Artificial intelligence in computing has paved the way toward new and smarter processes, enabling more accurate predictive analytics, autonomous systems, and more. Recently, more advanced AI – generative AI – which leverages machine learning algorithms has emerged, opening new doors to even smarter capabilities than before.
Generative AI, developed partly from generative adversarial networks (GANs), is a type of AI that involves training two neural networks to work together to generate new data. Over the past year, generative AI has experienced a meteoric rise in popularity with the advent of AI-powered chatbots, such as ChatGPT.
In manufacturing, generative AI is also becoming increasingly important in smart manufacturing solutions alongside other advanced technologies like digital twins, augmented and virtual reality, and industrial internet of things (IIoT) tools. With its potential to optimise manufacturing processes, improve product design, and enhance the overall efficiency of the manufacturing industry, it’s unsurprising that generative AI’s overall market value in the sector is projected to rise from US$225 million in 2022 to US$6,963.45 million by 2032.
But this isn’t the only advanced technology making waves in the industry. The use of the industrial metaverse – a virtual representation of the physical world – is becoming more commonplace as more manufacturers digitally transform, enabling the integration of digital and physical systems. Together, generative AI and the industrial metaverse are revolutionising the manufacturing industry by enabling greater efficiency, flexibility, and innovation.
The potential of generative AI in manufacturing
Generative AI is a powerful tool for manufacturers because it allows for the creation of new designs, processes, and products that would be difficult or impossible to achieve through traditional methods. By using generative AI, manufacturers can gain higher levels of process optimisation, waste reduction, and overall quality improvements. Additionally, generative AI can help manufacturers to identify new opportunities for innovation and growth.
Generative AI case study: aviation industry
Generative AI is already being applied and tested within the aviation industry to improve communications and enhance the customer service experience, in addition to implementing it for improving inventory management. Additionally, aircraft manufacturers can tap generative AI for manufacturing aircraft parts, optimising the designing and prototyping process with the help of AI-powered automation and digital twins.
More steps are being taken within aviation in preparation for more advanced AI use – the European Union Aviation Safety Agency (EASA) released its AI Roadmap 2.0 earlier in May, outlining a detailed plan for integrating AI into the industry.
Generative AI case study: automotive industry
Another example of generative AI in action is generative design software in the automotive industry. Manufacturers have begun using this AI-powered software to create a wide range of new and complex vehicular system designs thanks to the large number of data and simulations that it can produce.
Generative AI also empowers vehicle manufacturers with the ability to more deeply analyse machine and sensor data in vehicles for highly accurate predictive maintenance forecasting. This analysis using historical data helps identify issues much earlier, allowing manufacturers to take proactive steps to prevent and fix issues for greater efficiency and less waste.
How generative AI can improve the industrial metaverse
Although generative AI has enormous potential in the manufacturing industry’s digital transformation journey, it’s not the only advanced digital solution that’s driving the sector forward. The rise of the manufacturing metaverse has also led to greater process optimisation than before, thanks to the ability to create virtual worlds through the help of digital twins.
The use of digital twins in manufacturing has helped manufacturers gain greater flexibility as they can simulate operational processes, machine inputs, and automation in a virtually replicated version of real-world systems. By combining the power of generative AI with the industrial metaverse, manufacturers can achieve higher levels of efficiency, agility, and innovation.
For instance, the digital twins created using generative AI can be more accurate, can analyse more real-time data, and improve energy use compared to traditional AI and machine learning algorithms. Deloitte’s 15th annual Tech Trends report also states that generative AI – when paired with new spatial computing and the industrial metaverse – will be a new “growth catalyst” that enables manufacturers to not just attain new advances within their industry, but also elevated capabilities.
Potential challenges of using generative AI in the industrial metaverse
The potential benefits of using generative AI in the industrial metaverse are numerous. However, there are also challenges for manufacturers to consider when using such advanced AI-driven tools and solutions.
AI processes can be taxing for organisations that are not properly equipped to run such resource-intensive functions due to the large amount of data required. Given how demanding generative AI algorithms can be, manufacturers must know how to balance their resources so that existing infrastructure can keep up with the everyday operational demands within the organisation.
Furthermore, there are four other general risks involved when using generative AI. As outlined in PwC’s risk management playbook for AI, these are data risks, model and bias risks, prompt or input risks, and user risks.
Manufacturers must be privy to these risks and how to manage them. These include developing the right AI governance strategies, ensuring data is not corrupted, preventing the use of data influenced by user error, and more.
Preparing your organisation for generative AI
Generative AI is having a significant impact on smart manufacturing and the industrial metaverse. As Industry 4.0 matures further, these newer and more advanced smart manufacturing technologies can drive manufacturers closer to their digital transformation goals to achieve increased efficiency and flexibility while reducing cost, waste, and downtime.
However, manufacturing leaders must understand that they cannot improve their operations if they cannot identify the areas to address. With the help of neutral benchmarking tools and maturity assessment frameworks like the Smart Industry Readiness Index (SIRI), they can look forward to vast organisational improvements to take their operations to the next level.