Simulation And Modeling Questions Medium
Sensitivity analysis in simulation and modeling refers to the process of evaluating how changes in input variables or parameters affect the output or results of a simulation model. It is a technique used to understand the sensitivity or responsiveness of the model to different inputs and to identify which inputs have the most significant impact on the model's output.
The main objective of sensitivity analysis is to assess the uncertainty and variability in the model's output by systematically varying the input parameters within their specified ranges. By doing so, it helps in identifying the key drivers or influential factors that significantly affect the model's behavior and outcomes.
Sensitivity analysis can be performed using various methods, including one-factor-at-a-time (OFAT), factorial design, Latin hypercube sampling, and Monte Carlo simulation. These methods allow for the exploration of different combinations and ranges of input values to observe their impact on the model's output.
The results of sensitivity analysis provide valuable insights into the robustness and reliability of the simulation model. It helps in understanding the relationships between input variables and output results, identifying critical parameters that require more accurate estimation or further investigation, and guiding decision-making processes by highlighting the most influential factors.
Overall, sensitivity analysis is a crucial step in simulation and modeling as it enhances the understanding of the model's behavior, improves the accuracy of predictions, and supports informed decision-making by quantifying the impact of input uncertainties on the model's output.