry the highest potential risk, and how to align maintenance actions with what matters most to the business.
Without it, maintenance schedules often reflect habit instead of need. Teams may over-maintain non-critical equipment or overlook hidden vulnerabilities in high-impact systems. A criticality assessment matrix solves this by providing a logical hierarchy of asset importance built on data.
When done correctly, the analysis sharpens maintenance strategies across the board. It supports failure mode and effects analysis (FMEA), prioritizes work orders, and flags where predictive maintenance will deliver the highest ROI.
It also enhances your ability to allocate resources in line with equipment criticality, reducing unnecessary tasks and reinforcing uptime where it counts.
How to Perform a Criticality Analysis Using Hard Data
The strength of any criticality analysis depends on the quality and consistency of the data behind it. To remove guesswork from the process, maintenance teams need to start by defining objective criteria that reflect the actual impact of equipment failures.
This encompasses aspects like:
- Likelihood of failure: Based on historical data, operating conditions, and known failure patterns.
- Severity of consequences: How a failure affects production, safety, compliance, or customer commitments.
- Detection capability: The potential to identify an issue before it escalates into a failure.
From there, each asset is scored using a criticality assessment matrix. This gives maintenance teams a standardized way to assign a criticality level—from low to high—and apply that rating consistently across the asset base.
In a CMMS, these scores can be tied directly to assets, work orders, and maintenance schedules. At this point, data drives real results as it prioritizes inspections, adjusts maintenance intervals, and shifts resources based on actual risk.
How to Calculate Risk Priority Number (RPN)
Many teams rely on the Risk Priority Number (RPN) to quantify asset risk in a structured way. It’s a core metric within failure mode and effects analysis (FMEA) and one of the most effective tools for prioritizing maintenance actions based on hard data.
The formula is simple:RPN = Severity × Occurrence × Detection
Each factor is typically rated on a scale from 1 to 10:
- Severity measures the impact of the failure on operations, safety, or compliance.
- Occurrence reflects how likely the failure is to happen.
- Detection indicates how easily the issue can be spotted before causing damage.
The higher the RPN, the more attention an asset or failure mode demands. High RPN scores typically signal where mitigation strategies are most urgent, particularly in systems with low detection capability and severe outcomes.
What CMMS Data Should I Analyze?
When performing a criticality analysis through your CMMS, the goal is to evaluate each asset based on data that reflects its real operational behavior, cost impact, and risk profile. That means focusing on datasets that go beyond surface-level performance metrics.
Failure Records
Go deeper than how often an asset fails. Analyze the failure mode, time to detect, and time to resolve. This will show the actual operational cost of each breakdown and help determine whether failures are random or tied to systemic issues. The high occurrence of the same failure type flags the asset for reassessment under your criticality rating.
Downtime Behavior
Track how failures affect surrounding systems. If a single point of failure halts a production line or causes safety concerns, it’s likely a high-criticality asset. Use CMMS data to understand the frequency, duration, and ripple effects of downtime across your process flow.
Work Order Trends
A high volume of work orders tied to a specific piece of equipment could indicate poor asset health, excessive wear, or ineffective maintenance strategies. Use this data to understand if the asset is demanding too much of your team's time or if it’s being over-maintained relative to its value.
Maintenance Cost Concentration
Cost is more than budget tracking. It also reveals asset burden. When labor, materials, and emergency purchases consistently pile up on the same machines, it’s a sign they might be absorbing maintenance resources that could be redirected elsewhere. Criticality analysis helps rebalance that load.
Preventative and Predictive Maintenance Data
Assess whether current schedules are actually preventing failures or just masking deeper problems. If an asset breaks down despite routine PM, it could indicate the wrong maintenance strategy.
Or it may signal that the asset's risk level has changed and wasn't updated in the CMMS. Predictive insights, when available, provide a stronger baseline for evaluating the true likelihood of failure.
Asset Role and Redundancy
Consider whether the asset supports a mission-critical process or if redundancies exist. Equipment with no backup and a high operational impact gets a higher score, even if it rarely fails. Your CMMS should capture asset dependencies and linkages to help uncover these hidden risks.
The Importance of Criticality Analysis and CMMS
Every maintenance team faces the same pressure: too many assets and not enough time or resources. That pressure only gets worse when priorities are unclear. A criticality analysis integrated within a CMMS solves this problem.
It creates more structured decision-making, defines where maintenance should focus, and ensures that time, budget, and effort are aligned with operational risk.
Reducing Downtime and Costs
The clearest benefit is better control over unplanned downtime. By identifying which assets are high-risk or mission-critical, you can direct maintenance efforts where they prevent the most disruption.
Failures stop being surprises and become opportunities to intervene early. You can see this in action when criticality ratings are tied to real condition data and failure history inside the CMMS.
This precision changes how maintenance resources are used. Instead of evenly distributing technician time and spare parts, teams can focus on assets with a high likelihood of failure or high operational impact.
Such a shift reduces unnecessary interventions and cuts reactive costs tied to emergency repairs, last-minute purchases, or unexpected line stoppages. This degree of cost control is critical in high-pressure environments where every hour of downtime affects output or revenue..
Improving Decision-Making
CMMS data alone can tell you what happened. But when it’s layered with a criticality analysis. An integrated criticality analysis goes further by explaining why it matters and detailing what should happen next.
Maintenance planning, staffing, and strategy become data-driven. Tasks are prioritized based on necessity rather than routine.
Maintenance schedules reflect the level of risk each asset presents, while capital investment decisions get backed by evidence like criticality scores, work order history, and rising maintenance costs.
Conducting Criticality Analysis: A Step-by-Step Guide
Criticality analysis only delivers real value when treated as a structured, rigorous process. Turning it into a ‘check-the-box’ style procedure misses the point entirely.
For maintenance and reliability teams, a structured analysis gives shape to how you prioritize tasks, manage risks, and justify decisions across the asset lifecycle.
Here’s a step-by-step approach that performs well under operational pressure and delivers long-term returns.
1. Identify the Assets to Be Evaluated
Start with your full asset register. But rather than defaulting to a generic list export from your CMMS, focus on assets that are functionally relevant to your production goals or safety-critical processes.
This includes not just primary machines but also their components and subsystems tied to them. For example, it’s not enough to evaluate a chiller. You must also assess the compressor, control unit, and power supply independently, as their failure could disrupt performance.
Because you need to comprehensively access primary machines and their related components and subsystems, it’s important to introduce and implement criticality analysis in phases, depending on the size of your operation. Begin by mapping your primary machines and developing staggered implementation.
Including too many assets too early can dilute the analysis. So, a focused scope, guided by operational relevance, makes the results actionable from the start. You can prioritize your primary machine groups and build your implementation phases accordingly.
2. Define the Scoring Framework
Without clear evaluation criteria, criticality analysis becomes subjective and inconsistent. Standardization is key. Build a scoring structure based on quantifiable data and consistent logic.
Typically, the evaluation covers three pillars: severity, likelihood, and detectability.
- Severity must consider real consequences such as production downtime, health and safety implications, or regulatory exposure.
- Likelihood should be rooted in equipment performance data. Pull historical failure rates, inspection reports, and condition monitoring trends. If you're working with predictive systems, this is where their insights move from noise to strategic input.
- Detectability evaluates how early your team can catch a failure in progress. A motor that gives vibration warnings well before breakdown scores differently than a solenoid that fails without any precursor.
Here, your ability to act before damage defines your true control over asset health.
3. Score and Classify Each Asset
Now, it’s time to quantify. Each asset receives a numerical score per criterion, typically on a scale of 1 to 10. These are then multiplied or weighted depending on your model, resulting in a criticality score or RPN (Risk Priority Number).
The most important factor here isn’t the math itself, it’s consistency. If two different engineers score the same asset, they should get similar results. This level of repeatability is only possible if your scoring definitions are concrete, with no room for interpretation.
After scoring, group assets by criticality level. High criticality doesn’t just mean “important.” It means the asset demands more frequent inspection, higher maintenance effort, and proactive risk management.
Document why each asset was classified the way it was, and feed that logic back into your CMMS for traceability.
4. Link Scores to Maintenance Strategy
Classification is only useful if it informs action. Assets with high criticality must have corresponding maintenance schedules, where predictive and condition-based strategies should be concentrated.
If your most critical assets are still running on fixed-interval PMs, there’s a mismatch between risk and effort. You're also decreasing the hard value your operation adds to the bottom line through cost control.
Medium-criticality assets may justify streamlined preventative maintenance with some periodic condition checks. For low-criticality items, consider run-to-failure maintenance—conserving resources while accepting controlled risk.
Let the criticality score determine not just how often maintenance occurs but who performs it, how long tasks take, and what level of redundancy or parts coverage is required.
5. Review and Refine Over Time
Criticality isn’t static. As operations scale, equipment ages, and production priorities shift, your scoring must evolve. Build a review cadence into your maintenance planning cycle—quarterly or biannually—and ensure the analysis reflects current plant realities.
Pay close attention to feedback loops. If an asset classified as low criticality continues to trigger emergency work orders or production delays, your evaluation missed something.
Use those cases to refine your scoring framework—not as exceptions, but as opportunities for continuous improvement.
Optimizing Maintenance Using Criticality Outcomes
Once assets are evaluated and categorized, the real work begins. Criticality analysis isn’t useful unless it translates into clear maintenance actions. The goal is to optimize the deployment of time, tools, and personnel.
This involves adjusting not only task frequency but also methods, urgency, and team assignments. Let’s examine how this unfolds:
Example 1: Low-Criticality Asset
Take a secondary conveyor belt that moves packaging materials. It has a history of minimal issues, doesn’t impact upstream production, and has low repair costs. Its criticality score is low.
That asset doesn’t need monthly inspections or tight PM intervals. A run-to-failure approach might be entirely acceptable, especially if a spare belt is stocked on site and can be swapped quickly.
You also don’t need senior technicians involved here, this is the kind of asset that can be handled by entry-level techs without disrupting operations.
Using criticality analysis, you can shift resources away from this equipment and toward higher-impact areas without increasing risk.
Example 2: High-Criticality Asset
Now consider a main process pump in a bottling line. It has no redundancy, and when it fails, the entire line halts. The maintenance history shows recurring component issues every 8 to 10 months. Its criticality score ranks at the top of the scale.
This is where predictive maintenance should be concentrated. Condition monitoring data like vibration trends and temperature shifts should feed directly into your CMMS to trigger alerts or inspections.
Work orders for this asset require high-priority routing, and parts availability needs to be guaranteed—ideally through minimum stock thresholds or vendor agreements.
Technician assignments should also reflect the criticality. Senior maintenance personnel or reliability engineers should handle diagnostics, repairs, and long-term maintenance planning.
Developing Mitigation Strategies
Once high-criticality assets are identified, the next step is to reduce their risk exposure. Here, we need to look beyond scheduled tasks and into structural improvements.
- Increase detection: Add or calibrate condition monitoring sensors to detect early signs of failure. If faults are consistently undetected until it's too late, improving detection is the fastest way to reduce overall risk.
- Add redundancy: For assets without backups, consider whether parallel systems or hot-swappable components are feasible. More than prevention, redundancy buys you time to respond.
- Strengthen spare part readiness: Critical assets should have strategic inventory coverage. Waiting days for delivery isn’t an option when a point of failure threatens operations.
- Adjust maintenance intervals: CMMS scheduling must reflect the asset’s failure behavior and criticality rating. Extend intervals on low-risk assets and tighten them for high-priority ones, using real data as the driver.
Why Tractian’s CMMS Is Built for Criticality-Driven Maintenance
A CMMS that supports criticality analysis demands action. It can’t just store your data, it has to act on it. An active CMMS automates priorities, connects real-time equipment behavior to maintenance workflows, and aligns every work order with operational risk.
Tractian’s platform was designed with this mindset from the start.
It’s an integrated solution built to connect asset condition, failure history, and business impact, giving maintenance teams the structure they need to prioritize and act with precision based on intelligent insights.
Instead of managing a backlog of disconnected tasks, teams using Tractian operate with a clear understanding of which assets demand attention, when, and why.
Criticality scores are embedded in the system’s logic, driving automation that reflects the real state of your operation.
As failure modes shift and equipment behavior changes, the system adjusts, learns, and helps your team understand what’s actually at risk.
Ready to apply criticality analysis to your strategy? Explore how Tractian's CMMS can keep your operation focused on what matters most.
