Factories are places of productivity — but also of risk. Heavy machinery, forklifts, high-voltage areas, and chemical storage zones all create environments where a single mistake can cause serious accidents. According to recent research, many of these “major accident risk zones” remain difficult to manage, even with strict training and safety protocols (Zhou et al., 2022). Education helps, but prevention requires more than reminders — it requires constant awareness.
This is where real-time location systems (RTLS) and geofencing change the game.
With RTLS, digital boundaries can be drawn around hazardous areas. If a worker carrying a tag approaches a restricted zone, the system can issue an immediate alert: a vibration on their badge, a buzzer signal, a light blinking, or even a message on a wearable screen. Instead of relying only on memory or signage, workers receive a context-specific warning in the moment it matters most.
But RTLS does more than prevent accidents in real time — it also enables continuous, context-specific training. For example, if a worker enters too close to a conveyor belt, the system can not only trigger an alert but also display or narrate why the zone is dangerous. This transforms safety from a one-time training session into an ongoing learning process, embedded in daily routines.
For managers, geofence data also provides insights. If certain zones are repeatedly breached, it could indicate that workflows need redesigning or signage is insufficient. Safety becomes proactive: instead of waiting for accidents to happen, data helps leaders anticipate risks and adjust processes.
At DynaWo, we believe safety shouldn’t stop at education. Workers need tools that reinforce awareness, in real time, without disrupting their jobs. Geofencing through RTLS provides exactly that — an invisible safety net that reduces risk, supports continuous training, and helps every worker go home safe at the end of the day.
Zhou, Q., Li, Y., & Zhao, X. (2022). Safety risk identification in industrial environments: A real-time approach. Sensors, 22(6), 2372. https://doi.org/10.3390/s22062372