Digital Transformation Nokia, Pöyry, Infosys Collaborate to Improve and Increase Usage of AI Framework
The artificial intelligence framework called ‘KRTI 4.0’ aims to achieve operational excellence for industry, utilities, transportation and infrastructure organisations. The framework makes use of advanced digital concepts such as AI, cognitive/machine learning and machine-to-machine (M2M) for industrial environments.
Finland – Nokia recently announced that it will partner with Pöyry, and Infosys, a global leader in next-generation digital services and consulting, to further enhance and accelerate the adoption of KRTI 4.0, an artificial intelligence (AI) framework for operational excellence.
The KRTI 4.0 (TM) framework applies AI, cognitive/machine learning and machine-to-machine (M2M) capabilities to the industrial environment. The framework also addresses complex and expensive lifecycle management challenges faced by industry, utilities, transportation and infrastructure organisations across operational technology (OT) systems. The applied methodology identifies critical enterprise systems and assets. It also provides a deep understanding of their behavior to unlock and create new value for customers by reducing system maintenance costs and expensive operation shutdowns, improving reliability and enhancing employee and environmental safety.
The AI framework uses predictive and prescriptive analytics, that empowers decision makers with real-time knowledge on the best and the most effective operating and maintenance options for their OT systems, leveraging tools such as real-time dashboards, Rams (Reliability, Availability, Maintainability, Safety) modelling capabilities, augmented reality, chatbot functionality and more enabled by highly secure and reliable connectivity.
Chris Johnson, Global Head of Enterprise at Nokia, said: "A key component in realising the promise of Industry 4.0 is ensuring global IoT connectivity across the supply chain, the factory and the distribution networks. Dedicated wireless networks based on LTE & 5G, along with Wing, offered in cooperation with our mobile network operator partners and coupled with our Impact platform, are a key enabler in solving this global IoT connectivity challenge. We are excited to be working with other IoT industry leaders to help make Industry 4.0 a reality."
Richard Pinnock, President, Energy Business Group at Pöyry, said: "Our KRTI 4.0 (TM) framework using Rams modelling methodology puts the Pareto principle's 80/20 rule at the heart of the decision-making process. We know the criticality of each part of the asset and focus our data collection strategy and analytical predictive capabilities where it matters most. In KRTI 4.0 (TM) real-time data from critical assets is converted to information with innovative computing and business intelligent algorithms enabling proactive prescriptive decision making. This is the difference; and for this to be made possible, industrial-grade secure IoT connectivity is key. This is where Nokia steps in to bring market-leading IoT connectivity solutions and expertise to the KRTI 4. 0 (TM) framework".
About the Solution
• The KRTI 4.0 (TM) model-based data driven framework incorporates the Pöyry Rams methodology, which defines the criticality of every asset contributing to the functioning of an OT system.
• Infosys' Nia knowledge-based AI platform continuously executes complex, advanced analytics and machine leaning models and exchanges information with the Rams model to identify any inherent risk in operations on the overall system.
• Nokia provides the pervasive, secure industrial IoT connectivity and network analytics for integrating with data and devices from different OT systems, including Impact IoT platform, SI Suite - advanced visualisation, Scene Analytics - machine learning video analytics, dedicated wireless networks based on LTE and 5G along with its Worldwide IoT Network Grid (Wing) offering – sold in conjunction with mobile network operators – which supports dedicated IoT operations, billing, security, data analytics, and more. This data can then flow into the Nia system for the execution of forecasting models.
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