Companies are often faced with the need to make supply chain investment decisions to support new product introductions while there is still significant uncertainty in the demand expectations. This situation forces tradeoffs in supply chain capacity investments versus the risk of lost sales. To address this tradeoff, we have proposed a methodology that leverages expert input for quantifying demand uncertainty assumptions, simulation to develop demand scenarios that combine existing products and new product introductions, Mixed Integer Linear Programming optimization to determine the most efficient investment strategy for each demand scenario and visualizations to enable the practical exploration and comparison of a large number of optimized scenarios, uncovering more profound insights into the tradeoffs between supply chain investments and the risk of lost sales. Our work shows that incorporating demand uncertainty into a robust scenario analysis can reveal the capacity bottlenecks and available opportunities of a companies’ supply chain to meet increasing demand. Furthermore, we discovered that the most cost-effective strategies for covering demand are not always intuitive. Lastly, we demonstrated the power of visualizations in identifying the less intuitive tradeoffs that cannot be identified with more simplistic tools.