Predictive vs. preventive maintenance: choosing the right strategy for commercial facilities

Every facilities leader wants to reduce downtime – but deciding where to invest maintenance resources is more difficult than ever. The real question isn't whether predictive or preventive maintenance is better. It's how to apply each approach where it creates the greatest value.

When evaluating predictive vs. preventive maintenance, facility leaders often ask which approach is best. The answer is rarely one or the other.

Preventive maintenance provides operational structure and consistent costs, whereas predictive maintenance adds data-driven precision to reduce unnecessary maintenance and highlight failure risk.

Preventive maintenance (PM) follows fixed schedules at time- or usage-based intervals, regardless of the asset's actual condition. That consistency makes planning and budgeting easier, but it can also make maintenance inefficient. In practice, some assets are serviced too often, while others are not serviced enough.

By comparison, predictive maintenance (PdM) is a more sophisticated approach that uses sensors and monitoring systems to respond to the performance and condition of assets in real time. It prompts maintenance only when necessary and detects developing faults before they lead to failure, rather than waiting until the next scheduled service. The catch? That level of technology comes at a cost and requires a certain level of expertise.

For most commercial buildings, it’s not a question of which is best, but how to strike the right balance between the two approaches.

What is preventive maintenance?

Preventive maintenance is a proactive strategy that services equipment on a set schedule – such as time or operating hours – instead of waiting for a failure to occur (also known as reactive maintenance).

The biggest benefit of this approach is predictability. Following a fixed maintenance schedule allows facilities teams to plan resources in advance, rather than scrambling to respond to unplanned failures. That same structure helps keep budgets under control by catching issues earlier to reduce costly emergency repairs and unplanned downtime.

Preventive maintenance scheduling can also incorporate compliance-related tasks, rather than having to book them in as a separate obligation. This means carrying out quarterly HVAC inspections and routine tasks such as air filter replacements with necessary warranty or regulatory inspections conducted at the same time.

What is predictive maintenance?

Predictive maintenance takes the proactive strategy a step further by shifting maintenance decisions from schedules to real-time data. It’s a condition-based approach that uses continuous monitoring – from sensors and building energy management systems (BEMS) or connected monitoring platforms – to track asset health and flag any anomalies before they become problems.

In commercial buildings, this means monitoring variables such as energy consumption, air temperatures and energy efficiency. When performance metrics move outside expected operating ranges, maintenance teams can investigate before a problem becomes a failure.

HVAC downtime and undetected operational faults have a considerable impact on operations, energy consumption and occupant comfort. A large study of commercial buildings analyzing more than 60,000 pieces of equipment showed that – on any given day – up to 40% of air-handling units (AHUs) and 20% of air-terminal units had a fault.

Preventive vs. predictive maintenance: a comparison

Both preventive and predictive maintenance strategies offer a vast improvement from reactive maintenance approaches, but for different reasons. Below, we’ve outlined the key distinctions that matter when deciding what works best for your organization.

Predictive vs. preventive maintenance cost comparison

Justifying the upfront investment required for predictive maintenance can be difficult, especially compared to a cheaper preventive maintenance strategy. When making any decisions, it’s important to think beyond short-term spend and consider the full lifecycle value that predictive maintenance unlocks.

Consider what each approach costs over the lifecycle of an asset. This can include potential emergency repairs, equipment replacements or unplanned downtime. A preventive maintenance strategy has its limitations: some assets can be overserviced unnecessarily, while gaps between service intervals leave organizations open to failures. This can cause operational disruption and emergency callouts that feel more like something out of the reactive maintenance strategy playbook.

Adopting condition-based monitoring makes a big difference. Johnson Controls data shows that customers using connected chiller monitoring experience an average 35% reduction in unplanned service expenses. This shows the lifecycle value that predictive maintenance approaches can deliver on critical equipment, which puts upfront investment into perspective.

Which assets benefit most from predictive maintenance?

The upfront costs of predictive maintenance are a reality many organizations can’t justify at scale. This is where asset tiering comes in to help you prioritize predictive maintenance upgrades in a strategic, cost-effective way.

Not every asset in a commercial facility has the same value. Categorizing equipment based on its organizational impact, safety risk and financial implications helps determine whether a predictive maintenance investment is worthwhile.

• Mission-critical systems are where unplanned downtime or failures carry immediate safety, operational or compliance consequences. Think: emergency generators or critical cooling. If they stop running, operations stop completely, or people can be put at risk. This makes these systems excellent candidates for predictive maintenance, as continuous monitoring will help avoid these issues.
• Business-critical systems are important, but not an emergency. A failure of these systems, such as chillers or boilers, may disrupt operations or make spaces unusable rather than dangerous. The consequences of failure are significant, but not as severe or immediate as those associated with mission-critical assets
• Comfort or non-critical systems are assets whose failure is inconvenient but not urgent. Examples include fan coil units in low-traffic zones. These can be addressed as part of routine maintenance.

Categorizing assets according to risk level, downtime cost and compliance exposure helps highlight where to invest in predictive maintenance. Mission-critical systems should be the priority, as they will deliver the highest return on investment across the lifecycle.

Where preventive maintenance still makes operational sense

Moving towards predictive maintenance doesn’t mean abandoning preventive strategies altogether. For most organizations, preventive maintenance remains at the core of operations and acts as the baseline for more advanced strategies.

Put simply, preventive maintenance is best suited to assets that are stable and predictable, where the constant real-time monitoring of predictive maintenance adds little value. Air-handling unit filters are a perfect example. Degradation is predictable – the main issue is clogging over time – and they’re easy (and relatively inexpensive) to replace on a scheduled basis.

Where predictive maintenance delivers greater strategic value

Predictive maintenance works best for assets where unplanned downtime or performance issues would significantly disrupt operations. These would fall into the mission- and business-critical categories and benefit from the precise, condition-based monitoring of predictive maintenance.

In a commercial HVAC environment, assets most suited to this approach might include equipment where workload varies based on occupancy, season or demand. For example, chillers in a commercial tower work at different intensities at certain times of year. Fixed service intervals don’t necessarily account for this variation.

To be successful, predictive maintenance requires enough clean, consistently structured historical data to establish performance baselines. This helps create a reliable system that can accurately flag anomalies.

The hybrid model: structuring maintenance around asset risk and data maturity

Adopting a hybrid model is the most sensible solution for organizations looking to develop an existing preventive maintenance strategy into a more condition-based approach.

That’s because not every asset requires the same approach, and a portfolio-wide transition isn’t practical.

The first step is to use the asset tiering framework to identify and prioritize mission- and business-critical assets for a more advanced approach. Other assets can be added later as part of a rollout, once results are seen.

The maintenance hierarchy runs from preventive through condition-based to full predictive maintenance. Condition-based maintenance (CBM) initiates maintenance activities when monitored performance measures indicate deterioration beyond acceptable limits. The BAS executes any resulting control adjustments, and the CMMS manages the maintenance response.

When a reading crosses that boundary, it prompts a maintenance response. It’s a level up from using a calendar to schedule maintenance, but it’s still a rule-driven approach: the system will only act when a specific point is reached.

Full predictive maintenance is more sophisticated because it uses data analysis and pattern recognition to predict failures long before a threshold is crossed. For example, the system might spot a subtle behavior change for a particular asset over a 30-day period. It could then predict, according to the performance pattern and rate of decline, that an intervention would be needed within two weeks.

To be successful, predictive maintenance requires a high level of reliable operational data and infrastructure that simply isn’t a reality for most facilities. The hybrid model allows for both by adapting to an organization’s data readiness and asset hierarchy.

Applying this framework to HVAC systems (and broader building assets)

HVAC offers a perfect example of how the hybrid model works in a commercial building, because HVAC assets call for different maintenance strategies.

Central equipment – such as chillers and air-handling units (AHUs) – is mission- or business-critical, making it a strong candidate for predictive maintenance. Johnson Controls connected chillers service illustrates what this might look like in practice. It monitors data from more than 200 points (more than a BAS) and advanced analytics, combined with a remote operations team reviewing around the clock to identify anomalies before they become operational issues.

At the other end of the spectrum, assets such as AHU filters or zone-level air distribution systems are well-suited to the structured nature of preventive maintenance. The hybrid model shows how the portfolio can be managed across a range of maintenance strategies.

This same logic applies across electrical systems, life safety systems and other critical infrastructure. A range of maintenance strategies will apply according to each asset's requirements.

A practical decision framework for facilities leaders

Choosing which maintenance strategy to apply and where will be an ongoing evaluation that changes as assets age and priorities shift. It’s useful for facilities leaders to have a consistent decision framework.

Asset risk and data maturity are the most important factors when deciding which strategy to adopt for each asset. Those upgraded to predictive maintenance should be high priority in terms of criticality. They should also have reliable, clean data that can be analyzed.

Downtime impact, budget availability and portfolio scale are also important when making these decisions. Ultimately, it comes down to optimizing return on investment, so prioritizing assets where the financial case is the strongest is key to decision-making. Determining factors could include costly downtime or budget allowance.

Building a smarter maintenance strategy

The most effective maintenance strategies aren't built around a single methodology. They're built around asset criticality, operational risk and the strength of your building's data. Whether you're looking to build upon an existing preventive maintenance program or expand predictive capabilities across critical equipment, Johnson Controls can help you identify the right approach for your portfolio.

Johnson Controls combines building automation, connected equipment monitoring, advanced analytics and expert remote support in a single ecosystem. This is how we help organizations modernize maintenance strategies without replacing existing processes.

FAQs

1. What is an example of predictive maintenance?

A good example of where predictive maintenance is effective is a commercial chiller.

Chillers operate at varying intensities depending on factors such as occupancy and demand, making fixed service intervals a poor fit. Condition-based monitoring can detect a drop in efficiency or an anomaly in operating temperatures weeks before it would appear on a scheduled inspection. This goal: avoid unplanned failure that would cause significant disruption in a commercial building.

2. What is the difference between preventive and planned maintenance?

Planned maintenance is a general term for any maintenance scheduled in advance. Preventive maintenance is a type of planned maintenance that’s performed at time- or usage-based intervals, such as every six months or 10,000 hours.

3. How does predictive maintenance differ from preventive strategies?

Preventive maintenance is scheduled based on a fixed schedule on expected wear and tear (often time- or usage-based). Predictive maintenance is prompted by real-time data and asset health.

In commercial HVAC, preventive maintenance might mean replacing AHU filters every three months. Predictive maintenance would replace them based on airflow or pressure readings.