Monte Carlo Staffing Simulator

Run thousands of staffing simulations to model FTE requirements under uncertainty. See probability distributions, confidence intervals, and risk profiles instead of single-point Erlang estimates.

Monte Carlo simulation generates thousands of random scenarios based on your input distributions to estimate staffing needs under uncertainty. Unlike deterministic Erlang models that give a single answer, Monte Carlo reveals the full range of possible outcomes.

How to use: Enter your historical call volume mean and standard deviation, average handle time, and target service level. The simulator runs 10,000 iterations, sampling from the distributions you define. Results show the staffing range at different confidence intervals (50th, 75th, 90th, 95th percentile).

When to apply: Use Monte Carlo when your volume is volatile (CV > 0.3), when you need to quantify risk ("What's the probability we'll need more than 120 agents?"), or when planning for worst-case scenarios. It complements Erlang-C by adding uncertainty modeling on top of queueing theory.

Key formula: For each iteration, sample volume from N(μ, σ²), compute Erlang-C staffing at that volume, then aggregate across iterations to build the probability distribution of required FTEs.