Monte Carlo Staffing Simulator

The Monte Carlo simulator replaces deterministic Erlang models with stochastic simulation. Instead of assuming fixed arrival rates and handle times, it samples from configurable distributions — capturing the real-world variability that Erlang ignores.

How practitioners use it:

• Run 10,000 simulations across different staffing scenarios
• Compare the probability distribution of service level outcomes, not just point estimates
• Identify the staffing level where you hit your target SL 95% of the time (not just on average)
• Model non-standard arrival patterns (bursty traffic, callback queues)

When to use Monte Carlo over Erlang: whenever your arrival pattern is non-Poisson, your handle times aren't exponential, or you need confidence intervals rather than point estimates. For stable, high-volume queues, Erlang-C is fine. For everything else — small teams, mixed channels, variable complexity — Monte Carlo gives you truth.