What is Product Reliability: Essential Points and Examples

Product reliability is a product’s ability to perform its intended function consistently and without failure under specified conditions for a defined period. For product managers, engineers, quality assurance teams, and business stakeholders, it directly shapes customer satisfaction, brand reputation, warranty costs, compliance, and profitability.

Key takeaways

Quick facts:

• Reliability describes how a product behaves over time under stated conditions.

• Product reliability is statistically measured from field returns and life testing, and it is largely determined during design.

• Warranty cost and recall exposure both follow from it.

This article explains what product reliability means, why it matters, how it is measured, and which strategies improve it in practice. It also looks at industry examples and newer AI-based approaches to reliability, including physics-aware engineering AI. It is written for product managers setting warranty terms, design and CAE engineers specifying life targets, quality assurance specialists running field data analysis, and executives who bear the cost of recalls.

Table of contents

• What is product reliability?

• Five key components of product reliability

• Importance of the reliability of a product

• How is product reliability measured?

• Examples of product reliability

• How to ensure product reliability?

• AI and product reliability

• Conclusions

• FAQ

• Sources

What is product reliability?

Product reliability means that a product performs its intended function consistently and without failure under specified conditions (the operating environment) for a defined period (the product lifecycle).

In reliability engineering, the definition is probabilistic and therefore measurable. Reliability equals the probability that an item performs a required function under stated conditions for a stated time interval.

Reliability is a critical aspect of product quality, customer satisfaction, and brand reputation. A company is considered reliable when its products have proven reliable, an important factor in daily purchasing decisions.

Few buyers would purchase a product that performs satisfactorily during the first mile and fails soon after.

Five key components of product reliability

The definition breaks down into five properties that can be specified and tested.

1. Consistency: A reliable product performs its function consistently over time.

2. Durability: A reliable product withstands wear and tear, maintaining its performance throughout its intended lifespan.

3. Quality: High-quality materials and production processes contribute to overall reliability.

4. Functionality: A reliable product must effectively perform its intended function under various operating conditions.

5. Longevity: A reliable product has a longer useful life, reducing the need for frequent replacements.

Importance of the reliability of a product

The significance of a product's reliability extends far beyond a customer's immediate satisfaction. It impacts various aspects of a business and its operations.

Customer satisfaction

A product's reliability influences customer satisfaction. When customers purchase a product, they expect it to function as intended for a reasonable period. Reliable products meet or exceed these expectations.

Customers who experience consistent, reliable performance from a product are more likely to develop a positive association with the brand, becoming loyal customers and potentially brand advocates.

Brand reputation

In an interconnected world, brand reputation is more important than ever, and it is formed in public, since owners publish failure reports and review scores are aggregated and searchable. Product reliability significantly influences how a brand is perceived in the market. A reputation for reliability can be a powerful marketing tool, attracting new customers and retaining existing ones. Conversely, a reputation for unreliable products can be devastating, leading to lost sales, negative reviews, and long-term damage to the brand.

Consider the following impacts:

• Reliable products enhance brand image and perceived value.

• A positive reputation leads to increased market share and customer base.

• A strong brand reputation can command premium pricing.

• Reliability helps differentiate products in competitive markets.

Cost efficiency with a strategic warranty policy

A well-crafted warranty policy, underpinned by product reliability, can significantly impact a company's long-term cost efficiency throughout the product life cycle. By focusing on reliability during the product development process and implementing a strategic warranty, companies can avoid costly issues afterward. This approach protects against unforeseen expenses and builds a reputation for quality, ultimately improving the company's bottom line.

The scale of the exposure is documented. In 2025, U.S.-based manufacturers paid $30.37 billion in warranty claims, set aside $33.41 billion in warranty accruals, and held $71.89 billion in warranty reserves at year-end, a 17% increase in reserves from the end of 2024. The average claims rate equaled 1.30% of product sales revenue, and the average accrual rate equaled 1.43%.

Reference: Warranty Week, "23rd Annual Product Warranty Report," April 2026

While investing in reliability and comprehensive warranty coverage might seem costly upfront, it often leads to substantial savings and benefits, as detailed below.

• Optimized warranty costs: A strategic warranty, backed by reliable products, can reduce the frequency and severity of claims, leading to lower overall warranty expenses.

• Reduced product returns: Products covered by a clear policy tend to result in fewer returns, saving on logistics and replacement costs.

• Lower customer support and service expenses: Products under a well-designed policy naturally require less customer support, reducing associated costs.

• Decreased likelihood of product recalls: Investing in reliability minimizes the risk of widespread issues that could necessitate costly recalls or mass replacements.

• Improved production efficiency: The focus on reliability often leads to better manufacturing processes, resulting in fewer defects and improved overall efficiency.

• Potential for higher profit margins: A strong warranty, backed by product reliability, can justify premium pricing and increase profit margins.

• Enhanced customer trust: A comprehensive warranty demonstrates confidence in product quality, which can boost customer loyalty and reduce churn-related costs by improving customer experiences.

Regulatory compliance

Strict regulations and standards in many industries govern product reliability and safety. Ensuring product reliability is not just a quality assurance measure; it is a critical step in meeting or exceeding these industry requirements. When they prioritize reliability, companies can better address complex regulatory compliance systems.

This strategy helps avoid potential legal issues and fines arising from substandard products, thereby protecting the company's reputation and its financial stability.

Two recent changes illustrate the current regulatory floor. In the United States, the FDA's Quality Management System Regulation took effect on February 2, 2026, amending 21 CFR Part 820 to incorporate ISO 13485:2016 by reference and aligning federal good manufacturing practice requirements with the international quality management standard.

Reference: U.S. Food and Drug Administration, "Quality Management System Regulation (QMSR)," February 2026

In the European Union, ecodesign requirements for smartphones, cordless phones, and tablets have been in effect since June 20, 2025. They set minimum durability thresholds covering resistance to drops, scratches, dust, and water; batteries capable of at least 800 charge cycles while retaining at least 80% of initial capacity; spare-part availability for at least seven years after the model leaves the market; and operating system updates for at least five years.

Reference: European Commission, "New EU rules for durable, energy-efficient and repairable smartphones and tablets start applying," June 20, 2025

The volume of corrective action conveys the stakes. In 2025, the National Highway Traffic Safety Administration recorded 997 vehicle safety recalls affecting more than 29 million vehicles in the United States, even as AI‑enhanced simulation crash testing for automotive design becomes more central to predicting and preventing safety‑critical failures earlier in the development cycle.

Reference: National Highway Traffic Safety Administration, "Vehicle Safety Resources," 2026

Beyond the immediate benefits, a strong track record in reliability and compliance builds trust with regulatory bodies and the so-called "industry watchdogs". This trust can prove invaluable, potentially easing future interactions with these entities and reinforcing the company's standing in the industry.

Adherence to reliability standards transcends mere legal obligation; it demonstrates a company's commitment to quality and customer safety, inspiring long-term customer loyalty and industry respect.

How is product reliability measured?

Measuring product reliability is a critical aspect of the product development and product management process. It involves various metrics, testing methods, and analytical techniques.

Practical formulas for product managers

• Mean Time Between Failures (MTBF): The average time between system failures during normal operation. MTBF = Total Operational Time / Number of Failures

• Failure Rate: The frequency with which a product or a component fails, often expressed as failures per unit of time. Failure Rate = 1 / MTBF

• Reliability Function R(t): The probability that a product will perform its intended function for a specified period under stated conditions. R(t) = exp(-Fail Rate * t)

The figure shows a simple comparison between two different products, with all other factors held equal.

• Availability: The proportion of time a product is in a functioning condition = MTBF / (MTBF + Mean Time to Repair)

• Mean Time To Failure (MTTF): Used for non-repairable systems, it is the mean time expected until a piece of equipment first fails = Total Operational Time / Number of Units

Data analysis for product management - FMEA and Weibull

The information collected through various methods is crucial for assessing current reliability levels and identifying areas for improvement. Teams also measure reliability not only with current field data, but with methods that predict future behavior across the product lifecycle. This process begins with collecting real-world performance information from products in use, known as Field Data.

Failure Mode and Effects Analysis (FMEA) is employed to identify potential failures and their impacts. Monitoring and controlling production processes to ensure consistency is achieved through Statistical Process Control (SPC). Additionally, Reliability Block Diagrams provide graphical representations of system reliability relationships, while Fault Tree Analysis offers a top-down approach to identifying potential causes of system failures.

Basic representation of the FMEA Risk Priority Matrix. X-axis = Severity of the failure mode, Y = Likelihood of the failure mode; Bubbles: Each represents a failure mode | Author

The idealized XY plot in the figure illustrates an FMEA risk priority matrix.

X-axis: Represents the Severity of the failure mode (FM) (scale of 1-10)

Y-axis: Represents the Occurrence or likelihood of the FM (scale of 1-10)

Bubble size: Represents the Risk Priority Number (RPN), which is typically calculated as Severity × Occurrence × Detection, and each bubble represents a specific FM

The general equation for Risk Priority Number (RPN) in FMEA is:

RPN = S × O × D, with 1 ≤ S, O, D ≤ 10 and 1 ≤ RPN ≤ 1000

Each factor is rated on a scale of 1 to 10, with 10 representing the worst case.

• S = Severity of the failure effect

• O = Occurrence, the likelihood of the cause

• D = Detection, the ability of existing controls to identify the failure before release

The RPN equation has significant limitations and has been the subject of debate and criticism because it is an "equation" based on qualitative considerations. Severity, occurrence, and detection are ordinal ratings, so their product carries no defined physical meaning. The product also weights the three factors equally, giving severity no precedence.

On the AIAG-VDA scale, severity ratings of 9 and 10 are reserved for effects involving safety or regulatory non-compliance, while a rating of 3 denotes a minor effect on the customer. A safety-critical failure rated severity 9, occurrence 2, and detection 3 produces an RPN of 54, while a minor defect rated severity 3, occurrence 6, and detection 6 produces 108. A threshold set at 100 would therefore require corrective action for the minor defect but not for the safety-critical failure.

The joint AIAG and VDA FMEA Handbook, published in 2019, replaced RPN with Action Priority as the prioritization criterion for automotive FMEA, mapping every combination of severity, occurrence, and detection to a High, Medium, or Low action priority for each failure mode through a fixed lookup table that gives severity precedence over the arithmetic product. The table covers all 1,000 rating combinations, so every failure mode receives an explicit priority. Many suppliers still report RPN alongside Action Priority, the current criterion for prioritizing corrective action.

The visualization allows engineers to identify high-risk failure modes:

• Bubbles in the upper-right quadrant represent high-severity, high-occurrence failures

• Larger bubbles indicate higher RPN values, suggesting priority for corrective actions

Finally, Weibull Analysis is a statistical method used to model and analyze life data, completing the comprehensive approach to information collection and elaboration in product management with greater versatility than RPN calculations and supporting forward-looking reliability testing.

Examples of product reliability

Highly reliable products can be found across various industries.

The examples below demonstrate how reliability can become a key differentiator and selling point across different industries.

Automotive Industry:

Toyota vehicles are renowned for being reliable. Many models run for hundreds of thousands of miles with minimal issues. The brand returned to first place for predicted reliability in the Consumer Reports 2026 rankings, which drew on owner survey data from more than 380,000 vehicles, even as manufacturers increasingly use AI applications in the automotive industry to improve safety, quality, and performance. Only Toyota and Subaru reached the above-average reliability band.

Reference: Consumer Reports, "Who Makes the Most Reliable New Cars?" December 2025

Tesla's electric powertrains have proven mechanically simpler than traditional combustion engines, and reliability gains were long expected from that simplicity, although Consumer Reports data show the outcome varies by vehicle age. Tesla's newer models reached their highest ranking to date, while its 5- to 10-year-old vehicles ranked last among the 26 brands assessed for used-car reliability, highlighting how AI-driven car development from concept to reality is reshaping expectations for long‑term performance.

Reference: Consumer Reports, "Which Brands Have the Best Long-Term Car Reliability?" December 2025

Electronics:

Apple's iPhone series has consistently received high ratings for its long-term performance, although independent failure-rate data for current models is not publicly available. What is now measurable is the regulatory floor described above: smartphones placed on the European Union market must meet defined drop, dust, and water-resistance thresholds and carry batteries that retain at least 80% of their initial capacity after 800 charge cycles.

IBM's mainframe computers are designed to be exceptionally reliable and are sometimes described as achieving 99.999% uptime. IBM now states that an IBM z16 or z17 running z/OS 3.1 or higher, in a GDPS continuous availability configuration, is designed to deliver up to 99.999999% availability, corresponding to roughly 316 milliseconds of downtime per year.

Reference: IBM, "Server and cyber resiliency with IBM Z"

Aerospace:

Jet engines from manufacturers such as Rolls-Royce are engineered for ultra-high reliability, with rigorous testing and maintenance schedules. In June 2025, Rolls-Royce certified the first Durability Enhancement Package for the Trent 1000, more than doubling the engine's time on wing, with a second package adding a further 30%. The work forms part of a £1 billion durability program targeting an 80% average increase in time on wing across the modern Trent fleet by 2027.

Reference: Rolls-Royce, "Rolls-Royce launches Durability Enhancement Package that will double Trent 1000 Time-on-Wing," June 12, 2025

Spacecraft equipment undergoes extensive reliability testing to ensure it can withstand the harsh conditions of space.

Medical Devices:

Pacemakers and implantable cardioverter-defibrillators (ICDs) are designed for extreme reliability, often lasting well over a decade in service. A 2025 study of 644 patients projected a median device longevity of 16.7 years for the Micra VR2 leadless pacemaker, with 91% of patients expected to require a single device over their lifetime, based on real-world pacing data.

Reference: Future Cardiology, "Device longevity of a leadless pacemaker family," July 16, 2025

MRI machines are built to operate continuously in hospital settings, with high reliability to minimize downtime and maintenance.

Consumer Appliances:

Miele high-end washing machines and dishwashers are known for their longevity and reliable performance, often lasting 20 years, and Miele publishes the basis for the claim. During the development of the W1 washing machine series, models and core components were run for 10,000 hours, approximately 5,000 wash cycles, corresponding to five cycles per week over 20 years. Miele states that this testing does not guarantee a 20-year service life for any individual machine.

Reference: Miele, "Legal notice"

Vitamix blenders come with long warranties that scale with the model: currently, 10 years of full warranty on the Ascent and other Smart System blenders and 7 years on the Legacy range. They are built to withstand years of daily use without significant performance degradation.

Reference: Vitamix, "10 Year Full Warranty"

How to ensure product reliability?

Ensuring product reliability is a multifaceted process that should be integrated throughout the product lifecycle.

Here are the key strategies to apply across the lifecycle.

Design for reliability

Incorporating reliability considerations from the earliest stages of product development is crucial. This approach, known as Design for Reliability (DFR), starts with identifying potential failure modes early in the design process. Reliability principles provide the framework for those design choices, shaping analysis, testing, and continuous improvement from the start. So the idea is that engineers can prevent many problems before they occur, reducing the need for costly redesigns or field fixes by focusing on reliability during the design phase.

With robust design principles, engineers can minimize sensitivity to variations and employ redundancy in critical systems to prevent single points of failure. Selecting materials and components with proven reliability records further ensures product dependability. Additionally, using computer-aided engineering tools for simulation enables thorough evaluation and optimization. Mechanical stress testing is also used to simulate long-term use through specific tests.

The highly accelerated life test (HALT) exposes designs to overstress conditions to identify weaknesses before market release. IEC 62506:2023, the current edition of the accelerated testing standard, notes that the method was originally misnamed "highly accelerated life test" because it reports the stress level at which the design reaches its limit rather than a life duration. Considering the entire product lifecycle, including manufacturing, transportation, and end-use conditions, helps anticipate and mitigate potential issues.

Quality control

Maintaining strict quality control throughout manufacturing is essential for ensuring product reliability. Key aspects include:

• Implementing statistical process control to monitor and improve manufacturing consistency.

• Conducting incoming quality checks on raw materials and components. Using durable materials and solid construction also helps products last longer.

• Performing in-process inspections at critical stages of production.

• Utilizing automated inspection systems for consistent and objective quality assessments. Environmental testing also assesses performance under extreme conditions by simulating factors such as temperature and humidity.

• Implementing traceability systems to track components and products through the supply chain.

• Continuously training staff on quality control procedures and the importance of reliability.

Quality control not only catches defects before products reach customers but also helps identify and address systemic issues in the production process.

Maintenance and support

Even the most reliable product requires proper maintenance and support. Strategies in this area include:

• Developing clear, comprehensive user manuals

• Offering preventive maintenance programs to customers

• Analyzing information collected from the field to identify common issues and develop solutions

Effective maintenance and support extend product life, enhance customer satisfaction, and provide valuable feedback for product improvements.

AI and product reliability

Artificial Intelligence (AI) is changing how product reliability is predicted and managed, with tools and methodologies to enhance product performance and longevity.

AI in design and testing

AI is making significant inroads in product design and testing phases, starting with generative design, in which AI algorithms generate multiple design options optimized for reliability and performance.

AI for product design leverages machine learning to predict potential failure modes based on historical simulation data.

Physics-aware AI copilots extend the approach into geometry. They generate CAD-ready design options from stated intent and constraints, then rank and filter those options across performance objectives before a full simulation is launched, so that reliability trade-offs are evaluated during concept selection rather than after a design is frozen.

Automated testing with AI-powered systems conducts and analyzes complex tests more efficiently than traditional methods. AI also helps select the optimal materials for specific applications. Additionally, ML algorithms aid in design optimization, fine-tuning designs to improve reliability while balancing cost and manufacturability.

These AI-driven approaches allow engineers to explore a wider range of design possibilities and identify potential reliability issues earlier in the development process.

AI in predictive maintenance

AI is transforming how companies approach product maintenance. AI in predictive maintenance can reduce downtime, extend product lifecycles, and improve overall reliability.

The financial case rests on downtime. Siemens estimated that unplanned downtime costs the world's 500 largest manufacturers approximately $1.4 trillion a year, equal to 11% of their combined revenue, increasing from $864 billion across 2019 and 2020. In automotive plants, an idle production line costs about $2.3 million per hour.

Reference: Siemens, "The True Cost of Downtime 2024," 2024

Here is a quick list of applications.

• In Predictive Analytics, AI algorithms can analyze information collected from sensors and IoT devices to predict when maintenance is needed, preventing unexpected failures.

• In Anomaly Detection, ML can identify unusual patterns in product performance that may indicate impending product failures.

• In Optimization of Maintenance Schedules, AI can help determine optimal maintenance intervals based on usage and environmental conditions.

• In Root Cause Analysis, AI-powered systems can identify the underlying causes of failures more quickly and accurately.

• With Digital Twins, AI-driven digital representations of products can simulate performance and predict maintenance needs.

Conclusions

Product reliability encompasses the technical aspects of design and manufacturing and impacts customer satisfaction, brand reputation, and profitability.

The engineering role in ensuring product reliability is more critical than ever, especially as products become increasingly complex and interconnected.

Focusing on reliable products throughout the product lifecycle enables the creation of products that meet and exceed customer requirements.

The integration of advanced technologies such as AI/ML offers new possibilities for enhancing a product's reliability, allowing engineers to predict and prevent failures with greater accuracy.

Deciding reliability while the design is still open

Every mechanism above shares one property: it is cheapest to act on before the design is frozen, and most of the evidence arrives afterwards. Field data comes from products already sold. Life testing comes from hardware already built. A model trained on a company's own simulation archive moves part of that evidence forward, returning predicted stress, temperature and deformation from the geometry itself, so a failure mode can be designed out during concept selection rather than discovered in a warranty claim. Neural Concept delivers this as an Intelligence Layer for Engineering for physical products, above the CAD and CAE tools already in use, with an AI Design Copilot that answers while the geometry is still open.

The effect is visible where a durability or safety target gates the programme. General Motors applied it to pedestrian safety across 11 vehicle programmes, returning assessments in seconds where the simulation chain took weeks. MAHLE explored 30 million design iterations on a radial blower, reaching 15% higher efficiency with 4 dB less noise. Neither replaces testing; both change how many candidate designs are judged before one is committed to tooling.

FAQ

What is the difference between product reliability and product durability?

Reliability is the probability that a product performs its function without failure over a stated period. Durability is the ability to resist physical degradation such as wear and fatigue. A durable housing does not make a product reliable if the control board fails first.

What is the difference between product reliability and product quality?

Quality refers to conformance to specification at the time of manufacture. Reliability describes conformance over time, under real operating conditions. A unit can pass every end-of-line quality check and still fail in the field six months later.

How do software reliability metrics differ from hardware reliability metrics?

Hardware metrics such as MTBF assume physical wear-out, so failure rates increase with age. Software does not wear out. Its defects are present at release, and failure intensity generally decreases as defects are found and removed; therefore, software reliability is modeled using defect density, failure intensity, and reliability growth models rather than wear-out distributions.

What is the bathtub curve in reliability engineering?

The bathtub curve plots failure rate as a function of time in three phases: a decreasing infant mortality phase driven by manufacturing defects, a flat useful life phase with a roughly constant random failure rate, and an increasing wear-out phase. Burn-in screening addresses the first phase, and scheduled replacement addresses the third.

What is the difference between accelerated life testing (ALT) and highly accelerated life testing (HALT)?

ALT is quantitative. It applies elevated stress to the product's actual failure mechanisms, then extrapolates life under use conditions using an acceleration model such as the Arrhenius model. HALT is qualitative. It increases stress until the design breaks, revealing the operating and destructive limits. IEC 62506:2023 names it the highly accelerated life test for that reason.

What reliability standards apply to medical devices and other life-critical products?

ISO 13485:2016 sets the requirements for a quality management system, and the FDA incorporated it by reference into 21 CFR Part 820, the Quality System Regulation, effective February 2, 2026. ISO 14971 covers risk management, and IEC 62304 covers medical device software life cycle processes. Outside healthcare, IEC 61508 defines safety integrity levels for electrical and electronic systems, with sector derivatives such as ISO 26262 for road vehicles.

How is the ROI of reliability improvements calculated relative to warranty and field-failure costs?

To calculate the return, compare the incremental cost of the improvement with the avoided cost of failure. Avoided cost is estimated from the current claims rate, the average cost per claim, and the installed base exposed over the warranty term. U.S. manufacturers averaged a warranty claims rate of 1.30% of product sales revenue in 2025, providing a starting baseline when internal data is thin.

What is reliability-centered maintenance (RCM), and how is it implemented?

RCM is a structured method for determining the maintenance an asset requires in its current operating context. SAE JA1011 sets the minimum criteria: a compliant process answers seven questions covering functions, functional failures, failure modes, failure effects, failure consequences, proactive tasks, and default actions. SAE JA1012 provides the implementation guidance.

Sources

The standards named in the text, which are not linked above:

1. ISO 13485:2016 — medical devices, quality management systems; incorporated by reference into 21 CFR Part 820 by the FDA's Quality Management System Regulation, effective 2 February 2026.

2. ISO 14971 (risk management for medical devices) and IEC 62304 (medical device software life cycle processes).

3. IEC 61508 — functional safety of electrical, electronic and programmable electronic systems, with the sector derivative ISO 26262 for road vehicles.

4. IEC 62506:2023 — methods for product accelerated testing, the current edition covering ALT and HALT.

5. SAE JA1011 (evaluation criteria for reliability-centred maintenance processes) and SAE JA1012 (a guide to the RCM standard).

6. AIAG and VDA FMEA Handbook, 2019 — introduces Action Priority in place of the risk priority number.

7. Regulation (EU) 2023/1670 — ecodesign requirements for smartphones and tablets, applicable since 20 June 2025.

The data sources are linked inline in the sections above: the Warranty Week annual report, the European Commission, Consumer Reports, IBM, Rolls-Royce, Future Cardiology, Miele, Vitamix and the Siemens downtime study.

Appendix — abbreviations

• MTBF — mean time between failures, for repairable systems; MTTF — mean time to failure, for non-repairable systems; MTTR — mean time to repair

• R(t) — the reliability function, the probability of surviving to time t

• FMEA — failure mode and effects analysis; RPN — risk priority number; AP — Action Priority

• S, O, D — severity, occurrence and detection, the three FMEA ratings

• SPC — statistical process control

• FTA — fault tree analysis; RBD — reliability block diagram

• DFR — design for reliability

• ALT — accelerated life testing, quantitative; HALT — highly accelerated life test, qualitative

• RCM — reliability-centred maintenance

• Bathtub curve — failure rate over time: infant mortality, useful life, wear-out

• QMSR — the FDA's Quality Management System Regulation