Packaging is crucial to both the protection and presentation of a product. It’s essential for companies to optimize it, but balancing quality with efficiency can be difficult, especially in a heavily manual workflow. Packaging vision systems are a critical step in achieving both sides of this dynamic.

These systems use machine vision to either fully automate a process or support employees in manual workflows. Knowing how they can transform your packaging lines is the first step to using this technology to its full potential.

What Is Machine Vision?

To know how to restructure your packaging lines around machine vision, you first need to understand what this technology is. Simply put, machine vision — sometimes called computer vision — uses artificial intelligence (AI) to analyze visual data like photos or video feeds.

You likely use machine vision every day, even if you don’t realize it. It’s the same technology behind the facial recognition features that 64% of all smartphones use today. However, its potential use cases go far beyond biometric security. Machine vision can also detect product defects, interpret text, measure objects’ physical dimensions and more.

AI models are ideal for many kinds of visual inspections because AI is better at detecting patterns or subtle details in data than humans. It can also perform this analysis much faster while delivering the same accuracy every time.

Now that you understand machine vision, here are five ways you can apply it to packaging lines.

1. Automate Quality Inspection

One of the most impactful ways to use packaging vision systems is to apply them to quality control. Instead of manually inspecting boxes, you can send them past cameras and sensors connected to machine vision systems. The AI can then alert workers of any defects.

Manual quality inspections typically slow lines down because workers must stop the line to view a package from all angles. Machine vision, by contrast, can analyze boxes from all angles as quickly as they move down a conveyor belt while still being more accurate than humans. Some facilities can inspect 80,000 products an hour with a near-zero failure rate because of these systems.

2. Improve Picking Accuracy

Product picking is another ideal application for machine vision in packaging lines. Manual picking is slow and error-prone, making it easy to package the wrong item or take too long to package the right one. You can overcome these obstacles by using machine vision to guide employees or automated picking systems to the products they need.

Machine vision can read labels to immediately verify an item’s identity. Applying this both to packaging orders and products on the shelf lets AI identify what products a shipment needs, direct employees to it and verify that they’ve picked the right one. That way, you spend less time finding needed orders and prevent picking mistakes.

3. Identify Areas of Improvement

The more you use packaging vision systems, the more data about errors or packaging efficiency you’ll generate over time. This information can reveal areas to improve your packaging line to enable ongoing optimization. Because machine vision is so versatile, these improvements can apply to a diverse range of goals, too.

Given that 32% of American consumers want sustainable packaging options, you could use computer vision to monitor package sustainability. Intelligent camera systems could reveal where you’re leaving too much room in a package, leading to waste, where you could use less material or even highlight other, more sustainable materials to use.

4. Minimize Labeling Errors

Labeling errors are another common source of inefficiency and waste in packaging operations that machine vision can address. AI is an ideal tool to double-check labels because it can read the entire thing at once instead of line-by-line and doesn’t get tired or distracted.

Machine vision can analyze a package’s contents to inform employees or other machines down the line exactly what’s in it. Then, different AI-enabled cameras can verify these workers or systems applied the correct label in the right location and orientation. If any errors arise, data from them will reveal trends over time, showing you where mistakes come from and how to prevent them.

5. Optimize Palletizing

Packaging vision systems can also help you optimize your palletizing processes. Machine vision cameras over pallets can analyze packages’ size, shape, orientation and location in real-time. If they can read labels, they can account for their contents, too. With this data, AI algorithms can direct employees where and how to place each box to maximize space and ensure shipment accuracy.

These directions are most effective when simple, as simplicity prevents confusion and increases efficiency. A screen could light up as green when workers have placed boxes correctly and red if they’re incorrect. These quick responses enable optimized palletizing without sacrificing productivity.

Making the Most of Packaging Vision Systems

It’s important to remember that, like any other tool, packaging vision systems’ efficacy depends on their use. In light of that, here are some things to keep in mind when designing and implementing these technologies.

First, remember that AI’s accuracy depends on its data, as evidenced by bad data costing organizations $12.9 million a year. For machine vision systems, that means you must ensure they can “see” what they’re analyzing clearly. Provide ample light, place barriers around the analysis area to block backgrounds and review your specific system to consider any unique vision concerns. Be sure to calibrate these systems regularly.

Next, you must recognize that it’s easy for these projects to go wrong, considering their cost and complexity. The answer to this challenge is to approach machine vision slowly and carefully. Start by applying it to a single, relatively straightforward use case. Record the entire process and monitor relevant KPIs closely to determine its success. Once you see a return, refer to what you’ve learned to inform more effective rollouts in future machine vision investments.

Finally, acknowledge that workers’ roles will change as you automate more processes. You may need more technical, AI-centric skills as you implement more packaging vision systems than you need manual labor. Instead of turning to outside hires for this expertise, offer upskilling programs to existing employees. This will minimize turnover and mitigate tech talent shortages.

Packaging Vision Systems Have Many Applications

Machine vision is a revolutionary technology for the warehousing and logistics industry. Once you know where to apply packaging vision systems, you can use them to transform your packaging lines.

As supply chains across the globe embrace digitization and agile processes, automation will become increasingly common. Capitalizing on tools like machine vision today will ensure you remain competitive tomorrow.