Case study · 2 pages

Visualizing Risk Precursors

What lines up before a high-consequence incident?

Serious, low-frequency incidents have complex precursors that are hard to see. This brief shows how Insight Risk Systems uses Bayesian network analysis to reveal and rank those precursors, with results from more than 11,000 incident records at a power generation and distribution company, modelled separately for outages, injuries, environmental issues and near misses.

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Why high-consequence, low-frequency incidents have precursors that are harder to see than the ones behind recordable injuries.
How Bayesian network analysis ranks the relative contribution of each precursor, so interventions go where they matter most.
Results from more than 11,000 incident records at a power generation and distribution company, modelled separately for outages, injuries, environmental issues and near misses.
The project deliverables, from multi-label text classification to filling gaps in missing incident data.

Who should read it?

Operations, maintenance and safety leaders with large incident databases and limited time to read them.

Solution

Visualizing Risk Precursors

Who should read it?

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