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Service organizations must identify inefficient processes to optimize resource allocation and reduce costs. The complexity of process data, characterized by high volume, velocity and variety, poses significant challenges. Traditional operations management and process optimization methods often fall short, lacking the data-driven approaches necessary for making evidence-based decisions.

SiMLQ's key strength is its ability to automatically prescribe data-driven recommendations to optimize processes, with minimal data and manual effort required. Existing tools are either focused on descriptive and predictive analytics or require extensive manual labour to tune the models. SiMLQ bridges the gap, enabling reduced data-to-simulation time, capacity planning aligned with demand, and prescriptive analytics.

SiMLQ is versatile and can be applied to a wide range of service systems, including healthcare (emergency departments, long-term care homes), cloud computing (load planning, data services), retail (customer journey analytics, call centre workforce planning, supply chain management), and logistics (scheduling, transportation coordination). Our flagship product SiMLQ-Emergency is tailored to improving the performance of emergency departments.

Automated network learning employs a unique hybrid approach combining queue mining and machine learning. It effectively approximates system load even with minimal or missing resource and queueing information. With its flexible intake of contextual attributes, it enables digital twin simulations for comparative analysis of system changes.