Digital twins enable organizations to replicate real-world environments in a virtual setting, unlocking powerful capabilities for optimization and AI model development. This session showcases how a healthcare sterilization facility used a high-fidelity digital twin to identify operational bottlenecks and improve efficiency without disrupting live operations. It also highlights how synthetic data generated from the twin can train computer vision models for scenarios that are difficult or impossible to capture in reality, such as safety violations or rare events. Beyond healthcare, the approach demonstrates broad applicability across industries—from manufacturing to energy—by combining simulation, analytics, and AI in a single environment.
Presenters: William Collis and Paul Gavin, SAS
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