(Panel) Combining IT & OT: Creating & Deploying Digital Twins to Solve Real-World Problems
Atrium Ballroom A
The convergence of 4D modeling, real-time data analysis, and advanced simulation has accelerated the evolution of digital twins, or synchronized virtual replicas of real-world assets, processes, people, and systems. In this session, industry practitioners share how they’re building and operating digital twin ecosystems for decision making, performance prediction, product innovation, and more. Learn how to define and prioritize initiatives, connect real-time and historical data, and integrate digital twins into existing workflows. Key themes include: Defining digital twins : Scoping initiatives and identifying high-value use cases; simulation models vs. fully synchronized digital twins IT/OT integration : Connecting PLCs, SCADA, IoT platforms, and edge devices with enterprise systems (ERP, MES, PLM); handling protocols, APIs, and data normalization Data pipelines : Ingesting, synchronizing, and integrating real-time sensor data with historical and engineering data; managing latency, quality, and time-series consistency Modeling, simulation & lifecycle : Building accurate representations for forecasting and optimization; versioning, updating, and validating models as physical systems change Infrastructure & performance : Designing for scale across cloud, edge, and on-prem; managing compute, storage, and network constraints to support continuous data flow and real-time insights Interoperability & digital thread : Integrating with existing applications and workflows; connecting data across the asset lifecycle (design, build, operate, maintain) Security & governance : Ensuring traceability, access control, regulatory compliance, and protection of OT environments Deployment & impact : Moving from pilots to production; measuring improvements in uptime, throughput, maintenance costs, decision speed, etc. Core use cases : Predictive maintenance, asset monitoring, layout and process optimization, quality control, workforce training, sales and marketing, etc. Common challenges : Data silos, poor data quality, inconsistent standards, integration complexity, skills gaps, cybersecurity risks, etc. Enabling technologies : Reality capture and 3D pipelines, XR for visualization and interaction, and AI/ML for prediction and optimization
