In a recent article on this blog site we looked at how Ishikawa diagrams can be used to represent the causal drivers or factors for risk. However, these schematics lack the statistical models necessary for quantifying which factors are contributing most to the top event.
Perhaps one of the biggest problems with all causal analysis techniques are the conclusions that risk analysts draw from their assumptions, yet they often fail to test these postulations. In this blog we are going to look at how causal factors in a Bowtie or Ishikawa diagram can be investigated by "adding-in" the relevant statistical models. Our aim here is to identify which factors contribute most to a top event by considering both their frequency of occurrence as a driver but also how each variable intertwines and correlates in a network of factors to spawn an outcome.
Perhaps one of the biggest problems with all causal analysis techniques are the conclusions that risk analysts draw from their assumptions, yet they often fail to test these postulations. In this blog we are going to look at how causal factors in a Bowtie or Ishikawa diagram can be investigated by "adding-in" the relevant statistical models. Our aim here is to identify which factors contribute most to a top event by considering both their frequency of occurrence as a driver but also how each variable intertwines and correlates in a network of factors to spawn an outcome.
