What role do statistical distributions play in input modeling?

Statistical distributions are fundamental in input modeling as they effectively describe the probabilistic nature of input variables. In simulation modeling, inputs often have inherent variability and uncertainty, which means they cannot be represented as fixed values. Instead, statistical distributions allow us to model these inputs as random variables that follow a certain pattern, characterized by parameters like mean and variance.

By accurately representing the variability in input data, statistical distributions enable simulations to reflect real-world scenarios and provide insights into how different factors can influence outcomes. For example, a normal distribution might be used to model the distribution of daily customer arrivals, capturing the fact that while there is an average, actual arrivals will vary.

This understanding of input variability is crucial for performing meaningful analyses, making informed decisions, and assessing risks. It forms the backbone of many simulation studies, allowing practitioners to evaluate the likelihood of different outcomes based on the stochastic nature of the inputs.