What is prescriptive maintenance?

It is widely used across a diverse range of industries, wherever heavy, interconnected industrial assets are core to business processes.

As more enterprises make the shift from outdated, reactive maintenance strategies toward more modern, proactive maintenance ones, prescriptive analytics is emerging as one of the most sophisticated solutions on the market. Many advanced organizations deploy prescriptive strategies alongside predictive ones, using the latter to identify a problem and the former to recommend solutions.

With the help of smart sensors and enterprise asset management (EAM) platforms, modern organizations can collect more information than ever about their assets. Prescriptive maintenance helps put that data to use, recommending maintenance tasks to resolve issues before they result in equipment failure or unplanned downtime.

Predictive maintenance (PdM) is a maintenance plan that uses operational data and real-time condition monitoring (CM) to predict when assets are likely to fail. Prescriptive maintenance takes this approach one step further by recommending specific maintenance tasks technicians can take to resolve the issues and even evaluating the financial and operational contingencies of its recommendations.

Prescriptive maintenance depends on predictive maintenance for its core functionality—providing data-driven recommendations to resolve maintenance issues before they result in asset degradation or failure.

As organizations increasingly embrace IoT technology and advanced analytics as part of their asset management strategies, demand for prescriptive and predictive solutions is increasing. According to a recent report, the global market for prescriptive and predictive analytics solutions was worth USD 25.40 billion in 2024 and was expected to grow to USD 156.14 billion by 2032.

All prescriptive maintenance (RxM) strategies follow a five-phase process that involves data collection from IoT sensors, data analysis using prescriptive and predictive techniques, and task automation.

Here’s a closer look at each phase.

Modern industrial assets like oil and gas pumps, turbines and transformers are equipped with thousands of IoT sensors that collect data pertaining to asset performance in real time. Data collected by these sensors typically includes critical variables like temperature, vibration, energy consumption and flow rates.

This data is then transmitted to a condition monitoring (CM) platform, cloud environment or edge computing system for further analysis. During this stage, prescriptive platforms also import historical data about asset performance to compare with current conditions.

Organizations must set baseline performance expectations for each asset they intend to monitor, establishing normal operating profiles under ideal conditions. Without baselines, prescriptive maintenance systems can’t detect anomalies or identify patterns that might indicate a developing failure mode.

The second stage of the prescriptive analytics workflow involves applying predictive analytics and statistical algorithms to predict failures before they occur. Relying on performance baselines and data collected during phase one, the system can now predict when certain assets are likely to fail.

For example, a predictive model that’s been collecting and analyzing data around a vehicle engine might determine that the engine has a 99% probability of failure within 6 weeks. This information is based on increases in vibration that it’s detecting compared with historical operating data.

Phase 2 is where traditional predictive maintenance strategies and systems end, allowing technicians to act based on the information generated.

Prescriptive maintenance introduces an additional layer of intelligence, building on the insights gathered by predictive methods in phases 1 and 2. In the third phase, a prescriptive system can evaluate potential responses and recommend actions based on variables it is programmed to consider.

For example, if a prescriptive system learns in phase 2 that a bearing is likely to fail soon, it might recommend one of these actions:

• Replace the bearing within a specific time frame.
• Adjust operational parameters to accommodate the faulty bearing.
• Reduce operating speed to accommodate maintenance tasks.
• Schedule the maintenance during an upcoming shutdown to avoid unplanned downtime.
• Increase lubrication.

In addition to maintenance and performance data, some organizations add data from other parts of the enterprise, such as capital planning, parts inventory and work scheduling, for a prescriptive system to consider.

Most modern prescriptive maintenance strategies automate and enhance workflows with the use of a computerized maintenance management system (CMMS). When properly integrated, a modern CMMS plays a critical role in RxM, serving as both a system of record and an execution layer for prescriptive maintenance activities.

As a system of record, the CMMS serves as the authoritative source of business information for all maintenance activities. As an executor of prescriptive maintenance tasks, a CMMS can be set to automate manual tasks, such as generating work orders, ordering replacement parts and keeping maintenance schedules up to date.

Integrating a CMMS into a prescriptive maintenance strategy helps streamline maintenance activities, reduce reliance on manual effort and improve execution.

The final stage of the prescriptive maintenance workflow is ongoing. Continual learning is an AI training technique that involves sequentially training a model for new tasks while preserving what it already learned. As a CMMS generates work orders and maintenance teams complete them, machine learning algorithms constantly learn from the outcomes of each maintenance task that’s completed.

Eventually, when a prescriptive learning system has gathered enough information, it gains insights into which interventions it recommended prevented asset failures and which didn’t. These insights are valuable at an operational level, informing both the system’s future recommendations and how technicians set and manage baselines.  

Organizations adopt prescriptive maintenance strategies because they deliver benefits that go beyond the operational and execution stages of asset maintenance. When practiced effectively, prescriptive maintenance can impact an entire organization, improving worker safety, making capital planning more efficient and increasing overall productivity:

• Less unplanned downtime: Unplanned downtime is one of the most expensive sources of operational disruption in industrial sectors. According to a recent report, unplanned downtime costs manufacturers an estimated USD 50 billion annually, with an average hourly cost of approximately USD 260,000 per hour. Prescriptive maintenance helps predict failures and recommend corrective actions to prevent them, significantly reducing downtime in machinery and equipment.

• Increased operational efficiencies: Prescriptive maintenance helps organizations prioritize their maintenance activities based on risk and business impact—not just asset health and performance. By viewing maintenance efforts through a capital planning lens, prescriptive strategies help illuminate maintenance tasks that will have impact on business operations, not just on the health of a particular asset.

• Fewer emergency repairs: Emergency maintenance, a type of reactive maintenance performed when a piece of vital equipment breaks down, is problematic even when it doesn’t cause unplanned downtime. Emergency repairs often result in increased costs from overtime labor and expedited parts shipping and can create dangerous conditions for workers. Prescriptive maintenance reduces the likelihood of emergency repairs by enabling planned interventions that address issues before they escalate.

• Stronger asset lifecycle management: Asset lifecycle management (ALM) is the process organizations rely on to keep assets running smoothly throughout their lifecycle. Prescriptive maintenance improves ALM by providing real-time visibility into asset health and future performance. When integrated with a CMMS, prescriptive maintenance strategies can help organizations make more informed decisions about their assets during every stage of their lifecycle, including planning, installation, operation and retirement.

• Increased reliability: Prescriptive maintenance strategies help organizations identify failure modes early and address them before they result in equipment failure. This approach helps reduce the likelihood of a catastrophic failure in the short term and makes equipment and processes more reliable over time.

• Enhanced worker safety: Equipment failures pose a significant safety risk, especially in industrial environments that operate heavy assets. Prescriptive maintenance helps organizations identify developing problems before they become hazardous events, reducing the likelihood of workplace safety incidents.

• Improved sustainability: As sustainability initiatives become more common at the enterprise level, prescriptive maintenance is emerging as an effective tool to help organizations achieve their goals. Properly maintained assets consume less energy, produce fewer emissions and waste, and require fewer replacement parts. Also, by constantly gathering and analyzing data from thousands of IoT sensors, organizations gain valuable insights into how their assets perform and which maintenance actions help make them more sustainable.  

Prescriptive maintenance is used across all asset-intensive industries, including manufacturing, oil and gas and mining. Here’s a look at how those industries and others use it to increase asset reliability, lower costs and improve worker safety:

Manufacturing companies use prescriptive maintenance primarily to monitor production equipment, robotics and large assembly systems.

Modern, AI-powered platforms can analyze vibration, temperature, fluid levels and other key performance indicators (KPIs) and help identify equipment issues before they result in failures.

Common applications for prescriptive maintenance in the manufacturing industry include:

• Computer numerical control (CNC) machines
• Industrial robots
• Conveyor systems
• Packaging equipment
• Compressors

Oil and gas facilities rely on large, complex assets operating without interruption (and in demanding environments) for their core business processes.

Prescriptive maintenance helps monitor and maintain a wide range of critical components, including pumps, compressors, pipelines and turbines, ensuring downtime stays at a minimum.  

Prescriptive maintenance also helps improve worker safety in the oil and gas sector. Heavy machinery failure can cause injuries and even fatalities. Advanced analytics helps identify and fix issues on cranes, pumps and oil rigs before they result in dangerous conditions.

Mining equipment is often operated under extreme conditions that can accelerate wear and tear and lead to degradation.

Prescriptive maintenance with a fully integrated CMMS helps maintenance technicians monitor operational conditions and asset performance for various heavy assets that are critical to core operations, including:

• Haul trucks
• Crushers
• Conveyors
• Excavators
• Processing equipment

Real-time analytics tools give maintenance teams an accurate picture of how the environment and conditions are affecting their equipment and help them address issues before they interrupt production.  

The transportation and logistics industry relies primarily on prescriptive maintenance to service large vehicle fleets that transport people and goods over long distances.

Vehicles equipped with IoT sensors collect data at the source for further analysis on a CMMS or in the cloud. Prescriptive maintenance applications in the transportation and logistics industry include:

• Commercial trucking fleets
• Railway networks
• Aircraft
• Maritime craft

Modern hospitals rely on large, complex assets to support patient care and streamline processes. Given the criticality of the environment and the interconnected nature of the systems, these assets must be highly reliable.

In hospital settings, prescriptive maintenance helps support various systems:

• MRI machines
• CT scanners
• Ventilators
• Laboratory equipment
• Building infrastructure systems

Prescriptive maintenance systems for healthcare improve equipment uptime, patient safety and regulatory compliance.

As AI, IoT technology and ML applications continue to advance, prescriptive maintenance is poised to become a foundational component of modern maintenance programs. Future systems will likely integrate increasingly sophisticated algorithms, larger datasets and even more powerful predictive models to deliver accurate recommendations.

Organizations that can combine prescriptive maintenance with predictive maintenance, CMMS platforms and advanced EAM systems will be in the best position to realize benefits from these technologies as they mature. In some ways, prescriptive maintenance represents a new era in maintenance management, with organizations that can use it better being able to replace outdated, reactive practices with more efficient, automated, proactive ones.

But the future of prescriptive maintenance will have its challenges too. Prescriptive and predictive analytics systems are only as strong as the data they receive, and many organizations still struggle with a wide range of issues surrounding their data, including these examples:

• Incomplete or missing maintenance histories and failure records
• Poorly documented work orders
• Inconsistent asset naming conventions
• Siloed operational data

When built on a strong foundation of good data, prescriptive maintenance can help organizations use data-driven intelligence across all their maintenance processes, extending asset lifecycles and improving productivity. But if the underlying data is poor, even the most advanced predictive and prescriptive platforms won’t get off the ground.