We believe that computer vision is no longer just a tool for innovation teams – it’s now a frontline technology for driving real, measurable value across safety, quality, and efficiency in operations.
We recently launched our new webinar series, lifting the lid on getting started with computer vision (also known as AI Vision).
In this second webinar in the series – ‘High-impact computer vision in supply chain: real-world applications and ROI’ – our expert Technical Account Manager, Abi Anderson, led us through:
Why the time for computer vision is now
Computer vision: the basics
Real world relevance of computer vision in supply chain
High-impact use cases
Key takeaways and why they matter
If you would like to watch the video of the webinar – hosted by Chrissie Jamieson, our Vice President of Marketing – you can find it here.
(and if you prefer a more TL;DR version, here are our five takeaways to kick things off)
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AI Vision at scale requires depth, security and flexibility. See how Viso Suite delivers.
Health and Safety use cases are technically simple to implement, detecting people, forklifts, and near misses are among the easiest tasks for computer vision models. More importantly, this area often suffers from underreporting and limited oversight.
By automating incident detection and reporting, organizations can better understand risk profiles, reduce accidents, and enhance compliance: without adding headcount.
2. Existing camera infrastructure can be re-used
Why it matters:
Most warehouses already have CCTV systems installed. The ability to integrate with existing infrastructure dramatically lowers the barrier to entry and cost of implementation. This makes computer vision immediately viable without significant capital expenditure or operational disruption.
Running computer vision “at the edge” means faster processing and greater privacy. In time-sensitive environments – like a forklift nearing a pedestrian – low latency can be the difference between a near miss and an injury. This also avoids the cost and risk of streaming sensitive video data to the cloud.
4. Defect detection shifts quality assurance from spot checks to full coverage
Why it matters:
Instead of manually inspecting a fraction of goods, AI Vision allows visual inspection of every pallet or package. This drastically increases detection rates for damaged goods, reducing returns, reshipment costs, and customer dissatisfaction. It’s a transformative upgrade to quality assurance without scaling staff.
5. Process monitoring builds long-term behavioral change
Why it matters:
With computer vision, you can enforce process adherence automatically, whether it’s PPE compliance or quality assurance (QA) checks. Escalating alerts (from warnings to management interventions) drives compliance. Over time, teams internalize these expectations, and infractions decline. That’s a sustainable path to a safer, more efficient operations.
Supply chain analysis with the Viso Suite
Setting the scene: why supply chain and why now?
Since 2020 e-commerce has surged, bringing with it a 55% increase in demand for fulfillment centers in both the US and UK. That translates into millions of square feet of new warehouse space, and a corresponding 40% increase in the need for warehouse workers.
But workers aren’t scaling at the same rate. Labor shortages are hitting hard, and warehouse operators are under increasing pressure to do more with less. These trends are driving a rapid growth in automation across the supply chain, with warehouse automation seeing a compound annual growth rate (CAGR) of nearly 16%.
The combination of labor constraints, facility expansion, and escalating operational complexity makes supply chain environments ripe for computer vision.
What is computer vision and how does it work?
AI Vision enables machines to “see” and understand images and video in real-time. In the supply chain context, this means existing CCTV infrastructure can do much more than simply passive surveillance.
The process is surprisingly straightforward:
1. Data collection: capture footage using your existing cameras
2. Labeling and training: annotate relevant visual elements (such as people, forklifts, and pallets) and train AI models to recognize them
3. Logic and action: define business logic to trigger actions (for example, an alert if a person walks too close to a moving forklift)
4. Deployment at the edge: deploy models directly on-site to both reduce latency and enhance privacy
5. Analysis and insights: use the data to track trends – like near misses or defect rates – and inform strategic decisions
This is not about replacing people: rather it’s about augmenting human oversight and improving visibility in real-time.
AI vision application of forklift and vehicle safety management
Use cases: where AI Vision delivers real ROI
The real-world applications of AI Vision are limitless, but three in use today stand out for their accessibility, impact, and alignment with current operational challenges.
1. Health and Safety monitoring
AI vision can pro-actively detect near misses – close calls between people and machinery – that typically may go unnoticed and unreported. This data helps build a risk profile for each facility, empowering HSE teams to intervene before accidents occur.
Because large objects like people and forklifts are easy to detect, this use case is technically simple and feasible to pilot, and fast to deploy. It’s also transformational.
If every 300 near misses typically results in one major injury, improving visibility alone is literally a lifesaver. Automated incident capture means less time spent on investigations and more on prevention. Over time, trend analysis can reveal whether interventions are working and crucially where to focus next.
2. Defect detection in fulfilment
Today, many warehouses still rely on spot checks. But what if you could inspect every pallet, every time, in the same way and with minimal oversight?
AI Vision enables automated defect detection by training models on specific products or packaging. This could be damage to pallets, tears in wrapping, or manufacturing flaws.
Even with solely CCTV footage, you can detect and divert damaged goods before they ship: all saving money on re-work, re-shipping, and customer returns. The challenge is building a robust dataset of defects.
The smartest move? Start recording data now so that when you’re ready to pilot and build, you’ve already got footage to work with.
3. Process adherence and compliance
From PPE enforcement, to quality assurance process verification, and ISO safety standard compliance, every repeatable visual task can be monitored with AI Vision.
Consider for a moment how often processes go unchecked simply because no one has the time or visibility to enforce them. With AI monitoring, you can detect non-compliance in real time and trigger escalating alerts: audible sirens, lights, or perhaps automated emails to bring this to the attention of supervisors.
Over time, habitual compliance improves. Organizations typically see a 95% reduction in escalations year-on-year, simply because expectations are clearly – and consistently – enforced.
With AI Vision, cameras are able to ‘see’ and ‘understand’ in real-time
Some questions from the floor
Q1: Can I use my existing cameras?
Yes. Most AI vision setups integrate seamlessly with standard CCTV infrastructure. In some cases, like forklift monitoring or top-down QA stations, you might add specialized views, but it’s often not necessary.
Q2: What kind of alerts are possible?
Everything from sirens and lights to SMS and email alerts. You can also build escalation pathways that notify managers with visual evidence attached.
Q3: Does this run in the cloud or on-site?
Inference happens on-site (edge deployment) for speed, privacy, and cost. Only metadata is sent to the cloud for dashboards and trend analysis.
Q4: Where does GenAI fit in?
As data accumulates, GenAI will play a role in forecasting risk, surfacing insights, and answering questions like, “Which sites are trending toward non-compliance?” or “Where are we likely to see an incident next?”
Parcel damage detection in warehouses
Final thought: start small, prove fast
If you’re wondering where to begin, health and safety is the answer.
It’s technically simple, business-critical, and fast to demonstrate ROI. You’ll detect near misses within days, and the benefits—in reduced risk, better oversight, and stronger compliance—scale quickly from there.
Computer vision is no longer an experimental technology. In a world where warehouses are growing, workforces are stretched, and safety is paramount, it’s a strategic imperative.
And the best time to start? Yesterday.
(but today will do)
Join us for the next webinar in the series
Our third webinar in the series – ‘Revolutionizing workplace safety with AI vision’ – takes place on July 15, 3.00pm BST. Learn how AI-powered computer vision transforms workplace safety with real-time monitoring, risk reduction, and ROI.
