Mean Time Between Failure (MTBF) Explained

For businesses in today’s world of quick shipping and order fulfillment, the reliability of machinery and systems is more important than ever. Companies across various industries must overcome the challenge of minimizing downtime — or at least predict when repairs will become necessary — to avoid unexpected shutdowns and the financial losses and reduced customer satisfaction that come with them. This is especially important today, as an hour of unplanned downtime in 2022 cost businesses at least 50% more than it did just two years prior, according to Siemens’ “The True Cost of Downtime 2022” report. The report also estimates that Fortune Global 500 industrial companies lose almost $1.5 trillion a year through unplanned downtime, a 65% increase from 2020.

Given these financial pressures and potential losses, how can businesses take a more proactive approach to maintaining their equipment? To effectively balance routine maintenance and the slowdowns necessary to accommodate them, businesses need quantifiable metrics that inform when and where repairs should be made to minimize interruptions and maximize profits. This article explores a critical metric for predicting when equipment will fail — mean time between failure (MTBF).

What Is Mean Time Between Failure (MTBF)?

MTBF is a critical metric used to measure the time between two consecutive failures in a system’s or component’s, which indicates reliability during normal operation. Maintenance teams, schedulers and decision-makers use this metric to calculate the average time between failures, increasing the accuracy of downtime predictions, proactive repair scheduling and equipment investment decisions. By accurately calculating, analyzing and improving MTBF, businesses can create a smoother and more reliable flow of goods throughout their operation, positioning themselves to better meet customer expectations.

Businesses often use MTBF alongside other important key performance indicators (KPIs), such as mean time to repair (MTTR), to effectively schedule repair teams for regular maintenance and testing before there’s a problem, thereby minimizing unexpected equipment failures. This approach helps companies more effectively deploy their technicians and engineers without needing to bring in more hires or increase costs. This strategic focus on reliability can be a key differentiator in competitive markets, especially those relying on global supply chains prone to delays and interruptions.

Key Takeaways

• Mean time between failure (MTBF) is a reliability metric used to predict equipment performance and inform maintenance and investment decisions. A high MTBF shows a lower likelihood of equipment failure, while a low MTBF may suggest that new maintenance policies or more reliable equipment could improve operations.
• MTBF is calculated by dividing total operational hours by the number of failures within that period. It’s influenced by design quality, user handling and operating conditions, among other factors.
• Businesses can improve MTBF through proactive measures, both in maintenance policies and through improvements from vendors and equipment makers that minimize the strain on equipment. By improving MTBF, businesses can reduce costs, extend the life of equipment and increase customer satisfaction.

Mean Time Between Failure Explained

MTBF is a particularly valuable metric for businesses that rely on continuous capabilities or in situations where equipment failures can result in significant financial losses or safety risks. A high MTBF suggests that equipment or systems fail infrequently, allowing businesses to maintain their operational continuity and minimize losses from interruptions. A low MTBF, on the other hand, may point to aging equipment, a poorly trained workforce or other inefficiencies that can significantly impact the bottom line. But because MTBF is a descriptive, not prescriptive, metric, it can only give averages to guide further study. Analysts and decision-makers can then use this as a starting point for investigations into why failures are occurring and where they can make improvements.

For long-term planning, businesses can track trends in MTBF over time to inform decisions regarding asset management and acquisition. By analyzing MTBF data for all their equipment, companies can identify patterns and potential weaknesses in their operational processes. This analysis empowers businesses to implement targeted improvements, leading to better resource and investment allocation for high-quality components or more reliable systems. For example, if a machine’s MTBF is increasing and repairs no longer fully fix the problem, it may be time to replace the machine entirely. Otherwise, unexpected breakdowns will likely increase — in both frequency and severity — leading to increased downtime, tied-up maintenance staff and, ultimately, revenue loss. Through regular analysis of MTBF, often with the aid of automated software-generated reports and alerts, businesses can better prioritize capital investments, maximize investment returns and build sustainable, long-term growth.

Machinery Uptime Vs. Downtime During an Average Workday

How to Calculate Mean Time Between Failure

Calculating MTBF is a straightforward process that requires only two numbers: total operational hours and the number of times a machine failed over that same period. Once those figures are determined, either through manual tracking or automated data collection tools and sensors, failures are divided into the total to calculate MTBF. Written as a formula, MTBF is:

MTBF = Total operational hours / Number of failures

For example, say a delivery truck operates for a total of 1,800 hours in a year. During this period, the truck failed and required maintenance five times. Using the MTBF formula, the calculation would be:

MTBF = 1,800 hours / 5 failures = 360 hours

This result shows that, on average, the trucks run for about 360 hours before a failure is likely to occur. Businesses can then use this metric to plan maintenance and reliability assessments, such as bringing trucks in for tune-ups and routine checks after they pass certain benchmarks. In this case, maintenance teams would perform routine tune-ups every 300 hours of operation to minimize the chance of trucks’ reaching their predicted failure point without maintenance. These kinds of proactive steps help companies optimize their service schedules and improve equipment uptime, while minimizing unexpected breakdowns and unplanned business interruptions.

8 Factors Influencing Mean Time Between Failure

A variety of factors can influence MTBF, each playing a significant role in how frequently failures occur and how they can be mitigated. These factors apply across the entire life cycle of machinery, ranging from initial equipment design to end-of-life performance monitoring. By understanding these issues, businesses can enhance their operational reliability and reduce downtime. Here are eight key factors that significantly impact MTBF.

1. Design Quality

   Some equipment is more durable than others, and businesses should have a general sense of the quality and design strengths and weaknesses when determining reliability and MTBF. When purchasing new equipment, companies should ask the manufacturer for an estimated MTBF to inform expected long-term return on investment (ROI) calculations. A well-designed system that accounts for potential stressors and operational demands is less likely to fail prematurely, especially if those designs include guidance and easily accessible parts for repair, replacement and routine maintenance.

2. Manufacturing Process

   The processes used to manufacture equipment can also impact its reliability, and even strong designs can lead to unreliable equipment if they’re not coupled with strong quality control standards and high-quality materials. Inconsistencies in the manufacturing process can introduce defects that shorten the lifespan of components and decrease the MTBF, as well as increase the complexity and scope of repairs when they become necessary. Through careful research and vetting, companies can choose reliable equipment vendors and maximize their MTBF for all new machines as they grow and replace outdated assets.

3. Operating Conditions

   Operating conditions, including temperature, vibrations and environmental exposure, significantly affect MTBF. Equipment used in harsh or variable conditions is typically more prone to failure unless specifically designed to withstand such environments. For example, a company may have two factories, one on the coast and one inland, with radically different MTBF, as the extra humidity rusts and wears down machinery faster than the dry inland air. To extend MTBF, businesses should choose machinery built with specific environmental conditions in mind, as well as regularly monitor performance and adjust environmental conditions when possible.

4. User Handling

   It’s impossible to eliminate errors and mistakes entirely, but they can be mitigated by robust staff training and the elimination or improvement of unnecessarily complex workflows and machinery. By referencing detailed operational guidelines, employees can minimize improper use and overloads, reducing wear and tear and failures. These guidelines are most helpful when they are accessible and readily available, often posted near the machinery itself or in digital handbooks. This ensures that both new and established staff, as well as repair technicians, can be confident that they’re using the most up-to-date and efficient methods when operating or repairing equipment.

5. Wear and Tear

   While the goal is to extend MTBF as far as possible, businesses must be realistic — all equipment will eventually fail, after all. But even with that foregone conclusion, companies can take steps to extend machine life and predict when failures will occur. Natural wear and tear over time degrades the performance of components, and regular maintenance and timely replacement of worn-out parts are critical in managing this degradation. Before, during and after repairs, maintenance technicians can leverage technology, such as Internet of Things (IoT) sensors, to track key performance metrics and gain insights into where wear and tear is occurring and what steps are necessary to get everything back up to speed.

6. Technology and Material Advancements

   Even businesses with highly efficient machinery may still have room to improve their MTBF when new technology or materials enter the market. Companies often integrate new features and techniques into existing systems to enhance their durability and reliability. For example, human-robot collaborative technology, known as “cobots,” can be installed on assembly lines to enhance worker performance and safety. Additionally, new materials, such as stronger metal alloys or 3D-printed components, can help businesses increase the durability or customizability of their equipment, increasing MTBF without replacing the entire machine. These advancements may also increase the volume and/or quality of goods, expanding inventory capabilities.

7. Quality of Maintenance

   One of — if not the — biggest factors influencing MTBF is the quality and frequency of maintenance performed on equipment. Carefully planned and executed preventive maintenance helps technicians address potential issues before the machine fails. But when failures do occur, technicians need the right tools and techniques to ensure that they repair the machinery correctly, rather than relying on shortcuts that may result in further breakage in the future. Companies can track and measure technician performance using employee-focused metrics, such as technician productivity, first-time fix rates, MTTR and other relevant KPIs. By prioritizing expert repair and maintenance, businesses can make sure that MTBF remains high, and, when the time comes, the right equipment is replaced at the right time to maintain output and high-quality standards.

8. Software Reliability

   In systems where software plays a critical role, software bugs, decay or compatibility issues can lead to system failures, even if physical machinery components are undamaged. To minimize these risks, businesses rely on software engineers with relevant expertise to run regular software updates and rigorous testing. These updates can also enhance other aspects of equipment, bringing in new features as they become available. For example, software inside manufacturing equipment can be upgraded to integrate the latest technology, such as artificial intelligence or machine learning, to improve diagnostics and increase transparency of performance.

Benefits of Measuring Mean Time Between Failure

Beyond the direct benefit of reducing equipment failures, measuring and improving MTBF offer numerous advantages across various business operations. Here are eight specific benefits afforded by measuring MTBF and how a company can leverage them to build a more sustainable and competitive brand.

• Enhanced product design: By regularly measuring MTBF, companies gather critical data about which components or systems frequently fail and under what circumstances. Engineers and designers can use this data to refine product designs and increase the durability and reliability of machinery. These improved designs lead to fewer failures, reducing repair and replacement costs while boosting market competitiveness.
• Optimized maintenance schedules: Maintenance managers use MTBF to estimate equipment’s expected operational lifespan, allowing them to schedule inspections, service and replacements before failures are likely to occur. This minimizes unexpected breakdowns and ensures that equipment operates at peak efficiency, while extending an asset’s life, maximizing productivity and informing new equipment purchases.
• Efficient resource allocation: By predicting potential breakdowns with MTBF, companies can better manage inventory levels for both finished goods and supplies. Strategic resource allocation helps businesses maintain a lean operation, reduce waste and improve the bottom line without harming the customer experience. For example, businesses can temporarily increase production before scheduled maintenance to make sure customer demand can be met while equipment is turned off. Similarly, raw material inventory can be allocated accordingly, preventing clutter and excessive carrying costs while production is halted for maintenance.
• Effective risk management: Measuring MTBF allows companies to identify and assess areas with high risks of failure. Due to limited resources, businesses often must choose which weaknesses to address first, and MTBF data helps companies prioritize risks that can cause severe operational disruptions, financial loss or safety incidents. By understanding and managing these risks, companies can maintain a safer working environment and effectively protect their assets and profits.
• Increased customer satisfaction: Reliable products and minimal service interruptions boost customer satisfaction by making sure customers can trust that the business will fulfill their orders quickly and accurately. Customers satisfied with their experience are more likely to provide positive reviews and recommend products to others, contributing to the company’s long-term growth and success.
• Enhanced brand reputation: Companies that consistently produce durable and reliable products — without overexerting their equipment and causing failures — are better positioned to serve customers and outshine the competition. This reputation for quality can become a key differentiator in competitive markets by attracting more customers and opening up new business opportunities.
• Reduced downtime: One of the most direct benefits of improving MTBF is reducing the time equipment needs to be shut off, thereby minimizing halted production until repairs and maintenance are completed. With fewer failures, production lines run more smoothly and for longer periods without interruption. This efficiency boosts output and reduces the costs associated with downtime, such as lost productivity and expedited shipping costs for parts, replacement equipment and delayed customer orders.
• Effective life cycle management: By tracking MTBF over time, companies can better predict when machinery is slowing down or breaking, helping business leaders schedule equipment replacements and budget for capital expenditures with greater accuracy. This detail-oriented approach allows a company to rely on up-to-date technology and practices, while strategically phasing out older equipment to minimize the negative impact on operations and finances.

Industry Application of Mean Time Between Failure

MTBF is used throughout different sectors, each with its own unique set of challenges and operational demands. These differences come from the various types of equipment that industries use and how technicians in the field maintain and repair that equipment. Below are four industries that can use MTBF to manage their assets and improve reliability.

• Manufacturing

  According to Siemens’ “The True Cost of Downtime 2022” report, the average manufacturing facility suffers 20 downtime incidents a month. For many manufacturers, equipment quality and performance are the primary drivers of their output and, therefore, success. MTBF gives manufacturers insights into machinery performance, helping managers prioritize production line improvements where they will have the biggest impact. High MTBF values are indicative of reliable machinery and a smoother production process, which can be analyzed to inform future workflows, equipment purchases and product designs. Low MTBF, however, indicates frequent stoppages and should be addressed through more robust maintenance practices and/or higher-quality equipment investments.

• IT and Data Centers

  For service providers in the IT and data center sector, shutdowns and equipment failures do more than just halt their customers’ direct operations; they can impact the functionality and security of every department that relies on this critical digital infrastructure. Technicians and IT professionals can track the MTBF of their equipment — both on-premises and at customer facilities — to ensure that servers and network equipment can continuously operate, often by building redundant, backup systems to switch to when systems are being worked on or approaching a projected failure period. Maintaining a high MTBF is especially important for critical IT infrastructure, as it ensures that data processing and storage services remain uninterrupted. To prevent failures and data loss, many providers offer cloud-based software, such as an enterprise resource planning (ERP) system, to allow customers to focus on running their businesses, while outsourcing much of their IT responsibilities to external experts in the field.

• Healthcare

  In healthcare, equipment reliability is a matter of life and death. Healthcare companies can use MTBF to track and predict the reliability of medical devices and equipment, including MRI machines, patient monitoring systems and life support machines. Healthcare facilities can also use MTBF data to schedule maintenance activities, balancing the impact on patient services with care quality and regulatory standards. A longer MTBF means that healthcare providers can more confidently trust that their equipment will function properly when needed, an essential part of patient care and safety. Lower MTBF metrics suggest that more reliable equipment may be necessary to consistently deliver high-quality care and good patient outcomes.

• Defense and Military

  Due to its high-pressure and unpredictable nature, the defense and military industry demands equipment that can perform reliably under extreme conditions. MTBF is a key factor in the design and procurement of military hardware, from communication systems to vehicles and weaponry, as this equipment must reliably function for entire military operations or else troops and other personnel risk major consequences. The military uses MTBF data to conduct rigorous testing before deployment and to develop in-the-field maintenance schedules, ensuring that equipment is available and ready whenever and wherever it’s needed.

10 Strategies for Improving Mean Time Between Failure

To improve MTBF, businesses must understand exactly why equipment is failing and where other weaknesses are prevalent, such as consistent user error or subpar materials. Because there’s no one-size-fits-all approach to MTBF, decision-makers should consider a variety of strategic measures and implement the ones that cater to their specific needs. After improvements are made, analysts should carefully track how they impact operations, informing future strategies and course corrections. Below are 10 potential strategies to improve MTBF.

1. Enhance design quality: Improving the design quality of components and systems can significantly improve MTBF. This involves choosing suppliers that offer high-grade materials, incorporating strategic redundancies and emphasizing maintenance priorities in new designs. These efforts can both extend equipment’s operational life and reduce the likelihood and cost of failures.
2. Implement rigorous testing: Rigorously testing equipment before deployment and after maintenance can address potential failures and give technicians new insights into other, potentially widespread issues. Stress, endurance and environmental testing confirm that equipment can withstand operational demands and help businesses establish clear guidelines on the optimal conditions for getting the most out of machinery.
3. Adopt preventive maintenance: Effective preventive maintenance requires regular inspections and the systematic replacement of worn parts before they fail. Maintenance workers typically schedule these repairs based on historical data and manufacturer recommendations. Regularly scheduled, temporary downtime can reduce the occurrence of unexpected breakdowns and increase overall efficiency.
4. Improve manufacturing processes: By enhancing precision and control, businesses can optimize their manufacturing processes, significantly improving the quality of final products and the strain put on equipment. Additionally, quality control measures throughout the manufacturing process help identify defects and inefficiencies that could potentially jam systems and lead to increased wear and tear and equipment failures.
5. Use data and analytics: Modern businesses have access to sophisticated data analytics tools that can provide detailed, real-time insights into equipment performance and failure trends. Many business solutions, including ERP systems, leverage historical data and predictive analytics to predict when a failure is most likely to occur and make actionable suggestions to address issues before they happen.
6. Ensure proper usage and handling: Misuse or improper handling of equipment and systems often leads to increased wear and tear or outright failure, and operators must be properly trained to minimize user error and equipment damage. Additionally, this training should be ongoing and provide clear operational guidelines, helping to guarantee that both new and established employees use current best practices and safety protocols when operating machinery.
7. Conduct a root cause analysis: When failures occur, maintenance workers should identify the root cause, not just the symptoms, to make certain that repairs last. For example, if a specific machine arm is continually breaking, it may be from a weak internal mechanism rather than faulty arm components. By addressing underlying issues like this one, businesses can improve the overall reliability of a system rather than waste time and resources with temporary stopgaps. Additionally, some root causes may be systemic issues, and identifying one can lead to organization-wide fixes, significantly improving the operation as a whole.
8. Establish condition-based maintenance: With the rise of IoT devices, mobile sensors and other sophisticated monitoring technology, businesses can analyze the conditions of machinery in real time, quickly identifying any changes in conditions and performance. Using this information, often accessible through customizable dashboards or automatically flagged alerts, maintenance teams can prioritize urgent fixes when conditions change and thereby minimize major losses.
9. Identify the most frequent causes of failure: Through analysis of maintenance records and failure data, businesses can identify patterns and common causes of failures throughout their organization. By making systemic improvements, rather than just chasing one problem at a time, businesses can improve their maintenance workflows and significantly improve MTBF.
10. Minimize repair times: Enhancing repairs can also improve MTBF by minimizing downtime and improving the first-time fix rate. This can be achieved by having spare parts readily available and investing in higher-quality tools and better technician training. Businesses can also use advanced diagnostic tools and software to address other, potentially unrelated issues when making repairs. This proactive approach saves time and resources by reducing the number of service calls needed to maintain day-to-day operations, as well as reducing the overall number of repairs.

Minimize Mean Time Between Failures With NetSuite

Businesses can’t effectively predict when their equipment might fail without understanding the ins and outs of each machine. But gaining that transparency typically requires more data than businesses can manually collect and act on in a timely manner. With NetSuite Field Service Management, businesses can leverage real-time tracking, advanced scheduling and predictive maintenance capabilities to proactively manage their assets. Managers can use NetSuite’s drag-and-drop scheduling and dispatch features to allocate their maintenance teams more effectively, delivering better results and communication without taking on new hires.

NetSuite Field Service Management

NetSuite Field Service Management’s mobile optimization gives both technicians and the home office access to real-time job and technician status updates. This transparency helps ensure that resources are effectively allocated where and when they need to be, increasing first-time fix rates and customer satisfaction. And with NetSuite’s asset management capabilities, business leaders can monitor all equipment, from installation to decommission, from one centralized location. NetSuite empowers businesses to focus less on playing catch-up and repairing faulty assets and more on using their equipment to better serve their customers and grow.

NetSuite Mobile App

As businesses work to gain, maintain and expand their competitive advantages, improving MTBF is a critical way to extend the operational lifespan of their equipment, reduce downtime and optimize maintenance processes. Furthermore, the insights gained from monitoring MTBF over time help managers foster a proactive maintenance culture, while also empowering business leaders to make informed decisions about asset management and investments. By emphasizing MTBF analysis, businesses can better serve their customers, create a more sustainable and effective production environment and, ultimately, increase profitability.

Mean Time Between Failure FAQs

What’s the equation for MTBF?

Mean time between failure (MTBF) is calculated by dividing total operational hours by the number of failures during the given period. The formula is:

MTBF = Total operational hours / Number of failures

This metric provides an average time that equipment will run before requiring maintenance or repairs.

What is considered a good MTBF?

A good mean time between failure (MTBF) rating varies significantly across different industries and equipment types, but a relatively high MTBF typically indicates better reliability. Simple systems or those designed with an emphasis on longevity will likely have a much higher expected MTBF than systems that require regular maintenance and adjustments.

How do you calculate reliability from MTBF?

Generally speaking, the higher the mean time between failure (MTBF), the more reliable the equipment is. Calculating this figure requires some complex mathematics, so businesses will typically rely on sophisticated software to automatically calculate and report any changes in equipment’s reliability.

What is an acceptable MTBF?

An acceptable mean time between failure (MTBF) depends on the specific requirements and context of the equipment’s use. For high-pressure industries, such as aerospace or healthcare, a longer MTBF is crucial to minimize risks. In less-critical applications, a shorter MTBF may be acceptable if the cost of downtime and maintenance is lower. It’s up to the individual company to weigh the costs and benefits of equipment before investing and establishing maintenance protocols.

What are MTTF and MTTR?

Mean time to failure (MTTF) is a similar metric to mean time between failure (MTBF) but is used for non-repairable or one-time-use systems, such as light bulbs, as it calculates the average time to complete failure. Mean time to repair (MTTR), on the other hand, measures the average time required to repair a system or component after a failure. MTTR and MTBF are often used together to calculate expected up- and downtime for equipment.

How do you calculate MTTR and MTBF?

Mean time to repair (MTTR) and mean time between failure (MTBF) are calculated in similar ways, with both comparing the time spent on a specific task (repairing or operating, respectively) with the number of times that task occurs.

The formula for MTTR is:

MTTR =Total downtime / Number of repairs

The formula for MTBF is:

MTBF =Total operational hours / Number of failures

What is the mean time between defects?

Mean time between defects (MTBD) is similar to mean time between failure (MTBF) , but rather than focusing on equipment, MTBD focuses on the production process, specifically the average time between flawed goods. It’s calculated by dividing the total operational time by the number of defects detected, providing insights into the quality and consistency of the production process. It’s important to remember that MTBD, like other metrics, is only an average and not a perfect predictor of future behavior.