When organizations strategically monitor the health and performance of their most valuable assets, they reduce their operational costs and decrease the likelihood of breakdowns that might result in 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.

Maintenance tasks are essential to preserving the health and productivity of an organization’s assets. However, their cost can vary greatly and can impact the organization’s profitability. Improvements in decision-making capabilities generated by a strong APM approach help organizations avoid performing excessive and unnecessary maintenance.

Using 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.

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, but today they can get the same data in real time. In a 2022 case study, 51% of organizations said they would prioritize increasing operational efficiency over their other initiatives.¹ 

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, 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 , while it might own a fleet of vehicles it uses from time to time, the health and performance of the vehicles is not critical to its everyday business operations. Therefore, these two organizations would value the same asset differently. When you’re starting to form your APM, 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 as well as the automation of critical asset workflows, APM software, such as an enterprise asset management (EAM) system, is crucial to executing an effective strategy. EAM combines software and services to help organizations maintain, control and optimize their operational assets.

With the amount of data being generated these days via IoT, maintenance managers are relying more and more on management software like EAM equipped with AI-enhanced data analysis to help 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.

Today’s most effective APM approaches rely on the ability to schedule maintenance regularly to prevent equipment breakdown and to continuously assess performance in real time. These two strategies, preventive and predictive maintenance are key to the success of many APM initiatives. 

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. This data is then ingested by an AI-enabled EAM or CMMS where advanced data analysis tools and processes like ML identify, detect and address issues in real time.

Based on this data and the subsequent analysis, algorithms are used to 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%.²

Perhaps the most widely used metric for evaluating the success of any maintenance program is the MTBF of the assets it has been tasked with repairing. MTBF is a simple formula that calculates the average amount of time between needed repairs on a piece of equipment by dividing the total time of operation by the number of failures during that time. A maintenance department whose assets have a high MTBF is usually assessed as “strong.”  

As well-known and widely used as MTBF, overall equipment effectiveness (OEE) (link resides outside of ibm.com) is another popular metric for measuring asset performance. It focuses on three metrics: Availability, performance and quality of equipment.

“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, and “quality” evaluates the rate of production of products deemed “good count” compared to ones it produces that have defects or require reworking. To determine the asset’s OEE score, these three factors are multiplied together.

Two more metrics for measuring the success of an APM program are how quickly and effectively maintenance technicians close the work order tickets they’re assigned and the overall percentage of completed versus un-completed 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. Put simply, the better a maintenance department is at performing preventive maintenance, the rarer breakdowns requiring emergency intervention should be.