Management Science Research Centre (MSRC) Seminar: Opher Baron
Opher Baron
Rotman School of Management, University of Toronto
Business Process Intelligence in Congestion-Driven Systems: From Event Logs to Real-Time Digital Twins
Date: Friday, April 10, 2026
Time: 10:00 AM - 11:00 AM
Location: Bronfman Building, Room 046
Abstract
Many modern business processes operate under congestion: stochastic demand, shared and limited resources, complex routing, and tight service-level constraints. From healthcare and public services to logistics, financial operations, and large-scale customer support, these systems exhibit nonlinear behavior where small disruptions propagate quickly and performance deteriorates sharply.
This talk examines how Business Process Management can evolve to address congestion-driven environments by integrating process mining, queueing theory, simulation, and machine learning into AI-enabled digital twins.
Using large-scale event-log data from real deployments, we illustrate how descriptive analytics must move beyond static dashboards toward data-driven process models that reveal how processes actually unfold in practice鈥攃apturing routing variability, rework loops, synchronization delays, and resource contention. Process mining provides structural visibility; queueing-aware modeling explains performance; machine learning supports prediction under uncertainty.
Building on these foundations, predictive and comparative analytics enable counterfactual 鈥渨hat-if鈥� evaluation of staffing, routing, prioritization, and scheduling policies before implementation. Finally, prescriptive analytics embedded within real-time digital twins allow organizations to intervene proactively鈥攁nticipating congestion, reallocating capacity, and mitigating cascading delays.
Drawing on industrial deployments through SiMLQ, this talk demonstrates how combining process intelligence with congestion-aware operational modeling transforms BPM from retrospective analysis into continuous, real-time decision support. The implications extend across service industries where variability and resource contention define performance, resilience, and competitiveness.
