A simple guide to the bowtie
Causal Analysis
Figure 1. A bowtie can be used to determine the risk treatment.
The risk bowtie is a popular, simplistic, method of risk analysis, providing visual insight into causal factors and consequences, enabling management response and controls design.
The bowtie involves disaggregating a risk event into its core event, the causal factors and potential impacts.
Side note: the bowtie is widely used in industrial, mining and safety environments. In these analyses, the event is often referred to as a hazard, and controls referred to as barriers. Root causes can be traced back to the risk source. Degradation in controls can also be identified through so-called escalation factors, which provide insight into control performance and monitoring.
An understanding of the bowtie can inform controls design:
Controls placed to the left of the bowtie can prevent the risk event from occurring.
Causal factors can also be traced back to a root cause, beyond which its considered uneconomic to implement controls.
The left of the bow tie can also be used to identify data and metrics, which inform predictive key risk indicators, to provide warning signals regarding increasing likelihood of a potential risk event.
Controls placed to the right of the bowtie, support the organisation to continue to operate through and or recover from an event. These controls are considered corrective.
Metrics focused on identifying that the event has occurred are referred to as ‘detective’, enabling rapid management response and serve to limit the impact of risk events.
Causal factors that are not associated with a control can represent a ‘control gap’.
Pros
Provides an easily understood visual representation of a risk event.
Enables risk treatment decisions, encouraging decision makers to identify control gaps and understand the difference between preventive and corrective controls.
Cons
Provides a simplified view of the risk and the relationships between the underlying factors.
Provides a static representation, and is generally used to analyse historical events, rather than predict future events.
