In this article, a review of the main techniques proposed for the analysis of random components in stochastic simulations is presented. Two main topics are discussed: classical modeling using univariate distributions and advanced modeling techniques. The use of a library is introduced in the first topic; this library is named Simple Analyzer and can be downloaded for free from the web. In the second topic, techniques for input analysis that are currently not included in available commercial software of simulation experiments are discussed. Also the fitting of data of multimodal, correlated and time-dependent input components, and the incorporation of parameter uncertainty in the input model are analyzed.