Equipment and machines are the backbone for any manufacturing activity. Only if machines are properly managed and maintained, you can ensure optimum production activity. However, no machines are permanent – they may fail after reaching certain threshold, disrupting your production activity.

But can you predict when your machines are going to fail or better do you have a metric when failures would occur during a machine’s operating cycle? This is where we introduce an important metric – Mean Time Between Failure (MTBF) for evaluating the reliability of your machinery. This metric not only helps you assess the frequency of failures but also helps in determining when you need to go for maintenance.

This blog explores what Mean Time Between Failure (MTBF) is all about, how it is calculated, importance in the maintenance domain, key parameters that have a bearing on it and its drawbacks. Lastly, we will discuss how a mobile enterprise asset management (EAM) solution helps in improving the maintenance efficiency.

Overview:

What is MTBF and How It Is Measured?

Mean Time Between Failure (MTBF) is a reliability metric that lets you calculate the average time an asset that has been repaired operates, before it fails. This way, you get insights into how often your equipment is going to fail during normal working conditions. MTBF is calculated using the following formula:

MTBF = Total Operating Time/Number of Failures.

Let’s take a simple example. If a machine runs for 1,000 hours and fails two times within its operating cycle, its MTBF would be 500 hours. When you derive higher MTBF, you experience better reliability and minimal frequent failures.

You can apply MTBF to individual assets or aggregated assets across varied types or locations within your industrial setup. This way, you can customize your maintenance strategies, based on how your machines operate.

4 Reasons Why MTBF Is Essential for Your Maintenance Operation

Having got an overview of what MTBF is all about, let’s understand why MTBF is mandatory for your maintenance operations:

1. Predictive maintenance

We are living in the age of data and here’s where predictive maintenance comes into picture which relies more on data, rather than on intuition. Once you have the MTBF data, you can analyze and predict when equipment is likely to fail. This proactive approach helps you schedule maintenance in a systematic way – as per a pre-defined schedule. This way, you reduce surprise breakdowns, thereby improving your equipment’s operational continuity.

2. Resource distribution

When you properly comprehend MTBF, you can easily distribute resources more effectively. This means, you can plan your activities well ahead that includes scheduling parts and personnel in advance. This translates to minimized downtime, thereby facilitating seamless operations.

3.Cost control

When you drive higher MTBF values, you are rest assured of lower maintenance costs. This is because you tend to minimize repairs that are required over time. When you minimize repairs, you spend less money on maintenance, which ultimately leads to higher savings for your company.

4. Potential for performance improvement

Using Mean Time Between Failure metric, you can compare the reliability of different systems or components. This comparative analysis lets you identify areas where you can improve and maximize the performance across your operations.

Factors That Impact Mean Time Between Failure

There are a couple of factors that considerably impact the MTBF of your equipment:

Quality of design: The quality of design can make or break an equipment. This means the intrinsic durability of equipment has a major bearing on its reliability. If systems are well designed and meets all the required quality parameters and have been well tested for certain stressful conditions, they are less likely to fail early.
Operational environment: Typical environmental factors such as temperature, humidity, and vibration can drastically affect the performance of the equipment. This means when your equipment operates in harsh climate and its design is not meant to withstand such conditions, it has a lower MTBF. On the contrary, equipment that is designed to operate in harsh conditions will have a higher MTBF.
Preventive maintenance practices: If you plan and implement regular preventive maintenance programs for your equipment, your MTBF will always be higher. This is because, you tend to identify potential issues beforehand to avoid escalation into critical failures.
Component quality: The quality of parts incorporated in the machinery has a direct bearing on reliability of the equipment. When there are higher quality components or that meet stringent quality parameters, they have longer operational periods between failures.

Drawbacks of Mean Time Between Failure

Benefits aside, MTBF has its own drawbacks as outlined herewith:

Data specific measure: Mean Time Between Failure (MTBF) relies completely on statistical data, i.e, historical failure data. Though this data makes sense and might seem accurate, however, its accuracy depends on the completeness and quality of that data. When you derive incomplete data or data that lacks consistency, it could lead to misleading inferences about system reliability.
Average value: Mean Time Between Failure (MTBF) is an average value and doesn’t consider variations in failure frequency or severity. An example is a system that could derive a high MTBF but can still experience a series of failures that could critically halt the operations.
Non-suitability for non-repairable items: For items that cannot be repaired or are classified as archaic, the MTBF metric doesn’t make sense. Instead, Mean Time to Failure (MTTF) should be used for such categories of equipment.

Despite its drawbacks, Mean Time Between Failure (MTBF) is still a good measure to improve your assets’ lifespan and improve your maintenance efficiency over time. However, relying solely on manual methods are error prone and considerably consumes your organization’s time and resources. Instead, automating the process or leveraging a full-fledged mobile enterprise asset management (EAM) solution can work wonders for your maintenance department. Let’s decode how a mobile EAM solution can significantly improve your equipment’s lifespan, deriving a better Mean Time Between Failure (MTBF).

3 Key Benefits of Using a Mobile EAM Solution to Improve MTBF

Leverage a mobile EAM solution that can play a critical role in improving your Mean Time Between Failure (MTBF), thereby enhancing the overall efficiency of your maintenance team. Some of its benefits include:

1. Real-time data analytics

A mobile EAM solution that integrates with AI and IoT offers real-time data analytics, which help assess your equipment performance periodically. When you can assess your equipment performance regularly, you can easily predict any critical issues before hand and fix them at the earliest.

2. Triggering of alerts

If you set triggers for specific Mean Time Between Failure (MTBF) touch points, a mobile EAM solution can ensure prompt maintenance interventions. This way, you minimize manual calculations, thereby optimizing your resource management.

3. Access to historical data

When you empower your technicians with mobile devices that can integrate with your in-house ERP systems, they can access historical data. This data helps them to connect with experts remotely to fix potential issues at the earliest. This fosters better decision-making during maintenance operations.

In the above context, explore a right mobile EAM solution like that offered by Propel Apps that perfectly aligns with your organizational requirements in terms of fostering good practices in improving your plant maintenance. Whether it is improving your critical Mean Time Between Failure (MTBF), or getting access to critical maintenance data, a right mobile EAM solution will always make a huge difference in ensuring optimal utilization of your assets.

Propel Apps’ mobile EAM solution transforms plant maintenance and asset management operations using Oracle EAM, SAP Plant Maintenance, design thinking, and enterprise mobile implementations. To know more about our solution and how it can improve your overall MTBF, fostering good maintenance practices, schedule a free demo with us.

Final Thoughts

Mean Time Between Failure (MTBF) is a critical metric to understand the performance of your repairable equipment. If you rightly measure this metric, you can improve operational efficiency using predictive techniques, optimized resource management, cost minimization and enhanced equipment lifespan.

Above all, if you adopt the right mobile EAM solution for your maintenance strategy, you empower your workforce with real-time data that helps them in efficient decision-making. Hence, now is the time to improve your Mean Time Between Failure (MTBF) to make your maintenance operations shine – increased productivity, smiling workforce and healthy revenue generation.