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Sweden: Industry 4.0

Pöyry and Infosys Introduce Artificial Intelligence Framework

| Editor: Alexander Stark

The Krti 4.0 model based data driven framework applies AI, cognitive/machine learning and Machine to Machine (M2M) capabilities to the industrial environment. (sample image)
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The Krti 4.0 model based data driven framework applies AI, cognitive/machine learning and Machine to Machine (M2M) capabilities to the industrial environment. (sample image) (Source: Pixabay / CC0)

Pöyry and Infosys announced the release of an Artificial Intelligence (AI) Framework for Operational Excellence. The framework overcomes some complex and expensive lifecycle management challenges faced by industry, utilities and infrastructure organisations across operational technology (OT) systems, the companies announced.

Malmö/Sweden — The framework branded Krti 4. applies AI, cognitive/machine learning and Machine to Machine (M2M) capabilities to the industrial environment. The applied methodology identifies critical enterprise systems and assets and provides a deeper understanding of their behaviour to unlock and create new value for customers. Krti 4.0 is designed to reduce system maintenance costs and expensive operation shutdowns, and improve reliability, employee and environmental safety.

The framework incorporates the Pöyry Rams (Reliability, Availability, Maintainability, Safety) methodology, which defines the criticality of every asset contributing to the functioning of OT systems. Infosys' Nia knowledge-based AI platform continuously executes complex, advanced analytics and machine learning models, exchanging information with the Rams model to identify any inherent risk in operations of the overall system. Krti 4.0's open and intuitive machine-to-machine (M2M) interface provides for seamlessly connecting with different OT systems for collecting data.

The solutions provides real-time knowledge on the best and the most effective operating and maintenance options for their OT systems. Krti 4.0 achieves this by using predictive and prescriptive analytics within acceptable risk levels.

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