In today’s dynamic business environment, enterprises are under pressure to release high-value investments that are locked up due to obsolete technology, retiring workforce, regulatory implementations and environmental commitments.
To address these challenges, Infosys has partnered with consulting and engineering expert, Poyry and connectivity expert, Nokia to develop KRTI 4.0, an AI-powered framework for operational excellence.
KRTI 4.0 helps industry, utility and infrastructure organizations overcome complex and expensive lifecycle management challenges across operational technology (OT) systems.
The KRTI 4.0 framework applies AI, cognitive, machine learning and machine-to-machine (M2M) capabilities to industrial environments.
This methodology identifies critical OT systems and assets to provide a deeper understanding of their behavior so that enterprises can unlock and create new value for users.
KRTI 4.0 is designed to dramatically reduce system maintenance costs and expensive operation shutdowns by improving reliability and enhancing employee and environmental safety. It does this by enabling predictive maintenance, pervasive connectivity and knowledge sharing across the enterprise.
For industries across the globe trying to improve operations, increase profits and reduce costs, KRTI 4.0 offers a solution to unlock both capacity and capital to achieve their goals.
KRTI 4.0 has been developed to provide a single touch-point for enterprises to manage plant operations and maintain production systems. It acquires real-time information from field assets and uses advanced analytics to determine current asset health. It seamlessly interfaces with Reliability Models and correlates asset health and risk impact analysis to provide detailed insights about the risk exposure of the asset. The risk exposure is rolled up from plant-level to the enterprise-level enabling decision-makers to initiate actions by way of planning for inventory and operations.
KRTI 4.0 is focused on improving two attributes – Increasing Mean Time To Failure (MTTF) and Mean Time To Repair (MTTR) and aims to deliver increasing availability and profitability to customers. KRTI 4.0 can be leveraged by asset and capital-intensive industries such automotive, energy, mining, oil and gas, paper and pulp, pharma, power generation, utilities, waste and wastewater treatment and more.
KRTI 4.0 is a unique digital framework that leverages Pöyry’s Reliability, Availability, Maintainability, and Safety (RAMS) modeling along with AI capabilities from Infosys and powered by secure and persuasive connectivity from Nokia. It eliminates unknown risks that may not have been considered during plant design such as downtime of a particular asset caused by operational degradation, inferior specifications or quality issues.
KRTI 4.0 is a combination of the unique and complementary strengths of three leading global corporations, namely, Poyry, Nokia and Infosys.
Pöyry is an international consulting and engineering company that serves clients across power generation, transmission and distribution, forest industry, biorefining and chemicals, mining and metals, infrastructure, and water and environment industries. Their services include advisory, management consulting, engineering, operations, and security. They specialize in delivering smart solutions and working with the latest digital innovations.
Nokia has deployed over 1,000 mission-critical networks with leading utilities, railways, air traffic controllers, mining companies, banks and healthcare institutions around the globe. Leading enterprises across industries are leveraging their decades of experience building some of the biggest and most advanced IP, optical, and wireless networks on the planet. The Nokia Bell Labs Future X for industries architecture offers a framework for how companies can seize the Industry 4.0 opportunity.
Infosys is a global leader in technology services and consulting. We enable clients in 45 countries to create and execute strategies for digital transformation. From engineering to application development, knowledge management and business process management, Infosys helps clients find the right problems to solve, and guides them to solve these effectively. Our team of over 200,000 innovators across the globe are differentiated by their imagination, knowledge, and experience across every project we undertake.
Achieve operational excellence in a dynamic business environment with KRTI 4.0
For green field implementation, Reliability based Modeling would help plant owners to select the most optimal design with clearly laid out CAPEX and OPEX required for the entire life of the plant. There is a clear view of plant availability along with known risks in operations which needs to be mitigated by meticulous planning. In brown field implementation, KRTI 4.0 can help re-access the design and identify areas to optimize operations to reduce costs and increase asset reliability.
KRTI 4.0 collects real time information from plant assets using pervasive and secure connectivity. The machine learning environment identifies patterns of behavior from the collected data to forecast and predict anomalies. The impact of the predicted anomaly is correlated with the RAMS framework to identify the impact and risks associated with assets and their failure. This simplifies decision making with detailed insights.
Powered by a knowledge platform, KRTI 4.0 learns from technical manuals, operator knowledge, maintenance logs, etc., and converts this data into knowledge ontologies. This allows enterprises to manage and enhance learnings and reduce Mean Time To Repair by quickly identifying root causes and use standardized approaches for resolution.
Energy consumption patterns help identify variations in asset health. This not only forecasts energy needs but also optimizes energy consumption across every plant, thereby reducing the carbon footprint.
Machine learning and deep learning-based forecasting models help operations teams to identify risks related to environmental and human safety, and adopt the right strategy to eliminate risk and failures.