Numerical modelling of spot welding processes (RSW) can provide valuable insights in the behaviour of the process. Previous studies examined phenomena such as the formation of intermetallic compound layer in dissimilar welding [1], transport phenomena [2] and the impact of the electrode geometry on the weld formation [3]. These numerical models potentially can enhance the industry by predicting RSW quality in advance, enabling adjustments to welding schedules, where needed, to ensure optimal weld quality. However, the translation of current numerical model predictions to real-world welding scenarios faces some challenges by the uncertainties inherent to the process and input parameters. This paper explores uncertainty in spot welding numerical models, aiming to transcend current deterministic model predictions. In a first step, identification and quantification of real-world uncertainties is performed by assessing variations in input parameters. This includes both process inputs (e.g., welding time, electrical current and electrode force) and external factors (e.g. material properties and extra contaminants). Subsequently, the identified uncertainties are integrated into a deterministic model, considering both types of input parameters. The numerical model used for this is a 3D multi-physical finite element model capable of providing a temperature history and final nugget shape. Through uncertainty propagation the distribution characterising the output is derived, providing a more realistic representation of the real-world scenario of spot welding. Validation, the final and crucial step before industrial application, involves comparing the temperature history and weld nugget geometry obtained through simulation with measurements. For this, the determination of the temperature field relies on post-process microstructural analysis. Acknowledging the practical limitations of destructive examination for validation in industrial settings, this paper uses the application of non-destructive validation methods. This validation process highlights practical applicability, improving the predictive capabilities of numerical models for RSW in an industrial setting.