Nokia has announced that it will partner with international consulting and engineering company Pöyry and next-generation digital services and consulting provider Infosys to further enhance and accelerate the adoption of KRTI 4.0™.
KRTI 4.0 is an artificial intelligence (AI) framework for operational excellence. It applies AI, cognitive / machine learning and machine-to-machine (M2M) capabilities to the industrial environment. This addresses complex and expensive lifecycle management challenges faced by industry, utilities, transportation and infrastructure organisations across operational technology (OT) systems.
The framework identifies critical enterprise systems and assets. This provides a deep understanding of their behaviour which can help to reduce system maintenance costs and expensive operation shutdowns, improve reliability, and enhance employee and environmental safety.
KRTI 4.0 uses predictive and prescriptive analytics that gives users real-time knowledge of the most effective operating and maintenance options for their OT systems. It does this by leveraging tools such as real-time dashboards, RAMS (Reliability, Availability, Maintainability, Safety) modelling capabilities, augmented reality, chatbot functionality amongst other systems. All this is enabled by 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 and 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 of the Energy Business Group at Pöyry, said: “Our KRTI 4.0 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 real-time data from critical assets are 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.”
Nitesh Bansal, Senior Vice President and Global Head of Engineering Services at Infosys Ltd., said: “At Infosys we are focused on helping our clients maximise the potential that digital services including IoT have to offer. Through KRTI 4.0, we are focused on maximising the value that our clients are able to derive from their existing assets.
“We are pleased to have Nokia join this alliance, to accelerate the adoption of the framework at an enterprise scale, while ensuring security, legal and regulatory compliance.”
The KRTI 4.0 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 learning 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.