There are few if any systems in use to categorize and analyze the causal factors leading to significant incidents. This presentation explores a new categorization methodology and provides causal factor data analysis of real incidents that shows how the process contributes to improving safety.

Detailed accident investigation reports were reviewed from 40 major incidents containing literally hundreds of causal factors. Repeated passes were made through the causal factors to identify commonalities. Thirty-two discrete categories of causal factors were identified. Detailed definitions were written and multiple examples provided for each category. The categories were then tested with a new set of causal factors to ensure they were both exhaustive and mutually exclusive. The twenty-eight final categories were then put into question form for future conversion to an expert system.

The analytical advantages of the categories were tested with a group of five major crane incidents from around the world. The incident reports were reviewed and the causal factors identified. The methodology was used to categorize the causal factors and a Pareto chart was developed. This simple chart allowed the discovery of repeated causal factor categories and uncovered where corrective actions for identified root causes of the casual factor categories were inadequate.

This methodology not only assists the users in categorizing causal factors, it assists in identifying generic causes (cuts across organizational boundaries) and assessing the effectiveness of corrective actions for root causes. It has also formed the basis for building an expert system to identify causal factors, which is currently under development.

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