Citation

Roelen ALC, Lin PH, Hale AR. Safety Sci. 2011; 49(1): 5-10.

Copyright

(Copyright © 2011, Elsevier Publishing)

DOI

10.1016/j.ssci.2010.01.022

PMID

unavailable

Abstract

Event analysis is needed to learn and improve safety. In air transport, 'occurrences' are routinely reported by pilots and air traffic controllers, and in-flight data analysis systems automatically monitor aircraft system behavior and capture parameter threshold exceedances. The safety analyst of a large airline has to analyze dozens of occurrences each day. To understand why events happened the analyst has to go beyond the given information and make causal inferences. The analyst is able to do this for causal factors closely related in time and space to the event itself by applying individual knowledge and expertise. But typically the result of the analysis is ad hoc reaction to each individual event. Systematic analysis is needed to find areas of improvement for factors that are further removed from the event (latent factors). New tools are needed to help the analyst in this respect. There is a need for models that represent possible causal event sequence scenarios that include technical, human, and organizational factors. Building such models is a huge task, and requires the combination of detailed knowledge of all aspects of the system, processing huge amounts of data, a substantial mathematical background and the ability to capture this all in a user friendly software tool to be used by the safety analysts. Experience in Causal Modeling of Air Transportation System (CATS) in the Netherlands and similar projects in FAA and Eurocontrol in aviation shows that this is indeed a formidable task, but it has to be done to further improve safety.