Precise and forward looking simulation input modelling is pivotal to achieve accurate simulation modelling results and therefore sound decision making. We present an input modelling method that allows the aggregation of quantitative empirical data and quantified expert opinions via the process of Bayesian updating of prior distributions with expert input. Through this combination of input sources, the entropy reducing information of each input source is utilized through a formal method to reduce the uncertainty about the unknown estimated input parameters. While various methods exist to parameterize simulation models, many face limitations under realistic assumptions. Simulation modelers use empirical data for simulation model parameterization, yet this method faces limitations if the modeled process undergoes changes or when there are various viable sources of data on the modelled process.
By using input from forward looking experts for model parameterization, modelers can attempt to overcome such limitations though at the cost of risking other biases. The simulation model parameterization method presented here seeks to overcome the downsides of both these methods. For this method, we draw on a large existing body of research from academic fields as diverse as actuarial sciences, reliability engineering and Kalman filtering. We derive exemplary applications challenges in simulation input modelling to demonstrate the method. In a final benchmark to alternative model parameterization and data aggregation methods, we demonstrate the impact of the varying methods.