The use of artificial intelligence (AI) is revolutionizing the way in which Environment, Health, and Safety (EHS) processes are managed. EHS departments have been slow to adopt technology, relying on spreadsheets and emails, while other business areas have implemented advanced customer databases and automated maintenance management systems. AI can help EHS managers take proactive measures to improve safety while supporting other business functions.
Here are five ways in which AI is transforming EHS processes:
Traditionally, near miss reporting relied on people to identify and report safety concerns. However, with AI, computer vision can supplement worker-reported near misses with automated reports, resulting in a more consistent measure of events. This provides EHS managers with a more accurate picture on which to base decisions.
AI can automate housekeeping checks using computer vision to flag up obstacles left in areas that should be clear, such as walkways, thus leaving EHS managers with more time to talk to people and manage concerns that arise.
Rather than needing to “catch” people not wearing PPE, EHS managers can leave CV to monitor when it is – and isn’t – worn. AI will help EHS managers to spot patterns of behavior, such as people working further away from changing rooms being less likely to wear appropriate PPE. This gives EHS managers a better understanding of where problems occur, allowing them to identify the reasons for non-compliance and develop solutions.
Compute vision can provide rich data about pedestrians in vehicle zones, vehicles crossing pedestrian zones, or vehicles and pedestrians coming into close proximity. EHS managers can use this data proactively to prevent accidents, such as improving lighting, signage, or training.
In summary, AI is a powerful tool for EHS managers to manage safety more effectively. Fortunately, configuring an AI system such as computer vision is easy, and EHS managers do not need to be experts in machine learning to use these tools.
Automated supervision, monitoring and risk prediction in areas of safety, quality and productivity.
