Input data for simulation models

Since input data drive a simulation model, it is important to understand how to correctly identify what data are required as well as how to properly collect them. This paper is an introduction to identifing and collecting input data for creating simulation models. The paper starts with the first key steps to any simulation project: determining the objectives, identifying the performance measures and identifying the options to be tested. Starting with these both defines the scope and helps in correctly identifying what input data are needed. The paper then describes how to identify the required input data for designing a simulation model. It then discusses data collection strategies for existing and non-existing data. One of the great strengths of using simulation as a decision support tool is its ability to reflect random behavior. However, this requires the definition of where and how randomness occurs. This is done through the use of distributions. This paper discusses the use of a number of standard distributions and their typical applications.