Explainable Artificial Intelligence (XAI) stands as a crucial area of research essential for advancing AI applications in real-world contexts. Within XAI, Global Sensitivity Analysis (GSA) methods assume significance, offering insights into the influential impact of individual or grouped parameters on the predictions of machine learning models, as well as the outcomes of simulators and real-world processes. One domain where GSA is particularly valuable is engineering optimization. When designing a mechanical component, it is crucial to meticulously set up the optimization problem by selecting parameters that play a significant role and are, therefore, suitable for assignment as variables. This choice significantly influences the outcome of the optimization procedure, by making it ineffective if the wrong variables are chosen. This study presents GSAreport, an open-source software we recently developed, and assesses its performance on real-world test cases from crashworthiness optimization. We examine the impact of parameters of different natures on the performance of a crash box, for varying dimensions and sample sizes. Our findings underscore the relevance of our tool as (1) an instrument for gaining a deeper understanding of the features contributing to component performance and (2) a preliminary step to reduce dimensionality in optimization, thereby enhancing algorithm efficiency while maintaining flexibility. We highlight the potential of GSAreport as an interdisciplinary and user-friendly tool capable of expediting design processes and enhancing the overall understanding across diverse application areas.