Stochastic analysis can be a valuable exercise for electric companies interested in assessing and mitigating the risks posed by the uncertainty fundamental to resource planning. This study demonstrates the use and value of EPRI’s new Stochastic Planning Inputs Tool (SPI-Tool) for efficiently and accurately characterizing these uncertainties, a key part of performing a stochastic analysis. In this study, SPI-Tool is used to generate stochastic samples of future loads, natural gas prices, and carbon prices. These samples are evaluated using an open-source production cost model (PCM), optimizing dispatch for each sample over a 20-year planning horizon in a simple demonstration study. While Monte Carlo-based stochastic analysis can be computationally intensive, this study also demonstrates that parallelization can significantly reduce computational burden—achieving a considerable reduction in total computation time. Overall, the demonstration study illustrates how risk-focused insights can be systematically developed to support the selection of a robust resource portfolio.