Abstract: Human factors have been identified as one of the major root causes to most catastrophic incidents in processing industries. However, the estimation of failure probabilities due to human factors with time; in other words, the ability to learn from the process history has received little attention. In this study, a hybrid methodology is developed which combines a conventional Swiss Cheese model with Bayesian theory to predict the failure probabilities by human factors. Accident sequence precursor data within a period of interval time is utilized to generate the dynamic mode. This methodology is then applied to offshore safety assessment study. The result shows that the failure probabilities of human factors can be predicted for the desired time interval. It is proven that the approach has the ability to learn from process history and act as a predictive tool that provide early warnings toward human deficiency. A robust action plan can then be taken to enhance the overall human performance and ultimately to reduce the likelihood of major incidents.