Industrial
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In complex industrial environments, where downtime can cost millions and operational visibility is critical, plant managers and operators need more than dashboards. They need an intelligent, immersive understanding of their facilities. Digital Twins application, built on top of the IRIS Foundry™ platform, delivers exactly that— harnessing the power of real-time data, AI insights, and digital twin technology to create a living, interactive model of the plant that elevates decision-making, performance, and responsiveness. [1]
The journey begins with a dynamic AI-powered Digital Twins dashboard that offers a comprehensive view of plant performance in real time. By leveraging contextualized data and models built into IRIS Foundry, key metrics like Overall Equipment Effectiveness (OEE) are front and center, with drill-down capabilities into 24-hour, 7-day, and 30-day trends. Plant managers can see the contributing factors to OEE, quickly understand how different units (such as the hydro, FCC, or crude units) are performing, and compare their output and efficiency.
Beyond OEE, the dashboard also tracks:
Every data point connects directly to a real asset within the digital twin, allowing users to transition seamlessly from performance metrics to root cause analysis and targeted action. This level of visibility allows operators to stay ahead of potential issues and continuously improve performance. [2]
Alert management is enhanced with intelligent classification by severity, duration, and operational risk to enable quick triage. Users can filter alerts by date range or severity and immediately identify which ones remain open or have been resolved. AI models evaluate each alert’s potential impact, referencing historical patterns and real-time data to provide root cause analysis and actionable recommendations.
Work order management is also fully integrated with the digital twin model, showing daily trends in the creation and completion of tasks. It categorizes work orders by type—preventive, corrective, emergency, predictive, and inspection—helping organizations shift toward more predictive maintenance strategies. Distributions by status (e.g., complete, in progress, overdue) and priority help ensure that the most critical tasks are front and center. AI-driven insights support a shift from reactive fixes to condition-based and prescriptive maintenance, reflecting a more mature maintenance strategy. [3]
Through intuitive navigation based on ISA-95 hierarchy, plant assets are organized from the enterprise level down to individual components. Every component-whether a storage tank, pipeline, or heat exchanger-is part of a real-time digital twin that reflects its operational state.
Each asset page includes:
This enables a deep, contextual understanding of each asset and its operational state, making it easier to diagnose and act on issues. [4]
One of the most powerful features of the application is its fully interactive 3D plant model that brings the digital twin to life. Color-coded indicators highlight assets with critical alerts or maintenance activities. Users can click into any asset, zoom in for details, and instantly access sensor data, documentation, or alert history.
The model seamlessly integrates with the asset hierarchy, allowing users to jump from a list view to a full 3D visualization. For instance, selecting a process pipeline in the hierarchy will automatically highlight it within the 3D model, showing its exact location and operational state. This integration bridges the gap between logical data and physical reality, enhancing operator awareness and accelerating response time. [5]
An embedded AI copilot acts as a real-time decision support tool by providing actionable insights for each alert. When an anomaly is detected, for example, a high-temperature alert, the system presents:
Operators can either acknowledge the alert or go a step further—generate a fully populated work order directly from the alert. The system auto-populates relevant details, including procedures, and suggests available operators by shift. With just a few clicks, a task can be accurately assigned and dispatched.
The system goes beyond planning and into execution readiness. When a work order is created, the application checks warehouse inventory to confirm the availability of required tools and parts. It identifies potential blockers—like an unavailable control valve—and pinpoints where the needed items are stored within the warehouse.
This real-time validation ensures work orders are not only created efficiently but are also actionable, preventing wasted time and unplanned delays.
Need to locate a specific asset? Just ask. With a simple prompt like “Show me heat exchanger 006 in 3D,” the system highlights the exact location within the 3D model. From there, users can review work orders, inspect past alerts, and understand surrounding asset conditions.
This natural language capability removes friction from plant operations and dramatically shortens time-to-insight. [6]
This application offers more than just visualization—it brings together immersive 2D/3D modeling, real-time data, AI-driven insights, and a fully integrated digital twin framework to empower plant managers and operators. Built on the IRIS Foundry platform, it unifies plant data, contextualizes it with AI, and delivers it in an interface that’s intuitive, responsive, and highly actionable. Whether it’s monitoring performance, responding to alerts, planning maintenance, or navigating a massive facility in 3D, users have everything they need at their fingertips.
In an era where industrial operations are more dynamic and demanding than ever, this kind of intelligence is not optional-it's essential for staying agile, minimizing downtime, increasing efficiency, and maintaining a competitive edge.
[1] https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/digital-twins-the-key-to-smart-product-development
[2] https://www.grandviewresearch.com/press-release/global-digital-twin-market
[3] https://www.bain.com/insights/the-factory-of-the-future-could-boost-productivity-by-30-percent-or-more-global-machinery-and-equipment-report-2024/
[4] https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/2025-smart-manufacturing-survey.html
[5] https://matterport.com/learn/digital-twin/manufacturing
[6] https://medium.com/geospatial-intelligence/3d-visualization-for-digital-twins-f2c8818bbde2
Prateek is president of SymphonyAI’s industrial division and executive chairman of SymphonyAI’s enterprise IT division. With over 20 years of technology leadership and extensive experience in the enterprise, telecom, and automotive industries, Prateek brings a unique background in machine learning and AI, product strategy, operations, product technology, engineering, and sales to SymphonyAI. Prateek has extensive experience with highly engineered systems and expertise in B2B and consumer technology, deep learning, cloud virtualization, enterprise software, mobile applications, and information life cycle management. Before joining SymphonyAI, Prateek served as EVP and CTO at Cerence, where he was responsible for Cerence’s technology vision, R&D, and professional services, and rolling out Cerence technology and solutions to more than 65 automotive customers across the world and more than 450 million cars on the road. Before Cerence, Prateek served as general manager of AI and IoT products at View, responsible for leading product strategy, defining and driving product roadmap, and supporting M&A activity to accelerate growth. Before View, he served as VP of product and solution management at Polycom, chief strategy officer at HighQ, and VP of product strategy at Accusoft, which acquired Adeptol, a company Prateek founded. Prateek previously worked for several companies, including EMC, Sapient, Cognizant, and NEC. Prateek holds an MBA and a Bachelor’s of engineering degree in instrumentation and process control.