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From healthcare and pharmaceuticals to food and beverage, manufacturing processes across the globe are still inefficient. Despite engineering teams’ best efforts, below-par product design, lack of effective communication and human error lead to almost $8 trillion of waste per year.
Needless to say, this significantly impacts a company’s bottom line — and the environment — making it a critical problem. Therefore, manufacturing companies are exploring various solutions such as computer vision to increase efficiency, optimize manufacturing 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 leverages a large amount of data, processes input images, labels objects on these images and finds patterns within them. Although this technology has been around for years, recent advancements have meant that today’s systems are now 99% accurate compared to 50% less than a decade ago.
Yet only 10% of organizations currently use computer vision to boost their business operations. However, more and more manufacturing companies are in the process of investigating or implementing this technology as the benefits become more visible. Now’s the time to dive deeper.
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Understanding how computer vision sees
Data collection is key for a computer vision system to work without glitches. First, cameras and sensors set up in the assembly line capture images and upload them to a server. Then, the system learns to identify the various parts and stages of the production process and classify 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 continually evolves and improves itself.
To illustrate this, suppose you are running a pharmaceutical production line. In that case, a computer vision system can enable you to accurately verify pills’ size, shape variations, defects or total count. When there is a problem during the manufacturing process, you will receive alerts, analysis reports and actionable insights via notifications on connected devices.
Range of manufacturer use cases for computer vision
From equipment breakdown to poor planning and quality control issues, numerous factors can cause bottlenecks and slowdowns in the manufacturing process. But computer vision systems can detect and track the movement of products and machinery on a factory floor, allowing manufacturing companies to remedy these issues.
For instance, through computer vision, companies can monitor equipment and machinery to identify signs of wear and tear. This way, project managers can schedule maintenance and repairs more effectively, thus reducing downtime. When equipment and machinery are in good working condition, businesses can maintain production levels, reduce the risk of occupational accidents and meet health and safety requirements.
Another prime use of computer vision is to improve product quality. Manufacturers understand that ensuring their products are up to standards, 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 may miss, improving product quality and reducing waste.
On top of everything, implementing a computer vision system enables businesses to detect improper safety gear and equipment usage, overcrowding on scaffolding and falling objects while also assessing safety levels. Therefore, this technology can help prevent accidents and save thousands of people from work-related injuries.
Considerations for implementing computer vision
Computer vision is a rapidly evolving technology and has the potential to shake up the manufacturing industry. But it is crucial for companies to understand the realities of implementing 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 that it will do the same for another.
Therefore, to make more informed decisions, avoid overspending and determine which computer vision solution will be the most useful, companies need to:
- Identify their specific needs and set targets.
- Research available computer vision options.
- Conduct pilot tests to assess the performance of the solution.
- Ensure that the solution can scale to meet their future needs as they grow.
Even though computer vision solutions have the power to help manufacturers save time and money, implementing them can involve a significant investment.
This is because, before deploying a solution, organizations need to prepare the infrastructure and do the necessary groundwork, which means investing in cameras, installation and data-gathering 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 work environment for their employees. However, as this technology offers unique solutions for each use case and requires expensive hardware, manufacturing companies must set specific goals to optimize their usage of computer vision.
Sunil Kardam is the SBU head of logistics and supply chain at Gramener.
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