SiMLQ makes process optimization easy
SiMLQ-Emergency leverages machine learning and queueing theory to automatically create scalable, data-driven process simulators for Emergency Departments. It focuses on optimizing medical staff and resource allocation, and reducing flow and wait times by turning event data into actionable insights.
Harness the power of actionable insights to drive exceptional outcomes
Innovate Hybrid Analysis
Automated network learning utilizing a unique hybrid approach that combines queue mining and machine learning techniques
Resouces and Queue Insight
Effectively approximates system load even with minimal or missing resource and queueing information.
Adaptive Data Handling
Flexible intake of contextual attributes.
Digital Twin Simulation
Enables digital twin simulations for comparative analysis of system changes.
Proven Real-world Success
Successfully tested on real-world hospital and cloud computing data.
Data-driven Scalable Simulation
Leveraging extensive data to create highly adaptable process models, ensuring simulations accurately reflect real-world dynamics.
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