What manufacturers need to know about optimizing operations with computer vision

From healthcare and pharmaceuticals to food and beverage, manufacturing processes around the world are still inefficient. Despite the best efforts of the technical teams, substandard product design, lack of effective communication and human error lead to almost $8 trillion waste per year.

Needless to say, this has a significant impact on a company’s bottom line — and the environment — making it a critical issue. That is why manufacturing companies are exploring different solutions, such as computer vision, to increase efficiency, optimize production processes, reduce waste and stimulate innovation.

In a nutshell, computer vision is an area of ​​artificial intelligence (AI) that allows computers to interpret and understand visual information from sources such as images and videos. It uses a large amount of data, processes input images, labels objects on these images, and finds patterns in them. While this technology has been around for years, recent advancements have meant that today’s systems are now 99% accurate compared to 50% less than ten years ago.

Still alone 10% of organizations currently use computer vision to boost their business operations. However, more and more manufacturing companies are exploring or implementing this technology as the benefits become more apparent. Now is the time to dive deeper.

Understand how computer vision sees

Collecting data is essential to keep a computer vision system running fault-free. First, cameras and sensors arranged in the assembly line record images and upload them to a server. Then the system learns to identify the different parts and stages of the production process and to classify defects and deviations based on the type and severity of the problem. As the system receives more data and feedback from the assembly line, it constantly evolves and improves itself.

Let’s say you run a pharmaceutical production line to illustrate this. In that case, a computer vision system can allow you to accurately verify size, shape variations, defects or the total number of pills. When there is a problem during the production process, you receive alerts, analysis reports and actionable insights via notifications on connected devices.

Series of use cases from computer vision manufacturers

From equipment failure to poor planning and quality control issues, many factors can cause bottlenecks and delays in the production process. But computer vision systems can detect and track the movement of products and machines on a factory floor, helping manufacturing companies overcome these problems.

For example, computer vision allows companies to monitor equipment and machinery to identify signs of wear and tear. In this way, project managers can plan maintenance and repairs more effectively, reducing downtime. When equipment and machines are in good working order, companies can maintain production, reduce the risk of occupational accidents and meet health and safety requirements.

Another important use of computer vision is to improve product quality. Manufacturers understand that it can be a real challenge to ensure that their products meet standards, are free of defects and comply with regulatory requirements, especially when dealing with large quantities. Computer vision can help them accurately inspect products at high speeds and find even the smallest defects that human operators might miss, improving product quality and reducing waste.

In addition, the implementation of a computer vision system enables companies to detect improper use of safety gear and equipment, overcrowding on scaffolding and falling objects while assessing safety levels. Therefore, this technology can help prevent accidents and accidents save thousands of people of work-related injuries.

Considerations when implementing computer vision

Computer vision is a rapidly evolving technology and has the potential to shake up the manufacturing industry. But it’s crucial for companies to understand the realities of deploying this innovative technology before jumping on the bandwagon.

Since every product and its defects are unique, implementing a computer vision model that works for one product line does not guarantee it will do the same for another.

Therefore, to make informed decisions, avoid overspending, and determine which computer vision solution is most useful, companies should:

• Identify their specific needs and set goals.
• Explore available computer vision options.
• Run pilot tests to assess solution performance.
• Make sure the solution can scale to meet their future needs as they grow.

While computer vision solutions can help manufacturers save time and money, implementing them can require a significant investment.

This is because before deploying any solution, organizations need to prepare the infrastructure and do the necessary groundwork, which means investing in cameras, installation, and data collection tools.

The bottom line is that computer vision is transforming the manufacturing industry by harnessing the power of visual information. It enables companies to increase product quality, reduce waste and create a safer working environment for their employees. However, since this technology offers unique solutions for each use case and requires expensive hardware, manufacturing companies must set specific goals to optimize their use of computer vision.

Sunil Kardam is SBU Head of Logistics and Supply Chain at Grammener.