We Turn Data into Actions
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SiMLQ makes process optimization easy
SiMLQ leverages machine learning, and queueing theory to automatically create scalable, data-driven, process simulators. It focuses on congested systems, optimizing resource allocation, boosting productivity, and reducing costs by turning event data into actionable insights.
SiMLQ - From Data to Action!
Our patented technology uses Simulation, Machine Learning, and Queueing Theory to create data-driven simulations that empower process managers like you to seamlessly compare process KPIs and make informed and confident decisions on:
We tackle challenges in highly uncertain and congested environments
Resource Management
How can we relieve system bottlenecks by improving the usage of existing resources?
Customer Experience
How can we reduce wait times for different customer types to enhance service quality?
Process Improvement
What impacts can be expected from actions taken to improve current processes?
We collaborate with leaders in healthcare, technology, and service industries to drive process optimization.
Enhance Experience in ER and Long-term Care Facilities
By simulating patient flow, staff allocation, and resource utilization, SiMLQ helps reduce wait times, improve patient outcomes, and optimize daily routines, especially in emergency departments and long-term care facilities.
Optimize Load Planning and Data Services
By predicting demand and managing resources efficiently, SiMLQ helps identify bottlenecks, improve server utilization, and ensure seamless data delivery, leading to better performance and cost savings in load planning and data services.
Improve Retail Efficiency
By simulating customer interactions and staff workflows, SiMLQ identifies inefficiencies, predicts behaviors, and improves service delivery in customer experience and workforce planning within the retail industry.
Streamlined Scheduling and Delivery
By simulating logistical scenarios, SiMLQ identifies inefficiencies, optimizes routes, and streamlines scheduling, leading to timely deliveries, reduced costs, and improved transportation coordination.
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.