A well-known source of uncertainty for mining operations comes from errors in the estimation of the geometallurgical variables, such as mineral grades and recoveries. Accordingly, relevant decisions like the economic envelope should be based on several outcomes or scenarios reflecting the uncertainty, rather than on a single model of the deposit. A number of techniques have been proposed to deal with the abovementioned problem. These techniques can be classified into one of the following types: (1) post-evaluating the variability of a fixed solution (example, one scheduling or pit), (2) integrating variability into the decision process (example: computing a few plans and choosing the "best" one), and (3) creating flexibilities by leaving some decisions open so they adapt to the scenarios (example: real options and stochastic programming). In this paper we present an approach close to the second technique, but, instead of using a few scenarios, we construct a large number of them (in this case conditional simulations of a deposit). Then, for each scenario, we compute an optimal solution, namely, a final pit or a schedule, depending on the example. We also construct several reliability indexes and many possible decisions sets, ranked according to their reliability. The idea of using a large number of scenarios is to accurately understand and quantify the trade-off between the promises in business value (but also in tonnage and others) and how much reliability can be expected from the decisions that backup them. We apply the methodology to a real scale problem, in which conditional simulations of the deposit are produced with the turning bands method, and perform the study of the trade-off of the final pit values and tonnages, as well as long-term schedules of the mine.