When a critical asset like an expensive piece of machinery or infrastructure breaks unexpectedly, it affects customers and can cost companies millions. To be successful, businesses need to have clear, real-time visibility into the condition of their assets and a plan to keep them running smoothly and make repairs quickly when things break. Enterprises are constantly looking for new ways to optimize performance, increase reliability and extend asset lifespans—all without adding unnecessary costs.

This is why asset reliability is such a critical component of business strategy. Before we dive into it, let’s take a look at some relevant terms.

The term “asset” can refer to both physical and non-physical items that companies own and use to create value. Examples of physical assets include machinery, factories, office supplies, production plants, assembly lines, vehicle fleets, buildings and civil infrastructures. Examples of non-physical assets include software, intellectual property, trademarks and patents.

Asset reliability is the ability of an asset to perform under certain conditions over a specified period without breaking down. To be considered “reliable,” an asset asset must perform at a certain level and comply with all regulatory requirements surrounding its operation.

The terms asset reliability and asset availability are easy to confuse, but there are several key differences worth noting. First, availability measures the operational capacity of an asset over time. In other words, how long can a piece of equipment perform the tasks associated with its operation successfully. Reliability, on the other hand, refers to an asset’s ability to function without downtime or disruption under certain conditions. An asset is only deemed “reliable” when it operates without unexpected shutdowns in order to perform necessary repairs.

While reliability and availability are both measured in percentages, it’s possible—even likely—that these percentages will differ even when referring to the same piece of equipment. For example, a piece of equipment operating at 100% reliability might only be 90% available if 10% of the time was used to perform critical, planned maintenance necessary to keep it running.

In order to take a proactive approach to asset reliability, maintenance managers rely on two widely used metrics: mean time between failure, (MTBF) and mean time to repair (MTTR). Both KPIs help predict how assets will perform and assist managers in planning preventive and predictive maintenance. First, let’s look at MTBF and MTTR.

Both MTBF and MTBR can be calculated using simple mathematical formulas. Here’s the formula technicians use to calculate MTBF: 

MTBF = Total operating time / Number of failures in a specific time period

For example, if a piece of equipment is used for 20,000 hours and fails 5 times during that period, its MTBF would be 20,000 hours / 5 failures = 4,000 hours. In other words, this equipment can be expected to fail every 4,000 hours. Armed with this information, operators can plan maintenance activities to ensure equipment doesn’t break down unexpectedly, resulting in costly downtime.

While knowing an asset’s MTBF is critical to keeping it performing at peak levels, it doesn’t help operators determine how much time they will need to repair it. This is where MTTR comes in. To calculate MTTR, operators first need to know how much time it takes to perform the following tasks on an asset:

• Notify asset maintenance teams
• Let the broken equipment cool before it can be worked on
• Perform repairs and reassemble any necessary items
• Test the equipment thoroughly before restarting production

Here’s the mathematical formula operators use to calculate MTTR:

MTTR = Total downtime / Total number of failures over a specific time

For example, if over the course of a year, a system failed 10 times, resulting in 20 total hours of downtime, its MTTR would be: 20 hours / 10 repairs = 2 hours. In other words, it takes, on average, 2 hours to repair this piece of equipment each time it breaks.

Like MTBF, MTTR is used to determine asset reliability and, more specifically, to allow operators to measure the efficiency of their maintenance programs and to make adjustments where necessary.

Both preventive and predictive maintenance are maintenance strategies used by business leaders to increase asset reliability.

Preventive maintenance relies on condition monitoring to help managers strategically plan for asset repairs and downtime in a way that minimizes the impact to the overall business. Predictive maintenance takes the maintenance capacity of preventive maintenance one step further. Sensors collect data in real-time that is then fed into an enterprise asset management (EAM) or computerized maintenance management system (CMMS), where AI-enhanced data analysis tools and processes like machine learning (ML) spot issues and help resolve them. This information is then used to build predictive models of an asset’s performance over time and help spot potential problems before they arise.

One of the ways maintenance managers refine and improve predictive analytics to increase asset reliability is through the creation of a digital twin.

Digital twin technology

Digital twin technology allows for the creation of a virtual representation of an asset that spans the entire asset lifecycle and is subject to the same conditions as the real asset. Digital twins use real-time data, simulations and machine learning to aid decision-makers in the management of their most critical assets.

Digital twins can be created for assets as exotic as manned-spacecraft or as common as a wind turbine. As in predictive analytics, sensors connected to the physical object collect data from the real world that is then mapped onto a virtual model. By monitoring the asset’s digital twin, managers can spot crucial insights into how the asset is reacting to its environment and develop strategies to improve its reliability.

Business leaders know how important it is to have a deep understanding of when their assets are likely to fail so they can take immediate action to reduce risk to overall business operations. Asset performance management, or APM, helps decision-makers enhance asset management insights with automation, analytics and artificial intelligence (AI) capabilities.

Through AI-powered remote monitoring, root-cause analysis, Failure Mode Analysis (FMEA), computer vision and predictive maintenance, APM enables the modern enterprise to reduce unplanned repair work, manage risk, extend asset lifecycles and increase profitability.

Enterprise asset management (EAM) and CMMS

Enterprise asset management (EAM) is an asset management system that combines software and services to help organizations maintain, control and optimize the quality of operational assets throughout their lifecycles. With the amount of data being generated via IoT, maintenance managers are relying more and more on management software and AI-enhanced data analysis to help them make more informed decisions. The goal of EAM is always to improve equipment reliability, increase productive uptime and reduce operational costs.

Many EAM initiatives work in tandem with a computerized maintenance management system (CMMS) to help maintenance departments centralize vital asset information. A CMMS tells maintenance managers where an asset is, what kind of services or repairs it requires and who should perform them. A strong CMMS can enhance maintenance planning by making the information about an asset immediately accessible and auditable.

Asset reliability offers modern enterprises peace of mind when it comes to their most valuable resources. By deploying the most cutting-edge technologies available, coupled with rigorous maintenance management strategies and adherence to key metrics like MTBF and MTTR, enterprises can reduce costs, increase asset dependability and maximize their return on investment (ROI) in their most valuable assets.

The benefits of strong asset reliability include the following:

• Increased uptime: Having good asset reliability means top-performing assets run at peak levels for longer without increasing their risk of failure. By performing preventive and predictive maintenance as part of an overall maintenance strategy, businesses can reduce the number and frequency of breakdowns.
• Reduced costs: When asset reliability improves, maintenance labor, inventory, and working capital costs go down—it’s that simple. Reliability programs help managers plan strategically for downtime and perform repairs only when the impact on business continuity can be minimized.
• Improved worker safety: Another benefit of asset reliability is the improvement of workplace conditions for maintenance workers. By reducing the likelihood of equipment failures, workplaces avoid accidents and injuries associated with unexpected equipment failures.
• Smarter asset management: The continuous monitoring of assets through an EAM or APM approach to improving asset reliability generates insights into current and future (expected) asset states that improve the decision-making capabilities of maintenance managers.

Asset reliability depends on a strong, coordinated approach to asset management that incorporates the most recent technological solutions available. The IBM Maximo Application Suite is a fully integrated platform that helps companies improve asset reliability through better maintenance operations.

IBM Maximo enables the evolution of timed scheduling maintenance to condition-based, predictive maintenance informed by real-time insights. It has a proven track record of helping enterprises boost asset performance, extend asset lifespan and reduce costs and downtime.