USING BAYESIAN NETWORKS TO MODEL RISKS AND DEFINE SAFETY INDICATORS FOR PREVENTING INDUSTRIAL ACCIDENTS: CASE STUDY RESULTS
Abstract
The paper presents the use of risk assessment for selecting key performance indicators (KPIs) in the risk management context. The work is based on a case study of methanol unloading operation from a tanker-ship to the liquid cargo terminal at Luka Koper, d.d. By expanding on the potential major accident scenario, the consequence model was used to build the probability model (Bayesian Belief Network). The latter was related to the candidate quantitative key performance indicators that have a potential to be used in the risk management process. A sensitivity analysis (related to the values of some indicators), and related uncertainties are included. The approach has been found suitable for use in the industry. However, the conditional probabilities of the model nodes need to be set in a transparent and trustworthy manner.
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