Randomness Modeling in Supply Chain Simulation

Stochastic simulation models utilize probability distributions to represent a multitude of randomly occurring events. Theoretical distributions are commonly used to model the randomness of a real process because they help to smooth data irregularities that may exist due to the values missed during the data collection phase. These distributions can be selected either by fitting a distribution to the data collected, or based on the known properties of the process being modelled. The incompatibility between specific characteristics of the theoretical distribution and assumptions of simulation and mathematical calculus present an actual problem in supply chains. The paper is based on the analysis of mentioned contradictions. Different approaches to deal with theoretical probability distributions in supply chains are described in the paper.