Fuel performance codes are essential for predicting fuel rod behavior in nuclear reactors. These codes comprise a complex system of models with empirical constants that require calibration against experimental data. To support this calibration, this study conducts a preliminary sensitivity analysis using the Morris Method on a time-dependent model of Rod 1 in assembly IFA-432 from the Halden Reactor Project, employing the fuel performance solver OFFBEAT. Sensitivity analysis is typically performed on scalar outputs and for time-dependent models, ideally covering all time steps to capture input impacts on outputs. However, analyzing each time step in long series leads to an unmanageable number of sensitivity measures, complicating interpretation. To address this, key moments or time-averaged outputs are used to determine influential parameters. In this study, we aim to reduce reliance on expert judgment in selecting these moments by applying Principal Component Analysis (PCA) to generate reduced-dimensional outputs for sensitivity analysis, providing a more objective, data-driven approach to identify key parameters. We propose comparing these metrics-based on specific irradiation stages, time-averaged value, and PCA-derived quantities-for a deeper understanding of parameter influence on model output. This study offers a practical approach for future calibration by prioritizing key parameters, reducing computational complexity. At the conference this sensitivity analysis will be extended to additional rods within the IFA-432 assembly to ensure a robust calibration process adaptable across varied experimental conditions.