Research & insights

Research behind SiMLQ.

Explore research, publications, and media covering SiMLQ's work in simulation, machine learning, queueing systems, digital twins, and data-driven operational decision support.

Featured research

Applied evidence from real service systems

Healthcare operationsDigital twinsEmergency Departments2026

From Descriptive to Predictive Analytics in Healthcare Service Operations

How process mining, queueing, simulation, and AI can move Emergency Departments from retrospective reporting toward predictive, comparative, and prescriptive decision support.

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Emergency Department redesignPatient flowCounterfactual analysis

Reducing PIA by Over 50% While Generating New Patient Flows

A comprehensive assessment of an Emergency Department redesign at Southlake Regional Health Centre, examining physician initial assessment, patient volume, length of stay, and other flow measures using counterfactual analysis.

Preprint, not peer reviewed. Separate research study, not the Erie Shores case study.

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Faculty focusRotman Management

On the Power of Modelling

Opher Baron

Professor of Operations Management, Rotman School, and SiMLQ co-founder

SiMLQ co-founder Opher Baron discusses why modelling matters, how digital twins support operational decisions, and why SiMLQ began with healthcare.

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Broader research

The technical foundation behind SiMLQ

Applied across complex service and operational systems.

Simulation qualityMethodology

A Framework for Measuring the Quality of Business Process Simulation Models

A framework for evaluating how accurately business-process simulation models reproduce observed system behaviour.

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Digital twinsService systems

Digital Twins in Service Systems: The Process Twin Loop in Courts

A framework for moving from operational data to process maps, simulation, intervention testing, and continuously updated digital twins in complex service systems.

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