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(eng): Risk management processes play a fundamental part in any business context and rely on accurate conduction of the risk assessment stage. Risks are commonly evaluated according to the preliminary definition of suitable parameters mainly aimed at highlighting their severity and the frequency of occurrence. However, it may be interesting to integrate the human factor as a parameter of evaluation, being human activity directly related with many risks of diverse nature. This contribution develops the traditional Failure Modes, Effects and Criticality Analysis (FMECA) [1] for quantitative risk analysis from a Bayesian Network (BN)-based perspective, which reveals to be useful to make more accurate predictions about parameters’ values. The main purpose consists in providing a framework for analysing causal relationships for risk evaluation and deriving probabilistic inference among significant risk factors. These parameters are represented by linguistic variables and include the human factor as a key element of analysis.