How to increase the reliability of industrial assets?

Increasing the reliability of industrial assets is one of the greatest challenges for maintenance engineers and managers. Reliability is directly linked to a piece of equipment’s ability to perform its intended function without failure during the planned operating period. When reliability is low, it compromises productivity, raises maintenance costs, and puts operational safety at risk.

Unexpected failures lead to unplanned downtime, loss of efficiency, rework, and even delays in order delivery. Beyond the financial impact, a plant with low reliability creates uncertainty throughout the entire production chain, adding pressure on maintenance teams and reducing the company’s competitiveness in the market. Consequently, measuring and improving reliability is not just a best practice — it is a strategic necessity.

In this article, we will explore how to identify the main signs of low reliability, understand the key indicators used to measure it, and apply effective strategies to improve reliability. In addition, we will show how Industry 4.0 technologies and Dynamox solutions support this process, making maintenance smarter, predictive, and data-driven.

What are the signs of low asset reliability?

Low reliability can be detected through recurring indicators and asset behaviors. These signs serve as alerts for engineers and managers, indicating that maintenance is not effectively preventing failures. Below are the main signs:

High rate of corrective maintenance

When most interventions occur on an emergency basis, it means the maintenance strategy is predominantly reactive. Corrective maintenance is not only more expensive but also increases risk exposure, reduces operational predictability, and consumes resources that could be allocated to higher-value activities. For instance, companies that maintain this approach face greater difficulty in improving reliability, as they lack process control and resource optimization.

Maintenance costs above 5% of RAV

The annual maintenance cost compared to the Replacement Asset Value (RAV) is an internationally recognized benchmarking indicator. When this figure exceeds 5%, it shows that assets are generating excessive expenses relative to the capital invested. This may result from frequent failures, lack of standardized procedures, or an overemphasis on corrective actions. Meanwhile, world-class companies keep this index close to 3%, demonstrating much more efficient asset utilization.

Low MTBF

MTBF (Mean Time Between Failures) measures the average time between repairable failures. A low MTBF means the asset cannot operate for long periods without issues, directly impacting plant reliability and increasing pressure on maintenance teams, who must intervene more frequently. As a result, low MTBF is often associated with the absence of continuous monitoring or well-structured preventive actions.

Recurring failures due to basic causes 

Issues such as misalignment, imbalance, and inadequate lubrication are among the leading causes of mechanical failures. When not addressed systematically, these conditions repeat constantly, reducing asset lifespan and increasing maintenance costs. Therefore, such recurring failures strongly indicate poor reliability management, as these are relatively simple problems to monitor and correct.

Low operational availability

Reliability is closely tied to asset availability. If critical equipment experiences frequent failures, production loses the ability to meet planned deadlines and volumes. This results in delays, lost contracts, and even damage to the company’s reputation.

What are the main reliability indicators?

Asset reliability is measured using specific maintenance indicators that assess operational performance and intervention efficiency. These metrics help identify recurring failures, monitor maintenance team effectiveness, and compare internal results with market benchmarks.

MTBF (Mean Time Between Failures)

MTBF represents the average time between repairable failures of an asset. It measures operational reliability, indicating how long, on average, equipment runs before a failure occurs.

Formula:

Exemple: If a piece of equipment operated for 1,000 hours and had 5 failures, the MTBF would be 200 hours.

The higher the MTBF, the more reliable the asset. A low MTBF reveals that the equipment requires frequent interventions and cannot sustain continuous operation for long periods.

MTTR (Mean Time to Repair)

MTTR measures the average time required to repair an asset after a failure. It evaluates maintenance efficiency and the impact of asset downtime on production.

Formula:

Exemple: If there were 4 failures and the total repair time was 40 hours, the MTTR would be 10 hours.

Meaning, the lower the MTTR, the faster the maintenance response. Reducing this indicator means optimizing processes, training teams, and anticipating diagnostics through predictive maintenance.

OEE (Overall Equipment Effectiveness)

OEE measures the overall effectiveness of equipment, considering three main factors: availability, performance, and quality. It is an essential indicator in discrete manufacturing, where product and batch variability exists.

Formula:

• Availability: The proportion of time the asset was actually in operation.
• Performance: The production speed compared to the ideal.
• Quality: The proportion of defect-free products.

Example: If Availability is 90%, Performance is 95%, and Quality is 98%, the OEE will be: 0.90 × 0.95 × 0.98 = 83.7%.

Low OEE values indicate hidden productivity losses, recurring failures, and low reliability.

Maintenance costs as % of RAV

This indicator compares annual maintenance costs to the Replacement Asset Value (RAV), helping assess whether expenses are at acceptable levels.

Formula:

Exemple: If a plant spends $5 million on maintenance per year and the asset replacement value is $100 million, the index will be 5%.

The lower the percentage, the more efficient the asset utilization. Comparing this index with market benchmarks helps identify if the plant is overspending on maintenance.

How to calculate asset reliability?

Reliability is defined as the probability that an asset will perform its intended function without failure during a specific time interval. This calculation is essential in maintenance engineering because it helps predict failure risks, optimize maintenance plans, and support investment decisions for equipment renewal or replacement.

The most commonly used model assumes that failures follow an exponential distribution, where reliability depends directly on MTBF (Mean Time Between Failures).

The formula is expressed as follows:

Where:

• R(t) = reliability at time t (in percentage)
• t = mission or operating time
• e = Euler’s constant (≈ 2.71)
• λ (lambda) = failure rate (inverse of MTBF → λ = 1/MTBF)

For example:

If an asset has an MTBF of 200 hours, its failure rate is: λ=1/200=0.005.

• For 50 hours of operation:

• For 150 hours of operation:

In other words, the longer the mission time analyzed, the lower the estimated reliability.

Applications of reliability calculation

Reliability calculation should not be seen merely as a mathematical exercise but as a strategic tool for maintenance and asset management. Above all, by translating probabilities into practical insights, it guides decisions that directly impact availability, costs, and operational safety.

• Defining maintenance windows: Enables planning preventive or predictive interventions based on acceptable failure probability, optimizing production downtime.
• Comparing equipment and suppliers: Allows evaluation of different asset options using objective data, supporting purchasing decisions.
• Risk management for critical assets: Helps identify which equipment requires greater attention, considering safety, productivity, and cost impact.
• Optimizing spare parts inventory: By predicting failures more accurately, it reduces the need for excessive stock and immobilized capital costs.

In practice, using reliability calculations transforms historical failure data into consistent forecasts. This gives companies greater control over their assets and creates conditions to align maintenance with production goals and strategic planning.

Limitations of reliability calculation

Although useful, this model is a simplification that assumes a constant failure rate, which does not always reflect reality. Key limitations include:

• Bathtub curve: Assets typically go through three phases — infant mortality (high initial failure rate), useful life (constant failure rate), and wear-out (increasing failure rate). Reliability calculation is only fully valid during the stable phase.
• Insufficient historical data: Without reliable MTBF records, results may be inaccurate.
• Variation in failure modes: Different causes can impact reliability differently, requiring complementary analyses such as FMEA (Failure Modes and Effects Analysis).
• Need for advanced models: For complex assets, Weibull analysis offers greater accuracy by modeling different failure distributions.

Therefore, these limitations reinforce that reliability calculation should be part of a broader toolkit. For comprehensive analysis, it is essential to combine mathematical models with practical indicators, continuous monitoring, and technical knowledge of the assets.

How to ensure indicators reflect reality?

For reliability indicators to be truly useful, they must accurately represent the actual performance of assets. Otherwise, if applied incorrectly, they can lead to distorted analyses and poor decisions. Some essential precautions include:

• Clearly define what constitutes a failure: It is necessary to standardize criteria. A failure may be understood as a complete asset shutdown, partial loss of function, or even performance degradation. Without a clear definition, MTBF and other metrics lose consistency.
• Determine the correct analysis period: Choosing time intervals that are too short can create distortions, while overly long periods may mask critical failures. Ideally, measurement frequency should align with the asset’s operational cycle and the maintenance strategy.
• Perform industrial benchmarking: Comparing internal indicators with external references — such as ABRAMAN surveys and international standards (SMRP, ISO 14224) — helps assess whether plant reliability is at competitive levels.
• Avoid manipulation or misuse of indicators: Metrics can be inflated or adjusted to appear better than they really are. To ensure data reliability, it is essential to maintain traceability, transparency, and consistency in the collection and analysis process.

Therefore, ensuring that reliability indicators accurately reflect reality is essential for guiding strategic decisions. More than just calculating numbers, it is about creating consistency in data collection, standardization in analysis, and transparency in results. Only then do indicators cease to be isolated statistics and become reliable tools for improving operational efficiency and reducing risks.

How to improve reliability in practice?

Improving reliability depends on a combination of well-defined processes, proper use of technology, and team training. There is no single solution — rather, a set of consistent practices that ensure greater availability, fewer failures, and better asset lifecycle utilization. Key practices include:

Standardization of maintenance procedures

Well-defined and documented procedures reduce reliance on individual technician experience, consequently, ensuring repeatability and safety during interventions. Most importantly, this prevents rework, minimizes execution errors, and enables efficient training for new employees. Standardization also improves predictability and reduces variability in maintenance quality.

Data-driven predictive maintenance

Predictive maintenance uses IoT sensors to continuously monitor variables such as vibration and temperature. These data are sent to cloud-based analytics platforms, where algorithms and artificial intelligence detect early signs of failure. Manual and sporadic data collection is replaced by continuous, automated diagnostics, increasing asset reliability and reducing unplanned downtime.

At Dynamox, this approach is enabled by wireless sensors, gateways, and the Dynamox Platform, which integrates data collection, analysis, and management into a single ecosystem.

Structured asset management

Following standards such as ISO 55000 and adopting criticality analysis methodologies ensures that the most important assets receive priority maintenance. This way, planning intervention windows based on concrete data aligns equipment availability with production goals, avoiding unnecessary interruptions and optimizing resources.

Elimination of chronic failure causes

Most recurring failures stem from basic issues such as poor lubrication, misalignment, and imbalance. Addressing these causes systematically, with proper monitoring and predictive maintenance, is essential to extend equipment life. Thus, this practice reduces repetitive occurrences and frees teams to focus on higher-value activities.

Adoption of Industry 4.0 technologies

Integrating physical assets with digital systems enables much more precise reliability management. Tools such as digital twins allow simulation of failure scenarios and evaluation of maintenance strategies before implementation. Moreover, continuous data collection and interoperability between legacy systems and modern platforms make maintenance more efficient and strategic, aligned with productivity and competitiveness goals.

How does Dynamox help improve reliability?

One of the biggest challenges in the industry is turning scattered data into actionable insights to reduce failures and increase asset reliability and availability. Many plants still rely on manual inspections, isolated spreadsheets, or corrective strategies that raise costs and reduce competitiveness.

Dynamox addresses this challenge by providing a complete ecosystem that transforms monitoring into maintenance intelligence:

• DynaLogger sensors and Dynamox Lens sensors: Continuously monitor critical asset condition data, detecting early signs of failure.
• DynaGateways for automated data collection: Centralize sensor information and send it for cloud analysis.
• Dynamox Platform: Provides dashboards, reports, and real-time alerts to support data-driven decisions.
• DynaDetect (AI applied to maintenance): Performs automatic failure diagnostics, accelerating technical team response.
• DynaNeo (digital twins): Consolidates information in an integrated panel, enabling asset health monitoring and decision-making support.

Through this integration, Dynamox helps transform maintenance into a strategic process. Companies gain greater predictability, reduce unplanned downtime, optimize costs, and — most importantly — increase the reliability of critical assets.

Discover Dynamox solutions and start your journey to improve your plant’s reliability with safety, efficiency, and expert support.

Frequently asked questions about increasing reliability – FAQ