Bow-Tie Analysis and AI - How to Improve Workplace Safety

Bow-tie analysis helps safety teams map hazards, preventive barriers, recovery measures, and outcomes in one clear view. 

When paired with AI-powered computer vision, that framework becomes easier to monitor in day-to-day operations, especially in environments with forklift, pedestrian, and racking risks.

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Article highlights:

• The bowtie model shows how hazards can progress into a top event and where barriers can reduce risk.
• AI-powered computer vision can help teams detect unsafe behaviors, close-proximity events, and route issues in real time.
• Warehouse and manufacturing teams can use this approach to strengthen prevention and mitigation controls.
• Computer vision can add another line of defense for forklift-related risks, including speed, location, and wrong-way travel.
• Short event clips and reporting give managers evidence they can use to inspect damage, coach teams, and reduce repeat risk.

This article explains how bow-tie diagrams and AI-powered computer vision can work together to improve workplace safety.

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What Are Bow-Tie Diagrams?

Bow-tie diagrams are widely used to improve safety in high-hazard industries, but the bowtie model can provide a better understanding of how to prevent harm in any industry.In particular, if you are looking for insights on ways to use new technology – such as AI-powered computer vision (CV) – to reduce risk, bow tie risk assessment could be the tool you need. To illustrate this, we'll use a 5-step example from a warehouse.

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How To Build A Bow-Tie Risk Assessment In 5 Steps

Before you add computer vision or other controls, the bow-tie risk assessment needs a clear structure. 

Use these five steps to define the hazardous event, map likely causes and consequences, and assess whether each barrier is strong enough to prevent or reduce harm.

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Step 1: Identify The Hazardous Event

Your incident records and hazard analysis will help you to identify warehouse hazards.

Imagine you've had several incidents where forklift trucks (FLTs) have hit racking, causing objects to become unstable, fall, or nearly fall, with or without injuries. Repeated events like this are where a structured risk framework helps teams surface contributing factors and controls.

Define this hazardous event in the center of the bow tie (the yellow circle in our example).

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Step 2: Enumerate The Hazards

For the event you've described, list all the hazards you can think of related to that event. Don't worry about whether a single hazard or a combination leads to the event.

In the orange section of our bow tie diagram, we've listed hazardous activities that might contribute to an FLT hitting the racking – driving too fast, using the wrong route, or a pedestrian causing the FLT to maneuver too close to the racking to avoid a collision.

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Step 3: List The Outcomes

Exclude highly improbable outcomes – for example, unless you are storing flammable items, it is unlikely that a falling box will cause a fire. However, you should include a range of possible outcomes.

For example, consider an object falling off the shelf when no one is around, leading to financial losses, or a load falling in front of a vehicle and causing it to swerve or brake.

You should also include outcomes where a falling object directly injures a worker. These are described in the red section of the bow tie.

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Step 4: Evaluate The Barriers

There are two types of barriers: things that stop the hazardous event and those that limit its impact.

If a fire were the central event, controlling ignition sources and combustible materials would be on the left (the blue area), and fire alarms, emergency lighting, and evacuation procedures would be on the right (the green zone).

Recovery measures may not stop the hazardous event, but they do reduce the severity of the outcome.

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Preventing forklift hazards with barriers

Barriers that support incident prevention in this FLT example could include speed limits enforced by vehicle controls, training, observation, signage, and separation of routes for vehicles and pedestrians.

These safety barriers block the progression from hazard to hazardous event. Once an FLT has hit the racking, your key barriers might be reporting and inspecting the damage, as well as correcting any stacking problems. This is shown in the green part of the bowtie diagrams.

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Step 5: Assess The Barriers

Controlling risk requires multiple lines of defense. Speed limiters may fail or be disabled, preferred routes can become blocked, and people may make wrong decisions.

Barrier effectiveness can degrade over time – people might forget to report a hazardous event, which is why real-time incident reporting can strengthen follow-up. Traditional approaches offer limited options – vehicles can be inspected more often, and workers can be reminded of the importance of reporting.

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Where computer vision fits into the bow-tie model

Computer vision (CV) allows you to add extra lines of defense. If you are familiar with Reason's Swiss Cheese model, this is like adding an extra slice of cheese with fewer holes.

The bow-tie model helps identify additional controls on either side of the hazardous event – what can we do to prevent the FLT from hitting the racking, and if that occurs, how can we effectively recover from and mitigate any harmful outcomes? 

This is where a digital bowtie approach becomes useful: computer vision can help teams monitor key behaviors and conditions between periodic barrier reviews.

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How Does Computer Vision Strengthen Barrier Effectiveness?

When adding AI safety technologies like computer vision as an extra defense layer, it enhances safety protocols by allowing real-time workplace safety monitoring. 

For implementation context, this guide to integrating computer vision into your existing EHS tech stack explains how CV fits into current safety workflows. 

It can also support critical control management by giving teams a more continuous view of whether key controls are being followed.

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Prevention

With computer vision for safety, you can detect when FLTs travel too quickly, when they are in the wrong location, and even when they travel the wrong way along a route.

It also detects pedestrians using a vehicle-only route. This capability to monitor and measure these hazardous activities improves risk detection and helps teams prevent hazardous events.

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Mitigation

Rather than relying on individual drivers to report collisions with racking, CV can detect close-proximity events and feed short video clips to a manager for risk assessment.

If a collision has occurred, the manager can arrange for an inspection of the racking and address any stacking issues if necessary.

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Redefining Safety With Bow-Tie Analysis And Protex AI

The bowtie methodology serves as an effective framework for improving safety. It illustrates the sequence of hazards, prevention, event, recovery, and outcomes, giving safety teams and stakeholders a shared visual language for managing risk. You can review existing barriers and identify opportunities to use Protex AI’s workplace safety software, such as computer vision, to minimize risk.

Protex AI uses computer vision to analyze real-time video feeds and detect potential safety hazards before they occur. This proactive approach alerts employees to potential dangers and helps prevent accidents and injuries.

Our software provides detailed reports that enable businesses to identify trends and take proactive steps to improve safety. Watch our product demo and discover how Protex AI can help your business.

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