Category: Asset Reliability

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Understanding Machine Health Score and its Importance in Asset Reliability

Understanding Machine Health Score and its Importance in Asset Reliability

Machine health is the underlying propeller for asset reliability in the manufacturing industry. Machines that are maintained in optimal conditions are increasingly available for mission-critical operations. They are also at a lower risk of defective performance or sudden failure. This helps operations and maintenance teams move towards total plant reliability and depend on assets for uninterrupted production.

For decades, this understanding has pushed the preventive maintenance approach in manufacturing setups. Maintenance leaders are keen on monitoring individual machines and performing maintenance activities at predefined intervals. But this expensive approach forces them to carry spare inventory at all times and carry out replacements even before the remaining useful life of the component has been realized. Furthermore, the root cause analysis of declining machine conditions is rarely ever performed and similar problems keep happening repeatedly.

As manufacturing leaders are prioritizing maximum production uptime and plant reliability, they are attempting to figure out a holistic metric that can report overall plant condition. From a cost and ROI standpoint, machine health score is emerging as the metric that can generate real-time business intelligence and guide strategic decision-making. This article will cover what machine health score is and how it’s imperative for managing asset reliability.

What is Machine Reliability?

Machine reliability is the measure of how dependable a machine is in consistently performing its intended functions without unexpected breakdowns or failures. It indicates how trustworthy the machine is in maintaining its operational capabilities over time under normal operating conditions.

What is Machine Health Based on?

Before delving into machine health score as a metric, it’s important to understand what exactly machine health is made up of. Machine health is based on three main factors:

Maintenance requirements: For any equipment, basic maintenance standards and instructions are defined by the original equipment manufacturer (OEM). Furthermore, industry regulators can define equipment maintenance mandates to ensure a safe production environment for shop-floor workers. Machine health takes into consideration these standards and regulations to determine a frame of reference. If a machine is performing as per the pre-defined frame of reference, its maintenance needs can be predicted and planned. Any deviation from the standards can, however, give rise to unique maintenance requirements and indicate diminishing machine health.

Operating conditions: The operating condition of a machine is another important factor in determining its overall health. With cloud-enabled condition monitoring and predictive maintenance solutions, equipment conditions can be tracked in real-time. Advanced data analytics is deployed to capture machine vibrations and report them in the form of detailed frequency spectrum and waterfall diagrams. These in turn can be used to check anomalous behavior and deteriorating machine conditions, even before a physical inspection is due to be performed.
Running history: Finally, the running history of a machine tells a lot about its health. The number of times a machine has broken down since installation, the duration of every breakdown, and underlying cause of machine failure, and the impact on total production throughput are covered in running history. Historic machine performance and availability data need to be recorded and analyzed to determine the useful life of a machine. The useful life can, in turn, help determine the health in which the machine may be operating.
All these factors, when measured collectively, can generate machine health insights. Quantifying these insights results in the generation of machine health scores, which can be used by maintenance and operations teams to plan maintenance events.

What is Machine Health Monitoring?

Machine health monitoring refers to the systematic process of assessing and tracking the operational condition and performance of industrial machinery. It involves the continuous evaluation of factors such as maintenance requirements, operating conditions, and running history to gauge the overall health and reliability of machines. This monitoring is crucial for preemptive maintenance planning, as it enables early detection of potential issues, thereby reducing the risk of unexpected breakdowns and optimizing equipment uptime and performance. Machine health monitoring typically utilizes advanced technologies like predictive maintenance solutions, condition monitoring systems, and digital reliability platforms to generate actionable insights and machine health scores. These scores provide quantifiable metrics that aid maintenance teams in making informed decisions and implementing effective maintenance strategies to ensure optimal operational efficiency and prolonged asset lifespan.

Understanding Machine Health Score

Machine health score is essentially a scoring system used to monitor the overall equipment effectiveness by way of performing regular condition checks. It is reported in the form of an absolute percentage or a percentage range to indicate the machine’s condition or reliability. To arrive at a quantifiable number or score, a series of health checks are performed on the machine. If the machine under investigation is checked 10 times and passes the checks 7 times, then a health score of 70% can be assigned to it.
These checks are sophisticated in nature and capture the machine’s condition, running history, and maintenance requirements. If the pass or fail status of the health check is indeterminate over a large number of checks, then deeper machine investigation is needed. During standard checks, however, the following score ranges can be generated:
Machine Health Score Meaning of score
N/A Either insufficient data exists to determine machine health or the health check isn’t performed to make the machine health score available
0% – 20% Machine is operating at a very high risk of breakdown due to frequently sustained wear-tear and/or high consumption of energy
21% – 40% Machine has a high risk of failure due to heavy wear-tear and high energy consumption
41% – 60% Machine is going through repeated wear, that is diminishing its health and putting it at risk of sudden breakdown
61% – 80% Machine is experiencing intermittent wear and sub-optimal energy consumption, causing a small risk of potential failure
81% – 100% Machine is working in near-optimal condition and is highly reliable. There is a low or no chance of failure due to wear-tear.
Various digital reliability mechanisms can report machine health scores with an error margin of 10-20%. Solutions that deploy predictive maintenance technologies such as edge diagnostics and digital twins are more accurate than others. Regular reporting of machine health scores can empower maintenance teams with invaluable intelligence and allow them to organize informed maintenance events.
In conclusion, the machine health score becomes a much more useful indicator of plant reliability than total production downtime. For both discrete and continuous production environments, maintenance teams can realistically track the availability and reliability of all available assets. Whether machines are grouped together or distributed over vast geographies, a unified dashboard can inform maintenance decision-makers about machine conditions. With a holistic understanding of what machine health is and how it is scored, maintenance professionals can select digital reliability solutions that can help in the accurate calculation and reporting of industrial machine health.
Infinite Uptime’s digital reliability solutions perform advanced monitoring and analysis to report the most accurate machine health score for digitized plants. Plant leaders in industries such as Cement, Steel, Mining, Automotive, Tyre, Chemicals, Paper, FMCG, Pharmaceuticals, Glass, Oil & Gas, etc. have witnessed a sustainable increase in asset availability and performance with our digital reliability platform. Our patented vibration analysis technology and syndicated reliability reports allow maintenance leaders to minimize production downtime and improve industrial machine health in the long term.
Get in touch with our experts or book a demo now to understand how our solutions fit your plant.
FAQs
Machine health encompasses maintenance requirements, operating conditions, and running history, all of which determine the machine’s reliability and performance. It’s critical because optimal machine health minimizes breakdowns, ensures operational safety, and supports continuous production.
A machine health score quantifies the overall equipment condition based on regular health checks. It is typically reported as a percentage, reflecting the machine’s reliability level. Scores range from 0% to 100%, indicating varying degrees of risk of failure or optimal performance.
Predictive maintenance uses IoT and AI technologies to monitor machine conditions in real-time, enabling proactive maintenance actions. By predicting failures before they occur, predictive maintenance helps maintain high machine health scores by addressing issues preemptively.
Machine health scores provide actionable insights for maintenance teams to plan interventions effectively. They prioritize maintenance efforts based on the criticality of each machine, optimize spare parts inventory, and minimize downtime, ultimately improving overall asset reliability.
Digital reliability solutions, such as edge diagnostics and digital twins, enhance the accuracy of machine health assessments. They integrate data from various sensors and systems to generate reliable health scores, empowering maintenance teams with precise information for decision-making.
Machine health scores offer a holistic view of plant reliability by aggregating data from multiple assets. They enable maintenance managers to monitor the health of entire production lines or facilities, facilitating strategic planning to enhance overall operational efficiency and uptime.
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Asset Reliability
Asset Reliability Transformation: The Maintenance Perspective

Asset Reliability Transformation:
The Maintenance Perspective

Persistent volatility in global trade and evolving geo-economic climate have propelled even the more conservative businesses towards digitalization and asset optimization. With better control of production costs and seamless visibility of the supply chain, even the harshest market conditions can be toughed out. As more and more manufacturing leaders come to realize this, asset reliability has taken center stage in Industry 4.0-related technologies and production best practices.
But what does asset reliability mean for operation and maintenance managers? How does the concept translate into viable strategies? In this article, we will discuss asset reliability transformation, how more reliable plants can be built and maintained, and what it would mean for the asset maintenance teams of tomorrow.

What is Asset Reliability?

Reliability as a term often revolves around adherence to defined standards, a set of expectations, and consistent performance. For most industrial assets, reliability means performing within pre-defined operating conditions and delivering expected results. In a production environment, therefore, an increasing focus is shifting toward asset reliability management. Reliable assets:

  • Experience lesser breakdown and component failures
  • Are available whenever they are required in the production process
  • Safer and more sustainable to operate
  • Adhere to regulatory and quality standards
  • Minimize net maintenance costs and effort

Although, throughout Industry 4.0, there has been a gradual buy-in from manufacturing leaders for prioritizing asset reliability, the ‘how’ part of it has been remiss. Operations and maintenance managers understand the need for reliable assets in their production plants, but how to make available assets more reliable is still not well understood.

With asset reliability transformation, the status quo is changing and manufacturing industries are beginning to develop scalable and sustainable strategies to improve asset reliability.

What is asset reliability transformation?

Asset reliability transformation takes into consideration the acquisition, operation, maintenance, and complete useful life of every industrial asset. The entire transformation journey can be mapped in the following steps:

1.Acquisition:
At the very first stage of asset acquisition, it is critical to determine whether the asset is designed and built for reliable performance or not. Furthermore, once acquired, the asset should improve the net plant reliability, integrating with the existing infrastructure and asset ecosystem. These prerequisites can be featured in every project plan along with standard regulation and acceptance tests that are performed at the time of new asset acquisition.

2.Discipline:
Once an asset has been installed and started functioning, the operation teams start focusing on asset control. This includes defining the workflows, planning, and scheduling asset utilization, determination of precision work conditions, and adopting CMMS (Computerized Maintenance Management Systems). Factoring in the standard wear of components, spares management must also be an important driver to ensure continued asset reliability.

3.Care:
Meticulous asset care directly contributes towards improved asset reliability. From adoption of standard operating procedures to a strategic maintenance approach; asset care includes cleaning, lubrication management, equipment calibration, and maintenance management, spares inventory, and operator care. Moving away from reactive and preventive maintenance to adopt more advanced prescriptive and predictive maintenance models can lay a foundation for more available and reliable assets.

4.Analytics:
For effective analytics to happen and provide business intelligence, processes and tools need to be in place for capturing relevant information. Key metrics to monitor and measure performance needs to be identified and regularly tracked. With AI and IoT-enabled solutions, predictive analytics can be used to diagnose hidden failures, minimize the risk of asset breakdown, and drive reliability objectives.

5.Optimization:
Asset optimization requires constant monitoring of machine health while assessing risks, challenges, and opportunities for driving reliability objectives. OEE (Overall Equipment Effectiveness) forms the premise for ensuring asset reliability, which can strategically build toward total plant reliability.

6.End Of Life (EOL):
Ultimately, End of Life management for all plant assets is also essential for maintaining sustainable production practices and pursuing reliability in a responsible manner. Before spares are installed or assets are replaced completely, performing root cause analysis for failure and capturing breakdown circumstances is critical. Information captured at this stage should serve as insights for managing new assets. Disposal standards for assets must also conform to regulatory norms.

The maintenance perspective in asset reliability transformation

Among these six steps of the asset reliability transformation journey; the maintenance perspective is clearly captured in the ‘discipline’ and ‘care’ of assets. Assets that are cared for and maintained in optimal working conditions are less likely to fail or break down. At the same time, digitalized monitoring mechanisms allow for safer and more effective maintenance strategies.

Advanced predictive maintenance and digital reliability solutions can empower plant operation teams to build a connected enterprise that has a mine of asset intelligence. With the right information accessible and analyzed for generating meaningful insights, maintenance teams can lead the reliability transformation wave.

  • Visibility of all assets can be optimized with cloud and IoT-enabled technologies, and can capture asset data 24×7
  • Asset performance, condition, and need for intervention can be monitored in real-time with minimal human intervention
  • Plant-wide data can be predictively analyzed to plan and schedule maintenance events
  • Asset cleaning, lubrication, and maintenance can be strategically planned for minimal disruption in production schedules
  • Spares management can be streamlined and optimized by realizing the complete remaining life of assets and avoiding preventive part replacements
  • Key metrics such as MTTR (Mean Time To Repair), and Mean Time Between Failure (MTBF) can guide maintenance planning, making equipment more reliable and available
  • Root cause failure analysis and predictive analytics can provide helpful insights to guide asset acquisition and management
In sum, while asset reliability is a larger goal driving manufacturing leaders to look beyond asset management, it is rooted in asset maintenance and optimization through smart technologies. Acquiring assets that are built for reliable performance, caring for them, and optimizing their performance with intelligent interventions can drive reliability transformation. And predictive maintenance remains at the heart of it all.

Infinite Uptime’s digital reliability solutions are tailored to assist plant reliability teams in undergoing an effective asset reliability transformation. IoT-enabled asset health monitoring and predictive analytics are shared with plant leaders in industries such as Cement, Steel, Mining, Automotive, Tyre, Chemicals, Paper, FMCG, Pharmaceuticals, Glass, Oil & Gas, etc. Our patented vibration analysis technology and syndicated reliability reports allow maintenance leaders to maximize their plant reliability and minimize production downtime.

Get in touch with our experts or book a demo now to understand how our solutions fit your plant.

FAQs
Asset reliability refers to the consistent performance of industrial assets within defined operational parameters, ensuring minimal breakdowns, optimal availability, and adherence to safety and quality standards. For maintenance managers, it entails implementing strategies to enhance asset lifespan and efficiency through proactive maintenance practices.
Asset reliability transformation involves adopting comprehensive strategies throughout an asset’s lifecycle—from acquisition to end-of-life management—to maximize reliability and minimize downtime. It is crucial for improving operational efficiency, reducing maintenance costs, and achieving sustainable production practices in industries undergoing digital transformation.
Predictive maintenance utilizes IoT and AI technologies to monitor asset conditions in real-time, predict potential failures, and schedule maintenance proactively. By moving from reactive or preventive maintenance to predictive models, maintenance teams can optimize asset performance, extend lifespan, and enhance overall reliability.
IoT enables continuous monitoring of asset health, while AI analyzes data to generate actionable insights for maintenance planning. These technologies streamline maintenance processes, improve decision-making accuracy, and minimize human intervention, thereby optimizing asset reliability across manufacturing operations.
Asset reliability transformation involves stages such as acquisition (selecting reliable assets), discipline (establishing operational workflows), care (implementing maintenance strategies), analytics (utilizing data for insights), optimization (monitoring and improving asset performance), and end-of-life management (ensuring responsible asset disposal). These steps collectively ensure sustained reliability and operational efficiency.
Advanced analytics enable maintenance teams to monitor key metrics like MTTR and MTBF, perform root cause analysis for failures, and optimize maintenance schedules based on predictive insights. By leveraging these analytics, teams can proactively manage asset health, minimize downtime, and drive continuous improvement in reliability.
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Asset Reliability
Decoding plant reliability in manufacturing and a process to reach there.

Decoding plant reliability in manufacturing and a process to reach there

Process manufacturers typically operate in data-rich environments and know their plants inside out. While they know their assets and how their resources are deployed, they are often unaware of factors contributing to optimal asset performance. Even if this information exists within the manufacturing ecosystem, the plant maintenance and operational heads don’t really know how to use it to achieve optimum plant productivity. 

Studies reveal that frequent downtimes at process manufacturing plants can result in nearly $15000 per hour of lost revenue. Digitalization of the maintenance process and proactive asset performance management directly contribute to saving this cost. Prioritizing plant reliability becomes the only way to improve overall operations and mitigate unplanned downtimes. 

But what is plant reliability, and what processes need to be institutionalized to achieve it? In this article, we will discuss what reliability means for manufacturing and lay out a six-step process to devise a plant reliability strategy for a manufacturing plant effectively. 

What is Plant Reliability, and how can you measure it?

Any reliable system accounts for its safety and trustworthiness while ensuring minimal maintenance costs. For plant assets, reliability can depend on performance, condition, maintenance needs, and availability. An asset reliability check can be done based on factors like frequency of maintenance or repair costs, number of malfunctions, unexpected downtimes, and more.

Keeping plant’s maintenance up to the mark ensures that assets run 24/7 with fewer interruptions or unexpected delays due to frequent maintenance incidents. This leads to faster go-to-market, better output quality, employee productivity, and significantly lower operational costs or costs per unit.
Plant reliability in production can be quantitatively measured using the Overall Equipment Effectiveness (OEE). A popular metric for measuring manufacturing productivity, OEE factors in a product of availability (number of downtimes/uptime), performance (speed or run time of your processes), and quality (number of defects).
An OEE score of 100% indicates a completely reliable, dependable, and high-quality plant with maximum productivity. Therefore, calculating OEE and securing a top score should be part of your asset management’s best practices.

Six steps to creating an effective reliability plan at your plant

Every successful plan consists of a set of clear and executable steps. And the same principle applies to achieving top-notch plant reliability as well. Without a clear, planned route, it can be hard for you to envision your end goal – optimum plant and asset reliability management. Here are the six actionable steps that are essential to executing your plan successfully:

1. Building the right team

The right team can make or break reliability goals- from top to bottom.

Effective leadership, skilled personnel, and onsite-plant operations team must be aligned with accomplishing plant reliability goals.

Achieving reliability is a team effort and a continuous improvement process. Designated team champions have to be distributed within Operations, Maintenance, and Engineering along with sufficient alignment around their common goals & individual targets. This way, every individual is well aware of their role in constantly improving the plant and understands their dependencies on the other teams. There has to be also a Reliability Leader who helps drive this initiative

2. Creating the right mindset for reliability

For a successful plan, having the right mindset for asset reliability is as important as relevant skills, processes & technical understanding.

Since achieving reliability requires continuous effort, you can try to define your target numerically & align every department’s target accordingly. This target & its deadline needs to be agreed upon by each department-operations, maintenance, engineering, and the subsequent KPIs that befall them individually.

It is critical that all teams uphold this goal as their guiding principle and implement it through individual responsibilities every day.

 3. Adapting Predictive Maintenance (PdM) approach

Plant reliability is also heavily dependent on asset health & reliability. The approach towards asset reliability is centered around the plant maintenance methodology chosen.

An advanced framework like predictive maintenance alongside numerous assets and operations can speed up the process of obtaining plant reliability. By proactively anticipating flaws or anomalies within the plant and addressing them, reliability objectives can be progressively achieved.

And when you proactively work towards fixing them, you can see your maintenance costs and the dreaded plant downtimes plummet instantly. Also, by understanding what caused these failures, your teams can work towards optimizing their maintenance strategy in the future.

 4. Having a best practices checklist for assured equipment reliability

It is not enough to be proactive at one time; it has to become a process for excellence in reliability. For this, rigorous observation of what worked needs to be executed..

Best practices for different plant segments and units can be documented for standardized records and accessibility. Mainly for equipment reliability which requires defined steps, having a list of executables becomes highly essential. These practices are initially set for a particular type of machine in a plant -like a gearbox which can be applied for gearboxes across multiple plants for the same organization to benefit from learnings.

This can help your team focus on improving reliability at the plant, bit by bit, and avoid recurring mistakes alongside employing PdM.

 5. Prioritizing critical assets first

Your plant might have critical equipment that causes the most impact – both financially and operationally when down, like a kiln in a cement plant.

So to reduce this unpredictable impact, you can prioritize these assets and implement a model like Predictive Maintenance to fix issues on priority beforehand. Predictive Maintenance can also improve equipment reliability as it works toward assuring asset performance and health around-the-clock through continuous evaluation.

 6. Assessing your Reliability plan’s progress

Using your goals & best practices checklist, perform regular audits to check for any shortcomings. Organize audits as frequently as monthly or quarterly, based on your process durations. Check if your charted reliability program progress is aligned with your monthly/quarterly goals.

Continuous improvement requires continuous learning too. Make a detailed implementation plan with clear-cut steps for each task for every department involved and skill improvement and tool usage training at periodic intervals. Skills like vibration monitoring.

This practice keeps your plant and teams’ performance in constant check. Also, assessing highlights hidden improvement areas that may be hindering your plant’s reliability.

 Conclusion:

A well-articulated plant reliability plan and set targets can be the driving force towards rapidly fulfilling your reliability goals. Since consistency is key to realizing this goal, it requires a combined proactive effort from leadership, stakeholders, and staff. 

At Infinite Uptime, we provide cutting-edge solutions to implement predictive maintenance programs, seamlessly improving your plant’s reliability. With plant reliability, we help manufacturers across the globe see faster results by significantly improving plant efficiency, fewer downtimes, and better quality using predictive maintenance.

Want to achieve faster plant reliability?

Get in touch with us today to schedule a free demo!

FAQs
Plant reliability in manufacturing refers to the ability of production assets to consistently perform their intended function without unplanned downtime or failure. It is crucial because it directly impacts productivity, quality, and operational costs by ensuring continuous operation and minimizing disruptions.
Plant reliability can be measured using metrics like Overall Equipment Effectiveness (OEE). OEE factors in availability, performance efficiency, and quality to provide a comprehensive view of how well assets are performing. A higher OEE score indicates better reliability and operational efficiency.

Developing a plant reliability strategy involves:

  • Building the right team with aligned goals and responsibilities.
  • Creating a reliability-focused mindset across all departments.
  • Adopting Predictive Maintenance (PdM) to proactively manage asset health.
  • Implementing best practices for equipment reliability.
  • Prioritizing critical assets for maintenance interventions.
  • Regularly assessing and auditing the reliability plan’s progress to identify areas for improvement.
Predictive Maintenance uses data analytics and IoT to predict equipment failures before they occur. By proactively addressing issues, PdM reduces unplanned downtime, lowers maintenance costs, and enhances overall asset performance and reliability in manufacturing plants.
A strong focus on plant reliability improves manufacturing operations by ensuring continuous production, maintaining consistent product quality, enhancing worker productivity, and reducing operational costs associated with maintenance and downtime.
Implementing a robust plant reliability strategy leads to improved operational efficiency, increased equipment lifespan, reduced maintenance costs, enhanced safety, and better overall profitability for manufacturing plants. It also supports long-term sustainability and competitiveness in the market.
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Asset Reliability
Understanding Asset Optimization in Manufacturing
Asset Optimization By Infinite Uptime

Understanding Asset Optimization in Manufacturing

Asset Optimization By Infinite Uptime

Last updated on January 15, 2026

Mission-critical assets in manufacturing setups can make or break an entire value chain. An unmitigated asset breakdown or productivity decline can halt production for hours, or even days, resulting in huge revenue losses and unsafe work environments. The situation becomes even more complex for industries with distributed assets.To maintain assets in optimal condition and run the production process without disruptions, dedicated maintenance teams have to be deployed in various locations. Furthermore, investments are required in carrying spare-parts inventory and establishing strategic service contracts with Original Equipment Manufacturers (OEMs). While conventionally, these practices have been considered inevitable, a marked shift is happening towards predictive analytics and responsive maintenance solutions that can optimize asset performance.

This article explains what asset optimization means in a manufacturing environment, why it is critical for U.S. industrial operations, and how predictive maintenance solutions help maintenance and operations teams improve reliability, reduce downtime, and maximize asset performance.

What is Asset Optimization?

Optimization essentially means making something as effective, functional, reliable, and productive as possible. Asset optimization means optimizing the way that an asset is utilized and deriving maximum value from it. It also entails driving efficiency and reliability objectives by improving the Remaining Useful Life (RUL) of an asset and enhancing the Overall Equipment Effectiveness (OEE).

Asset Optimization depends on leveraging data-driven intelligence and predictive analytics to achieve business objectives and add to the bottom line. IoT-enabled technologies can be deployed to monitor asset conditions and analyze real-time data to determine maintenance needs.

Benefits of Asset Optimization

Optimal asset performance and availability have a dramatic effect on the overall productivity and throughput of a production plant. When assets are operating in optimal conditions, the following benefits are derived in discreet and process manufacturing industries:

Key benefits of asset optimization include:

  • Reduced unplanned downtime through condition-based maintenance

  • Lower maintenance and spare-parts costs

  • Improved asset performance and higher Overall Equipment Effectiveness (OEE)

  • Increased equipment reliability and operational stability

  • Extended asset lifespan by preventing premature wear and failure

List of asset optimization benefits including reduced maintenance costs, lower downtime, improved performance, greater reliability, and longer asset lifespan.
In addition to these, safer and accident-free production environments can be created, with reduced risk of catastrophic events that could cost life and property. An indirect, yet palpable effect is also observed on revenue, margins, customer satisfaction, Return On Assets (ROA), and Work-In-Progress (WIP) inventory.

Challenges in Asset Optimization

Despite the incredible benefits that asset optimization offers, it is quite challenging to manage asset performance towards optimization. Major roadblocks in asset optimization are:

  • Maintenance frequency: When manufacturing plants adopt breakdown maintenance (till failure) or scheduled (preventive) maintenance strategies, asset conditions often remain less than optimal. Either maintenance is performed when an asset breaks down or is performed periodically, irrespective of what the asset condition is. In both scenarios, it is impossible to extract the maximum use of an asset.
  • Lack of data: Real-time information about asset conditions is rarely available, especially if manufacturing plants rely on offline asset inspections. Even when regular equipment inspections are performed manually, gaps remain in the data and many alarming signs about deteriorating equipment conditions may go unnoticed.
  • Costly unplanned maintenance: For industries with distributed assets, unplanned maintenance in the event of machine breakdown proves to be very costly. A larger maintenance team needs to be maintained to cover the geographic distribution of assets. Spare parts and sub-assemblies need to be sourced at higher prices to fulfill urgent requirements. Not to mention, on-floor conditions are highly unsafe and hazardous for maintenance workers.
  • Poor flow of information: Offline machine inspections and decentralized maintenance events create silos of information within the manufacturing organization. Critical information about asset conditions is not shared in real-time with all concerned stakeholders, and maintenance teams operate independently as per their capabilities.
  • Ineffective utilization of resources: Both human and physical resources are utilized with limited visibility of the machine health and asset availability. Thus, maintenance activities are organized even when they are not needed and machine parts are replaced before their useful life is over.
Not only does it make the total cost of assets and maintenance higher, but it also creates a system acceptance of inefficient asset management practices. Planned downtimes become the norm and plant teams become resistant to change. Without a decided shift in the approach for asset performance management, asset optimization can be very difficult to achieve.

What is KPI in indusrtial asset Optimization?

evaluate how effectively physical assets are performing, maintained, and utilized to achieve maximum productivity, reliability, and cost efficiency. In manufacturing and process industries, KPIs help plant leaders and maintenance teams assess whether assets are operating at optimal conditions while supporting business goals such as higher throughput, lower downtime, and improved safety.

Common KPIs used in industrial asset optimization include:

  • Overall Equipment Effectiveness (OEE) – measures availability, performance, and quality of assets

  • Asset Availability – percentage of time equipment is ready for production

  • Mean Time Between Failures (MTBF) – evaluates asset reliability

  • Mean Time to Repair (MTTR) – measures maintenance efficiency

  • Unplanned Downtime – tracks unexpected asset failures

  • Maintenance Cost per Unit or Asset – assesses cost efficiency

  • Remaining Useful Life (RUL) – predicts how long an asset can operate reliably

By tracking these KPIs, industrial organizations can identify performance gaps, prioritize maintenance actions, and apply predictive and prescriptive strategies to keep assets running at peak efficiency. This data-driven approach enables better utilization of existing assets, reduces operational risk, and improves overall Return on Assets (ROA) without additional capital investment.

Asset Optimization through Predictive Maintenance

Predictive Maintenance solutions can help plant maintenance teams overcome the various challenges in asset performance management and ensure asset optimization across the plant. With a predictive approach, maintenance teams monitor asset conditions remotely with the help of cloud-enabled technologies. Vibration analysis, acoustics, thermography, oil analysis, and other remote condition monitoring techniques are deployed to track asset conditions while they operate as per schedule.

The machine health data is centrally collected and analyzed with the help of Industrial IoT (IIoT) technologies and accessible through responsive dashboards to concerned stakeholders. Since maintenance has to be strategized based on predictive insights, edge diagnostics, and advanced analytics are used to determine which asset is performing non-optimally and in need of attention. Such a focused approach to asset performance management has several benefits:

List of asset optimization goals including condition-based maintenance frequency, data-backed maintenance planning, improved machine health visibility, predictive fault notifications, reduced maintenance cost, real-time information flow, and extended remaining useful life.

Example of Industrial Asset Optimization in Practice

Consider a cement manufacturing plant operating multiple rotary kilns, ID fans, gearboxes, and conveyors—all mission-critical assets running 24/7.

Before Asset Optimization

  • Maintenance is largely preventive or breakdown-based

  • Kiln ID fan bearings fail unexpectedly every few months

  • Each failure causes 8–12 hours of unplanned downtime

  • Emergency spares are air-freighted at high cost

  • Energy consumption gradually increases, but the root cause is unclear

  • Maintenance teams react after problems occur, increasing safety risk

Despite regular maintenance, assets rarely operate at peak efficiency.

 

After Asset Optimization Using Predictive & Prescriptive Insights

The plant deploys condition monitoring and predictive analytics across critical assets.

  • Vibration and energy data reveal early bearing degradation in the ID fan weeks before failure

  • Predictive analytics estimate Remaining Useful Life (RUL) of the bearing

  • Maintenance is scheduled during a planned shutdown, avoiding production loss

  • Prescriptive insights recommend alignment correction to prevent repeat failures

  • Energy consumption drops as the fan returns to optimal operating condition

  • Spare parts inventory is optimized based on actual asset health

Measurable Outcomes

  • Unplanned downtime reduced by 30–40%

  • Maintenance costs lowered due to fewer emergency repairs

  • Asset lifespan extended by preventing secondary damage

  • OEE improves through higher availability and stable performance

  • Energy per ton of cement reduced, improving margins

  • Safer working conditions with fewer emergency interventions

A Practical Asset Optimization Checklist for Manufacturing

Asset optimization is not about fixing machines faster—it’s about ensuring assets operate reliably, efficiently, and predictably throughout their lifecycle. Below is a concise checklist that industrial teams can apply to drive real, measurable results.
1. Focus on Critical Assets
Identify equipment that has the highest impact on production, safety, energy use, and revenue. Start optimization where failures hurt the most.
2. Measure What Matters
Track core KPIs such as OEE, availability, MTBF, MTTR, unplanned downtime, and energy consumption to establish a clear performance baseline.
3. Monitor Asset Health Continuously
Track core KPIs such as OEE, availability, MTBF, MTTR, unplanned downtime, and energy consumption to establish a clear performance baseline.
4. Shift to Predictive Maintenance
Anticipate failures instead of reacting to them. Predictive maintenance allows issues to be addressed during planned stoppages—reducing downtime, cost, and risk.
5. Plan Maintenance Using Data
Base maintenance decisions on actual asset condition, not fixed schedules. Replace parts only when needed and optimize spare inventory.
6. Review and Improve Regularly
Conduct periodic reviews to validate results, refine strategies, and align maintenance actions with reliability and business goals.

Asset optimization succeeds when data drives decisions, failures are predicted early, and maintenance becomes proactive. Plants that follow this disciplined approach achieve higher reliability, lower costs, and better return on assets.

Conclusion

In sum, asset optimization ensures that all available assets are utilized optimally in a manufacturing environment. By tracking asset conditions in real-time and performing predictive analytics, maintenance activities can be scheduled to optimize asset performance. Improved flow of information within the manufacturing organization and data-backed planning of asset maintenance can improve net return on assets (ROA) and overall plant productivity.

In addition, Plant Energy Optimization helps manufacturers reduce energy waste and operating costs, while Prescriptive Maintenance provides clear, actionable recommendations to prevent failures before they happen—making asset performance even more reliable and efficient.

Infinite Uptime offers responsively designed predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in optimizing asset performance, explore the plant reliability solutions of Infinite Uptime.

FAQs

Asset optimization is the process of maximizing the performance, reliability, and useful life of equipment in an industrial plant by using real-time data, analytics, and targeted maintenance. It ensures that every asset operates at peak efficiency with minimal downtime. By improving visibility into machine health, asset optimization reduces operating costs, increases throughput, and supports safer, more reliable plant operations.

Asset optimization in manufacturing involves maximizing the efficiency, reliability, and productivity of critical assets to enhance overall operational performance and profitability.

Optimal asset performance reduces downtime, improves throughput, ensures safer working environments, and enhances profitability by maximizing asset lifespan and efficiency.

Challenges include ineffective maintenance strategies, lack of real-time data, costly unplanned maintenance, poor information flow among teams, and inefficient resource utilization.

Predictive maintenance uses IoT and advanced analytics to monitor asset conditions in real-time, enabling proactive maintenance planning and optimization of asset performance across manufacturing plants.

Asset optimization improves Return on Assets (ROA) by increasing asset availability, utilization, and lifespan while simultaneously reducing maintenance and operating costs. When assets are optimized, manufacturers produce more output from the same asset base without proportional increases in capital expenditure.