EthonAI is an AI-powered platform that helps manufacturers boost performance by detecting, monitoring, and preventing quality losses at scale. The software integrates with a facility's existing camera systems or off-the-shelf industrial cameras.
The platform offers no-code software tools for vision-based defect detection, process monitoring, and root cause analysis. This improves manufacturers' financial performance and helps reduce environmental impact by reducing waste, rework, and defects.
Users don't need to be data scientists. Thanks to the no-code interface, it's designed for manufacturing experts, like test engineers, process engineers and operators.
EthonAI's customers include leading manufacturers like Siemens, Roche, and Lindt & Sprüngli.
I spoke to Dr Julian Senoner, CEO of EthonAI, to find out more.
Poor quality costs companies time, money, and reputation
He explained that poor quality is a massive cost driver in all manufacturing industries.
According to Senoner, manufacturers are throwing away many products:
"If you look into semiconductor manufacturing, for example, it is not uncommon that they throw away 10 to 20 percent of the products they produce, which is a massive financial waste, but also harms the environment because you need to throw away these products at a cost and recycle them."
EthonAI can produce mass efficiencies at scale. Traditional visual inspection relies on human inspection or cumbersome, time-consuming rule-based systems.
And then, there is the challenge of increases in customisation in manufacturing, where product variants increase exponentially.
"Historically, you need hours or even days to develop a computer vision job for a particular product. If you develop 200 different product variants, it becomes an infeasible job. However, with our computer vision software, essentially, in five minutes, you can train a new computer vision model and put it out into the production line."
By aggregating process and quality data into a single platform, manufacturers can use EthonAI's advanced AI methods to analyse and improve the quality of their products and processes.
Using EthonAI, manufacturers have seen significant increases in productivity and over 50 percent reduction in quality losses.
Root cause analysis and horizontal platforms are crucial to meaning at scale
And the company's root cause analysis tools are critical to preventing future product defects. Only reacting to defects once they happen is insufficient. To avoid quality losses, manufacturers must proactively identify the underlying root causes to prevent similar defects in the future.
Senoner shared that EthonAI provides a point of difference with its horizontal platform.
"So we're not only focusing on the detection of the problems, but we also look at the entire production value chain, to help manufacturers improve everything around their quality management. And what we have seen is a strong verticalisation."
Many providers develop isolated solutions that fail to scale across factories at a time when manufacturers are looking for a solution where they can build something horizontally around that. As a result, EthonAI focuses on not only visual inspection but also root cause analysis.
"We investigate factory-like sensor data temperature and pressure. We look at the process behaviour and can identify early when processes start to behave differently and where quality losses are to be expected."
Furthermore, analytics provide deep historical insights over time to monitor both the efficacy of production health and design experiments.
"Platform thinking creates transparency. So we're not only doing visual inspections, but we're saving a lot of metadata about these inspections and creating transparency, by monitoring and analysing the number of defects over time."
The funding will help to develop EthonAI's technology further and bring its products to a wider market.