Real-Time Prediction for Targeted Safety Oversight

Though their approaches and exact responsibilities vary by country and by region, safety regulation organizations generally help to foster safe environments at work, at home, and at play for all people. Ultimately, employers and asset owners are responsible for reducing environmental, technical, and health hazards to keep employees and the public safe, overarching regulatory bodies can provide enhanced focus and comprehensive safety services to support these efforts.

Because it is impossible for any regulatory body, whether public or private, to inspect every single facility on a regular basis, a strategic and targeted approach is critical. As such, the best organizations leverage data and smart use of algorithms to deliver necessary safety oversight with a fixed level of resources.

Technical Safety BC is an independent, self-funded organization mandated to oversee the safe installation and operation of technical systems and equipment across British Columbia (BC). In addition to issuing permits, licenses, and certificates, they work with various industrial actors to reduce safety risks through assessment, education and outreach, enforcement, and research.

Conducting physical assessments is costly, and false positive inspections (sending safety officers to inspect sites where no apparent risk to the public or asset owner exists) can result in significant opportunity costs each year. Those same resources could be better allocated within the safety system; therefore, finding a way to more accurately predict hazards is of high strategic value to the organization, and it creates greater safety benefit to the public.

Go deeper: Build a predictive maintenance engine with GIS data

The Challenge

Technical Safety BC was looking to find more high-hazard sites while operating at the same resourcing level by introducing more sophisticated machine autonomy in the risk assessment process. Some of the challenges faced included:

• Uncoordinated heterogeneous data sources;
• Data quality;
• Speed of collaboration;
• Training challenges in the use of machine-recommended predictions.

To address these challenges, Technical Safety BC created a dedicated data analytics and decision science department responsible for integrating advanced analytics into all parts of the organization. The team chose Dataiku as the tool to bring efficiency gains to the data process.

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Thanks to Dataiku, Technical Safety BC is able to:

• Quickly prototype, test, iterate on, and deploy data-driven solutions.
• Easily reuse models over and over again that work well rather than writing separate queries for similar projects.
• Spend more time on innovative new ways to experiment with various models and work more efficiently.
• Build a culture of experimentation and rapid prototyping.
• Accomplish more for safety outcomes with a small team of data scientists.

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Technical Safety BC used Dataiku on a project that leverages machine learning predictions to find common features and signals relating to risk factors. The predictive model adapts quickly to reflect any emerging risks and automatically shifts resource allocations accordingly based on the latest knowledge. A/B testing was used to test the new model that predicts high-hazard sites.

Since the deployment of the new machine learning model developed with Dataiku, Technical Safety BC’s predictive performance improved by 85% for the electrical technology compared to previous methods.

Machine learning also significantly reduced the total number of mandatory inspections, which will help optimize safety officer time so they can apply their time and expertise to potentially higher-hazard situations and make a greater impact on safety.

In conclusion, this new approach offers improved performance, shorter deployment time, better scalability, and paves the road for future improvements in service to public safety.