In modern industrial management, data-driven decision-making is no longer a competitive advantage — it’s a necessity. The advancement of digitalization and the intensive use of sensors and algorithms have enabled maintenance practices to evolve — from simply reacting to failures to automatically recommending actions based on predictive analysis. This is where prescriptive maintenance comes into play.
More than just predicting future failures, prescriptive maintenance tells you what to do, when to do it, and how to act when an anomaly is detected. It leverages artificial intelligence, machine learning, and analytical models to transform large volumes of data into actionable decisions. As a result, it helps reduce unnecessary interventions, increase asset reliability, and optimize maintenance resources.
In this article, we explain what prescriptive maintenance is, how it works in practice, and the benefits it brings to different types of industrial assets. We also compare it with other approaches (corrective, preventive, and predictive) and show how sensors, integrated platforms, and algorithms can transform the way your plant performs maintenance.
What is prescriptive maintenance?
Prescriptive maintenance is an advanced, data-driven maintenance strategy that uses artificial intelligence (AI) and machine learning (ML) not only to predict failures but also to recommend specific corrective actions. This approach interprets trends, patterns, and operational deviations to determine what to do, when to intervene, and the best course of action to avoid failures or performance losses.
The key difference between predictive and prescriptive maintenance lies in the depth of analysis and the ability to generate automated recommendations. While predictive maintenance identifies abnormal behavior and anticipates potential failures using historical and real-time data, prescriptive maintenance goes further. It applies analytical models capable of simulating scenarios, calculating impacts, and defining the optimal response to a detected issue. In other words, predictive maintenance answers what is happening with the machine, while prescriptive maintenance tells you what to do about it.
To achieve this, prescriptive maintenance relies on algorithms trained with large volumes of operational data, combining variables such as vibration, temperature, electrical current, and more. AI then recognizes patterns, classifies events, learns from past failures, and continuously evolves — delivering increasingly accurate and tailored recommendations for each type of asset.
This approach is especially valuable in industrial plants with high data volumes and multiple critical assets. By automating part of the decision-making process, prescriptive maintenance shortens the time between detection and intervention, boosting operational efficiency and strengthening equipment reliability.
How does prescriptive maintenance work in practice?
Prescriptive maintenance operates through an integrated technological architecture that connects smart sensors, analytical platforms, and advanced algorithms to transform operational data into precise, automated decisions. The process involves four main stages:
1. Continuous data collection
It all starts with continuous asset monitoring via IoT sensors. These devices capture variables such as vibration, temperature, electrical current, and pressure in real time. The richness and frequency of this data are essential to feed predictive models and enable sensitive diagnostics — even for subtle behavioral changes.
2. Application of analytical and predictive models
Next, the collected data is analyzed using statistical models and machine learning algorithms trained to detect patterns and anomalies. This stage is similar to predictive maintenance: the system identifies deviations from normal behavior and estimates the likelihood of failure.
3. Generation of automated recommendations
This is where prescriptive maintenance stands out. Upon identifying a potential failure, the system not only issues an alert but also recommends specific actions. For example: replacing a component within a defined time window, adjusting operational parameters, or dispatching a technical team. These recommendations are based on failure histories, predicted consequences, and scenario simulations — ensuring greater accuracy.
4. Integration with asset management platforms
Finally, prescriptive maintenance integrates with asset management systems (EAM/CMMS), allowing work orders to be automatically generated based on algorithmic recommendations. This reduces response time, improves resource planning, and standardizes decision-making processes—amplifying the strategy’s positive impact.
Prescriptive maintenance anticipates failures and guides corrective actions by combining data, technology, and analytical intelligence, raising the maturity level of industrial maintenance.
What is required to implement prescriptive maintenance?
Implementing prescriptive maintenance involves more than simply acquiring new technologies. It represents a structural shift that requires high-quality data, robust analytical tools, and skilled teams. Below are the key requirements for effective adoption:
Sensor and gateway infrastructure
The foundation of prescriptive maintenance lies in continuous data collection. This requires a reliable ecosystem of sensors capable of monitoring variables such as vibration, temperature, electrical current, and lubrication. These sensors must be integrated with industrial gateways that ensure secure and automated data transmission to the central platform — eliminating manual steps and optimizing information flow.
Platform with predictive and prescriptive analytics capabilities
Beyond physical infrastructure, it is essential to have a digital platform that goes beyond simple monitoring. Prescriptive maintenance depends on algorithms capable of identifying patterns, predicting failures, and recommending corrective actions based on both historical and real-time data.
Historical data and model training
Prescriptive maintenance relies heavily on the quality and consistency of data. A reliable history of failure events, operational conditions, and past interventions must be included. This data feeds analytical models that use machine learning techniques to recognize patterns and generate accurate recommendations. The more robust the historical dataset, the more precise the diagnostics and automated suggestions will be.
Team training and enablement
Finally, transitioning to prescriptive maintenance requires technicians, engineers, and managers to be prepared to interpret data, validate AI-generated recommendations, and make evidence-based decisions. This involves training in data analysis, integration with other departments (such as production), and proficiency in the adopted technological tools. In short, a culture of reliability must be reinforced so that the team can act strategically and proactively.
What are the benefits of prescriptive maintenance?
Prescriptive maintenance represents a significant advancement over traditional approaches. Below are its main benefits:
Data-driven decision-making
By employing advanced algorithms, prescriptive maintenance eliminates guesswork and subjective judgment. Predictive analysis combined with automated recommendations enables technical decisions to be made based on concrete evidence — such as failure history, real-time operational conditions, and probabilistic simulations. This strengthens the reliability of interventions and reduces human error.
Cost reduction from failures and unnecessary maintenance
With data-driven recommendations, interventions occur only when there is a real risk of failure — avoiding premature replacements and unexpected corrective shutdowns. This helps reduce component waste, optimize labor usage, and prevent collateral damage, directly impacting asset cost reduction and return on investment (ROI).
Increased Mean Time Between Failures and operational availability
By anticipating failures and indicating the optimal time for intervention, prescriptive maintenance directly contributes to increasing Mean Time Between Failures (MTBF). Additionally, by reducing the number and duration of stoppages, it improves asset availability, making the plant more productive and resilient.
Faster and more effective actions
The key differentiator of prescriptive maintenance is the generation of automated recommendations. Instead of relying solely on human analysis, technology suggests actions based on databases and machine learning, ensuring speed and precision. This reduces response time and facilitates procedure standardization.
Cross-department integration and standardized responses
By centralizing data, analysis, and recommendations on a single platform, prescriptive maintenance enhances communication between departments such as maintenance, operations, and engineering. As a result, actions no longer depend on individual experience but follow defined technical criteria—promoting alignment, traceability, and continuous improvement.
Examples of prescriptive maintenance applications
Prescriptive maintenance is especially effective for industrial assets with high operational criticality, technical complexity, or high failure costs. Below are examples of prescriptive maintenance application across different types of equipment:
Electric motors
In drive motors, sensors are installed directly on the equipment body to monitor vibration and temperature, while current sensors are placed on the electrical panel. These data are continuously collected and analyzed by algorithms that detect abnormal patterns—such as increased vibration at specific frequencies, thermal deviations, or current spikes.
The prescriptive platform interprets these anomalies and recommends actions such as bearing replacement before failure, rotor imbalance inspection, or ventilation system checks. This allows the maintenance team to act proactively during a scheduled window, avoiding unexpected equipment downtime.
Industrial compressors
Compressors operate with multiple critical variables — pressure, vibration, temperature, and energy consumption. Sensors placed on components such as the head, casing, and discharge line enable real-time monitoring of thermal and mechanical performance.
Upon detecting, for example, an abnormal temperature rise combined with vibration pattern changes, the prescriptive system may recommend immediate inspection for valve wear or internal residue buildup. If symptoms persist, the recommendation may escalate to a planned shutdown with component replacement — preventing severe failures or production breakdowns.
Fans and exhaust systems
These assets, often used continuously, are monitored with vibration and temperature sensors on bearing housings and near motors. Prescriptive analysis identifies deviations indicating misalignment, dust accumulation on blades, or mechanical looseness — and prescribes corrective actions.
For instance, if vibration gradually increases and exceeds predefined limits, the platform may recommend dynamic balancing or preventive cleaning. The maintenance team can then schedule the intervention without emergency shutdowns, maintaining system reliability.
These examples demonstrate how prescriptive maintenance applies to various operational contexts, focusing on performance, safety, and cost-efficiency.
What is the difference between corrective, preventive, predictive, and prescriptive maintenance?
In industrial maintenance, different approaches coexist depending on the plant’s technical maturity, asset criticality, and available resources. Understanding the distinctions between corrective, preventive, predictive, and prescriptive maintenance is essential for adopting a strategy aligned with operational goals.
In this context, a comparative analysis shows that there is no single ideal approach — rather, complementary strategies should be applied based on the context of each asset. However, transitioning to predictive and prescriptive models represents a leap in technical maturity and reliability, which is essential for plants aiming for operational excellence and sustainable cost reduction.
When should each be applied?
- Corrective Maintenance: Recommended for low-criticality assets or redundant systems where failure does not compromise production, safety, or other equipment. It should be used with caution, as it is associated with unexpected downtime and emergency costs.
- Preventive Maintenance: Follows fixed time or usage intervals (operating hours, cycles, mileage). Suitable for equipment with predictable and well-documented failure modes, provided it does not lead to unnecessary replacements or premature interventions.
- Predictive Maintenance: Ideal for critical assets where failures must be anticipated through continuous monitoring. Best applied when sensors, historical data, and a trained team are available to interpret real operating conditions.
- Prescriptive Maintenance: Should be implemented in environments with higher digital maturity and robust data volumes. It is recommended for plants seeking to automate decision-making, optimize resources, and standardize interventions using AI-powered maintenance strategies.
Maintenance maturity evolution
The transition between maintenance types typically reflects the plant’s technological and organizational advancement. This evolution is described in established frameworks such as the Maintenance Maturity Model, originally developed by Winston Ledet (1999). Based on this model, industrial maintenance maturity can be divided into four levels:
- Initial stage: Dominated by corrective maintenance, with reactive actions focused on keeping operations running.
- Intermediate stage: Adoption of preventive plans and periodic inspections to increase predictability.
- Advanced stage: Use of sensors and platforms for predictive analysis based on real-time data collection.
- Prescriptive stage: Integration of data, AI, and machine learning to automate diagnostics and recommend specific actions — enhancing reliability and reducing costs.
This evolution is not linear or simultaneous across all assets. Many plants adopt hybrid strategies, applying different approaches depending on the criticality and maturity level of each piece of equipment.
Frequently Asked Questions (FAQ)
Does prescriptive maintenance replace predictive maintenance?
No. Prescriptive maintenance complements predictive maintenance. While predictive maintenance identifies early-stage failures based on data and trends, prescriptive maintenance goes further by automatically recommending the best actions to take. Both are part of an advanced maintenance plan, with prescriptive maintenance representing a more evolved use of data for faster, more accurate decisions.
What technologies are essential?
Smart sensors, industrial gateways, platforms with machine learning and artificial intelligence algorithms are fundamental. Integration with asset management systems (EAM/CMMS) is also necessary to operationalize recommendations and generate automated action plans.
Can any plant apply prescriptive maintenance?
In theory, yes. However, feasibility depends on the plant’s technical maturity, the volume and quality of available data, and the existing technological infrastructure. In environments with critical assets and high availability demands, prescriptive maintenance delivers greater returns. In contrast, for operations with low criticality or minimal digitalization, adoption should be gradual.
Is a large volume of data required to apply prescriptive maintenance?
Yes, the effectiveness of prescriptive maintenance depends on a robust and well-structured data foundation. Prescriptive models require historical records of failures, operational conditions, and interventions to accurately train algorithms. However, it is possible to start with basic data and expand the scope as the strategy matures.
Does AI actually decide maintenance actions?
AI in maintenance suggests actions based on historical patterns and predictive analysis, but the final decision still requires technical validation by the team. AI acts as a decision-support system — optimizing response time and reducing human error — without fully replacing expert judgment.
How Dynamox supports the evolution of industrial maintenance
Prescriptive maintenance represents the most advanced stage of industrial asset management. By combining continuous monitoring, predictive analysis, and artificial intelligence, it enables faster, more accurate technical decisions aligned with operational reliability goals. In this scenario, Dynamox acts as a strategic partner for industries aiming to evolve to this new level.
Our ecosystem combines IoT sensors, gateways, and a digital platform with intelligent algorithms. Together, these resources enable:
- Continuous data collection on vibration, temperature, and electrical current;
- Automated alert and diagnostic generation using AI (DynaDetect);
- Centralized asset health visualization (DynaNeo);
- Integration with management systems (CMMS/EAM) to close the maintenance loop.
With these solutions, Dynamox transforms raw data into actionable insights — helping teams prioritize interventions, prevent failures, and reduce operational costs.
Talk to a Dynamox specialist and discover how to evolve your plant toward smarter, safer, and more productive maintenance.