  Identifying and mitigating safety risks is paramount in a number of
industries. In addition to guidelines and best practices, many industries
already have safety management systems (SMSs) designed to monitor and reinforce
good safety behaviors. The analytic capabilities to analyze the data acquired
through such systems, however, are still lacking in terms of their ability to
robustly quantify risks posed by various occupational hazards. Moreover, best
practices and modern SMSs are unable to account for dynamically evolving
environments/behavioral characteristics commonly found in many industrial
settings. This article proposes a method to address these issues by enabling
continuous and quantitative assessment of safety risks in a data-driven manner.
The backbone of our method is an intuitive hierarchical probabilistic model
that explains sparse and noisy safety data collected by a typical SMS. A fully
Bayesian approach is developed to calibrate this model from safety data in an
online fashion. Thereafter, the calibrated model holds necessary information
that serves to characterize risk posed by different safety hazards.
Additionally, the proposed model can be leveraged for automated decision
making, for instance solving resource allocation problems -- targeted towards
risk mitigation -- that are often encountered in resource-constrained
industrial environments. The methodology is rigorously validated on a simulated
test-bed and its scalability is demonstrated on real data from large
maintenance projects at a petrochemical plant.
