Simulator-L™
Scenario & Modelling Engine
A Monte Carlo enabled enrolment and site activation simulator that projects enrolment performance, study timelines, and scenario outcomes — quantifying execution risk before capital is committed. Replace static spreadsheet timelines with probabilistic intelligence.
What it does
Three engines. Complete scenario intelligence.
01
Monte Carlo Simulation
Run thousands of iterations per simulation to produce probability distributions for enrolment timelines, site activation sequences, and study completion scenarios — not just single-point estimates.
02
Multi-Level Specification
Set parameters at study, country, and site level — overall target enrolment, country-specific regulatory timelines, individual site activation rates, and screen failure assumptions.
03
KPI Dashboards
Track total enrolment duration, FPI, LPI, screening volumes, dropout projections, and treatment milestones through custom visuals designed specifically for feasibility and project management workflows.
The methodology
Monte Carlo Intelligence
Designed by feasibility data scientists and project management professionals with hands-on experience across hundreds of clinical trials — the same domain-first design philosophy as Geo Matrix™ and the EKO™ logic.
01
Scenario Testing
Run 'what-if' analyses — what if site activation in Brazil is delayed 3 months? How does adding 5 sites in Poland affect enrolment probability? Test recovery scenarios before committing resources.
02
Multiple Predictive Models
Run and compare multiple predictive models side-by-side to evaluate different strategic scenarios. Use default mode for rapid assessments or advanced settings for detailed parameter tuning.
03
Probability Curves
Generate visual outputs including probability distributions, enrolment curves, and scenario comparisons that can be used directly in bid materials, sponsor updates, and strategic presentations.
For Proposals & Bid Defence
Include probabilistic enrolment forecasts in your proposals. Instead of 'we estimate 18 months', show 'there is an 80% probability of completing enrolment within 18 months based on 10,000 simulated scenarios.'
For Active Studies
Not just for proposals — project managers on active studies can model projected vs. actual enrolment, test recovery scenarios when sites underperform, and give sponsors data-backed timeline updates.
Quantify execution risk before capital is committed.
How it works
From parameters to probability curves.
Configure Your Study
Define your study parameters — target enrolment, geographic scope, site counts, regulatory timelines, and screen failure assumptions — at study, country, or site level.
Run Simulations
The Monte Carlo engine runs thousands of iterations, modelling enrolment trajectories, site activation sequences, and dropout scenarios to produce probability distributions.
Strategy-Ready Output
Receive probability curves, KPI dashboards, and scenario comparisons in a format your proposal team, project managers, and sponsors can use immediately.
1,000+
Iterations
Convergence-optimised per run
3 Levels
Configuration
Study, country, and site level
Side-by-Side
Scenario Comparison
Compare strategies instantly
Built for
Who uses Simulator-L™
Feasibility Teams
Replace static spreadsheet timelines with Monte Carlo simulation. Get probability curves that show realistic outcome ranges — not just single-point estimates — during tight RFP windows.
Project Managers
Model projected vs. actual enrolment on active studies, test recovery scenarios when sites underperform, and give sponsors data-backed timeline updates.
Strategy Teams
Run scenario planning and what-if analyses — stress-test country strategies, site counts, and timeline assumptions before committing resources.
Business Development
Include probabilistic enrolment forecasts in proposals. Show sponsors an 80% probability of completion within 18 months based on simulated scenarios — this level of rigour wins bids.
Ready to see Simulator-L™ in action?
Book a demo and see how Simulator-L™ replaces static spreadsheet timelines with Monte Carlo-powered scenario intelligence.