What is Mean Time Between Failure (MTBF)?
- Muiz As-Siddeeqi
- Oct 8
- 29 min read
Every minute your production line sits idle costs money. Every server failure disrupts customers. Every aircraft grounded for repairs means lost revenue. Behind these painful realities stands a deceptively simple metric that industries worldwide use to predict, prevent, and plan for equipment failures: Mean Time Between Failure (MTBF). Understanding MTBF isn't just about crunching numbers—it's about keeping operations running, customers happy, and businesses profitable.
TL;DR
MTBF measures the average operating time between equipment failures for repairable systems
Simple calculation: Total operating hours ÷ Number of failures = MTBF
Higher MTBF = more reliable equipment (fewer breakdowns over time)
MTBF differs from MTTF: MTBF is for repairable items; MTTF is for non-repairable items
Real-world benchmarks vary widely: consumer hard drives (1.2-1.5 million hours), servers (different failure rates by age), manufacturing equipment (depends on type and maintenance)
Critical for maintenance planning, spare parts inventory, and reliability engineering
What is MTBF?
Mean Time Between Failure (MTBF) is the average time that a repairable system or component operates without interruption before failing. Calculated by dividing total operating hours by the number of failures, MTBF helps organizations predict equipment reliability, schedule preventive maintenance, and make informed decisions about repairs versus replacements. Higher MTBF values indicate more reliable equipment with fewer breakdowns.
Table of Contents
Understanding MTBF: The Fundamentals
Mean Time Between Failure represents the average duration a repairable system operates before experiencing failure. This metric became critical when the U.S. Department of Defense needed standardized ways to predict electronic equipment reliability in the 1950s and 1960s.
MTBF applies only to repairable systems. Think of manufacturing equipment, computer servers, aircraft engines, or industrial pumps—systems that can be fixed and returned to service. When a production line motor fails, technicians repair it, and the line runs again. That downtime between operational periods is what MTBF helps quantify.
According to the Defense Acquisition University (updated October 2025), the DoD Handbook 791 defines MTBF as "the total functioning life of a population of an item divided by the total number of failures within the population during the measurement interval." This definition holds regardless of whether you measure in time, rounds, miles, events, or other life units.
The bathtub curve provides context for understanding MTBF. Equipment typically experiences three failure phases:
Early life (burn-in period): Higher failure rates from manufacturing defects or installation issues
Useful life: Relatively constant, random failures—this is where MTBF applies
Wear-out period: Increasing failures as components age
MTBF calculations assume equipment operates during its useful life period, when only random failures occur. This is why brand-new equipment and equipment nearing end-of-life often don't match MTBF predictions.
What counts as a failure? The definition matters enormously. For complex systems, failures are conditions that take the system out of service and require repair. Minor issues that don't halt operations aren't failures under standard MTBF definitions. Scheduled maintenance doesn't count either—MTBF measures unplanned downtime only.
How to Calculate MTBF
The MTBF formula is elegantly simple:
MTBF = Total Operating Time ÷ Number of Failures
Step-by-Step Calculation
Example 1: Basic MTBF Calculation
A warehouse operates a conveyor system 24 hours per day. Over 30 days, the system experiences 5 breakdowns. Here's the calculation:
Total operating time: 24 hours × 30 days = 720 hours
Number of failures: 5
MTBF = 720 ÷ 5 = 144 hours
This means the conveyor system averages 144 hours of operation between failures.
Example 2: Multiple Assets
As explained by IBM (July 2025), consider a motor operating 8 hours daily, 5 days weekly, for one year:
Total operating time: 8 hours/day × 5 days/week × 52 weeks = 2,080 hours
Failures during this period: 4
MTBF = 2,080 ÷ 4 = 520 hours
Example 3: Accounting for Downtime
A mechanical mixer runs 10 hours daily and breaks down twice over 10 days. First breakdown occurred at 25 hours (3-hour repair). Second breakdown at 50 hours (4-hour repair).
Total calendar time: 10 days × 10 hours/day = 100 hours Minus repair time: 100 - 3 - 4 = 93 hours actual operating time Number of failures: 2 MTBF = 93 ÷ 2 = 46.5 hours
Important Calculation Considerations
Operating time matters. A machine running 8 hours daily will accumulate the same MTBF operating hours three times slower than an identical machine running 24 hours daily. The machines have the same reliability, but calendar time differs.
Data collection is critical. Accurate MTBF requires precise tracking of operating hours and failure events. Modern Computerized Maintenance Management Systems (CMMS) automate this tracking, eliminating manual errors.
Statistical significance requires volume. MTBF calculations become more reliable with larger sample sizes. One machine experiencing two failures provides less statistical confidence than 100 machines experiencing 200 failures.
MTBF vs. MTTF vs. MTTR: Key Differences
These three metrics often confuse even experienced professionals, but understanding the distinctions is essential.
MTBF (Mean Time Between Failures)
For: Repairable systems
Measures: Average time between breakdowns
Example: Industrial pump that fails, gets repaired, and operates again
MTTF (Mean Time To Failure)
For: Non-repairable systems
Measures: Average time until complete, irreparable failure
Example: Light bulbs, batteries, disposable components
According to LogicMonitor (November 2024), MTTF applies to devices like spinning disk drives in consumer contexts—once they fail, replacement is the only option. The manufacturer would cite lifespan in MTTF terms.
MTTR (Mean Time To Repair)
For: Any repairable system
Measures: Average time to fix equipment and restore function
Example: If 10 repairs took 20 total hours, MTTR = 2 hours
The relationship: MTBF includes downtime, while MTTF doesn't. Mathematically:
MTBF = MTTF + MTTR
For most equipment, MTTR is relatively small compared to MTTF, so MTBF ≈ MTTF. However, for large, complex systems like production lines or aircraft, MTBF and MTTF can differ substantially.
Comparison Table
Metric | System Type | What It Measures | Time Includes Downtime? | Example |
MTBF | Repairable | Average operating time between failures | Yes | Server: 520 hours MTBF |
MTTF | Non-repairable | Average time until permanent failure | No | Light bulb: 2,000 hours MTTF |
MTTR | Repairable | Average repair duration | N/A | Motor repair: 2.5 hours MTTR |
MDT | Repairable | Mean downtime (includes delays beyond repair) | N/A | Total downtime: 4 hours MDT |
Industry Standards and Calculation Methods
Multiple reliability prediction standards exist, each with different approaches, advantages, and limitations.
MIL-HDBK-217F: The Military Standard
MIL-HDBK-217F (Military Handbook: Reliability Prediction of Electronic Equipment) remains the most widely known reliability prediction standard despite not being updated since December 1991. Originally developed for military electronic systems, it spread to commercial applications worldwide.
Two main methods:
Parts Count Method: Uses generic failure rates and quality factors for component groups. Faster but more conservative (predicts lower reliability).
Parts Stress Method: Factors in actual operating conditions, temperatures, electrical stress levels, and environmental conditions. More accurate but requires detailed circuit analysis.
The handbook provides failure rate models for integrated circuits, transistors, diodes, resistors, capacitors, relays, switches, connectors, and more across 14 operational environments (ground fixed, airborne inhabited, naval sheltered, etc.).
Critical limitation: As noted by Design News (October 2023), MIL-HDBK-217F reflects the electronic status quo of the late 1980s. Modern commercial electronics are considerably more reliable than military-grade components from that era, making raw MIL-HDBK-217F results extremely pessimistic.
ANSI/VITA 51.1: The Modern Update
A consortium including Boeing, Northrop Grumman, GE, and Honeywell developed ANSI/VITA 51.1 in 2008 (current version: 2013) as an add-on to MIL-HDBK-217. By applying ANSI/VITA 51.1 adjustments, component-level results improve by factors between 1 (switches, connectors) and 100 (resistors, capacitors). At the printed circuit board level, results typically improve by factors of 3 to 5.
Other Major Standards
Telcordia SR-332: Updated regularly with field failure data from telecommunications companies. More current than MIL-HDBK-217 but based on limited company submissions.
FIDES (French standard): Physics-of-failure approach accounting for design, manufacturing quality, and usage profiles.
Siemens SN 29500: European standard for electronic component reliability.
IEC 61709: International Electrotechnical Commission standard for electronic components.
NSWC Mechanical: U.S. Naval Surface Warfare Center handbook for mechanical reliability.
According to Cadence (April 2025), modern electronic design automation tools integrate these standards, allowing engineers to perform MTBF analysis directly within schematic capture environments.
Real-World MTBF Benchmarks by Industry
MTBF values vary dramatically across industries, equipment types, and operating conditions. Here are documented benchmarks from recent data.
Data Storage (Hard Drives)
Consumer-grade hard drives (3.5-inch): 1.2 to 1.5 million hours MTBF for top manufacturers. Enterprise-grade drives reach approximately 2 million hours for 2.5-inch models.
Reality check: At 8,760 hours per year, a 1.5 million hour MTBF translates to approximately 171 years—but this doesn't mean individual drives last that long (see Misconceptions section).
Backblaze, a cloud storage company, publishes detailed drive statistics. Their 2024 report (February 2025) analyzed 300,633 data drives. The 16TB Seagate model (ST16000NM002J) achieved an exceptional 0.22% annualized failure rate (AFR) in 2024, with just one failure all year. This translates to an extremely high MTBF.
Across all drive models, Backblaze observed that even identical drive models exhibit failure rate variations based on usage patterns, operating temperatures, and manufacturing batches.
Servers and Data Centers
Server failure rates follow a predictable age pattern. According to Statista data on annual server failure rates:
First year: 5% annual failure rate
Fourth year: 11% annual failure rate
Failure rates increase significantly as servers age due to component wear, outdated software support, and accumulating thermal stress cycles.
Intel Server Boards: Intel published MTBF estimates for their S1200RP Family (May 2013), though specific values require accessing technical documentation. Server board MTBF typically ranges from 50,000 to 200,000 hours depending on components and operating environment.
Manufacturing Equipment
Manufacturing MTBF varies enormously by equipment type, maintenance quality, and operating conditions.
World-class OEE targets: According to Allied Reliability (May 2024), Overall Equipment Effectiveness (OEE) serves as a more comprehensive metric than MTBF in discrete manufacturing. World-class OEE is 85%, which accounts for availability (uptime), performance efficiency, and quality rate.
For process industries where continuous operation is critical, establishing MTBF for highly critical assets and trending performance upward provides more value than OEE.
Benchmark ranges:
Industrial pumps: 8,000-50,000 hours depending on type and operating conditions
Conveyor systems: 5,000-25,000 hours
CNC machines: 10,000-80,000 hours with proper maintenance
Robotic systems: 40,000-100,000 hours for critical components
Aviation
Commercial aviation maintains exceptionally high reliability standards. While specific component MTBF values are proprietary, the industry's safety record reflects the effectiveness of reliability engineering.
According to Airbus accident statistics (2025), commercial jet traffic recovered to pre-pandemic operational levels in 2024, reaching almost 34 million flights. The aviation industry's accident rate per million flights has decreased dramatically over decades through improved design, maintenance practices, and reliability prediction.
Aircraft engines, landing gear, avionics, and hydraulic systems undergo rigorous MTBF analysis during design and continuous monitoring during operations.
Telecommunications and IT
Network equipment: Enterprise-grade switches and routers often specify 200,000+ hours MTBF.
UPS (Uninterruptible Power Supply) systems: 150,000-300,000 hours MTBF for quality units, though batteries within them have much shorter lifespans (typically 3-5 years regardless of MTBF calculations).
Case Studies: MTBF in Action
Case Study 1: Nuclear Reactor Primary Pump (2024)
Organization: RSG-GAS Reactor (Indonesia)
Component: JE01-AP03 primary centrifugal pump
Source: Nuclear Technology, Vol 211, No 4 (June 2024)
Challenge: The RSG-GAS reactor, a pool-type reactor using light water for moderation, cooling, and shielding, needed reliable maintenance scheduling for critical primary cooling system components.
Methodology: Researchers applied the Nonhomogeneous Poisson Process (NHPP) model to estimate MTBF and establish inspection timelines. The NHPP model accounts for reliability growth over time as maintenance improves system performance.
Results:
MTBF achieved: Approximately 42 days
Reliability growth value: 0.41
Application: Inspection timeline established based on MTBF value
Impact: This MTBF estimation enables the reactor installation to achieve higher safety, longer lifetime, and substantial reliability with a minor failure rate. Regular inspection schedules now align with predicted failure patterns rather than arbitrary intervals.
Key lesson: Even for critical nuclear infrastructure, MTBF provides actionable maintenance scheduling data when calculated using appropriate statistical models for repairable systems with reliability growth.
Case Study 2: Sugar Processing Plant Equipment (2018)
Source: International Association for Management of Technology conference paper (2018)
Title: "Improvement of process machinery availability and reliability: A case study of the production line in a sugar processing plant"
Challenge: A sugar processing plant faced persistent availability and reliability issues impacting production capacity and profitability.
Problems identified:
Insufficient or incorrect downtime data collection
Poorly trained maintenance staff
Delays in logistics and administration
No systematic MTBF tracking
Impact of poor MTBF management:
Unpredictable equipment failures
Excessive spare parts inventory (wrong parts)
Insufficient critical spare parts (right parts)
Higher maintenance costs
Reduced overall production capacity
Solution: Implementation of comprehensive Computerized Maintenance Management System (CMMS) software.
Results:
Accurate real-time MTBF calculations
Data-driven preventive maintenance scheduling
Optimized spare parts inventory based on actual failure patterns
Improved staff training based on failure mode analysis
Dramatically improved equipment availability
Key lesson: MTBF is only as valuable as the data quality behind it. Manual tracking produces insufficient data, while CMMS implementation provides the foundation for actionable reliability improvements.
Case Study 3: Water Treatment Plant Pump Analysis
Source: LLumin case study (July 2024)
Scenario: A water treatment plant operates three essential pumps. Each pump is critical to operations, making reliability prediction essential.
MTBF application:
Identified failure patterns across pump population
Scheduled preventive maintenance before predicted failure windows
Optimized pump rotation to equalize wear
Calculated optimal spare parts inventory
Technology integration: LLumin's CMMS+ software identified potential failure signs (heat increases, efficiency decreases) before scheduled maintenance inspections through machine-level analytics.
Results:
Reduced unplanned downtime by 40%
Extended pump operational life by 25%
Lowered maintenance costs through predictive interventions
Avoided critical service disruptions
Key lesson: Modern IoT sensors and analytics platforms transform MTBF from a historical metric into a predictive tool, enabling intervention before failures occur.
Why MTBF Matters for Your Business
MTBF drives four critical business functions that directly impact profitability, customer satisfaction, and competitive advantage.
1. Preventive Maintenance Planning
MTBF provides the foundation for intelligent maintenance scheduling. Rather than arbitrary time intervals or running equipment until failure, maintenance teams can schedule interventions before predicted failure windows.
Example: If a critical motor has an MTBF of 5,000 hours and currently shows 4,200 operating hours, preventive maintenance can be scheduled during the next planned production downtime window—avoiding unexpected failure during peak demand.
According to Infraspeak (February 2023), the higher the MTBF, the more reliable the asset. MTBF serves as a guideline for preventive maintenance scheduling and improves inventory management when estimated accurately.
2. Spare Parts Optimization
Knowing failure frequency allows precise spare parts inventory management. Too many spares tie up capital; too few cause extended downtime waiting for parts.
MTBF enables:
Minimum quantity calculations
Optimal reorder points
Lead time adjustments
Just-in-time delivery strategies
Fiix Software (June 2025) notes that MTBF tracking fine-tunes inventory approaches, resulting in lower costs and quicker repair times.
3. Repair vs. Replace Decisions
When equipment repeatedly fails despite repairs, MTBF data makes replacement decisions objective rather than emotional.
Decision framework:
Calculate current MTBF trend
Compare against baseline MTBF for similar equipment
Analyze total cost of ownership (repair costs + downtime costs)
Determine breakeven point for replacement investment
If all attempts to combat low MTBF prove unsuccessful, replacement becomes the financially sound choice despite the capital investment required.
4. Design and Procurement Decisions
During equipment selection, comparing MTBF values helps predict long-term reliability and total cost of ownership.
Important caveat: According to Splunk's analysis, reliability engineers can use MTBF to compare similar systems or components, but MTBF cannot be directly compared between different systems. Operating conditions, usage patterns, and environmental factors significantly impact reliability, making vendor-provided MTBF figures useful for relative comparison within product lines but not absolute guarantees.
5. Service Level Agreements (SLAs)
For service providers and enterprises dependent on third-party systems, MTBF directly relates to availability guarantees.
Availability calculation: Availability = MTBF ÷ (MTBF + MTTR)
If a system has MTBF of 10,000 hours and MTTR of 4 hours: Availability = 10,000 ÷ (10,000 + 4) = 99.96%
This availability percentage becomes the foundation for SLA negotiations and penalty structures.
6. Cost Management and Budgeting
MTBF enables accurate financial planning for maintenance operations.
According to Splunk, the tradeoff between cost and system dependability improvements typically follows a negative exponential curve. After a certain threshold, spending more on redundancy and reliability produces diminishing returns. MTBF analysis identifies optimal investment levels.
Common MTBF Misconceptions and Myths
Myth 1: "MTBF Means My Equipment Will Last That Long"
The reality: MTBF of 100,000 hours doesn't mean individual equipment operates for 100,000 hours before failing.
MTBF represents a population average, not an individual prediction. As Reliable Plant explains through a dramatic example:
The 25-year-old human analogy: If we calculated MTBF for 25-year-old humans by counting hours until the first failure, we might get 800 years (given extremely rare sudden deaths at that age). However, the actual life expectancy for 25-year-olds is approximately 80 years. Which number is correct?
Both are mathematically correct, but they answer different questions. MTBF addresses: "On average, how long between random failures in a population during the useful life period?" Life expectancy asks: "How long will individuals in this population ultimately survive?"
Myth 2: "50% of Items Will Fail by the MTBF Time"
The reality: This assumption is mathematically incorrect.
For systems with constant failure rates (the assumption underlying MTBF), the probability a system survives to its MTBF is approximately 37% (e^-1), meaning 63% fail before reaching MTBF.
This counterintuitive result stems from exponential distribution mathematics. IBM (July 2025) emphasizes that MTBF is an average time and does not guarantee any particular system will last the full MTBF period without failing.
Myth 3: "Higher MTBF Always Means Better Equipment"
The reality: MTBF context matters enormously.
A consumer-grade hard drive with 1.2 million hours MTBF operating in a temperature-controlled office differs dramatically from the same drive in a desert environment with temperature fluctuations and vibration.
MTBF values assume specific operating conditions. Equipment used outside those assumptions will not match predicted reliability.
Myth 4: "MTBF Accounts for All Failures"
The reality: MTBF only measures random failures during useful life.
According to Wikipedia (July 2025), MTBF calculations assume systems work within their "useful life period," characterized by relatively constant failure rates (the middle section of the bathtub curve) when only random failures occur. Systems haven't yet approached end-of-life or completed initial burn-in.
Systematic failures, design flaws, and wear-out failures fall outside MTBF's scope.
Myth 5: "We Can Directly Compare MTBF Across Different Products"
The reality: MTBF comparisons require identical conditions.
IBM notes that MTBF is highly dependent on operating conditions, usage patterns, and environmental factors specific to the system being measured. A good MTBF for one system might look completely different than a good MTBF in another similar use case.
Myth 6: "MTBF Tells Us Why Equipment Fails"
The reality: MTBF quantifies failure frequency, not failure causes.
MTBF provides a useful metric of failure count over time but doesn't explain why problems occur. Root cause analysis, failure mode and effects analysis (FMEA), and reliability engineering investigations determine failure causes.
A high MTBF doesn't mean breakdowns never occur—only that they occur less frequently.
How to Improve Your Equipment's MTBF
Improving MTBF requires systematic approaches across design, procurement, operations, and maintenance.
1. Invest in Quality Equipment and Components
The cheap equipment trap: Low initial costs often lead to frequent failures, expensive repairs, and unplanned downtime.
According to Coast App (September 2024), purchasing high-quality equipment at higher upfront costs saves money long-term through increased total uptime. Quality components from reputable manufacturers generally offer better MTBF than bargain alternatives.
Procurement checklist:
Request vendor MTBF data and methodology
Verify data comes from field performance, not just calculations
Compare total cost of ownership (TCO), not just purchase price
Review warranty terms as reliability indicators
Check third-party reliability data if available
2. Maintain Optimal Operating Conditions
Equipment operated within design specifications lasts longer.
Key factors:
Temperature control: Heat accelerates component degradation
Vibration management: Mounting, isolation, and alignment
Contamination prevention: Dust, moisture, chemicals
Power quality: Voltage stability, surge protection
Load management: Avoid prolonged overload conditions
Coast App emphasizes ensuring equipment operates and maintains optimal conditions as fundamental to MTBF improvement.
3. Implement Comprehensive Preventive Maintenance
According to UpKeep, the first step to improving MTBF is accurate data collection through maintenance software. The next step is using that data to proactively perform preventive maintenance.
High-impact preventive activities:
Proper lubrication schedules
Alignment and calibration checks
Cleaning and inspection routines
Filter replacements
Seal and gasket monitoring
Bearing monitoring and replacement
Thermal imaging for electrical systems
Time investment pays off: Regular preventive maintenance significantly reduces major breakdown frequency.
4. Train Operators and Maintenance Staff
Human error causes many equipment failures. According to the sugar plant case study (2018), poorly trained maintenance staff directly impacted MTBF rates.
Training areas:
Proper equipment operation within specifications
Early warning sign recognition
Correct maintenance procedure execution
Safety protocols that protect equipment
Documentation standards for failure tracking
5. Implement Condition Monitoring
Modern sensor technology enables predictive maintenance that catches problems before failures occur.
Monitoring technologies:
Vibration analysis
Thermal imaging
Oil analysis
Ultrasonic testing
Motor current signature analysis
Pressure and flow monitoring
LLumin (July 2024) describes how their CMMS+ software identifies potential failure signs like heat increases or efficiency decreases before scheduled maintenance inspections, creating actions that occur at the optimal time.
6. Conduct Root Cause Analysis
When failures occur, determine true causes rather than just fixing symptoms.
Root cause analysis methods:
5 Whys technique
Fishbone (Ishikawa) diagrams
Failure Mode and Effects Analysis (FMEA)
Fault Tree Analysis (FTA)
Apply corrective actions that prevent recurrence, not just quick fixes that address immediate symptoms.
7. Track and Trend MTBF Over Time
MTBF isn't static. According to IBM, MTBF provides a starting point that enables analysis of trends, helping understand overall maintenance strategy efficacy.
Tracking best practices:
Calculate MTBF monthly or quarterly
Compare against baseline and industry benchmarks
Identify improvement or degradation trends
Correlate MTBF changes with maintenance activities
Adjust preventive maintenance schedules based on trends
8. Optimize Redundancy and Backup Systems
For critical equipment, redundancy dramatically improves effective MTBF.
According to Splunk, redundancy significantly improves Mean Time To Repair (MTTR), increasing system operational duration and indirectly improving overall availability—which combines MTBF and MTTR.
Redundancy strategies:
Hot standby systems (immediate switchover)
Cold standby systems (manual switchover)
Load-sharing redundancy (multiple units operate simultaneously)
Component-level redundancy (dual power supplies, RAID storage)
MTBF Calculation Tools and Software
Manual MTBF calculation works for small asset populations but becomes impractical at scale. Modern tools automate calculation and integrate with broader reliability programs.
Computerized Maintenance Management Systems (CMMS)
CMMS platforms track operating hours, failure events, and maintenance activities automatically, providing real-time MTBF calculations.
Leading CMMS features:
Automated work order tracking
Equipment history logging
Automatic MTBF calculation
Preventive maintenance scheduling
Spare parts inventory management
Mobile access for field technicians
Featured platforms:
MaintainX: Asset management integration, IoT sensor compatibility
Fiix Software: Cloud-based CMMS with automated MTBF tracking
LLumin CMMS+: Real-time machine-level analytics and predictive failure detection
UpKeep: Mobile-first maintenance management
Reliability Prediction Software
For design-phase MTBF prediction, specialized software implements industry standards.
Capabilities:
MIL-HDBK-217F calculations
ANSI/VITA 51.1 adjustments
Multiple standard support (FIDES, Telcordia, IEC)
Component library databases
Stress analysis integration
What-if scenario modeling
Notable tools:
RAM Commander: Comprehensive reliability, availability, maintainability analysis
RelCalc: MIL-HDBK-217 calculator
Relyence: Support for all major standards with modern interface
Electronic Design Automation (EDA) Integration
According to Cadence (April 2025), modern EDA tools integrate MTBF analysis directly into schematic capture environments.
Allegro X System Capture features:
Automated MTBF calculation based on electrical stress data
Support for MIL-HDBK-217F, FIDES, SN 29500
Real-time reliability dashboard
Component-level failure rate analysis
Integration with thermal analysis
Automatic report generation
This integration allows engineers to assess reliability during design rather than after manufacturing—dramatically reducing cost and time for reliability improvements.
Online Calculators
For quick estimates or educational purposes, web-based calculators provide immediate results.
Example: Reliability Analytics Toolkit offers a MIL-HDBK-217F Parts Count calculator with:
Component selection by type
Environmental factor selection
Quality factor adjustments
Export to Excel for detailed analysis
Limitation: Simple calculators lack the data integration and historical tracking that CMMS platforms provide.
Pros and Cons of Using MTBF
Advantages
Single, standardized metric. MTBF provides a universal reliability measurement that stakeholders understand across departments and industries. Engineers, managers, and executives can discuss equipment reliability using common language.
Enables comparison. Organizations can compare equipment models, vendors, and operating strategies using MTBF data. According to Splunk, this benchmarking capability drives reliability improvements and informed procurement decisions.
Predicts maintenance needs. MTBF-based preventive maintenance scheduling reduces unexpected failures. Knowing approximate failure frequency allows proactive interventions during planned downtime windows rather than reactive scrambling during production periods.
Supports business planning. Reliability data feeds into financial models, service level agreements, warranty programs, and operational capacity planning. According to IBM, MTBF enables analysis of trends and understanding of maintenance strategy effectiveness.
Facilitates continuous improvement. Tracking MTBF trends over time reveals whether reliability initiatives are working. Improvements validate investments in training, equipment upgrades, or process changes.
Industry requirement. Many sectors mandate MTBF calculations for compliance, particularly aerospace, defense, telecommunications, and nuclear power. According to the Defense Acquisition University (October 2025), MTBF serves as a basic technical measure of reliability recommended for Research and Development contractual specification environments.
Disadvantages
Assumes constant failure rate. MTBF calculations assume systems operate in the useful life period with constant failure rates. This assumption breaks down during burn-in and wear-out phases.
Population metric, not individual predictor. According to Reliable Plant, MTBF predicts group behavior, not individual component lifespan. This distinction causes widespread confusion and misapplication.
Doesn't explain failure causes. MTBF quantifies failure frequency but provides no insight into why failures occur. According to IBM, determining MTBF gives a useful metric of failure count over time but doesn't explain problems.
Sensitive to operating conditions. IBM emphasizes that MTBF is highly dependent on operating conditions, usage patterns, and environmental factors. Equipment operated outside design specifications won't match predicted MTBF.
Can be manipulated. Vendors may present MTBF calculated under ideal conditions that don't reflect real-world operations. According to Server Fault discussions, some manufacturers no longer publish MTBF numbers due to their limited real-world relevance.
Requires significant data. Accurate MTBF calculation demands extensive failure data. Small sample sizes produce unreliable estimates. The sugar plant case study (2018) showed that insufficient data collection fundamentally undermined MTBF utility.
Doesn't account for failure severity. IBM notes that MTBF doesn't consider failure severity or operational impact. A 5-minute failure and a 5-hour failure both count as single events, despite vastly different business consequences.
May not reflect modern electronics. As Design News (October 2023) explains, MIL-HDBK-217F hasn't been updated in over 20 years and reflects 1980s electronics. Modern component reliability often far exceeds handbook predictions.
When to Use MTBF (and When Not To)
Use MTBF When:
1. Equipment is repairable. MTBF only applies to systems that can be fixed and returned to service. For non-repairable items, use MTTF instead.
2. You're in the useful life period. Equipment operates in the flat portion of the bathtub curve with relatively constant random failure rates—not during burn-in or wear-out phases.
3. You need maintenance planning data. MTBF provides the foundation for preventive maintenance scheduling, spare parts inventory, and resource allocation.
4. Comparing similar systems. MTBF helps compare equipment models, manufacturers, or configurations operating under similar conditions.
5. Contractual or regulatory requirements. Many industries mandate MTBF calculations for compliance or procurement processes.
6. You have sufficient data. Adequate sample sizes (many units or extended observation periods) provide statistical confidence in MTBF calculations.
Don't Rely on MTBF When:
1. Predicting individual equipment lifespan. MTBF describes population averages, not individual component behavior. Don't expect your motor to operate exactly MTBF hours before failing.
2. Systems have systematic failures. MTBF assumes random failures only. Design flaws, installation errors, or operator mistakes fall outside MTBF's scope.
3. Operating conditions are extreme or variable. According to IBM, MTBF depends on operating conditions. Equipment in harsh, fluctuating, or uncontrolled environments won't match laboratory-derived MTBF values.
4. Quality is more important than availability. According to Allied Reliability (May 2024), process industries struggling to identify quality losses may find MTBF provides limited value. In such cases, other metrics may be more relevant.
5. For non-technical stakeholder communication. MTBF's technical nature and common misconceptions make it problematic for general business communication. Availability percentages or downtime hours often communicate more clearly.
6. As the only reliability metric. According to Atlassian, MTBF works best when combined with MTTR, MTTD (Mean Time To Detect), and MTTA (Mean Time To Acknowledge), telling a more complete story about incident management effectiveness.
Better Alternatives in Some Contexts
Overall Equipment Effectiveness (OEE): For discrete manufacturing, Allied Reliability recommends OEE over MTBF. OEE captures availability, performance efficiency, and quality rate—providing comprehensive production system assessment. World-class OEE is 85%.
Failure Rate (λ): The reciprocal of MTBF, failure rate expresses probability more intuitively for some applications. Failure rate = 1 ÷ MTBF.
Availability: Combining MTBF and MTTR into an availability percentage often communicates more effectively than MTBF alone. Availability = MTBF ÷ (MTBF + MTTR).
Reliability Function: For probability calculations over specific timeframes, the exponential reliability function R(t) = e^(-t/MTBF) provides more detailed predictions than MTBF alone.
The Future of Reliability Metrics
Reliability engineering is evolving rapidly with technology advances, shifting MTBF's role and capabilities.
Predictive Analytics and Machine Learning
Traditional MTBF calculates historical averages. Modern predictive maintenance uses machine learning to forecast individual asset failures before they occur.
Current developments:
IoT sensors capturing real-time equipment condition data
Machine learning algorithms identifying failure precursors
Digital twins simulating equipment performance under varying conditions
AI-powered anomaly detection flagging unusual patterns
These technologies don't replace MTBF but enhance it, transitioning from "on average, failures occur every X hours" to "this specific asset will likely fail in Y days based on current condition."
Physics-of-Failure Approaches
According to Design News (October 2023), the electronics industry is gradually moving away from empirical handbook methods (like MIL-HDBK-217) toward physics-of-failure models.
Physics-of-failure benefits:
Accounts for actual failure mechanisms (electromigration, thermal cycling, etc.)
Reflects modern component technology
Considers specific operating profiles
Provides more accurate predictions for complex systems
Standards like FIDES incorporate physics-of-failure principles, representing the future direction of reliability prediction.
Industry 4.0 Integration
Manufacturing digitalization creates unprecedented reliability data availability.
Emerging capabilities:
Real-time MTBF calculation from automated production tracking
Automatic correlation between MTBF trends and operating parameter changes
Blockchain-based reliability data sharing across supply chains
Cloud-based reliability databases enabling cross-industry benchmarking
Criticism and Alternative Metrics
Design News (October 2023) argues that MIL-HDBK-217 and similar outdated handbooks should be abandoned. Critics note that:
Constant failure rate assumption is unrealistic for most modern systems
Field failure data in existing handbooks is decades old and limited to few companies
Modern electronics don't follow handbook predictions developed for 1980s technology
The future may see MTBF supplemented or replaced by more sophisticated metrics accounting for:
Time-varying failure rates
Component interaction effects
Real-time condition monitoring
Usage profile variations
Environmental factor integration
Sustainability and Lifecycle Considerations
Reliability engineering increasingly considers environmental impact and total lifecycle costs.
Future trends:
Designing for repairability and extended operational life
Circular economy principles affecting reliability targets
Carbon footprint integration into reliability decisions
Regulatory pressure for longer-lasting, more reliable products
MTBF will likely evolve to account for sustainability metrics, balancing reliability, repairability, and environmental impact.
Frequently Asked Questions (FAQ)
Q1: What is a good MTBF value?
A: "Good" MTBF depends entirely on context. According to IBM (July 2025), it's difficult and possibly inadvisable to define meaningful MTBF across different use cases. A good MTBF for one system might look completely different for another. Consumer hard drives (1.2-1.5 million hours) have different benchmarks than manufacturing pumps (8,000-50,000 hours). Compare MTBF within similar equipment types and operating conditions, not across categories.
Q2: Can I use MTBF for non-repairable items?
A: No. MTBF measures time between failures for repairable systems. For non-repairable items (light bulbs, batteries, disposable components), use Mean Time To Failure (MTTF) instead. According to LogicMonitor (November 2024), MTTF is specific to non-repairable devices where the manufacturer would describe lifespan in MTTF terms.
Q3: Does higher MTBF always mean better equipment?
A: Not necessarily. MTBF values assume specific operating conditions. Equipment used outside those conditions won't match predictions. Additionally, MTBF doesn't account for failure severity, maintenance costs, or total cost of ownership. A system with lower MTBF but faster, cheaper repairs might be more practical than high-MTBF equipment with expensive, lengthy repairs.
Q4: How does MTBF relate to warranty periods?
A: Manufacturers typically set warranty periods well below predicted MTBF to manage risk. If equipment has 100,000-hour MTBF, the warranty might cover 8,760 hours (1 year of continuous operation) or 3 years of typical use. Warranties protect against early-life failures during the burn-in period, which MTBF calculations specifically exclude.
Q5: Can I calculate MTBF for brand-new equipment?
A: Only through prediction methods like MIL-HDBK-217, not from actual field data. Accurate MTBF requires observing real failures over extended periods. For new equipment, manufacturers provide predicted MTBF based on component failure rates, similar equipment performance, or accelerated life testing. Actual field MTBF often differs from predictions.
Q6: Why do hard drives list multi-million hour MTBF when mine failed after 3 years?
A: This common frustration stems from MTBF misunderstanding. According to Reliable Plant, MTBF isn't suggesting individual drives last that long—it predicts population behavior. With 100,000 drives at 100,000-hour MTBF, expect one failure per hour on average across the entire population. Your individual drive might fail early, late, or never.
Q7: How often should I recalculate MTBF?
A: For active reliability programs, calculate MTBF monthly or quarterly. This frequency reveals trends without being dominated by random variation. According to IBM, MTBF enables trend analysis to understand maintenance strategy effectiveness. Annual calculations suffice for stable, mature equipment.
Q8: Does preventive maintenance improve MTBF?
A: Yes, significantly. According to UpKeep, proactive preventive maintenance dramatically reduces major breakdown frequency. Proper lubrication, alignment, calibration, and component replacement before failure all extend time between unplanned failures. The sugar plant case study (2018) demonstrated that comprehensive CMMS implementation with preventive maintenance dramatically improved MTBF.
Q9: Can MTBF predict when my specific equipment will fail?
A: No. MTBF predicts average failure frequency across populations, not individual failure timing. For specific equipment failure prediction, use condition monitoring, vibration analysis, thermal imaging, and other diagnostic tools that assess actual equipment condition rather than statistical averages.
Q10: Is MTBF still relevant with modern predictive maintenance?
A: Yes, but its role is evolving. MTBF provides baseline reliability metrics and enables historical comparison. Modern predictive maintenance enhances rather than replaces MTBF. According to LLumin (July 2024), IoT sensors and analytics transform MTBF from a historical metric into a predictive tool by identifying failure signs before they occur, but MTBF calculations still provide the statistical framework.
Q11: How does temperature affect MTBF?
A: Dramatically. Higher temperatures accelerate component degradation through increased chemical reaction rates, thermal stress, and material fatigue. The Arrhenius equation predicts that every 10°C temperature increase roughly doubles chemical reaction rates, potentially halving MTBF. MIL-HDBK-217 includes temperature factors (πT) that can change predicted failure rates by orders of magnitude.
Q12: What's the difference between MTBF and reliability?
A: Reliability measures the probability a system performs correctly for a specific duration. MTBF quantifies the average time between failures. They're related through the exponential reliability function: R(t) = e^(-t/MTBF). According to the Defense Acquisition University (October 2025), MTBF is a basic technical measure of reliability. Higher MTBF produces higher reliability at any given time.
Q13: Can I add MTBF values for series systems?
A: No. For systems where component failure causes total system failure (series reliability), system MTBF is less than any individual component MTBF. The relationship is complex: System failure rate = Sum of individual failure rates. Since MTBF = 1/failure rate, you can't simply add MTBF values. System MTBF requires proper reliability calculations accounting for component relationships.
Q14: Should I use MTBF or OEE for manufacturing?
A: According to Allied Reliability (May 2024), it depends on your manufacturing type. For discrete manufacturing (automobiles, furniture, smartphones), Overall Equipment Effectiveness (OEE) provides more value by capturing availability, performance efficiency, and quality rate. For process industries (chemicals, power generation) where continuous operation is critical, MTBF for highly critical assets often provides more meaningful metrics.
Q15: How do I improve MTBF for existing equipment?
A: Focus on five areas: (1) Optimal operating conditions (temperature, vibration, power quality, load management), (2) Comprehensive preventive maintenance (lubrication, alignment, calibration), (3) Operator and technician training, (4) Condition monitoring (vibration analysis, thermal imaging), (5) Root cause analysis when failures occur. According to Coast App (September 2024) and UpKeep, these systematic approaches significantly improve MTBF.
Q16: What data do I need to calculate MTBF?
A: Three essential data points: (1) Total operating time in hours for the equipment or fleet, (2) Number of failures during that period, (3) Clear failure definition (what constitutes a failure vs. minor issue). According to IBM (July 2025), define the system and operating conditions, collect start/end times for operation cycles, record failures, then divide operating time by failure count.
Q17: Can environmental factors affect MTBF calculations?
A: Absolutely. According to IBM and Splunk, environmental conditions significantly impact equipment reliability. MIL-HDBK-217 specifies 14 different operational environments (ground fixed, ground mobile, naval sheltered, naval unsheltered, airborne inhabited, etc.) with dramatically different failure rate multipliers. Humidity, vibration, contamination, and electrical environment all affect actual MTBF compared to predictions.
Q18: Is MTBF the same across all manufacturers?
A: No. Even identical equipment models from the same manufacturer can show MTBF variations due to manufacturing batch differences, component sourcing changes, and operating condition variations. According to Backblaze's 2024 drive statistics, even identical drive models exhibit failure rate variations. Always validate vendor MTBF claims with field data when possible.
Q19: How does redundancy affect MTBF?
A: Redundancy doesn't directly change individual component MTBF, but it dramatically improves system-level availability and effective uptime. According to Splunk, redundancy significantly improves Mean Time To Repair (MTTR) by enabling immediate switchover, indirectly improving overall operational continuity. For critical systems, redundancy provides higher availability despite unchanged individual component MTBF.
Q20: Should MTBF influence equipment purchase decisions?
A: Yes, but not exclusively. According to Splunk, reliability engineers use MTBF to compare similar systems, but it cannot be directly compared between different systems. Consider MTBF alongside: total cost of ownership, maintainability, spare parts availability, vendor support quality, energy efficiency, and operational flexibility. MTBF provides one data point in comprehensive procurement analysis, not the sole decision factor.
Key Takeaways
MTBF quantifies equipment reliability by measuring average operating time between failures for repairable systems, calculated as total operating hours divided by number of failures.
MTBF predicts population behavior, not individual equipment lifespan. The common misconception that MTBF equals expected equipment lifetime causes widespread misapplication.
Higher MTBF indicates more reliable equipment with fewer breakdowns, but "good" MTBF varies dramatically by industry, equipment type, and operating conditions.
MTBF differs from MTTF and MTTR: MTBF applies to repairable systems, MTTF to non-repairable items, and MTTR measures repair duration. Understanding these distinctions prevents analytical errors.
Industry standards like MIL-HDBK-217F provide prediction frameworks, but critics note they're outdated and may not reflect modern component reliability. Newer standards like ANSI/VITA 51.1 provide more realistic predictions.
Real-world MTBF benchmarks vary enormously: Consumer hard drives (1.2-1.5 million hours), servers (failure rates of 5-11% annually depending on age), manufacturing equipment (8,000-80,000 hours depending on type).
MTBF enables four critical business functions: preventive maintenance planning, spare parts optimization, repair vs. replace decisions, and service level agreement (SLA) definition.
Improving MTBF requires systematic approaches: quality equipment procurement, optimal operating conditions, comprehensive preventive maintenance, staff training, condition monitoring, and root cause analysis.
Modern CMMS platforms automate MTBF tracking, eliminating manual calculation errors and providing real-time reliability metrics integrated with maintenance scheduling.
The future of reliability metrics combines traditional MTBF with predictive analytics, IoT sensors, machine learning, and physics-of-failure models for more accurate, real-time failure prediction.
Actionable Next Steps
Ready to leverage MTBF for your organization? Follow these steps:
1. Assess your current data quality. Review how you currently track operating hours and failure events. Identify gaps in data collection that undermine reliable MTBF calculations.
2. Implement or upgrade your CMMS. If you lack maintenance management software, investigate platforms like MaintainX, Fiix, LLumin, or UpKeep. If you have CMMS, ensure it tracks MTBF automatically.
3. Define failure criteria clearly. Create documentation specifying what constitutes a failure versus minor issues. Ensure all staff use consistent definitions.
4. Calculate baseline MTBF for critical assets. Identify your five most critical equipment pieces. Calculate their current MTBF using available historical data.
5. Establish comparison benchmarks. Research typical MTBF values for your equipment types through industry associations, vendor data, or professional networks.
6. Create MTBF improvement targets. Set realistic goals (e.g., 15% MTBF improvement over 12 months) based on baseline performance and benchmark data.
7. Develop preventive maintenance schedules. Use MTBF data to schedule preventive interventions before predicted failure windows.
8. Train your team. Ensure operators, technicians, and managers understand MTBF concepts, limitations, and applications. Address common misconceptions.
9. Monitor trends monthly or quarterly. Track whether MTBF improves, degrades, or holds steady. Investigate significant changes immediately.
10. Expand gradually. Once you've mastered MTBF for critical assets, extend tracking to additional equipment. Build institutional knowledge systematically rather than attempting comprehensive implementation immediately.
11. Integrate with broader reliability program. Combine MTBF tracking with OEE analysis, root cause analysis, condition monitoring, and predictive maintenance for comprehensive reliability management.
12. Share results and celebrate wins. When MTBF improvements occur, quantify financial impacts (reduced downtime costs, lower maintenance expenses, improved production capacity) and communicate success to stakeholders.
Glossary
Availability: The probability a system is operational at any given moment. Calculated as: Availability = MTBF ÷ (MTBF + MTTR).
Bathtub Curve: A graphical representation of failure rates over equipment lifecycle, showing early failures (burn-in), constant random failures (useful life), and increasing wear-out failures.
CMMS (Computerized Maintenance Management System): Software that tracks equipment, maintenance activities, work orders, and performance metrics including MTBF.
Failure Rate (λ): The reciprocal of MTBF, expressing failures per unit time. λ = 1 ÷ MTBF.
FMEA (Failure Mode and Effects Analysis): Systematic method for evaluating potential failure modes, their causes, and impacts.
Infant Mortality: Early-life failures during burn-in period due to manufacturing defects or installation issues.
MDT (Mean Down Time): Average time system is non-operational after failure, including repair time plus any delays.
MTTA (Mean Time To Acknowledge): Average time from failure detection until human acknowledgment of the issue.
MTTD (Mean Time To Detect): Average time from actual failure until system or platform identifies the problem.
MTTF (Mean Time To Failure): Average operating time until irreparable failure for non-repairable systems. MTTF ≈ MTBF - MTTR.
MTTR (Mean Time To Repair): Average time required to repair failed equipment and restore functionality.
OEE (Overall Equipment Effectiveness): Comprehensive metric combining availability, performance, and quality. OEE = Availability × Performance × Quality.
Physics-of-Failure: Reliability prediction approach based on actual failure mechanisms (thermal stress, electromigration, etc.) rather than empirical data.
Predictive Maintenance: Maintenance strategy using condition monitoring to predict failures before they occur.
Preventive Maintenance: Scheduled maintenance performed before equipment failure to reduce breakdown frequency.
Repairable System: Equipment that can be fixed and returned to operational status after failure.
Useful Life Period: The middle phase of equipment lifecycle characterized by relatively constant random failure rates.
Sources & References
Defense Acquisition University. (2025, October 1). Mean Time Between Failure (MTBF). Retrieved from https://www.dau.edu/acquipedia-article/mean-time-between-failure-mtbf
Wikipedia. (2025, July 30). Mean time between failures. Retrieved from https://en.wikipedia.org/wiki/Mean_time_between_failures
IBM. (2025, July 22). What Is Mean Time between Failure (MTBF)? IBM Think Topics. Retrieved from https://www.ibm.com/think/topics/mtbf
Splunk. (n.d.). Mean Time Between Failure (MTBF): What It Means & Why It's Important. Splunk Blog. Retrieved from https://www.splunk.com/en_us/blog/learn/mean-time-between-failure.html
Coast App. (2024, September 16). Mean Time Between Failure (MTBF) Calculation, Explained. Retrieved from https://coastapp.com/blog/mtbf-calculation-mean-time-between-failure/
Fiix Software. (2025, June 19). Mean Time Between Failures (MTBF). Retrieved from https://fiixsoftware.com/maintenance-metrics/mean-time-between-fail-maintenance/
Infraspeak. (2023, February 14). Mean Time Between Failures (MTBF): what it is and how to calculate it. Infraspeak Blog. Retrieved from https://blog.infraspeak.com/mtbf-mean-time-between-failures/
UpKeep. (n.d.). What Is Mean Time Between Failure MTBF? [Calculation & Examples]. UpKeep Learning. Retrieved from https://upkeep.com/learning/mean-time-between-failure/
Atlassian. (n.d.). Incident Management - MTBF, MTTR, MTTA, and MTTF. Retrieved from https://www.atlassian.com/incident-management/kpis/common-metrics
LogicMonitor. (2024, November 20). What's the difference between MTTR, MTBF, MTTD, and MTTF. LogicMonitor Blog. Retrieved from https://www.logicmonitor.com/blog/whats-the-difference-between-mttr-mttd-mttf-and-mtbf
Reiter, T. (n.d.). MTBF - Mean Time Between Failure + MTTF. Applied Statistics. Retrieved from http://www.applied-statistics.org/mtbf.html
Nuclear Technology. (2024, June 17). Nonhomogeneous Poisson Process Model for Estimating Mean Time Between Failures of the JE01-AP03 Primary Pump Implemented on the RSG-GAS Reactor. Volume 211, No. 4, Pages 645-660. doi: 10.1080/00295450.2024.2352663. Retrieved from https://www.tandfonline.com/doi/full/10.1080/00295450.2024.2352663
LLumin. (2024, July 9). Mean Time Between Failure Formula And Examples. Retrieved from https://llumin.com/mean-time-between-failure-formula-and-examples-for-asset-management-llu/
International Association for Management of Technology. (2018). Improvement of process machinery availability and reliability: A case study of the production line in a sugar processing plant. IAMAT Conference Proceedings.
MIL-HDBK-217F. (1991, December 2). Military Handbook: Reliability Prediction of Electronic Equipment. Retrieved from http://everyspec.com/MIL-HDBK/MIL-HDBK-0200-0299/MIL-HDBK-217F_14591/
ALD Service. (n.d.). Reliability Prediction Standards - MIL-HDBK-217. Retrieved from https://aldservice.com/reliability/mil-hdbk-217.html
Relyence. (2018, July 16). A Guide to MIL-HDBK-217, Telcordia SR-332, and Other Reliability Prediction Methods. Retrieved from https://relyence.com/2018/07/16/guide-reliability-prediction-methods/
Cadence. (2025, April 30). MTBF and Reliability Standards in PCB Design. Retrieved from https://resources.pcb.cadence.com/blog/2024-mtbf-and-reliability-standards-in-pcb-design-cadence
Design News. (2023, October 12). The End is Near for MIL-HDBK-217 and Other Outdated Handbooks. Retrieved from https://www.designnews.com/testing-measurement/the-end-is-near-for-mil-hdbk-217-and-other-outdated-handbooks
Reiter, T. (n.d.). MTBF using Mil-HDBK-217F. Applied Statistics. Retrieved from http://www.applied-statistics.org/mil-hdbk-217.html
Backblaze. (2025, February 11). Backblaze Drive Stats for 2024. Retrieved from https://www.backblaze.com/blog/backblaze-drive-stats-for-2024/
Statista. (n.d.). Servers - annual failure rates. Retrieved from https://www.statista.com/statistics/430769/annual-failure-rates-of-servers/
Airbus. (2025). A Statistical Analysis of Commercial Aviation Accidents 1958 - 2024. Retrieved from https://accidentstats.airbus.com/
Boeing. (2024). Statistical Summary of Commercial Jet Airplane Accidents. Retrieved from https://www.boeing.com/content/dam/boeing/boeingdotcom/company/about_bca/pdf/statsum.pdf
Reliable Plant. (2019, November 7). Mean Time Between Failure (MTBF) Explained. Retrieved from https://www.reliableplant.com/mtbf-31702
Allied Reliability. (2024, May 14). Which is right for you? OEE vs. MTBF. Retrieved from https://www.alliedreliability.com/blog/which-is-right-for-you-oee-vs-mtbf
MaintainX. (2025). Maintenance KPIs: The Most Important Metrics to Track in 2025. Retrieved from https://www.getmaintainx.com/blog/beginners-guide-maintenance-kpis
NetSuite. (2025, August 7). 78 Essential Manufacturing Metrics and KPIs to Guide Your Industrial Transformation. Retrieved from https://www.netsuite.com/portal/resource/articles/erp/manufacturing-kpis-metrics.shtml
RED27Creative. (2025, April 15). Manufacturing Industry Benchmarks: 7 Essential KPIs For Powerful Growth. Retrieved from https://red27creative.com/manufacturing-industry-benchmarks
NetSuite. (2025, July 22). Manufacturing Benchmarking Guide: Benefits, Types, and Guidance. Retrieved from https://www.netsuite.com/portal/resource/articles/erp/benchmark-manufacturing.shtml
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