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AI Preventing Safety Incidents
Ensuring safety compliance is the duty of on-ground HSE officers. A ratio of 1:200 (safety officers to workers) for the usual running of factories/refineries and 1:50 for high-risk operations like construction has been stipulated as ideal by IOSH, but it is not uncommon for this figure to touch even 1:500. To add to that, the area of supervision under a single safety officer can be as high as 34000 square feet!
A single HSE officer must keep their eyes peeled for compliance violations across all the activities that are going on in their zone of supervision. In exactly 100 such activities, they can expect 19 safety violations and 2.8 incidents just waiting to happen on average. This is where the concept of YOLO comes in.
YOLO – You only look once
AI can observe and report PPE non-compliance issues in real-time, enabling the reviewer to take corrective action before something goes wrong. AI powered solutions continuously monitor visual or sensor-based sources of data at your workplace for indications of compromise. Depending on its training, AI can detect a wide range of operational risks to processes, assets and people. You ONLY HAVE TO LOOK ONCE!
Why YOLO?
Monitoring the workforce for HSE non-compliance is a monotonous part of an HSE officer’s job. They are additionally responsible for several other tasks like planning and reviewing policies, advising trainers, preparing safety education, conducting risk assessments, identifying bottlenecks, preparing safety plans, etc., which demand their own, much more justified share of time and attention.
AI Responding to Safety Incidents
Despite everything we do, accidents are bound to happen. But how we respond to such situations determines everything from the potential damage to repercussions and even repetition of similar mistakes in future.
An ideal accident response involves assessing the damage, securing the scene, and reporting. But contemporary siloed approaches to HSE hamper planning and coordination, and with it, the ability of the administration to respond to incidents efficiently.
For proper planning and coordination, visibility across the entire chain of command is imperative. This is where the unity of data and interface comes into play. AI dashboards can unite all relevant data and automate processes generating insights, ensuring they are available to all important stakeholders in the hour of need.
Then, every incident is an opportunity to learn something new. RCA (Root Cause Analysis) is the process of determining what went wrong and contemporary RCA methods, just like contemporary incident responses, are bogged down by a lack of detailed endpoint data, process logging, and event correlation.
How good is AI with RCA? Turns out, AI can help with all 3 stages of RCA – detection, analysis and remediation. It can help eliminate blind spots, provide better prioritization context and implement long term solutions, not just workarounds.
Can you trust AI for Safety? – A Story of learning from Mistakes
How good is AI with RCA? Turns out, AI can help with all 3 stages of RCA – detection, analysis and remediation. It can help eliminate blind spots, provide better prioritization context and implement long term solutions, not just workarounds.
Statistically, the deployment of AI proved to be a game changer for a petrochemical enterprise in the APAC region. Within a month into deployment of T-Pulse (an outstanding HSE AI), dangerous workforce exposures went down by 80%, the number of non-compliance issues reduced by 10X and a gain of 8% was observed in overall productivity.
Summing Up
Usually, organizations are constantly reminded that health and safety failure is expensive. Of course, it is, but that is just one side of the coin. Pragmatically, the benefits are much greater. A Comcare paper reveals an average return of $5.81 for every dollar invested by companies into workplace HSE programs.
Despite that, with the global market getting increasingly competitive, HSE is one of the worst hit departments. The global HSE budget was cut by over 13% between 2019 and 2022 and further cuts are still planned. The staff numbers too have been reduced by 22% since 2010.
