Is the data from proximity warning systems useful for making better workplace safety decisions?

Proximity warning systems (PWS) are widely used in industrial environments to provide real-time alerts and generate retrospective data that can significantly enhance workplace safety strategies. But how exactly can this data support smarter, evidence-based health and safety decisions? 

In this article, we explore key ways PWS data helps safety professionals identify risks, prevent incidents, and improve site operations. 

Identifying high-risk zones or times of day 

Let’s start by examining how PWS data can highlight the locations and timings of high-risk interactions between people and moving machinery. 

Many proximity warning systems record GPS coordinates or zone data for each interaction. Safety teams can use this data to create heat maps that reveal where dangerous interactions most frequently occur. These visual tools help identify hazardous zones, poorly designed routes, or areas where machines and pedestrians routinely come into close proximity. 

With this insight, teams can redesign walkways, adjust traffic management plans, or introduce time-based restrictions - for example, tackling risks that peak during shift changes or at busy loading times. 
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Monitoring near-miss trends to prevent future incidents 

Proximity warning system data is particularly valuable for understanding near-miss patterns. Advanced PWS solutions, including tag-based systems, can record which operator, pedestrian, or machine was involved in a near miss. 

Systems like SensorZone also allow teams to link supplementary information - such as job roles or specific activities - to each machine and tag. This added detail helps identify recurring risk patterns linked to certain tasks, operators, or locations. The result is more targeted interventions, from additional training to changes in procedures, before an incident occurs. 
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Supporting incident investigations with reliable data 

When an incident happens, PWS data can provide vital facts that support thorough, evidence-based investigations: 

• Establishing the timeline – Time-stamped records reconstruct the sequence of events leading up to, during, and after the incident. 
• Locating the incident – GPS or zone data pinpoints the exact location, helping identify contributing hazards. 
• Identifying involved parties – System logs connect machines, operators, and pedestrians to the event. 
• Understanding pre-incident behaviour – Movement data and prior alerts can reveal unsafe practices or missed warning signs. 

This data strengthens accountability, facilitates root cause analysis, and informs preventive actions. 
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Supporting safe machine maintenance 

Some proximity warning systems offer telematics-style data, tracking machine hours, maintenance status, or inspection intervals. By monitoring this information, safety teams can ensure equipment is regularly checked and maintained. This reduces the risk of mechanical failures - such as brake faults or steering issues - that could lead to collisions or other dangerous incidents. 
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Summary 

Proximity warning system data offers far more than real-time alerts. When used proactively, it helps organizations: 

• Identify high-risk zones and times 
• Spot near-miss patterns before they result in harm 
• Strengthen incident investigations 
• Keep machinery safe through data-led maintenance 

Ultimately, leveraging PWS data empowers safety teams to move from reactive to preventive strategies, fostering safer workplaces, reducing downtime, and improving compliance.