37% Of Firms Plan To Invest In Software For Asset Failure Prediction In 2021

37% Of Firms Plan To Invest In Software For Asset Failure Prediction In 2021

According to the Verdantix annual survey of 259 managers within operations, maintenance, engineering, and process safety, 37% mentioned that they plan to invest in software for asset failure prediction in 2021. Two per cent said they would invest in commercial software for the first time, 10% will replace existing in-house software with commercial software, 17% will replace existing commercial software with software from a new vendor, and 8% will upgrade existing commercial software. Software that can predict asset failure help firms mitigate unplanned downtime, avoid disastrous asset failures, and optimize maintenance work.

Firms that have invested in asset failure prediction software include Alcoa, the world’s eighth largest producer of aluminium; Aker BP, a Norwegian state-owned oil exploration firm, and Saudi Aramco, a Saudi Arabian state-owned fully integrated oil and gas firm. Alcoa worked with Senseye, a UK-headquartered predictive maintenance specialist, to implement AI powered predictive analytics to catch asset failures and achieved a 30% enhancement of operational efficiency. Aker BP worked with Cognite, a Norway-headquartered DataOps specialist, to drive their digital transformation, implement smart monitoring solutions and a well surveillance system that uses a digital twin to detect early signs of well failure. This is expected to generate savings of $22.5 million per year in operating costs. An oilfield service operator worked with Flutura, a digital twin simulation software provider, to accurately monitor the pressure pumping operations in its hydraulic fracturing facilities. A 97% prediction of failure rate was achieved, reducing job schedules by 10% and fleet utilization was increased by 30% to 40%

To learn more about improving asset management with digital technologies, register for the Verdantix event: “Next And Best Practices: From Asset Reliability To Digital Twins”.