Simulation to Infer Future Performance Levels Given Assumptions

Abstract

Decision makers deal with uncertainty when considering future scenarios. Performance levels depend on multiple influences with uncertain future values. To estimate future performance, managers make assumptions about likely future scenarios and uncertain future values of performance components. To evaluate decision alternatives, the “best” and “worst” case outcomes are compared. Monte Carlo simulation can be used to simulate random samples using decision makers' assumptions about performance driver values, and those random samples can then be combined to produce a distribution of likely future outcomes. Inferences from a simulated distribution of outcomes, given assumptions, can then be made to inform decision making and to adjust assumptions.