It’s an honor to share that AspenTech has been recognized as a Leader in the 2022 Verdantix Green Quadrant^®: Asset Performance Management Solutions report. In it, AspenTech’s APM suite is acknowledged for its strengths in asset health monitoring and failure prediction, performance optimization, and asset lifecycle management.

This leadership position reinforces why customers are choosing AspenTech for a more proactive approach to asset health. We’re helping industrial customers understand that with the right APM solution, one that can be easily deployed and scaled ‘out-of-the-box,’ they can quickly begin to improve operational efficiencies and drive ROI regardless of where they are on their digitalization journey.

Our APM suite, which includes Aspen Fidelis™, Aspen Mtell^®, Aspen ProMV^®, Aspen Process Pulse™, and Aspen Unscrambler™, combines machine learning with predictive analytics to anticipate issues before they occur. In the report, Verdantix states: “with market leading capabilities in asset health monitoring and failure prediction, firms across the process industry can expect to use the AspenTech APM suite to effectively identify and pre-empt asset failures.”

Using AspenTech’s APM solutions, customers eliminate downtime with actionable insights that help reduce costs, safety concerns and environmental impact. Some recent examples include:

• GSK deployed predictive and prescriptive maintenance solution to improve robustness of supply chains by preventing equipment downtime. With 35 days of advance warning of potential issues, GSK benefitted by avoiding lost batches, 50% reduction in lifecycle maintenance costs resulting in tens of millions of dollars in savings.


• OCP Ecuador applied AI powered prescriptive maintenance solution to accurately identify equipment failures and reduce maintenance costs. Early and accurate failure prediction resulted in 25% reduction in yearly maintenance costs and 20% increase in equipment uptime.


• With prescriptive analytics, MOL Group, aimed to maximize equipment uptime and energy efficiency to meet decarbonization goals. Solution delivered multi-million-dollar impact on business by providing 78 days lead time allowing for early maintenance intervention.


• LG Chem deployed a prescriptive analytics solution with predictive Degradation Agents as a part of a comprehensive world-wide digitalization program to increase reliability and avoid unplanned shutdowns. At one site, thanks to the ease and speed of deployment and its staff’s technology adoption, LG Chem realized $3.6M USD benefits in a year by avoiding production loss.


• Sardeolica applied a predictive maintenance solution across 57 wind turbines in its 250GWh wind farm to predict gearbox and generator degradation before failures occurred with the goal of reducing maintenance costs and extending the useful life of the wind turbines. With extreme early warnings of imminent failures between 2-5 months, Sardeolica was able to transform its maintenance program to schedule service before failures within low-wind periods, resulting in easier repairs and a 10% reduction in maintenance costs per year.


• An international bulk chemicals producer used process quality analytics to conduct multivariate statistical analysis that sorted through 2.5 years of ethylene furnace data in a few short months. The automated analysis identified the specific process adjustments to reduce fuel gas consumption, saving over $400K USD in one month, on less than half of the site’s furnaces.


• A Latin American pipeline company used a system-wide modeling solution to outline the impacts of costs and risks in maintenance strategies. The application studied three different maintenance mitigation scenarios: immediate repair, delayed repair, and run-to-failure. The computations indicated the high probabilistic impact for each scenario on spares inventory, capacity, use of redundant equipment to define a low-risk minimal cost mitigation choice that has delivered $7 million USD in protected value to date.


• A metals and mining company has deployed a leading-edge predictive analytics solution across more than 300 of its assets. It is managed by essentially one person, and the company has improved availability enough to get full return on investment in less than six months.


• A downstream Oil and Gas company employed process quality analytics as part of an ongoing CO₂ emissions reduction effort. Not only were they able to reduce CO₂ emissions by 2% in a matter of months, but also reduced flaring by 28% and receive real time alerts when multivariate behaviors indicate process health will result in increased emissions or off-spec product.


• Braskem Idesa applied process quality analytics to increase time between reactor cleanings by 20%, all with existing data. Models were online within weeks to alert to conditions that contribute to accelerated reactor fouling as well as predict when future cleaning would be needed, enabling advanced planning.

For more information on our APM solutions visit our AspenTech Asset Performance Management solutions page.