Criticality: The Measure That Matters. Are You Falling Short?

Summary

Ask 10 engineering teams what's considered "critical," and they’ll deliver 10 different answers. Most will say downtime, but is that really the full picture?

In any manufacturing facility, engineers utter the same refrain: "This machine is critical because when it goes down, we lose production." A logical starting point? Yes. Still, this kind of surface-level thinking misses the broader costs that can devastate operations long before a machine ever stops running.

What teams really need is a deeper view of asset criticality that reveals hidden costs and risks that standard models miss, whether that be safety hazards that could shut down entire facilities or spare part strategies that tie up millions in working capital. The smartest teams are moving beyond simple downtime calculations to capture the true cost of failure.
 

What is criticality, and why is it misunderstood?

Most criticality models stop at downtime catalysts—teams rank assets by their impact on production throughput, overall equipment effectiveness (OEE) or immediate revenue loss. While these factors matter, this narrow focus creates dangerous blind spots.

True criticality encompasses a much broader spectrum: safety implications, total cost of failure, procurement lead times, potential for cascading damage and strategic timing considerations. An asset might run perfectly for months while simultaneously creating safety risks, eating up inventory costs or positioning your operation for catastrophic failure during peak demand periods.

Though it’s tempting to think about cost solely in terms of downtime, consider the risk of safety incidents, excess inventory, brand reputation damage, regulatory penalties and opportunity costs, which all compound over time.


Furthermore, the most successful teams understand that criticality is dynamic and always changing. Take a seasonal production surge, for example. A part that’s moderately important during normal operations can become absolutely critical depending on the time of year or consumer demand.
 

Understanding downtime: The most visible risk

Downtime gets the spotlight for good reason. It's easy to track, directly tied to production metrics, and immediately visible to leadership. When a critical line stops, everyone knows exactly what it's costing per hour. Still, context matters. 

Downtime during a peak season or critical run can be exponentially more damaging, potentially costing up to 10 times more than the same failure during a different window. A breakdown during a major delivery can compromise a customer relationship worth millions in future revenue. Emergency repairs during off-hours can triple labor costs while introducing safety risks from rushed work. Whatever the scenario may be, it’s clear to see that not all downtime is created equal, and not all risks are downtime.
 

The hidden factors that should influence criticality

Though it’s important to consider and mitigate downtime risks, hyperfocusing on it can leave your facility vulnerable. Every team should also consider:

• Safety: Safety failures often exceed most downtime costs, both morally and financially. A single incident can result in injuries, regulatory penalties, insurance claims, and complete operational shutdowns—all of which can further impact company reputation.
• Cascading failures: The most expensive failures usually aren’t isolated events; they bring other assets down with them. Take a cooling fan, for example. If one fails, multiple downstream assets can overheat, causing more disruptions than originally anticipated.
• Spare parts and inventory: While building a “just-in-case” inventory seems like a proactive approach to keeping a plant floor running, overstocking may actually do more harm than good. Parts can degrade and expire over time, become obsolete, and consume space and inspection time—oftentimes costing more than emergency procurement.
• Lead time and supplier risk: Global sourcing delays are also creating time-sensitive issues. It’s not uncommon to experience 17-week lead times for high-value assets, and a critical failure during a supply shortage can leave operations idle for months.
• Replacement costs and labor complexity: The sticker price of a spare part only tells half of the story. Installation complexity, required skill levels, alignment precision, and downtime duration all multiply the true cost of replacement. Poor installations can trigger serious failures, while complex repairs pull skilled technicians away from preventive work and create bottlenecks.

Technology can help technicians mitigate full-spectrum criticality concerns

Modern technology offers a proactive countermeasure to many of these hidden risks, particularly a strategy known as predictive maintenance (PdM). PdM leverages sensor technology and advanced machine learning algorithms to monitor and detect subtle changes in machine performance, such as shifts in vibration or temperature. These changes can hint at potential wear or looming malfunctions, helping technicians take action before a critical breakdown occurs. Rather than reacting to failures, teams can detect and address potential issues long before they cause harm, disruption, reputational damage or any other measure of criticality listed above.

Technicians applying PdM technologies can proactively manage:

• Safety: Pressure buildups, temperature spikes or abnormal vibration patterns often precede dangerous failures, allowing maintenance teams to intervene before safety is compromised. This reduces the risk of injury, regulatory violations or asset loss.
• Data-driven inventory: Predictive insights enable a shift from fear-based stocking to need-based ordering. Teams collect data showing when a part will need replacing and order accordingly, oftentimes reducing inventory carrying costs by 20-30% by eliminating waste and utilizing large inventory warehouses in other ways to improve production.
• Planned maintenance and efficient repairs: Smart PdM technology like wireless sensors make condition monitoring more accessible and accurate, so teams can plan complex repairs during optimal timeframes. Advance notice helps ensure the right technicians and tools are ready, minimizing disruption and preventing skilled workers from being pulled away from other critical preventative tasks.

Ultimately, PdM technologies are deployed to prevent peak-time shutdowns and lead-time mishaps. With real-time data from sensors, alerts and inspections can come long before an abnormal metric becomes completely catastrophic. Such foresight gives facility managers peace of mind that their plant can sustain operations during peak hours, while also providing them enough warning to withstand increased lead times if need be.
 

Reliability can’t be improved, only preserved

The best way to think about maintenance is this: you don't "improve" reliability; you maintain it.  Every asset starts at its highest reliability point and degrades from there. Your job isn't to make things better than new. It's ultimately to slow the inevitable decline and predict failure.

This perspective shift is crucial for understanding criticality. Tools like vibration sensors, thermal imaging, and oil analysis don't improve asset performance—they help extend asset life and reduce the slope of degradation. The goal is preservation, not enhancement.
 

What to do next: Sense check your asset criticality approach

If a facility’s criticality approach is in need of a revamp, the best start is a simple self-audit. Engineers should be honest when answering questions like:

• Are we still ranking criticality based only on production impact?
• Do we factor in safety, cascade risk, inventory cost, and timing?
• Are we modeling lead times and supplier reliability into asset priorities?

If the answers are limited, it’s time to expand your facility’s risk factors, even involving operations, maintenance, finance, safety and procurement teams to build a more complete criticality model. Cross-function criticality models expose hidden risks and create alignment on priorities.

Downtime is only a piece of the puzzle, not the whole picture. By expanding the definition of criticality and using modern sensor technology and predictive analytics to assess these broader risk factors and time interventions, a plant can be primed for maximum success.