Quantifying simulation output variability using confidence intervals and statistical process control

Two types of variability can occur in model output: variability between replications and variability within each replication. The objective of the model combined with the type of output variability determines which tool is more appropriate for output analysis. Many output analysis techniques are used to translate simulation model results into a format that answers the model objective. This paper compares two tools for output analysis: confidence intervals and statistical process control. Each tool quantifies a different type of variation from the model results. As such, statistical process control is applied beyond monitoring the consistency of run data. A supply chain example with one factory, multiple parts, and multiple distribution centers is used throughout the paper to illustrate these concepts