Industrial assets rarely fail without warning. A motor draws more current than usual. A pump vibrates outside its normal range. Energy consumption climbs week after week. But operational data sits scattered across SCADA systems, historian databases, CMMS platforms, and thousands of sensor streams that were never designed to work together. By the time someone sees the pattern, you’re not planning maintenance. You’re responding to an outage.
AI-powered Digital Twins change that equation.
A Digital Twin continuously syncs a virtual model of a physical asset with real-time operational data. AI agents use that model to detect early anomalies, identify degradation patterns, and surface failure risks before they become production problems. They don’t just generate alerts. They recommend corrective actions, trigger work orders, and identify the right window to intervene, before downtime is unavoidable.
This webinar goes beyond the concept. You’ll see a live Digital Twin built on Microsoft Fabric and Esri ArcGIS, applied to a real industrial asset scenario.
A Unified Data Foundation
Microsoft Fabric and OneLake unify telemetry, maintenance history, ERP data, and sensor streams into a single governed environment for complete asset visibility.
Predictive Intelligence in Practice
AI continuously evaluates asset behavior against expected conditions, assesses risk, and surfaces the right corrective action before failure occurs.
Location Intelligence as Operational Context
Esri ArcGIS adds geospatial context to the Digital Twin, factoring in environmental conditions, geography, weather patterns, surrounding and site-specific factors that directly influence equipment performance.
Live Demo: Cooling Tower Digital Twin
For this session, we will use a cooling tower as the reference asset. Cooling towers are critical infrastructure across manufacturing, data centers, energy, and healthcare, and they generate exactly the kind of multi-source operational data that makes predictive maintenance complex. You will see how the Digital Twin monitors real-time performance, flags early deviation from normal operating conditions, and recommends action before a failure occurs.
Sharad Kumar | Director – Digital Transformation at Kanerika
Sharad leads digital transformation at Kanerika, driving enterprise data strategy across AI, ML, big data, governance, and advanced analytics. He manages multi-million dollar portfolios and advises C-suite executives across industries.
Pardeep Singla | Partner Solution Architect at Microsoft
Pardeep Singla is a Digital Cloud Solution Architect at Microsoft with expertise in Microsoft Fabric. He helps organizations design and implement modern cloud and data solutions that drive operational outcomes.
Sarah Battersby | Principal Product Manager at Esri
Sarah is a Principal Product Manager at Esri, leading ArcGIS GeoAnalytics products that bring spatial analysis to big data and cloud workflows. With a PhD in Geography and a background as a Principal Research Scientist at Tableau, she’s dedicated her career to making geospatial data easier and more trustworthy for everyone to use.
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See how AI-powered Digital Twins help industrial organizations detect failures earlier and move from reactive to predictive operations.
