5-6. Visualizing EHS Risks and Improvement Measures with Big Data Analysis

In the EHS (Environment, Health, and Safety) field, big data analysis is significantly contributing to risk visualization and the optimization of improvement measures. While traditional EHS management has been reactive, focusing on responding to accidents and incidents after they occur, using data allows for a proactive, prevention-based approach. Specifically, by integrating and analyzing vast amounts of data from IoT sensors, wearable devices, and environmental monitoring systems, companies can identify early signs of risk and enable swift action.

1. Identifying EHS Risks with Big Data

Many workplace accidents and equipment failures result from the accumulation of minor anomalies or near-misses. Big data analysis integrates and processes various data points—such as work environment data, operational history, health checkup results, working hours, and measurements of noise and dust—to extract unusual patterns. This process clarifies risk factors that a person might overlook and helps identify specific areas for improvement.

2. Real-Time Monitoring and Predictive Detection

When combined with AI, big data enables real-time anomaly detection. For example, continuously monitoring temperature and vibration data can predict equipment degradation, preventing serious accidents. Additionally, analyzing employee vital signs and work logs can provide early detection of fatigue or heatstroke risk, thereby strengthening safety management.

3. Application for Improvement Measures

By using the analysis results to plan improvement measures, companies can effectively reduce risks. For instance, if data shows that noise levels consistently exceed standards on a specific production line, the company can consider adding soundproofing or adjusting shift schedules. Identifying work procedures with a high probability of accidents can also lead to revising training programs and SOPs (Standard Operating Procedures).

4. Benefits for Management

EHS risk management that uses big data isn't just about on-site improvements; it also contributes to business strategy. In addition to reducing costs from accidents and environmental violations, it enables data-driven, reliable explanations for ESG (Environmental, Social, and Governance) ratings and disclosures to investors. This directly leads to sustainable management and increased corporate value.

Conclusion

The visualization of EHS risks through big data analysis is an essential method for advancing occupational health and safety and environmental management. By focusing on real-time risk detection, scientifically-backed improvement measures, and positive impacts on management, companies can significantly enhance the quality of their EHS management.