Reliability and its strategic impact on industry 

Reliability is one of the most important pillars of modern industrial management, as it is directly linked to safety, efficiency, and operational continuity. More than just a technical concept, it is a strategy that ensures assets, processes, and systems perform their intended function without unexpected failures, within a defined time frame.

In a scenario marked by increasingly complex operations, high competitiveness, and pressure for optimized costs, reliability evolves from being merely a metric to becoming a strategic differentiator. Companies that prioritize robust reliability practices are able to reduce unplanned downtime, extend asset lifespan, improve product quality, and enhance operational safety.

This article explains what reliability means, how it differs from related concepts such as availability and maintenance, which analytical methods can be applied, and the tangible benefits this approach brings to industry. Additionally, it highlights how Dynamox contributes to strengthening reliability through smart sensors, data integration, and digital platforms that support predictive maintenance. 

What is reliability? 

Reliability can be defined as the probability that an asset, system, or process will perform its intended function without failure during a specific period, under certain conditions. In other words, it is the ability to deliver consistent, predictable, and safe performance over time. 

In the industrial context, reliability goes beyond the mere availability of equipment. It involves the confidence that critical machines, production lines, or electrical systems will operate as expected when needed. This includes not only avoiding failures but also reducing risks, optimizing resources, and ensuring that assets maintain stable performance levels.

The importance of reliability is revealed across multiple dimensions: 

• Operational: Ensures continuity of production processes by reducing unplanned downtime. 
• Economic: Lowers costs related to emergency repairs, premature asset replacement, and productivity losses. 
• Safety: Protects employees and facilities by preventing failures that could lead to accidents. 
• Competitive: Enhances the quality and predictability of deliveries, strengthening the company’s market position. 

For these reasons, reliability is considered a strategic indicator that connects maintenance, engineering, quality, and asset management. Industrial plants that invest in reliable practices extend equipment lifespan and build a safer, more efficient, and competitive operating environment. 

The difference between reliability, availability, and maintenance 

In industrial settings, terms like reliability, availability, and maintenance are often used interchangeably. However, each has a distinct scope and impact on asset management, and understanding their differences is essential for structuring effective operational strategies: 

Reliability 

Refers to the probability that an asset will perform its function without failure over a defined period. It is a predictive metric: it measures the likelihood of equipment operating correctly, based on failure history, usage conditions, and design. Thus, an asset may be available but not necessarily reliable if it frequently fails shortly after intervention. 

Availability 

Availability represents the percentage of time an asset is actually in usable condition. It results from the combination of reliability and the efficiency of maintenance actions. Practically speaking, availability indicates whether the machine is ready to operate when needed. Plants with low reliability but fast maintenance may still achieve acceptable availability levels, though in a more reactive scenario. 

Maintenance 

Maintenance encompasses the set of practices, strategies, and interventions carried out to preserve or restore an asset’s condition. It can be corrective, preventive, or predictive, and aims to increase both reliability and availability. In other words, maintenance is the means through which actions are implemented to reduce failures, extend equipment lifespan, and improve operational predictability. 

In summary: 

• Reliability measures the probability of an asset performing its function without failure under specific conditions. 
• Availability measures the time the asset is operational. 
• Maintenance is the means by which reliability and availability are achieved. 

Understanding these differences is essential for maintenance managers to move beyond simply “fixing quickly” and instead focus on building reliable systems that require fewer interventions and operate with greater industrial safety and efficiency. 

Reliability analysis and assessment methods 

A system’s reliability does not depend solely on inspections or scheduled maintenance. It requires data analysis, vulnerability identification, and solution design based on structured methods that combine statistics, engineering, and risk management. 

Below are the main methods applied in industries seeking to reduce failures, increase availability, and make data-driven technical decisions: 

Failure mode and effects analysis (FMEA/FMECA) 

FMEA (Failure Mode and Effects Analysis) is a systematic methodology aimed at anticipating potential failures before they occur in operation. It involves three main steps: 

1. Identifying failure modes for each component or subsystem. 
2. Analyzing the effects these failures may have on the overall system. 
3. Determining probable causes and calculating the Risk Priority Number (RPN) based on three criteria: severity, occurrence, and detection. 

In practice, this method helps prioritize critical failures that directly impact safety, availability, and costs. 

FMECA (Failure Mode, Effects and Criticality Analysis) expands on this approach by including a quantitative analysis, calculating the criticality of each failure based on probability and consequence. This version is widely used in sectors such as energy, mining, and aviation, where the failure of a single component can lead to catastrophic risks. 

“Fishbone” or Ishikawa Diagram

The Ishikawa diagram visually and logically organizes the root causes of recurring failures. It classifies the origins of problems into major groups: Machine, Method, Manpower, Material, Environment, and Measurement (the “6Ms”). 

In reliability analysis, the diagram is useful for: 

• Identifying hidden causes of repetitive failures in critical equipment. 
• Mapping variables that impact performance (such as inadequate lubrication or lack of calibration). 
• Supporting action plans that eliminate the problem permanently rather than treating it superficially. 

This method is especially effective when combined with techniques like Pareto analysis (80/20), which helps prioritize the causes that contribute most to failures. Below is a visual example of the Ishikawa diagram applied in industrial contexts:

Fault Tree Analysis (FTA)

Fault Tree Analysis (FTA) starts with an undesired event — such as a circuit breaker failure, a critical motor shutdown, or the stoppage of a production line — and breaks down its causes into a hierarchical tree-like model. 

The technique uses logical AND/OR gates to represent the relationship between basic failures and the top event. This allows for: 

• Quantifying the system’s failure probability. 
• Identifying single points of failure. 
• Supporting decisions on redundancy, automation, and contingency planning.

FTA is widely used in high-criticality industries such as aviation, nuclear, oil & gas, and electric power, where reliability is directly tied to human safety and operational continuity. 

Life data testing and applied statistics 

Life data analysis is conducted in laboratories or in the field to measure the time until failure of components or systems. Based on this data, statistical tools are applied to project reliability over time. 

The Weibull distribution is the most commonly used model, as it adapts to different failure behaviors: 

β < 1: early failures (infant mortality); β = 1: random failures; β > 1: wear-out failures.

These tests allow for the calculation of key metrics such as: 

• MTBF (Mean Time Between Failures): average time between successive failures.
• MTTR (Mean Time to Repair): average time required to restore the asset. 
• Intrinsic Availability (Ai): ratio between uptime and total time (operation + maintenance). 

In practice, when combined with continuous monitoring and data integration, these methods enable evidence-based maintenance planning, failure curve forecasting, and optimization of spare parts inventory. 

Operational benefits of reliability 

Reliability is not just a technical indicator — it is a strategic factor that directly impacts safety, cost, productivity, and competitiveness. The higher the reliability of assets and processes, the lower the operational uncertainty and the greater the predictability of outcomes. Below are the main benefits: 

Enhanced safety 

Reliable assets operate within controlled parameters, reducing the likelihood of catastrophic failures that put people and facilities at risk. Reliability strengthens operational safety programs by minimizing incidents such as fires, explosions, or electrical discharges. Moreover, the use of continuous monitoring and failure analysis techniques ensures early risk detection, allowing teams to act before problems escalate. 

Reduced costs from failures and rework 

Unexpected failures generate direct costs (parts, emergency labor) and indirect costs (downtime, lost production, contractual penalties). Reliability acts preventively, reducing the frequency and severity of such failures. By prioritizing critical assets and increasing MTBF, companies reduce corrective interventions and avoid rework. At the same time, more efficient planning lowers the need for large inventories of spare parts. 

Greater operational availability

Reliability is directly linked to availability. The more reliable an asset is, the fewer interruptions it experiences, resulting in more productive time. Integrating reliability with predictive maintenance and asset management helps keep MTTR low, ensuring that any failures are resolved quickly. The result is a stable operation with no efficiency losses due to unavailability.

Improved product and process quality 

Asset failures affect not only production but also the quality of the final product. Unreliable equipment can lead to nonconformities, process variations, and raw material waste. When reliability is high, processes become more consistent, quality control is strengthened, and rejection rates decrease. This improvement directly impacts customer satisfaction and the company’s competitiveness in the market. 

Efficiency in asset management 

A well-structured reliability strategy provides trustworthy data and accurate indicators for decision-making. This enables managers to prioritize investments, plan maintenance proactively, and extend asset lifecycles. Furthermore, by integrating reliability with digital data analysis platforms, companies achieve greater maturity in asset management, aligning operations with international standards. 

Challenges in achieving high reliability 

Achieving high levels of reliability is a strategic goal for industries across various sectors, but the path involves overcoming technical, cultural, and organizational barriers. Below are the main challenges faced by maintenance, engineering, and asset management teams:

Increasing system complexity 

Industrial automation and digitalization have increased plant complexity. Today, a single asset may be connected to IoT sensors, supervisory systems (SCADA), maintenance software (CMMS), and enterprise management platforms (ERP). This interdependence makes failure analysis more challenging — a problem in one subsystem can trigger cascading effects, requiring advanced diagnostic tools and multidisciplinary teams to identify root causes and implement effective solutions. 

Lack of standardization and governance 

Without clear and standardized processes, reliability programs tend to be inconsistent. The absence of data governance, well-defined analysis methodologies, and uniform criteria for failure logging compromises the quality of information used to calculate indicators like MTBF and MTTR. Additionally, without standardization, different departments may interpret the same data differently, hindering aligned decision-making. 

Cultural resistance and insufficient training 

Implementing reliability-focused practices requires a mindset shift. Maintenance teams are often accustomed to reactive approaches, prioritizing immediate repairs over prevention. This culture hinders the adoption of predictive, data-driven methodologies. Moreover, the lack of ongoing training in failure analysis, reliability, and new technologies limits the evolution of teams toward Reliability-Centered Maintenance (RCM) practices.

Want to learn more about how people management impacts reliability?  Check out episode 15 of our podcast DynaTalks, featuring Ricardo Amaral from Compass. 

Challenges in data collection and analysis 

Data is the foundation of any reliability program, but it’s not always available in sufficient quality or volume. Incomplete, redundant, or scattered information across different systems makes accurate analysis difficult. Another challenge lies in transforming large volumes of operational data into actionable insights — something that requires integration tools, analytical algorithms, and data science skills tailored to industrial contexts. 

Limited integration between teams and systems 

One of the greatest barriers to reliability is fragmentation between departments and platforms. Without integration, information remains siloed: sensors collect data that never reaches the maintenance system, failure reports aren’t linked to asset planning, and decisions are made based on partial information. 

To address this, Dynamox provides open APIs and connectors that enable integration between sensor data, analytics platforms, and enterprise systems (ERP, CMMS, SCADA). This connectivity breaks down barriers between teams and departments, strengthening a unified view of asset health and enabling more robust reliability analysis. 

Reliability in the information age 

The rise of industrial digitalization has profoundly transformed how reliability is managed. In the past, assessments relied almost entirely on the experience of maintenance teams and manual records. Today, continuous information flow is the cornerstone for anticipating failures, planning interventions, and ensuring operational continuity. In this context, reliability extends beyond asset performance — it also encompasses the integrity and security of the data that supports strategic decisions. 

This shift aligns directly with the industry’s digital transformation journey, structured around key stages highlighted by Guillaume Barrault, CEO of Dynamox, in a recent LinkedIn post. It reinforces the importance of building a solid foundation for data collection, analysis, and protection — essential elements for advancing industrial reliability alongside digital evolution. 

Moreover, this context shows that investing in smart sensors and platforms is not enough. Companies must also adopt international standards for data protection and information security, ensuring trust throughout the entire process. 

Information security with ISO 27k certifications 

The massive collection of data via IoT sensors and its transmission to cloud platforms brings significant gains in visibility and operational efficiency — but also increases cyber risks. Data loss, alteration, or leakage can compromise both operational reliability and overall business security. 

To mitigate these risks, international standards from the ISO 27k family were created to standardize information security and data protection practices. Key certifications include: 

• ISO 27001: defines requirements for information security management systems.
• ISO 27017: provides guidelines for cloud environments.
• ISO 27018: focuses on personal data protection in cloud services.
• ISO 27701: addresses data governance and privacy.

These certifications ensure that data collection, transmission, and storage processes follow strict protocols for encryption, access control, and auditing — protecting digital assets just as physical ones are protected. 

Dynamox Certifications 

In practice, reliability in the information age requires technology partners that guarantee transparency, traceability, and security throughout the data lifecycle. That’s why Dynamox is certified in multiple ISO 27k standards, including ISO 27001, ISO 27017, ISO 27018, and ISO 27701.

This means that the entire continuous monitoring infrastructure — DynaLogger sensors, gateways, open APIs, and Dynamox Platform — operates under rigorous cybersecurity and data governance protocols. In addition to enhancing the technical reliability of monitored assets, this approach ensures that strategic plant information is protected against unauthorized access or data breaches. 

In a scenario where data is as critical as the equipment itself, reliability depends directly on information security. By combining continuous monitoring technology with international certifications, Dynamox strengthens both asset performance and the protection of industrial operations. 

How Dynamox strengthens industrial reliability 

Reliability isn’t built solely on good maintenance practices — it depends on the ability to transform data into precise technical decisions. In this regard, Dynamox offers a complete ecosystem that connects smart sensors, connectivity infrastructure, and an integrated platform, ensuring continuous visibility into asset health and strategic support for maintenance and reliability managers. 

Dynamox solution centralizes operational data and provides modeling tailored to each hierarchical level of the industry. This allows analysts to accurately evaluate data collected by technicians, while managers monitor strategic indicators that support decision-making. Data reliability is ensured within Dynamox Platform, meeting the analytical needs of all business layers. 

Continuous monitoring with sensors 

DynaLogger sensors capture critical variables such as vibration, temperature, and electrical current directly from rotating, electrical, and hard-to-access assets. Data collection is automatic and frequent, eliminating information gaps and enabling the early detection of incipient failures. 

Continuous monitoring also enables advanced predictive analysis, reduces reliance on manual inspections, and increases team safety by minimizing exposure to hazardous areas. 

Data integration in Dynamox Platform 

Sensor data is transmitted via DynaGateway and consolidated in the Dynamox Platform, a cloud-based analytical environment that centralizes data from multiple assets and variables. 

Within the platform, managers and engineers have access to configurable dashboards, historical reports, automatic alerts, predictive diagnostics, and integration capabilities with ERPs, CMMS, and supervisory systems via public API. 

Thus, industrial reliability depends on tools that deliver predictability, safety, and efficiency. With smart sensors, connectivity, ISO 27k international certifications, and an open integration platform, Dynamox positions itself as a strategic partner for companies seeking to increase asset availability and reduce maintenance costs. 

Want to learn how to apply this ecosystem to your operation? Talk to a Dynamox specialist and discover how to transform your reliability strategy with cutting-edge technology. 

Frequently asked questions about reliability – FAQ 

Which industries benefit most from reliability? 

All industrial sectors rely on the reliability of their assets, but some stand out due to their high criticality — such as mining, pulp and paper, steelmaking, oil and gas, food and beverage, energy, and data centers. In these contexts, an unexpected failure can lead not only to financial losses but also to safety risks and disruptions in the supply chain. 

How are MTBF and MTTR calculated? 

Among the most commonly used indicators to assess asset performance and guide maintenance strategies are MTBF and MTTR, which respectively measure failure frequency and repair efficiency: 

• MTBF (Mean Time Between Failures): calculated by dividing the total operating hours by the number of recorded failures. The higher the MTBF, the greater the reliability.
• MTTR (Mean Time to Repair): calculated by dividing the total repair time by the number of failures. The lower the MTTR, the more efficient the maintenance.

Both metrics are essential for evaluating equipment reliability and availability, as well as for guiding investments in technology and process improvements. 

Is reliability solely dependent on maintenance? 

No. Maintenance is one of the pillars of reliability, but not the only one. Well-designed projects, quality installation, standardized processes, proper equipment operation, and risk management are also key factors. Reliability should be approached as a multidisciplinary strategy involving engineering, maintenance, operations, and asset management. 

How does IoT technology support reliability?

IoT technology enables continuous monitoring of assets by collecting data from sensors installed on critical equipment. These data feed analytical platforms that generate predictive diagnostics and strategic insights. 

In practice, IoT increases operational visibility, reduces unexpected failures, optimizes interventions, and strengthens data-driven decision-making. Additionally, integration with management systems enhances traceability and ensures greater efficiency across the entire plant.