Evaluation of a task performance resource constraint model to assess the impact of offshore emergency management on risk reduction
In this age of safety awareness, technological emergencies still happen, occasionally with catastrophic results. Often human intervention is the only way of averting disaster. Ensuring that the chosen emergency managers are competent requires a combination of training and assessment. However, assessment currently relies on expert judgement of behaviour as opposed to its impact on outcome, therefore it would be difficult to incorporate such data into formal Quantitative Risk Assessments (QRA). Although there is, as yet, no suitable alternative to expert judgement, there is a need for methods of quantifying the impact of emergency management on risk reduction in accident and incidents. The Task Performance Resource Constraint (TPRC) model is capable of representing the critical factors. It calculates probability of task success with respect to time based on uncertainties associated with the task and resource variables. The results can then be used to assess the management performance based on the physical outcome in the emergency, thereby providing a measure of the impact of emergency management on risk with a high degree of objectivity. Data obtained from training exercises for offshore and onshore emergency management were measured and successfully used with the TPRC model. The resulting probability of success functions also demonstrated a high level of external validity when used with improvements in emergency management or design changes or real data from the Piper Alpha disaster. It also appeared to have more external validity than other HRQ/QRA techniques as it uses physical data that are a greater influence on outcome than psychological changes - though this could be because the current HRA/QRA techniques view human unreliability as probability of error rather than probability of failure. The simulation data were also used to build up distributions of timings for simple emergency management tasks. Using additional theoretical data, this demonstrated the model's potential for assessing the probability of successf or novel situations and future designs.