How IBM is Applying AI to Improve Operational Asset Performance

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Data from physical assets has become pervasive in the modern world. With inexpensive sensors, low-cost communications, and the prevalence of cloud computing, data is streaming off of physical assets and devices, and updating us about everything from our teeth brushing habits to how our elevators are running.

The revolution goes far beyond consumer technologies. Every second, assets such as manufacturing equipment, gas turbines, electric utility transformers, and transportation infrastructure are providing data on their current operating conditions. Through the connection between these assets and software for data capture, business leaders can know instantly if performance characteristics are exceeding baselines, predict potential failures, diagnose problems, and offer repair recommendations. Ultimately, this helps businesses financially optimize asset-related decisions. Now more than ever they are able to reduce maintenance costs, lower risks from asset failures, and improve overall resiliency of operations. The industrial world has changed.

However, not every organization is fully benefiting from this newly available asset data. Organizations continue to struggle with the high costs and limited efficacy of reliability programs. According to some estimates, up to 40% of all preventive maintenance costs are spent on assets with negligible effect on uptime and up to 30% of these activities are carried out too frequently. Some even estimate that total preventive maintenance hours can be reduced by 50-70%. With maintenance costs and asset performance under intense scrutiny, many asset-intensive organizations are looking for a better approach.

The challenge is that today’s reliability engineers, maintenance planners, and repair technicians often use a mix of manual tools, siloed spreadsheets, and institutional knowledge to make asset maintenance, repair, and replacement decisions. These disparate tools and spreadsheets are difficult to use, and in many cases, make it impossible to compare alternative asset optimization strategies. They typically do not incorporate real-time IoT data, analytics, or AI.

This is where asset performance management (APM) comes in. While many organizations have enterprise asset management (EAM) systems in place, they recognize the need to enhance their investments and optimize asset decisions. As pressures mount, APM can help them do this by enhancing the foundation of EAM with IoT, advanced analytics and AI.

Specifically, APM helps reliability engineers and maintenance managers understand asset criticality, current condition, and factors that may lead to downtime. While EAM helps organizations execute maintenance activities, APM enhances these activities by helping organizations financially optimize asset maintenance, repair, and replacement decisions. APM is about optimizing performance of operational assets by lowering costs, reducing risks, and improving resiliency.

To this end, today we announced IBM Maximo APM, our new suite of APM solutions. As organizations accelerate the pace of their asset maintenance journey, APM can provide the tools required to increase uptime and reduce maintenance costs.


If you’d like to see a real-life example, check out how we’ve been helping Sandvik put APM to use in the mining industry to cut maintenance costs here. If you are on your APM journey, click here to take our APM assessment and see where you are and what your next steps should be.

General Manager, Watson Internet of Things, IBM

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