Monte Carlo simulation
Alpine Peak Snow Resort
Built a 1,000-trial Monte Carlo simulation to test room-rate and overbooking strategy for a 50-room alpine resort, weighing expected profit against downside risk.
$450/night beats $350/night by +47.9% mean daily profit
Executive summary
The Sales Manager needed to choose a room rate and overbooking limit for peak season without a reliable way to weigh expected profit against downside risk. I built a stochastic profit model with four calibrated random inputs — Poisson-distributed online reservations and walk-ins, Binomial late cancellations under the resort's non-refundable policy, and Normal miscellaneous costs — and ran a 1,000-trial Monte Carlo simulation across a $350 vs $450 nightly rate and a 5% vs 10% overbooking allowance. The $450 strategy with a 5% overbooking limit won on every measure: +47.9% mean daily profit ($14,442 vs $9,767), non-overlapping 95% confidence intervals confirming the gap is real, and a lower chance of a bad day (0.6% vs 10.5% chance of profit under roughly $9,000). Demand volume, not cost control, turned out to be the dominant driver of profit, by a factor of about ten.
The problem
Alpine Peak Snow Resort's Sales Manager had to set a nightly room rate and an overbooking allowance for peak season, under three sources of uncertainty — booking volume, late cancellations, and daily operating costs — without a reliable way to compare strategies on both expected profit and downside risk.
The outcome
Built a single-day stochastic profit model combining four calibrated random inputs (Poisson bookings and walk-ins, Binomial cancellations, Normal misc. costs) and ran a 1,000-trial Monte Carlo simulation across two pricing scenarios. The $450/night strategy delivered a mean daily profit of $14,442 versus $9,767 at $350 — a statistically significant 47.9% uplift, confirmed by non-overlapping 95% confidence intervals, with a lower probability of a bad day.
Model comparison
Mean daily net profit1,000-trial Monte Carlo · higher is better
Architecture
The conceptual decision model — stochastic inputs, decision variables, and fixed inputs flowing through to daily net profit.