What is asset performance management (APM)?

Top-performing APM solutions combine software and asset management best practices to increase the performance and reliability of everything from a smart office building to a fleet of transport vehicles.

Using advanced capabilities such as predictive maintenance, AI-enhanced analytics and remote monitoring, APM solutions reduce equipment failures, improve asset reliability and extend asset lifespans while avoiding extra operational costs for organizations.

An asset is any piece of property that is (or can be) useful or valuable to an organization. The term can include both physical and non-physical assets, such as infrastructure and equipment, capital resources and intellectual property (IP).

Although talented employees contribute significant value to an organization, personnel are not assets in the technical sense. Human beings cannot be owned or controlled in the same way a business might operate a warehouse or hold voting shares in an investment property.

In essence, assets are valuable property used for business purposes. Generally, assets are divided into four major categories:

1. Fixed assets: Fixed assets are foundational and stationary assets intended as long-term critical infrastructure. Fixed assets might include buildings, such as offices, manufacturing plants, factory facilities or data centers, along with the main service machinery or components found within. Asset criticality analysis reports consistently rank fixed assets among the most important assets for business performance. Industrial asset management strategies prioritize maintaining fixed asset reliability because their failure can result in costly unplanned downtime. As part of regular equipment failure prevention, organizations will often use vibration sensors and other networked indicators to detect early signs of wear and prevent breakdowns and failures.
2. Mobile assets: As the name implies, mobile assets are property that moves around during daily operations. Mobile assets might include shipping vehicles, forklifts or other types of portable machinery. Because mobile assets do not have fixed locations by definition, organizations operating these types of assets benefit from Internet of Things (IoT)-enabled real-time asset monitoring, which tracks live location, operational and usage data. This data feeds predictive maintenance programs that help operators schedule service before a breakdown occurs in the field.
   
3. Digital or virtual assets: Assets such as software, data and IP continue to swell in value in the modern era. This trend includes the virtualized models of heavy machinery or physical systems known as digital twins, which are mission-critical for critical operations. Using advanced smart sensors, APM programs can simulate the live conditions of physical hardware or equipment, offering greater insight into machine or system performance than a typical visual inspection of the hardware. Through predictive analytics, digital twins can help evaluate machinery performance, predict and plan required maintenance and run simulated tests to assess the potential impact of planned workloads. Although digital twins, such as other virtual assets, do not experience physical wear and tear in a mechanical sense, they still require careful management to protect their integrity, security and availability.                                         
4. Production assets: Identified as the most critical components of an organization’s output, production assets are the equipment and systems directly used for manufacturing goods or delivering service. Assets used in this way are considered production assets whether they are fixed or mobile, virtual or physical. Production assets might include assembly lines, industrial robotics and operational oversight programs such as supervisory control and data acquisition (SCADA) software and systems. Because these types of assets are directly tied to an organization’s ability to operate and produce revenue, ensuring production asset operation is a top priority for any enterprise. APM strategies rely on predictive analytics and condition-based maintenance methodologies to maximize production asset uptime and improve operational efficiency. 
   

Across industries, organizations use asset performance management (APM) as part of their overall asset strategy to reduce asset failures, decrease unplanned downtime and minimize maintenance costs. Modern APM solutions also generally improve compliance capabilities, equipment diagnostics capabilities, asset connectivity and maintenance ecosystems.

Broadly, the benefits of effective APM include the following capabilities:

• Less unplanned maintenance: When organizations strategically monitor the health and performance of their most valuable assets, they reduce operational costs and the likelihood of breakdowns that might require unplanned maintenance. By deploying preventive and predictive maintenance tactics, they can improve their forecasting capabilities and schedule repairs when it suits them—not after an unexpected failure.
• Reduced maintenance costs: Maintenance tasks are essential to preserving the health and productivity of an organization’s assets. However, the costs of different tasks vary greatly and can impact the organization’s profitability. Improvements in decision‑making capabilities generated by a strong APM approach help the organizations avoid excessive and unnecessary maintenance.
• Improvements in asset uptime: Using artificial intelligence (AI) and machine learning (ML) capabilities to deploy predictive and preventive maintenance, modern APM solutions deliver improvements in asset availability and asset condition. Operators assisted by these new technologies are better able to recommend repair strategies that help reduce costly downtimes for assets the company relies on.
• Greater operational efficiency: Many asset-rich organizations today use Internet of Things (IoT) capabilities and artificial intelligence (AI) as part of an overall APM strategy to help them spot opportunities for greater efficiency. In the past, business leaders had to wait until the end of the month or quarter to review performance numbers. Today, they can get the same data in real time. With faster reporting, management and maintenance teams can respond to potential issues sooner, while advanced analytics inform better long-term planning.    
• Improved regulatory compliance: APM can strengthen the regulatory compliance capabilities of any organization by providing robust documentation throughout the entire manufacturing or production timeline. In addition to informing early-warning systems that predict necessary repairs, APM solutions generate continuous, auditable records of asset condition and maintenance activity. This data helps organizations demonstrate the due diligence required during official safety audits. By adhering to regulations, APM helps businesses do more than avoid costly fines and penalties; they can also protect an organization’s good standing and reputation.  

Enabled by a range of modern hardware and software solutions, asset performance management (APM) programs use a layered stack of technologies to collect, analyze and respond to asset data.

These APM capabilities are the key components of the APM technology stack.

Internet of Things (IoT) technology allows organizations to create connected networks of smart sensors for real-time asset monitoring. 

Vibration sensors and condition monitoring tools track equipment endurance and physical stress to flag early signs of wear. Computer vision‑enabled visual inspection technologies use video and photography to identify defects that human inspectors might overlook.

SCADA systems collate data from sensors across the entire production facility.

When the data is collected, SCADA solutions provide centralized visibility and control for equipment operators and management and serve as a primary data source for APM software.

APM technologies use artificial intelligence (AI) and machine learning to detect performance anomalies, enabling organizations to predict and prevent unexpected equipment failures and downtime.

Predictive analytics support condition-based and prescriptive maintenance, often incorporating generative AI tools to create high-level summaries to quickly draft reports and work orders on-demand. 

At the level of the APM stack, automation and management tools such as enterprise asset management (EAM) solutions and computerized maintenance management systems (CMMS) ingest and centralize information from the lower layers.

Depending on operational requirements, EAM and CMMS solutions will also automate recommended maintenance tasks and incorporate digital twin virtualized models for simulating asset performance and predicting operational outcomes.

Today’s top-performing asset performance management (APM) solutions use software, tools and a data-driven approach to help organizations achieve their digital transformation initiatives. APM software solutions help business leaders construct a unified strategy, increase their risk management capabilities and deliver operational excellence.

Here are three steps to building an effective APM program inside your organization.

Effective APM begins in the first stage of the asset lifecycle: planning. Before an asset is even acquired, decision-makers need to think about how operating and maintaining it fits into their broader operations. To do this assessment, they need to assess the asset’s value.

Asset valuation varies greatly between organizations and industries. For example, in a food delivery business, the health and performance of vehicles used to transport goods is going to be critical. However, for a software company, even if it owns a fleet of vehicles used occasionally, the health and performance of those vehicles are not critical to its daily business operations. Therefore, these two organizations would value the same asset differently.

When you’re starting to form your APM strategy, choose assets that are critical to business operations first. You can address the condition of your lower priority assets later.

One technique that has become increasingly valuable in APM is the creation of a digital twin. A digital twin is a virtual representation of an asset that allows operators to run tests and predict performance based on simulations. With a good digital twin, decision-makers can know how well an asset is likely to perform under the conditions they plan to subject it to.

Digital twins help operators and maintenance leaders spot performance issues and gain insights into possible improvements to maintenance plans.

When monitoring asset performance and the automation of critical asset workflows, APM software, such as an enterprise asset management (EAM) system, is crucial. EAM combines software and services to help organizations maintain, control and optimize their operational assets.

With the amount of data being generated these days through the Internet of Things (IoT), maintenance managers increasingly rely on management software such as EAM equipped with AI‑enhanced data analysis. This software helps them make smarter decisions.

In addition to EAM, many APM initiatives deploy 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 make critical information about an asset immediately accessible and auditable to its operators.

Modern APM approaches rely on preventive maintenance and predictive maintenance.

Preventive maintenance uses regularly scheduled maintenance activities to reduce the chances of assets breaking down. Downtime is planned by using best practices and historical averages, such as mean time between failure (MTBF).

Predictive maintenance continuously assesses and reassesses an asset’s condition by using sensors that collect data about the asset in real time. AI‑enabled EAM or CMMS systems ingest this data and advanced analysis tools and processes identify, detect and address issues.

Based on this data and the subsequent analysis, algorithms build models that predict when future potential problems with a piece of equipment might arise. Predictive maintenance has been proven to lower maintenance costs, decrease asset downtime by 35–50% and increase asset lifespan by 20–40%.

To determine whether your asset performance management (APM) strategy is working, it’s critical to select the right success metrics.

Key performance indicators (KPIs) let you know whether a strategic approach is successful and help business leaders understand the impact of their decisions. Choosing the wrong metric can give an inaccurate picture of an asset’s performance and cause companies to take strategic decisions that might be harmful over time.

Many factors influence how performance metrics are identified, including the industry your organization operates in, its size, the kinds of assets it owns and its overall business priorities. Here are a few proven methods companies use to measure the success of their APM initiatives.

Perhaps the most widely used metric for evaluating the success of any maintenance program is the mean time between failure (MTBF) of the assets it has been made responsible for repairing.

MTBF is a simple formula that calculates the average amount of time between needed repairs on a piece of equipment. It does this calculation by dividing the total time of operation by the number of failures during that period. A maintenance department whose assets have a high MTBF is assessed as “strong.”

The MTTR represents the average time that it takes from a breakdown or maintenance-required work stoppage until all requisite maintenance is performed and a piece of machinery or production has been restored. MTTR factors in the time it takes to diagnose, repair and return equipment to normal function. A shorter MTTR means faster recovery and less unplanned downtime. 

Overall equipment effectiveness (OEE) is determined by multiplying three metrics:

• Availability is determined by using the actual asset production time compared to its planned production time.
• Performance measures how well the equipment performs compared to its maximum potential.
• Quality evaluates the rate of production of products deemed “good count” compared to those defective units or items that require reworking.

Planned maintenance percentage (PMP) measures the amount of planned maintenance performed as compared to the total amount of maintenance performed. PMP calculates planned maintenance hours as a share of total maintenance time, combining both planned and unplanned work.

A higher PMP indicates a higher percentage of planned maintenance, a positive indicator that more resources are being applied to preventive and predictive maintenance as opposed to responding to unexpected breakdowns. Conversely, a low PMP would indicate a more reactive and likely more disruptive and expensive, approach to maintenance. A PMP of 80% or higher is recognized as the benchmark for a successful APM program.

Two complementary metrics for evaluating the success of an APM program include how quickly and effectively maintenance technicians close their assigned work order tickets. The second measures the overall percentage of completed compared with incomplete work.

Typically associated with long-term maintenance strategies, the frequency of emergency maintenance tasks metric provides insights into the effectiveness of a preventive maintenance program. The better a maintenance department is at performing preventive maintenance, the rarer breakdowns requiring emergency intervention should be.

While there are many key metrics for measuring the performance of an asset performance management (APM) strategy, return on investment (ROI) provides a view of the overall value of the entire program. ROI expresses that value in financial terms that can influence broader business operations. By calculating the ROI of an APM, decision-makers across an organization can justify APM-related expenses or optimize their asset strategy over time. 

The first step in calculating the ROI of an APM program is to establish a baseline of the total cost of maintenance fees. You also need to determine the frequency of machine breakdowns and the amount of unplanned downtime an organization experiences before or without an APM solution. This baseline provides the reference point needed to quantify the financial impact of implementing APM.

After establishing this baseline, an APM program is evaluated on various cost-saving and revenue-preserving categories, including the following metrics:

• Reduced maintenance costs
• Recovered production capacity
• Extended asset lifespans
• Reduced regulatory fees
  

While savings from reduced maintenance and recovered production can be realized quickly, long-term value derived from machinery optimization and risk mitigation takes longer to materialize. For this reason, it’s important to track APM savings over more than one budgetary cycle after establishing a robust baseline.

With enough time and data, an organization can calculate its ROI by subtracting the total cost of an APM program from the total financial value it generates. It then divides that result by the total cost to express the value as a percentage.

While specific use cases vary across a wide range of industrial applications, asset performance management (APM) solutions offer several valuable benefits to many different types of operations:

• Manufacturing: Manufacturing operations depend on the continuous and synchronized performance of interdependent machinery to produce a wide variety of goods and components. Relying on complex systems and machinery, such as assembly lines and industrial robots, a single point of failure can easily result in significant bottlenecks and downtime. Using condition‑monitoring tools such as vibration sensors and motor current signature analysis (MCSA)—a process that passively tracks and evaluates electricity flow through machinery—APM programs detect early signs of mechanical failure. They give maintenance teams more time for repairs or prophylactic improvements to prevent costly outages.
• Energy and utilities: From turbines to transformers, energy and utility companies rely on APM tools to monitor infrastructure across large, dispersed service areas. These tools include dedicated transformer‑monitoring platforms such as DNV Cascade and grid‑scale software such as GE Vernova Monitoring and Diagnostics. Because energy disruptions can lead to dangerous emergencies and steep regulatory consequences, APM programs are an essential component for ensuring resource readiness and delivery. 
• Transportation and logistics: For transportation and logistics companies, mobile assets such as fleet vehicles are critical to daily operations. APM systems provide IoT connectivity and inform predictive maintenance, allowing stakeholders to track variables such as real-time vehicle conditions and schedule service before equipment breakdown can cause unplanned shortages or bottlenecks. 
• Healthcare: Healthcare organizations apply APM strategies, such as condition monitoring and asset health monitoring, to maintain medical equipment where reliability is directly tied to patient health and safety. These techniques help ensure that critical devices remain available and compliant with any specific regulatory standards.