What Is Predictive Maintenance? Definition, Benefits and Steps for Effective Implementation

The promise of predictive maintenance is a scalable, site-wide maintenance system that won’t restrict the operations of individual factories and industrial teams. 

The entire predictive maintenance cycle is a highly efficient, self-contained loop. Edge sensors detect data, which generates analytics and catalyzes automated maintenance actions. By providing a direct path from data collection to real-world response, predictive maintenance systems actively reduce downtime and extend asset life. They also help with cutting costs, improving safety and meeting rigorous ESG objectives — all while preserving operational flexibility.

What is predictive maintenance?

Most experts would define predictive maintenance as a proactive, data-centric system for managing equipment. More specifically, predictive maintenance monitors the condition of equipment in real time, facilitating the continuous collection and analysis of operational data. By using sensors, simultaneous analytics and machine learning, predictive maintenance systems anticipate failures before they happen. 

With predictive maintenance, teams can better detect patterns and anomalies that precede equipment malfunctions. Predictive maintenance methods enable timely, targeted interventions that lead to improved overall efficiency. As a result, companies can service machinery exactly when needed. They can minimize unplanned downtime, avoid unnecessary maintenance and extend asset life. 

Many predictive maintenance deployments run Kubernetes and Linux. This approach ensures compatibility in a GitOps-driven environment, where teams manage all configurations and updates for software systems through a version-controlled repository. With GitOps, teams apply a software development mindset to their infrastructure management.

Why is predictive maintenance important?

Predictive maintenance has emerged as a key strategy for modern manufacturers. It is especially relevant in the face of growing production demands, labor shortages and Environmental, Social and Governance targets. 

Predictive maintenance techniques can directly contribute to smarter and more sustainable operations. Predictive maintenance helps manufacturers:

• Maximize uptime by resolving issues proactively
• Reduce maintenance costs through targeted interventions
• Enhance worker safety
• Achieve ESG and efficiency goals

Notably, predictive maintenance can support modernization on the move. Instead of pausing production or replacing entire systems, teams can add predictive approaches incrementally. By layering new capabilities onto existing assets and workflows, organizations can unlock new value without major disruption.

What makes predictive maintenance different?

Most traditional maintenance strategies prioritize the prevention of equipment failure. Predictive maintenance stands apart for its precision and adaptability. Rather than relying on fixed schedules or reactive fixes, predictive maintenance continuously evaluates asset conditions and responds dynamically. This intelligent, data-first approach helps avoid costly surprises and directs maintenance resources toward the right place at the right time. 

Predictive maintenance vs preventive maintenance

Preventive maintenance follows fixed schedules. Predictive maintenance acts based on actual equipment conditions. While preventive maintenance aims to minimize failures through routine servicing, it may lead to unnecessary interventions or fail to account for real-time degradation. Predictive maintenance adjusts dynamically based on data from sensors, which ensures that actions are taken only when truly needed. 

Predictive maintenance vs reactive maintenance

Reactive maintenance waits for breakdowns. Predictive maintenance prevents them through early detection. A reactive approach means downtime is inevitable, and repairs often come with higher costs and safety risks. In contrast, predictive maintenance identifies signs of deterioration before failure occurs, allowing for timely and low-impact interventions that keep systems running smoothly.

Predictive maintenance vs condition-based maintenance

Condition-based maintenance responds to visible signs. Predictive maintenance forecasts failures by monitoring performance data, environmental conditions, electrical signatures and more. While both strategies use asset condition to inform maintenance, condition-based maintenance typically reacts to thresholds or manual inspections. Predictive maintenance goes further by leveraging machine learning to understand patterns over time. These insights help teams anticipate issues earlier and more easily. 

How does predictive maintenance work?

Predictive maintenance starts with data. Predictive maintenance systems collect data from a network of smart sensors embedded across industrial equipment. These sensors capture real-time indicators such as vibration, temperature and acoustic signals. Instead of sending this data to the cloud, the system processes it at the edge — close to the equipment and sensors — with ruggedized computing nodes. 

These edge nodes perform on-the-fly analytics to detect anomalies or performance deviations. When unusual patterns arise, AI models interpret the signals and identify issues before they escalate.  These insights pass through an automation layer, where predefined policies determine the appropriate response. For example, the system might trigger a maintenance alert or queue up a repair task.

Essential types of predictive maintenance technology

To implement predictive maintenance effectively, organizations must deploy a coordinated set of technologies across the edge. 

Sensors are the frontline tools of predictive maintenance analytics. They capture detailed operational data like throughput, power quality and humidity in real time. Their accuracy and placement are critical to ensuring that systems receive accurate, actionable information.

Edge computing nodes are rugged, localized servers that process sensor data instantly. Industrial edge computing makes it possible for analysis to happen close to the machinery, which eliminates the lag of cloud connections and empowers instantaneous decision making.

An AI-powered software model will draw from the enterprise’s historical and real-time operational data. With each deployment, the model learns and adapts, increasing the accuracy of predictions over time.

Through GitOps automation, teams can push updates, policy changes and configuration rollouts in a repeatable, version-controlled manner.

Coordinating these technologies across distributed environments requires deep expertise in edge computing infrastructure, AI and automation. For many teams, the technologies are daunting. Field-tested solutions like SUSE Edge offer a cohesive platform for implementing and managing predictive maintenance. Platforms with a modular design are particularly helpful for teams looking to start small and scale efficiently.

Benefits of predictive maintenance

Because of its technical impact, predictive maintenance also delivers measurable business outcomes. Adopting a predictive approach to maintenance can provide several benefits to large-scale industrial organizations.

Increased uptime

Predictive systems identify potential equipment failures before they happen, enabling maintenance teams to act proactively. By intervening before a breakdown occurs, teams can significantly reduce unscheduled downtime and maintain steady, uninterrupted production.

Reduced maintenance costs

By pinpointing exactly when maintenance is truly needed, predictive maintenance helps prevent unnecessary part replacements and service calls. This level of precision lowers maintenance expenses and also helps minimize operational disruptions.

Improved safety

Predictive maintenance helps reduce the risk of hazardous incidents by identifying potential equipment failures before they occur. It will flag anomalies like leaks, component fatigue or abnormal operating conditions. As a result, predictive maintenance promotes a safer environment for workers on the factory floor and in the field.

Asset life extension

Timely, data-informed maintenance helps preserve equipment condition and prevent major damage. Predictive maintenance helps teams to intervene at the right time — before problems escalate — which extends asset lifespan and increases return on investment.

ESG alignment

A well-executed predictive maintenance strategy can reduce waste, energy consumption and emissions. By helping companies avoid over-maintenance and unexpected breakdowns, predictive maintenance contributes to the sustainability of operations.

Together, these benefits elevate predictive maintenance from a tactical improvement to a strategic capability. It enables manufacturers to modernize at scale, increase operational resilience and unlock competitive advantages.

Predictive maintenance examples

The internet of things, combined with advances in edge AI, make it possible for companies to use real-time data better than ever before. Manufacturers that embrace industrial IoT see related impacts on supply chain management, workforce safety and more. These capabilities are foundational to predictive maintenance.

Automotive manufacturing

BMW’s highly automated factories offer one predictive maintenance example. In these factories, precision is nonnegotiable. Robotic arms must operate in perfect harmony to keep assembly lines running smoothly. Even modest latency between edge devices and control systems can increase robotic collisions and production errors. By adopting a high-performance, edge native architecture with predictive maintenance software, BMW reduced latency from 50–60 milliseconds to just 1–3 milliseconds.

Maritime shipping

Danelec’s globally distributed fleet comprises more than 13,000 vessels. Over time, the company’s architecture became increasingly complex. In addition, manual provisioning was time-consuming and progressively burdensome. By adopting a modular, open edge architecture with sensor-driven monitoring and other predictive maintenance features, Danelec reduced deployment times by 75%.

These examples of predictive maintenance illustrate just two of the many IoT predictive maintenance use cases found in today’s enterprise landscape.

Challenges to implementing predictive maintenance

While the benefits of predictive maintenance are compelling, implementation is not without its hurdles. From technical complexities to organizational inertia, several barriers can slow down or stall predictive maintenance adoption.

Legacy lock-in

Many organizations still rely on proprietary hardware and software. Some legacy technologies may not immediately work well with modern predictive maintenance counterparts. Rigid infrastructure can make it difficult to connect sensors, integrate edge analytics or automate workflows.

Siloed systems 

Predictive maintenance thrives on the free movement of data. In existing environments, departmental silos or isolated platforms may trap information and prevent data flow. Inconsistent security policies and conflicting operational expectations, for example, can undermine the potential of predictive maintenance. 

Lack of modular practices

Without a consistent, template-based way to roll out predictive maintenance, implementation teams will have to reinvent the wheel at each site. Disconnected deployments can lead to incompatible configurations, higher support costs and slower ROI. 

Insufficient platform governance 

Without clear governance practices, organizations struggle to maintain uniformity across deployments. Such inconsistencies will complicate efforts to scale predictive maintenance and other systems alike.

Platforms like SUSE Edge help companies manage, reduce or eliminate these friction points. Designed with modularity in mind, SUSE Edge includes GitOps-driven automation and supports hybrid environments. Such platforms make it easier to effectively deploy predictive maintenance.

Implement predictive maintenance in seven easy steps

With the right technologies and processes in place, most companies can successfully implement predictive maintenance across their operations. The following steps can help teams modernize in place, unify fragmented systems and deploy predictive maintenance in a scalable and sustainable manner.

• Start with ROI and high-impact assets

Start with a clear target, such as reducing unplanned downtime on a designated line by a specific amount. Rank equipment based on failure cost and safety impact and choose a small but meaningful subset for a pilot. Document key metrics like current outage hours and maintenance spending. These metrics will serve as a baseline and help you measure the results of the pilot.

• Equip machines and secure the edge

Attach condition-monitoring sensors — like vibration, temperature or power meters — to your selected machines and connect them to a rugged edge gateway. Consider power, mounting, connectivity and encryption from day one. Data security is vital to the system’s integrity.

• Stream, clean and store reliable data

Stream sensor readings to your edge device, synchronize them by time, filter out noise and categorize events as either “normal” or “fault.” Use compression or down-sampling to minimize bandwidth without sacrificing data quality. 

• Train models and deploy to the edge

Use your data to train an AI model that can distinguish between normal operations and signs of potential failure. Validate the model using real examples of past breakdowns. Next, containerize the model and deploy it to the edge. Like any AI model, it will need regular monitoring for accuracy. And as your deployment scales, consider setting service level agreements for drift detection and retraining.

• Automatically connect insights to action

When the edge model identifies a risk, predefined automation policies will trigger actions like opening a work order or reserving parts. Setting up this connection requires aligning your edge analytics with policy triggers through tools like GitOps and then integrating with systems such as CMMS or ERP.

• Package for consistent rollouts

Turn your pilot into a repeatable package by bundling everything — sensor settings, software image, machine learning model and alert logic — into a deployment template. To help ensure consistency across rollouts and simplify compliance checks, customize the template for different sites or lines.

• Measure, improve and scale

After the pilot, compare key metrics with your original baseline. Work with colleagues to collaboratively interpret the results, refine your approach and identify where to scale next. In addition to further optimizing your predictive maintenance stack, a cross-functional review will build shared confidence in a broader rollout.

What is the future of predictive maintenance?

While automation and autonomy may be the long-term vision, industrial leaders remain pragmatic. Software should only get more control — like the ability to dynamically adjust performance thresholds — when the software is well-proven in production settings. Over time, each successful pilot builds trust. And each rollout confirms repeatability. 

Hybrid architectures are paving the way for this future. Heavy workloads like model training may stay in the cloud, while real-time scoring happens on local devices at the edge. This separation gives teams the flexibility to scale, switch providers or self-host. It also allows for adjustments to happen without disrupting the decision loops that keep production humming. 

A related evolution is the convergence of predictive maintenance with digital twins — virtual replicas of physical assets. Through physics-based simulations that integrate sensor data, some organizations are virtually modeling asset behavior under different scenarios. Findings from these simulations inform equipment planning efforts, model training and more.

AI and predictive maintenance

Today, AI is most effective when used as a supportive tool. It helps to surface anomalies, optimize performance thresholds and assist with analysis. AI can serve as a copilot that enhances productivity without triggering mission-critical actions. Over time and with validation, such tools may eventually take on more decisive roles.

AI is increasingly shaping how organizations refine and scale their predictive strategies. By enabling real-time data scoring directly at the source, edge AI has introduced an important layer of agility. On lightweight models or specialized chips, edge AI can reduce response times to just a few milliseconds. Because of the automated nature of AI predictive maintenance, teams are better set up to move from pilot to a full production rollout.

In parallel, organizations are exploring ways of ensuring data privacy while still supporting shared intelligence. With federated learning, a system only shares model updates rather than sending raw sensor data from each site to a central server. This approach preserves the confidentiality of process data while still allowing distributed teams to collectively refine their AI models. 

SUSE edge computing and IIoT for predictive maintenance

In IoT-based manufacturing, predictive maintenance has shifted from concept to cornerstone. Technologies like sensors, edge analytics and GitOps automation empower teams to better recognize failure patterns and forecast risks. Together, these components ensure that predictive maintenance systems remain accurate, responsive and scalable. 

For companies looking to implement predictive maintenance, especially in high-demand or mission-critical environments, the right foundation matters. It is possible to make the transition using open tools and best practices, but a partner like SUSE can greatly streamline the process. 

SUSE Edge offers a modular, open architecture that helps teams move from isolated pilots to global rollouts without disrupting existing infrastructure. The platform supports rapid deployment, real-time decision making and consistent policy enforcement across facilities. Fortunately, the shift to predictive maintenance does not mean you’re locked into a rigid solution. With SUSE Edge, you avoid vendor lock-in and stay in control of your infrastructure.

Dive deeper into industrial IoT — and read more edge computing manufacturing use cases — in the Gorilla Guide to Edge and Industrial IoT at Scale.

Predictive maintenance FAQs

How does edge computing enable predictive maintenance?

Edge computing enables predictive maintenance by bringing real-time processing power directly to the source of sensor data. Edge computing removes dependency on cloud connections and therefore reduces latency. It enables predictive analytics to happen close to the equipment — where speed, precision and uptime matter most.

Who uses predictive maintenance?

Industries with high-cost consequences for downtime have been early adopters of predictive maintenance. Many companies in manufacturing, aviation, energy, fleet logistics and data-center operations have benefited from its predictive insights. Within these sectors, key champions include reliability engineers, maintenance planners and IT or platform teams. 

What are the requirements for predictive maintenance?

Successful predictive maintenance programs require a secure, sophisticated and integrated technology stack. Real-time data collection forms the foundation of predictive maintenance systems. Sensors gather the data, robust edge or cloud infrastructure processes the data, and models act on the results. To be effective, a predictive maintenance system needs secure, encrypted data pipelines and zero trust security. It also requires tools to manage and retrain its machine learning models. Finally, it must integrate with maintenance management systems in order to enable automation and minimize manual intervention.

What is predetermined maintenance?

Predetermined maintenance refers to the servicing equipment at fixed intervals, typically based on manufacturer recommendations or historical averages. Sometimes referred to as time-based or calendar maintenance, predetermined maintenance does not account for the real-time condition of assets. As a result, predetermined maintenance leads to risks of over-maintenance and overlooked precursors to equipment failure.

What is emergency maintenance?

Emergency maintenance occurs after a failure has already happened, often unexpectedly. Emergency maintenance is typically unplanned, disruptive and costly — especially when the equipment failure causes safety risks or production downtime.