In the hot-dip galvanization process, the formation of dross particles in the zinc bath and dross build-up on zinc bath hardware, negatively impacts the quality of the produced coating. CFD simulations of the zinc bath provide insights into which process conditions favour the formation of these particles. However, using simulations to control the industrial galvanizing process in real time is uncommon, as their computation is extremely time-consuming. A reduced analytical model is therefore developed in Python that uses current process data to predict future dross formation rates in the galvanizing process and supports the user in improving product quality. This model utilizes energy balances to calculate the zinc bath’s mean temperatures. Mass balances are used to generate aluminum and iron concentrations and combined with thermodynamic relationships and kinetics to predict the global dross formation rates. The model is validated by comparison with process data provided by voestalpine.