This article delves into sensitivity analysis within simulation models of real systems, focusing on the impact of variability in independent input factors (x) on dependent system outputs (y). It discusses linear and nonlinear regression to analyse and represent relationships between input factors and system responses. This study encompasses three sensitivity analysis areas: factor screening, local sensitivity analysis, and global sensitivity analysis, highlighting their roles in understanding the significance of factors in simulation models. The practical application of sensitivity analysis becomes clear through a case study in a manufacturing system. The case study utilises the Simio simulation system to investigate the impact of input factors on production lead time and work in process (WIP). The analysis uses regression to quantify the impact of seven factors, showcasing the most significant ones with tornado charts and emphasising the application of sensitivity analysis to optimise system responses.

In the presented article, the initial phase is focused mainly on familiarization with the production focus of individual enterprises and their financial security, which provides a basis for the proposal of possible solutions to shortcomings. A more detailed study of production through simulation studies using Process Simulate software provides a more comprehensive view of production in the assessed enterprises, where the current course of production at workplaces is evaluated in detail, and suggestions are then offered to improve the revealed shortcomings. In the end, there is an assessment of the companies from the point of view of homogeneity, so that it is then possible to make an inter-industry comparison of the revealed errors and shortcomings, with the result of finding common recommendations for a wide industrial spectrum.