In resource-rich but data-constrained mining regions, deterministic estimates of equipment productivity often mask critical operational risks, leading to flawed strategic decisions on fleet investment, maintenance, and national resource forecasting. This paper bridges a key gap in the literature by introducing a probabilistic Overall Equipment Effectiveness (OEE) framework that quantifies uncertainty in the shovel–truck fleet performance at one of the world's largest copper mines. Using Monte Carlo simulation calibrated with extensive field data—including photogrammetry-based cycle production and dispatch logs—we model joint variability in availability, utilization, and performance efficiency. Results reveal wide OEE distributions: 8–52% (mean: 25%) for shovels and 27–53% (mean: 38%) for dump trucks, where low utilization, driven by suboptimal dispatch and operational coordination, is the dominant constraint. Critically, we demonstrate that probabilistic OEE is essential for robust, risk-aware planning in aging fleets. The framework offers a low-cost, transferable tool for evidence-based resource policy and operational optimization in developing economies.