The goal of this chapter is to provide an understanding of how simulation can be an effective business analytics technique for informed decision making. Our focus will be on applications and to understand the steps in building a simulation model and interpreting the results of the model; the theoretical background can be found in the reference textbooks described at the end of the chapter. Simulation is a practical approach to decision making under uncertainty in different situations. For example: (1) We have an analytical model and we would like to compare its output against a simulation of the system. (2) We do not have an analytical model for the entire system but understand the various parts of the system and their dynamics well enough to model them. In this case, simulation is useful in putting together the various well-understood parts to examine the results. In all these cases, the underlying uncertainty is described, the model developed in a systematic way to model the decision variables, when necessary describe the dynamics of the system, and use simulation to capture values of the relevant outcomes. This chapter sets out the steps necessary to do all the above in a systematic manner.
Kunnumkal, S. (2019). Simulation. In International Series in Operations Research and Management Science (Vol. 264, pp. 305–336). Springer New York LLC. https://doi.org/10.1007/978-3-319-68837-4_10