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Machine learning (ML), artificial intelligence (AI), and other advanced analytical tools can make an impact on employee and public safety.  Predictive Safety Analytics applications are data-driven algorithms that are applied to a wide variety of contexts, supporting both strategic safety initiatives and operational tactics.

These technologies are applied to predict which employees may be at risk for an incident or injury during a shift.  At the strategic level they reveal and quantify incident causal factors, providing fact-based guidance for improved training and safety policies.  More recently, Generative AI applications, such as Large Language Models, are providing deeper insights about safety incidents through conversational interfaces.

The Benefits of Safety Analytics

We all want our colleagues to return home safe and secure, with all limbs intact, with no injuries or trauma.  A focus on personal wellness also has an impact on the business.  Safety programs and processes, while protecting individuals, also bring benefits to an organization. Safety analytics can:

  • improve safety metrics such as OSHA or FRA reportable injury rates, DART (days away restricted time), lost time incidents (LTIs), and near misses;
  • reduce lost productivity and improve operational metrics, like manufacturing OEE;
  • reduce equipment damage;
  • reduce legal liability, litigation and settlement costs, and medical treatment payments;
  • reduce insurance premiums;
  • supply information to assist managers in safety-related coaching.
  • Improve management/labor relations;
  • support managers in attaining their safety-related performance metrics;
  • aid safety professionals in measuring safety program effectiveness and in designing impactful policies, programs, and training;
  • contribute to corporate sustainability goals.

These benefits delivered by safety analytics rely on data- and fact-based decision making.


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