Intelligent Machines: AI and Manufacturing

Intelligent Machines: AI and Manufacturing

As AI begins to be applied as a practical tool, what are the challenges manufacturers are facing using AI across their processes? Will manufacturing be transformed by AI?

 

 
Integrating AI in manufacturing is an incremental improvement and a revolutionary shift. Traditional manufacturing relied heavily on manual labour and mechanical processes, which, although practical, were often limited by human error and inefficiencies. Today, AI-powered machines can perform complex tasks with a level of precision and speed unattainable by human workers.

However, manufacturers’ adoption of AI in its current state of development is mixed. According to research by the Manufacturer, just 9% of manufacturing organisations are leveraging AI today. In some specific areas of process, such as machine vision in automotive OEMs, for example, the figure is nearly a quarter (24%).

The picture improves by looking at specific aspects of AI implementations—notably Generative AI (GenAI). Research from KPMG indicates that of the industrial manufacturers surveyed, 65% are developing their own GenAI solutions. However, 76% are just defining objectives, with 44% ready to allocate the budget to these projects. AI across the manufacturing landscape is gaining pace, if very slowly.

In addition, in their report Manufacturing Data and AI Predictions 2024, Tim Long, Global Head of Manufacturing, Snowflake, states: “Manufacturers should identify the areas with the biggest potential payback and where the biggest business challenges exist, then assess whether gen AI is the right solution to make the impact they need in those areas.”

Speaking to Silicon UK, Eleanor Lightbody, CEO at Luminance, commented: “AI also supports manufacturers in reducing emissions and improving sustainability. AI facilitates the drive towards Net Zero by providing an instant overview of a company’s existing ESG position. With a conceptual understanding of language, AI enables businesses to move beyond simple keyword searches and gain deeper insights into their environmental and compliance obligations.

Eleanor Lightbody, CEO at Luminance
Eleanor Lightbody, CEO at Luminance.

Eleanor continued: “Suppose a manufacturing company sought to better understand its sustainability measures. Instead of merely reporting on contracts that specifically mention the word’ sustainability,’ the AI would also identify contracts with relevant terms such as ’emissions’ or ‘waste management.'”

AI is then being applied widely, if tentatively, in manufacturing as businesses begin to understand how these tools could enhance their processes. The road to real transformative change will be long and full of challenges.

A new age?

To gain an insight into how AI could impact the manufacturing space, Silicon UK spoke with Theo Saville, co-founder and CEO of CloudNC, and began by asking: In your view, how is AI transforming the manufacturing sector in the UK? Can you highlight some key trends and areas of impact?

Theo Saville, co-founder and CEO of CloudNC
Theo Saville, Co-founder and CEO of CloudNC.

“The honest answer is that AI is not currently transforming UK manufacturing, as the kinds of AI you need to make a genuine difference for the sector haven’t been invented yet,” Theo succinctly stated.

“Manufacturing gets better and faster if you find ways to lower your overheads and costs and find ways to improve your yield and efficiency. However, recent developments in AI are mostly in the software realm – image and text generation and data sifting – and are based on large language models. For AI to be helpful in manufacturing, it must be extremely robust and deterministic, and not built on fuzzy datasets with slightly iffy logic. The kind of funny mistakes that we see from Chat-GPT and other solutions when it comes to answering queries and creating images would, if translated to a factory environment, wreck expensive equipment very quickly.

“My company, CloudNC, is on the vanguard of developing AI so it can make a genuine impact: our solutions automate CNC machining and programming, and it is already making manufacturers more efficient and productive. What we have made is one of the most advanced applications of AI in manufacturing that you can get – we’ve been working on it for nine years. However, it’s only been deployed correctly in the market for about three months. We’re right at the very start of finding out how AI can transform manufacturing.”

Beyond efficiency gains, what other benefits can AI offer to UK manufacturers? (e.g., improved product quality, design optimisation, sustainability)

“Right now, the benefits are the same as in other industries – AI offers faster access to information, so you can ask questions about complicated subjects and get much faster, accurate answers without having to know and understand all the detail. That’s something particularly applicable to manufacturing, given that previously your way of finding answers out would be to sift through textbooks or find someone with expertise to help.”

“Our CAM Assist AI is a good case in point – it’s a solution specifically built for one task, which is to accelerate precision machining through automating programming, thereby solving a big bottleneck in the system. That can save manufacturers an enormous amount of time.”

How can AI be harnessed to optimise production processes and resource utilisation in UK factories, leading to a more sustainable manufacturing environment?

“Solutions built to have an impact in manufacturing are few and far between as it’s a very challenging use case. That said, we will first see an impact in simple tasks like picking and placing in warehouses: areas where being very precise isn’t as much of a critical requirement, and if something goes a little bit wrong, it’s ok.

“In the future, the big difference that AI will make is in enabling greater efficiencies, which will in turn boost reshoring. The reason that manufacturing went abroad in the first place is that labour is cheaper overseas. However, if you can use AI to make machinists at home more productive, then labour stops being such a cost factor, and you can carry out more manufacturing closer to home.”

As AI plays a growing role in decision-making within manufacturing, what ethical considerations need to be addressed (e.g., bias in algorithms, job displacement)?

“Broadly speaking, most people don’t want to work in manufacturing, and that’s a big reason why there’s a skills gap. So, technology like ours isn’t displacing any jobs. Instead, it’s a tool that makes skilled people and newcomers alike more productive and making their jobs more interesting and better paid.”

What skills and workforce training are needed to ensure a smooth integration of AI into UK manufacturing operations and empower human workers to collaborate effectively with these intelligent machines?

“One nice aspect of AI is that it makes it easier to carry out complex tasks with a lower skill level. However, that means you need to build digital literacy in the next generation of workers so they can use the tools that companies like ours are building, so they can use them effectively. That might require replacing a few English lessons with software engineering at school.

Imagine a future where “intelligent factories” are commonplace in the UK. What would this future look like, and what are the potential benefits and risks to consider?

“There is a much greater risk than that of having lots of intelligent factories in the UK, which is: what if there are no factories or manufacturing base at all. A country needs to be self-sufficient in certain regards – defence, food – as otherwise you are vulnerable, especially if you’ve just lost your largest trading partner. The UK needs competitive factories to run effectively, and I hope our solutions will help them to run and run productively.”

Embracing AI

The future of AI in manufacturing is promising, with continuous advancements likely to bring even more transformative changes. As AI technology evolves, we can expect even greater integration of AI systems with other emerging technologies, such as the Internet of Things (IoT) and blockchain. For instance, the combination of AI and IoT can create intelligent factories where machines communicate with each other, making real-time adjustments to optimise production.

Additionally, AI-driven automation is expected to be critical in sustainable manufacturing practices. AI can help manufacturers reduce their environmental impact by optimising resource use and minimising waste. This capability is particularly crucial in an era where sustainability is becoming a significant concern for consumers and regulators alike.

Also, the shift towards AI-driven manufacturing requires substantial investment in technology and workforce training. Companies must be prepared to invest not only in AI systems but also in the continuous education of their employees to ensure they can work effectively alongside intelligent machines.

Intelligent machines powered by AI are revolutionising manufacturing, bringing unprecedented efficiency, precision, and innovation. From predictive maintenance to supply chain optimisation, AI enhances every aspect of manufacturing, driving the industry towards a more productive and sustainable future.

However, to fully realise AI’s potential, manufacturers must address the associated challenges and invest in the necessary infrastructure and training. As we move forward, the symbiosis between human ingenuity and artificial intelligence will undoubtedly shape the future of manufacturing, creating new possibilities and opportunities.

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