Date: 1/12/2018
Author: Descreye Solutions
Process simulation is the act of getting the mathematical or stochastic outcome of a process with known characteristics. Process simulation can take many forms including mathematical models, monte carlo simulations, discrete-event simulations, or agent-based models. Each of these methods of process simulating provides different insight into the process. Process simulation can be used in any field to gain insight into the complex behavior of a process. Some of the most common outputs of process simulations include sensitivity analysis, performance analysis, resource utilization, and cost analysis.
Process simulation is most useful when uncertainty or variability is inherent in the process. If there is not uncertainty or variability in the process, then the outcome characteristics are merely calculable. In that case a simulation is unnecessary. Process simulations help to show how the variability or uncertainty in a system can affect the overall performance. For instance, in a simple process with three steps that take the same amount of time on average, the variability of the cycle time for each step can have a tremendous impact on the amount of items that can be finished by the process in a given time period. Process simulation brings insight into the stochastic or mathematic impact on the performance of a process.
There are many tools (including OPS) that can be used for process simulation. Below is a link to a wikipedia page that includes many tools that can be used for process simulation.