Unexpected asset failures are costly. Even a single hour of unplanned machine downtime can cost manufacturers well over $100,000, and this number rapidly increases the longer it takes maintenance teams to remove and replace failed assets.
Remaining useful life (RUL) is a key manufacturing metric that enables organizations to estimate how long an asset will operate before failure. RUL is a key input for proactive maintenance and planning, helping organizations anticipate rather than react to emerging equipment issues.
Here’s what you need to know about the role of RUL in manufacturing, how to calculate remaining useful life and how RUL underpins predictive operational insight.
What is remaining useful life (RUL)?
RUL is the estimated time or usage before an asset reaches the end of its serviceable life. This metric may be applied to equipment, components or systems, and may be expressed in units of absolute time, cycle time or total remaining operating hours.
Remaining useful life focuses on prediction rather than fixed schedules. In other words, it’s not an exact science; it’s the process of estimating how long assets will last, given the current environment, performance and maintenance conditions.
Consider a production line machine owned by Company A that uses high heat to fuse parts. When purchased new, the estimated life span of this asset is five years. After two years of use, Company A carries out an RUL assessment.
Under ideal conditions, the RUL of the equipment is three years. In a manufacturing context, however, ideal conditions are impossible. From environmental stressors such as humidity, dust and debris to operational issues related to excess load conditions or challenges tied to unexpected parts degradation or failures, the actual RUL of the asset may be two years or less.
Why RUL is important for maintenance and reliability
Assessing RUL lays the groundwork for improved reliability programs. By understanding machine history and comparing this data with expected asset lifespan, manufacturers can design a maintenance strategy that addresses likely issues through scheduled maintenance and also addresses additional concerns through proactive monitoring and industrial asset management.
Using RUL, companies can:
- Enable timely maintenance interventions
- Reduce unplanned downtime
- Improve asset availability and performance
- Support predictive and condition-based maintenance strategies
Without RUL, companies may spend time and effort repairing assets that are likely to fail in weeks or months, in turn creating additional CapEx and OpEx costs.
How RUL is estimated in manufacturing
RUL may be time-based or usage-based. For example, a useful life estimate may be tied to the remaining time a machine has left before it fails. This time may be measured in weeks, months or years, and may represent total time remaining or total operational time.
Manufacturers may also choose to measure usage. In the case of a machine that produces components, maintenance teams may estimate the RUL as 10,000 cycles (including defects) or 8,000 components that do not require rework.
Key components of RUL estimations include:
- Condition monitoring: The current condition of the equipment impacts RUL calculations. Condition is typically observed by technicians or maintenance teams on the shop floor. For example, staff may see obvious wear and tear on moving parts or hear grinding that indicates the need for lubrication. More issues mean lower RUL, especially if they are not immediately addressed.
- Sensor data: Connected IIoT sensor data also informs RUL. Common sensor types include temperature, vibration, friction and pressure: Data collected by these sensors is collected by a computerized maintenance management system (CMMS) or similar tool and then analyzed to assess machine performance. If sensors indicate systemic issues, RUL decreases accordingly.
- Historical failure rates: It’s also important for teams to consider historical failure rates in RUL. Consider an asset that has failed once in the last month but has failed 20 times over the past year. While a short-term snapshot may indicate improvement, it may simply be noise without long-term context. Combining historical data with condition and sensor information provides more context for RUL.
- Maintenance records: Preventive maintenance records offer a way to identify recurring issues and find root causes. Addressing these causes can help extend RUL by solving the underlying problem rather than spending time and effort on symptoms. For example, if a machine experiences weekly power failures that are resolved but never solved, its RUL will steadily drop. Maintenance record analysis, meanwhile, may point to an underlying infrastructure issue that’s causing the problem. Solving this issue eliminates the symptom and boosts RUL.
In practice, RUL estimations combine statistical, digital and physical information to create a comprehensive view of remaining asset life.
It’s worth noting that RUL isn’t static. Regularly reviewing asset status and production line conditions is essential to ensure maintenance activity is targeting the top causes of reduced life cycles.
Data sources that support RUL calculations
Data drives RUL calculations, and this data comes from multiple sources. RUL relies on data such as:
- Equipment condition
- Operating context
- Load conditions
- Maintenance history
- Asset usage and duty cycles
To make the most of RUL data sources, manufacturers need technology frameworks capable of collecting, analyzing and applying actionable insight. In practice, this requires a combination of CMMS and manufacturing execution systems (MES) that are connected to ERP software tools and backed by intelligent scheduling systems.
RUL and predictive maintenance
Reactive maintenance uses failure as a starting point. As a result, work begins in tandem with unexpected downtime. Teams must work as quickly as possible to carry out root cause failure analysis (RCFA), create a repair or replacement plan, ensure they have access to the right parts and equipment, carry out the work and bring machines back online. Even with a solid MRO in manufacturing strategy, reactive maintenance is expensive and time-consuming.
Predictive maintenance, meanwhile, uses machine data to schedule regular evaluation and repair, and track key indicators tied to unexpected failures. By intervening before failure occurs, manufacturers can improve scheduling, minimize unnecessary maintenance and reduce the risk of unplanned downtime.
RUL helps enable predictive maintenance analytics by providing a baseline. Measured and compared over time, RUL serves as a warning signal that machine condition may be deteriorating.
For example, Mark’s Manufacturing estimates the RUL of its most critical production line asset at four years, based on environmental, operational and maintenance data. Two months later, the company carries out another RUL estimate. This time, the RUL comes in at three years and six months.
The result prompts action. Why? Because in just two months, the remaining useful life prediction of the asset has decreased by six months. This suggests a change in condition or operation that must be identified and addressed.
<h2>RUL and asset lifecycle management
Asset lifecycle management looks to balance spending and asset performance optimization to get the best of both worlds. In practice, this means identifying the best path forward for an asset after a failure or disruption. Should the equipment be repaired, overhauled or entirely replaced?
RUL offers several benefits for asset lifecycle management, including:
- Determining whether to repair or replace an asset
- Timing capital investments more accurately
- Extending asset life without increasing risk
- Aligning maintenance with long-term asset strategy
- Assessing asset salvage value and depreciation
When combined with machine condition data and maintenance history, RUL helps teams identify the most cost-effective path forward.
Best practices for calculating RUL in manufacturing
Five best practices help companies accurately calculate RUL in manufacturing:
1. Start with critical assets
Critical assets should be prioritized when performing RUL calculations. If these assets fail, they will cause both immediate production shutdowns and performance bottlenecks down the line.
2. Focus on data quality and consistency
Accurate RUL depends on data quality and consistency. Data formats and collection processes should be standardized, and companies should conduct regular evaluations to ensure that collected data matches physical performance.
3. Combine condition monitoring with historical data
As noted above, condition and machine health monitoring is made more effective when combined with historical data. This provides a big picture look at machine performance and the interconnected factors that impact RUL.
4. Integrate RUL insights into maintenance planning
RUL insights should inform maintenance planning. For example, if the RUL of an asset is quickly falling, targeted maintenance tasks may be required to identify underlying causes.
5. Continuously validate and refine models
RUL fluctuates based on workload, usage, environmental conditions and the effectiveness of maintenance strategies. As a result, manufacturers must continuously validate and refine models to ensure accuracy.
RUL turns asset uncertainty into predictive insight
RUL is an essential metric for modern manufacturing and maintenance practices.
Alone, RUL helps companies identify assets that may be nearing the end of their useful life span and make plans for their replacement. In combination with condition, operations and maintenance data, RUL improves predictive maintenance efforts, reduces repair costs and functions as a key capability in data-driven manufacturing.
See how ATS can help your team capture and analyze data that underpins predictive maintenance strategies. Let’s talk.
References
Kuklis, J. (2025, December 8). Predictive maintenance with generative AI: Senseye anticipates when there will be trouble at the factory. Siemens Blog. https://blog.siemens.com/en/2025/12/predictive-maintenance-with-generative-ai-senseye-anticipates-when-there-will-be-trouble-at-the-factory/
