Modeling and simulation are critical disciplines for analyzing and predicting the behavior of complex systems without manipulating real-world operations. By creating and experimenting with digital representations of systems, analysts can test new policies, identify bottlenecks, and explore potential outcomes in a controlled, cost-effective environment. The purpose of this document is to conduct a detailed comparative analysis of three primary simulation methodologies—discrete-event, continuous, and Monte Carlo—to empower technical professionals to select the most suitable technique for their specific analytical needs.

To fully appreciate the differences between these powerful methodologies, it is essential to first understand the formal definitions of modeling and simulation.

Modeling is the process of representing a model which includes its construction and working. This model is similar to a real system, which helps the analyst predict the effect of changes to the system. In other words, modelling is creating a model which represents a system including their properties.

Simulation of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system. In other words, simulation is the process of using a model to study the performance of a system.

To effectively compare these methodologies, it is first necessary to understand the fundamental ways in which systems are classified.