Optimizing EHS Protocols and ML-Powered Safety Management

Industry Technology
Region Global
Solution Optimizing EHS Protocols
Context
The case study highlights how an energy equipment manufacturing company improved its Environment, Health, and Safety (EHS) management using a machine learning (ML)-powered solution. The system accurately categorized incidents, enabling a detailed analysis of safety reports. By implementing advanced classification techniques and NLP tools, the company identified root causes, reduced worksite incidents at several locations, and enhanced decision-making. The solution's flexible, scalable design allowed for greater visibility into safety risks, resulting in improved safety protocols and fostering a proactive approach to incident prevention.
Problem statement

Our client, a well-known name in the energy equipment manufacturing and services sector, faced significant challenges in addressing and reducing worksite incidents, with safety being a top priority. To tackle this issue comprehensively, Environment Health and Safety (EHS) leaders needed to gain a deeper understanding of the types of incidents occurring within their worksites. Their objectives were to identify and analyze reported incidents, discern patterns among incident types to pinpoint closely linked categories, investigate the detailed reasons behind these incidents, and strategize effective plans for mitigating future occurrences.

Impact

  • A hybrid framework accurately categorizes incidents, offering a clear view of types.
  • A flexible multi-label tagging system allows tagging with multiple classes for deeper insights.
  • Analysis identifies root causes across 20 incident classes.
  • NLP-based tools enhance visibility, offering granular insights for better decisions.

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