Predictive Maintenance: Preventing Failures Before They Happen
“If it ain’t broke, don’t fix it” might sound practical, until you find yourself scrambling to recover at the point of failure. Reactive maintenance is a common practice in facility management. It can look like a cost-effective solution in the short term, since you’re not investing in ongoing monitoring or routine upkeep. But having to respond to emergencies can lead to costly downtime, disrupted operations, and uncomfortable or even unsafe environments for occupants and workers.
Predictive maintenance is an emerging solution that supports proactive facility maintenance by using smart building systems to flag potential maintenance issues before they happen. It allows facility management and operations teams to intervene early, plan smarter and keep everything running smoothly–saving time, money, and stress in the process.
What is predictive maintenance?
Predictive maintenance is a proactive strategy that relies on condition monitoring using real-time data, efficient inspections with sensors and IoT devices and advanced analytics to track the health and performance of critical assets. The aggregated data is then used to detect early signs of wear and forecast when equipment is likely to fail, so you can optimize the timing of replacements and repairs.
Predictive vs. preventive vs. reactive maintenance
Approach | Definition | Advantages | Disadvantages |
Reactive Maintenance | Perform maintenance after a failure occurs | No planning needed Low initial cost | Unpredictable failures Costly downtime and repairs Safety risks Shortened equipment lifespan |
Preventive Maintenance | Perform maintenance on a fixed schedule, regardless of equipment condition | Reduces the chance of failure Easy to plan and budget | Can lead to over-maintenance Wastes time, money, and parts |
Predictive Maintenance | Perform maintenance only when needed, based on real-time data and analytics | Most efficient use of resources Minimizes downtime Reduces costs long-term | Requires investment in sensors, software and data management |
Predictive, preventive, and reactive maintenance strategies greatly differ in their timing, data requirements, and overall effectiveness. By performing maintenance only when it's truly needed, predictive maintenance avoids the drawbacks of both reactive (too late) and preventive (too early) approaches.
5 benefits of predictive maintenance
Predictive maintenance offers a host of benefits which help to optimize resource allocation and maintain smoother, safer facility management operations across the board. These benefits also compound over time. As systems compile lifecycle data and teams become more proficient in interpreting insights and acting on alerts, the accuracy of predictions improves.
1. Reduced unplanned downtime and long-term costs
One of the most significant advantages of predictive maintenance is its ability to minimize unplanned downtime. Timely repairs significantly reduce the risk of unexpected breakdowns, which can lead to costly service or production delays and lost revenue.
2. Extended asset and system lifespans
Data from predictive maintenance systems can be used to optimize operating conditions, ensuring that equipment runs within its ideal parameters. By analyzing data from sensors and other monitoring devices, teams can fine-tune settings and make necessary adjustments to minimize stress on equipment components. Meanwhile, early intervention prompted by predictive maintenance helps to prevent the deterioration of critical building assets, prolonging their overall lifespan.
3. Better resource allocation
Predictive maintenance can assist with more strategic maintenance schedules, ensuring that resources are allocated efficiently. The maintenance workforce can prioritize maintenance tasks based on criticality and risk as dictated by data insights, avoiding emergency overtime expenses or high staffing costs associated with excessive maintenance activities.
Additionally, a predictive maintenance strategy helps streamline spare part inventory management, ensuring that the necessary components are readily available when required, without overstocking or tying up capital in inventory.
4. Improved safety and compliance
Facilities can reduce the risk of accidents and injuries by proactively identifying potential equipment failures and addressing them before they escalate and become hazards. Detailed records of equipment health, performance, and maintenance history–which are efficiently stored and tagged in a thorough predictive maintenance strategy–can also be used to demonstrate compliance with safety regulations and industry standards.
5. Sustainability and energy efficiency
Predictive maintenance identifies inefficiencies in equipment operation, such as excessive energy consumption or suboptimal performance. Additionally, by extending asset lifespans and minimizing the need for frequent replacements, predictive maintenance contributes to the sustainable use of resources and reduces waste generation.
5 steps to implementing a predictive maintenance process
Implementing a predictive maintenance process requires careful planning, the right tools, and a commitment to continuous improvement. Here's a practical roadmap to guide you through the implementation process.
1. Assess current maintenance strategy
First, you’ll need to assess your current maintenance practices. Conduct an audit of your existing approach to identify pain points, inefficiencies, and areas for improvement, considering:
Asset performance history: Review failure frequency, downtime trends, and associated costs for key equipment.
Data availability and quality: Check if relevant operational and maintenance data is being collected (e.g., sensor data, logs) and assess accuracy, completeness and consistency.
System integration readiness: Evaluate whether existing systems (CMMS, SCADA, ERP) can support predictive maintenance tooling.
Resource constraints: Identify budget, time, or staffing limitations that could impact predictive maintenance rollout.
2. Identify critical assets and data sources
Next, create a comprehensive inventory of your facility's critical assets, including equipment, machinery, and infrastructure. This asset catalog will serve as the backbone of your predictive maintenance program.
If you’re planning to implement predictive maintenance in a staggered approach to commit costs slowly, first focus your efforts on assets that are essential to operations or expensive to repair.
Select critical equipment based on failure risk and business impact.
List available data sources, such as:
IoT sensors (temperature, vibration, pressure)
PLCs, SCADA systems
CMMS or ERP systems
It’s essential to document your assets as efficiently as possible for predictive maintenance. Consider using a digital twins to annotate and tag equipment with relevant documentation and maintenance history in an immersive virtual environment. This allows you to create a centralized knowledge base and add visual context which will streamline asset performance tracking.
3. Deploy analytics and AI tools
Once you have data, the next step is to make sense of it. You’ll need to incorporate a range of tools into a predictive maintenance strategy in order to do so.
Tool | Features |
Predictive Maintenance Platforms | Ingest data from sensors and devices Monitor equipment condition for real-time asset health insights Manage maintenance scheduling |
AI and Machine Learning Models | Anomaly detection flags abnormal patterns before failure Remaining Useful Life (RUL) estimations predict when maintenance will be required Root cause analysis helps uncover patterns leading to repeated failures |
3D Digital Twins | Integrates spatial data, live sensor feeds, and operational systems into photorealistic 3D visualizations Matterport’s AWS IoT TwinMaker integration converts static 3D models into live operational dashboards for predictive maintenance, troubleshooting, and system optimization. |
Business Intelligence Platforms | Visualizes data insights for decision makers. |
There are a number of analytics platforms available that aim to transform your facility's raw data into a decision-making asset. Which analytics platform you choose will depend on a variety of factors including industry, facility size, existing tech ecosystem, equipment types and deployment preferences.
It’s vital to choose tools with strong integration capabilities when setting up your analytics. By doing so, you’ll be able to sync predictive tools with your CMMS to set up thresholds, automate alerts, and generate work orders.
4. Plan and allocate resources
Implementing a predictive maintenance system requires careful planning and resource allocation. Develop a realistic timeline for rollout, taking into account factors such as staff training, equipment upgrades, and software integrations.
Matterport’s 3D digital twins simplify the predictive maintenance training process by providing a central knowledge base and enabling remote learning opportunities. With accessible and photorealistic digital twins, maintenance staff can quickly familiarize themselves with equipment and procedures, reducing the learning curve and minimizing disruptions to daily operations.
5. Monitor KPIs and continuously refine
As your predictive maintenance program takes shape, it's crucial to track key performance indicators (KPIs) to gauge its effectiveness. Some essential metrics to monitor include:
Equipment uptime and availability
Mean time between failures (MTBF)
Mean time to repair (MTTR)
Maintenance costs as a percentage of total operating costs
Percentage of maintenance activities that are planned vs. reactive
As you gain confidence in your program, consider expanding it to additional assets and facilities. Matterport's Pro3 camera makes it simple to scan new spaces and update asset information, enabling you to scale your predictive maintenance efforts with ease.
Applications of predictive maintenance in different sectors
Predictive maintenance has the power to transform operations across a broad range of industries. While the underlying principles remain consistent, implementation details vary based on the unique needs and challenges of each sector.
Manufacturing and industrial operations
In production facilities, techniques like vibration analysis, thermal imaging, and acoustic monitoring help to track equipment health. Take a look at how a digital twin in a manufacturing facility provides valuable context for maintenance planning and execution. By identifying potential issues before they cause production line stoppages, predictive maintenance improves quality and minimizes the consumer and revenue impact of downtime.
Energy
Safe operations are critical in the high-risk, high-impact energy sector. Equipment failures can lead to power outages, environmental hazards, and large-scale safety risks for both workers and the public.
In oil and gas operations, predictive maintenance monitors wear in drilling rigs, compressors, and pipeline systems, minimizing environmental risks.
Refineries use predictive analytics to monitor rotating equipment and heat exchangers, reducing unplanned outages.
Utilities suppliers apply strategies to manage the health of transformers, substations, and generation assets, supporting grid stability and efficient energy delivery.
Commercial real estate and property management
Digital twins allow property managers to visualize the performance of building systems like HVAC, elevators, and electrical infrastructure so that they can schedule maintenance with minimal inconvenience to occupants. By reducing disruptions and ensuring more consistent building performance, predictive maintenance directly contributes to tenant satisfaction and retention.
Hospitality and retail
In sectors where guest and customer experience is paramount, equipment failures can quickly lead to dissatisfaction and damage brand reputation. Explore our demo library to see where other customer-first facilities are using 3D digital twins to support predictive maintenance for mission-critical assets like kitchen equipment, climate control systems, and point-of-sale technology.
Healthcare
In healthcare facilities, equipment reliability is not just a matter of convenience—it can mean the difference between life and death. Predictive maintenance helps hospitals and clinics meet strict regulatory requirements while ensuring the highest levels of patient care.
Applications range from medical devices and diagnostic equipment to HVAC systems in surgical areas and backup power generators. Digital documentation allows maintenance teams to efficiently navigate the intricate layouts of hospitals. Matterport's St Joseph Hospital and Hull University Teaching Hospital demo models illustrate how 3D captures can provide critical context for healthcare maintenance operations.
How digital twins facilitate predictive maintenance
Matterport's digital twin technology creates a comprehensive visual record of facility assets, documentation and infrastructure, providing a strong foundation for predictive maintenance strategies.
Digital twins serve as a central repository
By consolidating facility and asset data from various sources, such as equipment manuals, maintenance records, and sensors, 3D digital twins provide a single source of truth for maintenance teams. This centralized approach streamlines access to critical information.
By embedding relevant information, such as manufacturer details, installation dates, and maintenance schedules within annotations directly within the 3D model, teams can quickly access the data they need to plan and execute maintenance activities.
Remote collaboration
Digital twins enable remote collaboration among maintenance teams, regardless of their physical location. By providing a shared virtual environment, digital twins allow experts to remotely assess equipment condition, diagnose issues, and guide on-site personnel through complex maintenance procedures.
Integration capabilities
Matteport seamlessly connects with a large range of predictive maintenance software and tools, so teams can monitor asset performance, detect anomalies, and predict potential failures with greater accuracy and precision.
Dimensionally accurate measurements
Dimensionally accurate measurements of facilities and assets eliminate the need for manual measurements and reduce the risk of errors. High levels of precision are crucial for predictive maintenance, as they enable teams to identify subtle changes in equipment performance that may indicate impending failures.
From predictive to prescriptive: the next evolution in maintenance intelligence
As predictive maintenance continues to evolve, the integration of AI and machine learning is paving the way for an even more advanced strategy: prescriptive maintenance. While predictive maintenance focuses on anticipating failures and optimizing maintenance schedules, prescriptive maintenance leverages vast amounts of historical data and sensor information to automate decision-making and recommend specific actions.
AI algorithms can not only predict when a failure is likely to occur but also suggest the most effective course of action to prevent it. By taking proactive measures now to implement predictive maintenance tools into your strategy, you can be ready to merge new prescriptive maintenance tactics as and when they emerge.
