What manufacturers need to know about optimizing operations with computer vision

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From healthcare and pharmaceuticals to food and beverages, manufacturing processes around the world are still inefficient. Despite the best efforts of engineering teams, poor product design, lack of effective communication, and human error lead to nearly $8 trillion in waste annually.

Needless to say, this significantly affects a company’s bottom line and the environment, making it an important issue. Therefore, manufacturing companies are exploring various solutions such as computer vision to increase efficiency, optimize production processes, reduce waste and drive innovation.

In a nutshell, computer vision is a field of artificial intelligence (AI) that allows computers to interpret and understand visual information from sources such as images and videos. It uses large amounts of data, processes input images, labels objects in those images, and finds patterns in them. Although this technology has been around for years, recent advances mean that today’s systems are now 99% accurate, down from 50% a decade ago.

However, only 10% of organizations are currently using computer vision to drive their business operations. However, more and more manufacturing companies are in the process of exploring or implementing this technology as the benefits become more visible. Now it’s time to dive deeper.

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Understanding how computer vision sees

Data collection is key to the smooth operation of a computer vision system. First, cameras and sensors on the assembly line take pictures and upload them to a server. The system then learns to identify different parts and stages of the manufacturing process and categorize defects and anomalies according to the type and severity of the problem. As the system receives more data and feedback from the assembly line, it continuously evolves and improves itself.

To illustrate this, let’s say you run a pharmaceutical production line. In that case, the computer vision system will enable you to accurately check the size, shape variation, defects or total number of pills. When an issue occurs in the production process, you’ll receive notifications, analysis reports and actionable insights via notifications on connected devices.

A series of manufacturer’s use cases for computer vision

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

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

Another major use of computer vision is to improve product quality. Manufacturers understand that ensuring that their products meet standards, are free of defects and meet regulatory requirements can be a real challenge, 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.

To top it all off, implementing a computer vision system enables businesses to detect improper use of safety equipment and facilities, overcrowding on scaffolding, and falling objects while assessing safety levels. Therefore, this technology can help prevent accidents and save thousands of people from work-related injuries.

Computer Vision Implementation Considerations

Computer vision is a rapidly evolving technology and has the potential to shake up the manufacturing industry. But it’s critical for companies to understand the realities of implementing this innovative technology before hitting the treadmill.

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

Therefore, to make more informed decisions, avoid unnecessary costs, and determine the most beneficial computer vision solution, companies should:

  • Identify their specific needs and set targets.
  • Explore the available computer vision options.
  • Perform pilot tests to evaluate the effectiveness of the solution.
  • Make sure the solution can scale to meet their future needs as they grow.

Although computer vision solutions can help manufacturers save time and money, their implementation can be a significant investment.

This is because before deploying the 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 leveraging the power of visual information. It allows companies to increase product quality, reduce waste and create a safer work environment for their employees. However, since this technology offers unique solutions for each use case and requires expensive equipment, manufacturing companies must set specific goals to optimize the use of computer vision.

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


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