Manual processes still play a vital role in manufacturing. From assembly and machine set-up to material delivery and logistics, on-site human activity is essential to delivery. In many industries, this situation is unlikely to change anytime soon.
in DeltiaWe believe there is great potential in finding ways to optimize these manual processes, but when it comes to figuring out how to improve them in the field, things get more complicated.
Data collection on the production line
First, there is little tracking data on manual processes. Manufacturing Execution Systems (MES) can help monitor and control production on the shop floor, but why Problems will occur.
Traditional methods just don't cut it: manual observations take a lot of time (and labor costs), data tracking is often flawed, and when you're spending all your time collecting granular data, it's hard to find time to actually make your business more efficient.
Identifying a focus for improvement is also difficult. Unless you have a dedicated team in place (which costs money) or a staff of a thousand eyes (which, dear reader, are in short supply given today's skilled labor shortage), you're out of luck.
Even the best teams sometimes need extra help.
Modern problems need modern solutions
So when large line monitoring teams are not feasible and the monster of too many eyes brings a bad vibe to the shop floor, production managers and process engineers need better ways to gather actionable insights. This is where computer vision and AI come in.
First, it's important to understand what the term “computer vision” means. IBM defines it as “a branch of artificial intelligence that uses machine learning and neural networks to teach computers and systems how to derive meaningful information from digital images, videos and other visual inputs, and how to make recommendations or take action when they spot defects or problems.”
This has huge implications for manufacturing: cameras installed on the factory floor can monitor many stations at once and easily collect thousands of data points.
“Amid a global shortage of skilled workers, computer vision is emerging as a driver of the digital transformation of production facilities. Through this technology, we enhance human capabilities and transform the production environment. Our vision propels us towards a future where skilled workers are supported by cutting-edge technologies, increasing their efficiency and effectively transferring their expertise. This vision is aligned with the growing demand for both the quality and quantity of products,” said Maximilian Fischer, co-founder and CEO of Deltia.ai.
To date, computer vision has been used primarily in the area of quality control, but tools that effectively combine the processing power of computer vision and AI have the potential to improve operations in many areas, including:
The computer vision-based platform helps document and digitize manual tasks, provides analytics on efficiency, and suggests ideas to reduce downtime.
- Improving worker capabilities
Implementing computer vision solutions can provide training and support, helping to fill the shortage of skilled labor.
Computer vision systems can provide instant alerts and ensure production lines meet safety standards, protecting people on the ground from accidents and errors.
- Improve business efficiency
Computer vision platforms can learn over time to provide more specific suggestions for improvement, and these systems also capture issues as they occur and send real-time notifications to factory managers who can adjust or stop operations as needed.
Computer Vision and Technical Debt
Not all solutions are created equal, and companies looking to transform their factory floor operations often don't have the time or money to take on a lot of technical debt.
The advantage of computer vision is that it is easy to install (even for a single production line) and requires minimal hardware to get started.
AI does the heavy lifting of calculations and analysis, providing users with easy-to-understand graphs and data points. With a minimal learning curve, process engineers and other employees responsible for monitoring output and efficiency can be onboarded easily. Time spent on face-to-face data collection can be spent on process improvement and training.
For production lines with short cycle times, AI can begin to identify patterns after just one or two shifts. Proof of concept is immediate and without large up-front investments.
Find the best solution for your site
Manufacturing companies that rely heavily on manual processes don't need to spend a lot of time and money on digital transformation.
Deltia.ai provides an AI-based process analytics and monitoring platform with cameras on the shop floor – no expensive cameras, complex calibration, or highly specialized lighting conditions required. The company is backed by leading investors Cavalry Ventures and Merantix, and is currently used by leading manufacturers such as Viessmann and Swiss technology group ABB.
ABB has increased productivity by 15 percent since implementing Deltia.ai and plans to roll it out to more production lines in the near future. The Viessmann plant in Legnica currently monitors 45 employees with Deltia.ai technology. According to Viessmann manager Bartłomiej Magiera, they have been able to increase productivity by 20 percent since implementation. Magiera expects productivity to increase by up to 50 percent once Deltia is added to all the plant's lines.
Deltia products are developed by people with a deep understanding of the everyday problems on the factory floor, resulting in real-time transparency and deep insight into how to improve the efficiency and quality of manual processes.
To learn more about how computer vision can help, check out Deltia.ai.
Silviu Homoceanu is the Founder and CTO of Delta.ai.
Silviu holds a PhD in Computer Science and is an expert in Computer Vision, and previously led the team developing a robot taxi service at Volkswagen.
