Mineral resource classification plays a critical role in communicating confidence levels, yet supporting methodologies such as drill-hole spacing analysis and geostatistical simulations are not consistently applied in routine updates of deterministic resource models. As a result, both local and global uncertainty quantification remain underutilized, and drilling requirements are often defined without a clear link to uncertainty reduction. This paper introduces a mineral resource uncertainty and drilling policy framework developed and applied at Compañía Minera Doña Inés de Collahuasi (CMDIC). The framework quantifies the uncertainty of each mineral resource model update when new data are available and provides an initial approach to determining drilling requirements based on CMDIC’s risk acceptance policies for different project stages. The proposed approach is a stochastic workflow that uses the current deterministic mineral resource model and database to generate geostatistical simulations. These simulations account for data quality, quantity, geological variability, and copper-grade variability. They form the basis for mineral resource classification with an explicit uncertainty quantification and provide an optimized drilling campaign to achieve desired risk levels subject to budget constraints. Because stochastic modeling updates faster than deterministic modeling, it provides timely insights from new drilling campaigns and delivers valuable insights for subsequent deterministic geological and grade modeling updates. The implementation of this workflow demonstrates its feasibility as a standard step following deterministic modeling, leading to cost-effective mineral resource development and management by aligning technical practices with the organization’s strategic objectives and risk preferences.

A pivotal aspect in the evaluation of mining projects is the classification of mineral resources, which directly influences the definition of mineral reserves and significantly impacts mine planning and operational stages. However, the current classification methodologies often need specificity regarding the methods and parameters employed and heavily rely on the qualified/competent person’s judgment. This study addresses these gaps by proposing a pioneering fuzzy approach to assess grade and tonnage uncertainties. By allowing for overlapping resource categories and directly incorporating economic, geological, metallurgical, environmental, and operational criteria, we aim to provide tools for decision-making and for the final classification and public disclosure of mineral resources and reserves. We demonstrate the potential of our proposed methodology through an application to an iron ore deposit case study and through a detailed discussion on its uses, contributions, strengths, weaknesses, and on whether it complies with international reporting codes.