Monte Carlo simulation models uncertainty by sampling input variables from their probability distributions thousands of times and aggregating the results into a distribution of possible outcomes — not a single point estimate.
In project management it turns PERT three-point estimates into a probabilistic completion forecast ("80% chance of finishing by week 14"); in operations it stress-tests schedules, capacity, and inventory policies against variability.