This paper is the first documented research effort on how simple meta-models can be used in simulation-based investment analysis. Modern computers allow the construction and simulation of near real-world emulating models, often referred to as “digital twins”, that offer requisite variety to real world phenomena, such as an industrial investment. These models can be extremely complex and computationally demanding which reduces the scope of their practical applications. This is where meta-models can help. Meta-models are simple black-box models that are fitted with the input-output -combinations from more complex models to be able to approximate complex model behavior. As the simple meta-models are very fast to solve they may be used to explore much larger solution spaces with considerably higher speed and less computing power needed than the original models. We demonstrate how the meta-modeling approach can be used in the context of metal mining investment analysis that is originally conducted with a dynamic system model constructed based on a real-world metal mining investment. We show how two simple meta-models, a linear regression model and a regression-tree model, can be used in gaining insight about a suitable financing-mix for the said metal mining investment.