Deterministic models describing heat transfer in cold chain, microbial growth and product quality evolution are widely studied. However, it is difficult to apply them in practice because of several random parameters of the logistic supply chain (ambient temperature varying due to season, product residence time in equipment), and of the product characteristics (initial microbial load, lag time, water activity). This variability can lead to different product evolutions (microbial load, weight losses, firmness and colour change) causing product losses and health risks. This study proposes a new approach combining the deterministic and stochastic modelling (Monte Carlo) to take into account the variability of the logistic supply chain and product characteristics. The developed methodology was applied to the cold chain of cooked ham including, display cabinet, transport by consumer and domestic refrigerator, to predict the evolution of state variables such as temperature and the growth of <em>Listeria monocytogenes</em>. The impact of inputs parameters was calculated and ranked to highlight the main sources of product degradation.