Abstract

This article describes a practical approach to applying predictive analytics techniques against safety incident and near-miss data to generate actionable insights that change safety outcomes in the field. Examples illustrate three critical ways to use safety data: 1) predicting where incidents are most likely to occur, informing where to place additional resources and effort; 2) understanding the combinations of causes and sub-causes that are creating incidents, improving the focus of safety programs; and 3) revealing which proactive safety activities will best mitigate incident types predicted to occur, increasing the effectiveness of preventive measures. The authors discuss typical data and implementation challenges and encourage companies to stop waiting for "perfect" data and, instead, start applying predictive analytics to deliver targeted safety insights to supervisors and workers in the field.

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