A Beginner’s Guide to Asset Performance Management (APM)

On Dec 23, 2023

Breakthroughs in technologies like Artificial Intelligence (AI) are changing how we think about operations management. As organizations move from a reactive approach to a proactive one, they can use technologies like the Industrial Internet of Things (IIoT), cloud, AI, and analytics to gain real-time data, actionable insight, etc., enhancing performance management to propel business growth.

This is where Asset Performance Management (APM) comes in. It provides a strategic approach to increase the efficient use of industrial assets. Moreover, with the growing need to optimize APM strategy, this market is projected to hit USD 4.7 billion by 2028.

In this article, we discuss what APM is, its role in asset management, implementation challenges, and future trends in asset management.

What is Asset Performance Management (APM)?

Asset Performance Management is a strategic framework to manage a company’s assets, i.e., infrastructure, equipment, human labor, etc. This strategy aims to maximize the value derived from available assets by optimizing performance during operations.

For example, an industrial manufacturer might develop and apply an APM strategy after noticing that the manufacturing equipment is not being utilized to its maximum potential. This can lead to lower production and, as a result, lower revenue.

Companies today rely on software-based APM solutions to monitor the health and performance of critical assets. They also inform companies whether their APM strategy is being executed as originally planned. These solutions use technologies such as IoT, AI, predictive maintenance, remote monitoring, etc, to measure the effectiveness of the APM strategy applied.

Companies can employ the following APM strategies:

Asset Criticality Analysis (ACA): Used to critically assess an asset’s likely consequence of failure and the highest risk posed to operations as a sa result.

Reliability Centered Maintenance (RCM): Used to assess a system’s risk and help develop strategies to reduce operational failures.

Asset Strategy Optimization (ASO): Used to increase asset reliability and decrease maintenance costs using advanced quantitive strategy modeling techniques.

Extending Asset Life and Maximizing Labor Productivity

One of the major goals of applying and executing an Asset Performance Management strategy is to extend asset life to its maximum operational potential. The benefits include cost savings on new assets, increased operational efficiency, reduced maintenance costs, and better safety and compliance.

But most importantly, successfully extending the life of assets has a deeper impact on labor productivity. This is because APM strategies compel industries to have better maintenance practices, lower downtime, improved resource allocation, enhanced worker safety, etc.

Some of the strategies used to extend asset life using APM include:

Asset Lifecycle Management: A strategy used to understand an asset’s complete lifecycle, from acquisition to disposal, to strategically plan everything from maintenance to optimal usage.

Real-time Monitoring: Using technologies like the Industrial Internet of Things (IIoT), real-time monitoring and evaluation can help measure the actual performance of assets to avoid downtime and asset failure.

Reducing Maintenance Costs and Time

Unplanned downtime, the resulting maintenance costs, and time spent to make the asset operational again are some of the leading problems industries face today. For instance, WSJ’s report estimates almost $50 billion lost annually by industrial manufacturers because of unplanned downtime resulting mainly from equipment failure.

One of the primary goals of incorporating Asset Performance Management strategies is to reduce unplanned downtime to, ideally, zero. This reduces unnecessary maintenance costs, prevents costly equipment breakdowns, and makes it easier to predict and sustain industrial operations.

Some of the APM strategies employed for this include:

Predictive Maintenance: By using modern AI/ML capabilities to analyze big data, this strategy can monitor an asset’s health and forecast maintenance.

Root Cause Analysis (RCA): This strategy emphasizes understanding the root causes of asset failures in a structured manner. Using this strategy, companies can avoid future unplanned failures instead of just temporary firefighting.

Maintenance Optimization: By using advanced analytics, industries can optimize maintenance schedules and resources in a way that doesn’t over- or under-optimize for the maintenance of assets.

Challenges in Implementing Asset Performance Management

While organizations do understand the importance of APM strategies, roadblocks can arise during execution. Modern challenges of implementing APM strategies include:

1. Maintaining Data Quality: The execution of any APM strategy can only be as good as the source data used to make conclusions about what needs to be done. If the data quality fails to accurately reflect the condition of assets, it will defeat objectives such as reducing downtime and maintenance costs, improving labor productivity, etc.

2. Growing Technological Complexity: With the emergence of Industry 4.0 and technologies like AI and IIoT, industries can increase operational efficiency. But at the same time, these systems also create adoption challenges. Especially, training the workforce so that APM strategies can be executed properly is a significant challenge.

This means you might need to train or hire resources to implement modern APM strategies, such as predictive maintenance, where the knowledge of AI and data analytics is important.

3. Measuring Performance: One key challenge of implementing an APM strategy is ensuring that performance is being measured accurately and that you have the right performance metrics in place to reflect the progress.

For example, it will be a challenge to understand how your APM strategy has helped reduce downtime. And whether this reduction correlates with the implemented strategy.

Concluding Note

Advanced AI systems, real-time data, and predictive analytics enable industries to create more reliable APM strategies. The end goal remains the same: