This paper describes a generic, pilot model that could assess risk attributed to error in continuing airworthiness (CAW) of air transport. The model addresses risk contribution from approved organizations involved in CAW process, to delivering an airworthy aircraft. The risk assessment method is based on Bayesian Belief Networks (BBN) to enable the translation of subjective judgments on risk into a quantified numerical output that can be better comprehended by non-subject experts. Furthermore, the use of evidence-based numeric data from case histories, when available, makes BBN more reliable and robust than other methods, as well as transparent and unambiguous. Quantification of risk is based on the conditional probability of a system error, multiplied by its potential consequence, presented in a numeric scale. In the model, the CAW process system is divided into 5-functional subsystems in which their error and non-error performance can be monitored, and error together with its consequence captured. From this information, conditional probabilities of system error can be computed. The subsystems are decomposed to causal chains, organizational hierarchies and weak points in the CAW processes and events where errors occur. Unlike traditional risk assessment methods, this model is primarily based on historical recorded data of experience as much as possible, relying on expert opinion to fill gaps where data is unavailable or not recorded. However, by identification of variables, the model encourages data to be maintained in future, so that expert opinion inputs could be replaced with actual data, and thereby increasing fidelity with time. As the model could be used to monitor the safety of an organization's output, it could be an essential management tool in the Safety Management System (SMS) of the organization. The model could also help to test the sensitivity of safety risk level to any of the contributory causes,including corporate policies that impact on CAW processes. The taxonomy used in the model would identify more important causal factors, and thereby help to prioritize the resourcing of preventive measures that could more effectively minimize risk. Sharing data with an approved organization, the model could also be used by a national aviation authority (regulator) to assess the risk level of approved organizations. An example would be the use of the model as a management tool to determine priorities in the implementation of a risk-based oversight concept. The model has been successfully tested with simulated CAW process data and is undergoing further development.
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