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AVEVA to acquire Maxgrip’s software assets for asset performance management

05 April, 2019
1 min read
AVEVA to acquire Maxgrip’s software assets for asset performance management
AVEVA to acquire Maxgrip’s software assets for asset performance management
MaxGrip, an AVEVA partner since 2017, works to optimize asset performance with Reliability Centered Maintenance (RCM) solutions.

April 5, 2019   AVEVA announced an addition to its Asset Performance Management (APM) portfolio with the acquisition of the software assets of MaxGrip, subject to approval from MaxGrip’s shareholders. MaxGrip, an AVEVA partner since 2017, works to optimize asset performance with Reliability Centered Maintenance (RCM) solutions.

AVEVA’s Asset Performance Management (APM) offering is a key element of the Company’s portfolio of asset life cycle solutions enabling digital transformation for its customers. It addresses the broadest dimensions of asset performance management through the use of design and engineering information, real-time and historical operational data, and maintenance execution workflows, together with model-based machine learning for predictive asset analytics, and augmented reality to ensure safe and efficient maintenance activities in the field.

MaxGrip’s software and asset library enables customers to define and implement RCM strategies based on asset criticality and risk. The libraries encompass over 900 critical asset types with failure causes and conditions and proven maintenance remediations. This represents over 20 years of experience in end markets that map closely to the sectors AVEVA serves, with implementationsin the oil and gas, chemicals, infrastructure, utilities, food & beverage and water & wastewater industries.

The combination of AVEVA’s portfolio with the capabilities of MaxGrip is intended to enable customers to realize high levels of reliability, availability, safety and efficiency in the operations of their capital assets and close the loop on managing fault scenarios with prescribed remediation actions based on best practices, moving from reactive and predictive maintenance to prescriptive maintenance.

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