5 ways AI is reinventing EHS processes

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EHS departments have lagged behind other business areas in adopting technology. While sales have high-spec customer databases, and production have automated maintenance management, EHS rely on spreadsheets and emails. AI provides the opportunity for EHS to be reinvented, giving EHS managers more time to take proactive measures to improve safety, while supporting other business functions in meeting their objectives.

Here there are five ways that AI is reinventing EHS processes in organisations.

1. Near miss reporting

Traditional approach

Near miss reporting relies on people to identify something as a near miss or safety concern, to find the appropriate paper or online form, complete it, and send it to the right person. The EHS manager might have only a handful of near misses reported each month.

With AI

Computer vision (CV) monitoring multiple CCTV streams can supplement worker-reported near misses with automated reports of defined near misses. This provides a more consistent measure of events.

Benefit

The EHS manager will have a more accurate picture on which to base decisions.

2. Housekeeping checks

Traditional approach 

Housekeeping checks require regular and time-consuming walk arounds. Although these provide an opportunity to talk to people, that adds to the time taken to complete the checks, and might result in some areas being missed.

With AI

Housekeeping checks can be automated using CV to flag up obstacles left in areas that should be clear, such as walkways. This could be tools or materials that could cause obstruction to workers day-to-day tasks.

Benefit of AI

Hazards can be flagged directly to those responsible for an area so that they can arrange for a clean-up, leaving the EHS manager with more time to talk to people properly, instead of dealing with housekeeping concerns.

3. Monitoring

Traditional approach

While some monitoring is already automated (eg smoke detectors trigger a fire alarm panel) other systems still rely on people to read meters or take temperatures. For example, legionella management schemes require the temperature of water from taps and water tanks to be measured and recorded.

With AI

The Internet of Things (IoT) allows instant reporting of out-of-range readings. For example, water temperatures and emergency lighting functioning. Machine learning can help the EHS manager to make sense of the additional data from IoT.

Benefit

The EHS manager can spend less time chasing people for temperature records and emergency lighting reports and more time managing concerns that arise, as soon as they are identified.

4. Vehicle management

Traditional approach

Being hit by a moving vehicle is the second most common cause of death at work in the UK, with 25 people killed in year 2020/21, and over 1000 people suffering reportable injuries. Wearable technology – which causes an alarm when a tagged vehicle and a tagged person get too close – has had some success, but it’s too easy for people to forget to wear their tag.

With AI

CV can provide rich data about pedestrians in vehicle zones, vehicles crossing pedestrian zones, or vehicles and pedestrians coming into close proximity. No additional infrastructure or tags for people or vehicles are needed.

Benefit

EHS managers will have better data about where problems could occur before anything serious happens. They can be proactive in measures to prevent accidents, such as improving lighting, signage or training. The EHS manager will be able to spend less time investigating accidents too.

5. PPE use

Traditional approach

Although hard hats, gloves and other personal protective equipment (PPE) are the last line of defence, they remain essential to protect workers in many jobs. The EHS manager can’t always be there to make sure it’s being worn. Individuals are criticised for their behaviours, without sight of a broader pattern of behaviour.

With AI

Rather than needing to “catch” people not wearing PPE, the EHS manager can leave CV to monitor when it is – and isn’t worn. AI will help the EHS manager to spot patterns – for example, perhaps people working further away from changing rooms are less likely to wear appropriate PPE.

Benefit

With a better picture of where the problems are the EHS manager can spend time identifying why PPE isn’t being worn in some areas, and what could be done to improve compliance. For example, moving the PPE storage, or providing different varieties for different work environments.

Summary

Fortunately, you don’t need a degree in machine learning or even a school qualification in computing to manage this new technology. AI tools make it as easy for an EHS manager to configure an AI system such as computer vision, as to create a flow chart in PowerPoint, or a spreadsheet in Excel. What are you waiting for? To learn more about how Protex AI is using camera software to help EHS safety managers embrace more proactive safety processes, chat to one of our product experts here 👈🏼

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