Over half of UK automotive industry companies now using AI for machine vision
The need for heightened inspection confidence, parts traceability and operational transparency across the supply chain are driving this advancement, as auto manufacturers and suppliers grapple with growing regulatory burdens and consumer mistrust.
Though it might be expected for original equipment manufacturers (OEMs) to embrace AI technologies to automate certain processes and decisions in production environments, the newly released “AI Machine Vision in the Automotive Industry Benchmark Report” reveals that, broken down by organisation, AI machine vision is being used by 56% of OEMs, 63% of tier 1 and 44% of tier 2 suppliers in the UK. This data reflects UK automotive industry companies’ commitment to quality, supply availability and customer satisfaction at the nexus between supply chains and emerging industry trends such as electric, hybrid, and autonomous vehicles.
In Germany, 49% of OEMs, 40% of tier 1 and 35% of tier 2 suppliers have embraced AI machine vision to date. While it’s possible others are exploring the benefits of this technology – around 20% of all respondents in the UK and Germany said they would like to learn more or have already started to procure AI machine vision tools – those who delay adoption of AI machine vision may find it more difficult to maintain a competitive edge.
“It’s concerning that nearly one-quarter (24%) of automotive business leaders in the UK and over one-third (34%) in Germany say there are not using any form of AI, such as deep learning, in their machine vision projects. It’s even more worrying that they don’t see the importance or relevance,” said Stephan Pottel, Automotive Manufacturing Practice Lead EMEA, Zebra Technologies. “The automotive industry is highly competitive, meaning automotive business leaders should be looking at ways they can build confidence in the quality of their products, especially as the rising shift toward hybrid and electric vehicle production.”
For automotive OEMs and suppliers who are preparing their engineers and plant-floor employees to think and act like data and AI specialists, new AI-powered machine vision software is unlocking higher levels of productivity, speed, accuracy and capabilities in challenging environments and with complex use cases.
Yet, feedback provided by the 500 automotive OEM and supplier decision-makers in the UK and Germany who participated in this benchmark survey illustrate a more polarised situation unfolding against a backdrop of renewed pressures around sustainability, transparency and performance.
The data also shows that when it comes to AI performance in machine vision projects, there is room for growth. Of those using AI in the UK, nearly one-in-five (18%) say their AI could be working better or doing more. “Some OEMs and suppliers expect more from their AI-powered machine vision applications—they’re setting down a challenge to machine vision partners, who can and must deliver on this expectation,” added Pottel.
The report also reveals several key challenges UK and German OEMs and suppliers face when using conventional optical character recognition (OCR) tools, which are widely used for reading serial codes, component and lot numbers, as well as vehicle identification numbers to ensure quality, traceability, and presence/absence checking. They claim there is a significant amount of training time required, and the older OCR tools are often unstable, difficult to use, and unable to handle complex use cases well – even by a well-trained engineer.
“The survey results show a surprisingly high level of dissatisfaction with traditional OCR tools, and a range of challenges that require a lot of investment for an unsatisfactory outcome,” said Pottel. “These older OCR tools do not meet the demands of modern manufacturing. However, new deep learning OCR models are available to deliver immediate value out of the box with no specialised skills or training required.”
In the last year, there has been an increase in the availability of deep learning OCR tools that come pre-trained using thousands of images, making it easy for people with no AI experience or data science skills to use. These deep learning OCR models are built specifically to handle complex use cases such as damaged and hard to read characters, reflective surfaces, and changing lighting environments. As a result of this innovation by machine vision system engineers, automotive OEMs and suppliers no longer have to expend resources trying to configure OCR tools or validate inspection outcomes. There are hardware and software tools which can provide the answers they need to deliver on quality and availability promises to stakeholders.
Key takeaways
- Fifty-six percent of UK auto industry leaders are using some form of AI in their machine vision systems.
- Nearly one-quarter (24%) of OEMs and suppliers in the UK say they are not using any form of AI and don’t see the use or relevance, highlighting the need to provide greater proof of value.
- Auto industry leaders are frustrated with the performance and labour burden of conventional OCR tools and need an easy way to automate visual inspections in complex use cases.
- Automotive OEMs and suppliers who want additional exclusive benchmark insights into how they compare to peers and competitors around machine vision applications and future AI-based automation goals can download the full report here.
Methodology
The survey was carried out among 250 machine vision decision-makers in the UK automotive industry and 250 machine vision decision-makers in the German automotive industry, in May 2023 by Censuswide.
All image rights are owned by the author of this press release.
back to overview