HAZARDOUS AREAS & SAFETY


Richard Jones, Vice President Sales Northern Europe, Confluent, says proactive, data-led safety prevents injuries, reduces downtime and strengthens ESG performance


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onventional safety on the factory floor is well established: hard hats, safety goggles, checklists on the wall, and regular inspections. They are as necessary as they are mundane.


In the modern factory, these reactive measures are no longer enough. Modern production lines are faster and more complex than ever. A single machine fault can escalate in minutes; the goal is stopping issues before they start.


To keep up, manufacturers are now using real-time data and AI to predict and prevent risks before they happen. Reactive safety is a race against time. Routine checks find issues eventually, but often not soon enough. A robotic arm drifts a few millimeters off alignment, damaging every part it handles. A refrigeration unit fails overnight, spoiling critical materials before anyone arrives. By the time anyone notices, production has halted — or worse, someone’s been hurt.


The US Bureau of Labour Statistics reported over 2.5 million non-fatal injuries and illnesses in 2023; here in the UK, over 60,000 non-fatal injuries were reported last year under RIDDOR regulations. These injuries won’t just affect the afflicted, either; the psychological damage of workplace accidents ripples out in any number of ways.


Human and financial cost Beyond the human cost, workplace incidents drain over $8 billion annually in direct expenses, according to the National Safety Council. There is pressure in every metric — financial, moral, logistical — to minimise such occurrences. As factories become faster and more automated, waiting for something to go wrong is no longer an option. As such, many manufacturers are adopting a proactive model, powered by real-time data streaming, IoT sensors, and AI analytics. Modern PPE is now “smart,” embedding sensors and connectivity into


A DATA-DRIVEN SAFETY NET


helmets, vests, and wristbands. These technologies continuously collect data from machinery, environmental monitors, and even wearable devices. AI models spot anomalies — a rise in temperature, a shift in vibration — before they turn into hazards. Data streaming platforms (DSPs) make this possible, processing and distributing sensor data in milliseconds so teams have a live view of plant conditions.


This visibility helps prevent breakdowns, reduce downtime, and improve both safety and efficiency. Smart helmets, for instance, can display augmented reality warnings directly in a


worker’s field of view. The real breakthrough comes when this data is connected to the wider monitoring system.


Imagine a stationary sensor detecting a spike in airborne particulates. When that data is cross-checked with helmet readings, the system can instantly identify which workers are in danger — and alert them in real time. That’s safety that works as fast as the environment changes.


The Institute of Electrical & Electronic Engineers published a study in September claiming that it could correlate environmental events to worker behaviour with over 85% accuracy — just using data streamed from wearable IoT and factory sensors.


Seeing the bigger picture with AI While predictive maintenance and wearables focus on specific assets, AI-powered risk assessment looks at the bigger picture. AI can analyse logs, historical incidents, and


8 NOVEMBER/DECEMBER 2025 | PROCESS & CONTROL


environmental data together to uncover patterns that humans might miss — say, a combination of machine vibration and humidity that often precedes a fault. When these systems detect risk, they can trigger a targeted intervention immediately. Pausing a line, alerting maintenance, or isolating equipment doesn’t require the manual review process that introduces delays. The need for constant connectivity might have been a fear here — but when a connection is unreliable, edge streaming can keep workers safe. In remote sites or offshore facilities, data can be processed locally, so hazards are still detected and addressed even if the network drops out.


Autonomous safety systems In the near future, safety systems will become increasingly autonomous. AI agents will orchestrate entire responses — rerouting production and adjusting shift patterns — within seconds. Over time, these systems will form the factory’s “nervous system,” constantly learning how to keep people and processes safe.


As adoption grows, regulators may soon treat these tools as best practice, or even a compliance standard. For workers, that shift will be profound, walking onto the floor knowing they’re protected by a data-driven safety net.


For too long, safety has been treated as a necessary duty, separate from productivity. That’s changing. Proactive, data-led safety prevents injuries, reduces downtime, and strengthens ESG performance.


Data is protecting the people who keep the factory running.


Confluent www.confluent.io


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