The world of manufacturing is driven by automation, efficiency and digitalisation. And now we have generative AI (GenAI) – a subset of deep learning – which is taking the factory floor to the next, impressive level and unlocking new possibilities for product innovation.
However, first we need to contextualise GenAI within the manufacturing industry. For one, it enables organisations to analyse data from thousands of productions cycles, simulations and decisions, all to improve speed and precision. Manufacturers now have the time and scope to run hundreds of ‘what if” scenarios without pausing the production line. This is virtual proof of concept designed for the sole purpose of exploring innovation and validating changes before implementing them.
Looking at a real-world example, product development, traditionally time consuming and costly, can be sped up by using GenAI to create dozens of iterations, each within a different set of material, performance, sustainability and cost parameters. Engineers therefore become both designers and curators.
Fundamentally, GenAI can optimise defect detection, which is essential for ensuring product quality and customer satisfaction.
Traditional defect detection methods typically rely on manual inspection or rule-based algorithms, which are time-consuming and prone to errors. GenAI models like generative adversarial networks (GANs) can generate synthetic images mimicking defects, lighting conditions and orientations.
“This approach accelerates the production of large datasets of synthetic images, which can be used to train, test and validate machine learning models in various applications, including computer vision, object detection, and image classification,” notes the research paper ‘Generative AI in manufacturing: a literature review of recent applications and future prospects’.
Responsible Gen AI usage
As we embed AI deeper into our factories, we must be vigilant about bias, transparency and data privacy. The manufacturing industry must therefore ensure that these AI-generated developments and designs can be audited, data is protected, and human oversight remains central. But GenAI use also comes with a greater need for technical proficiency, data literacy and adaptability. To that end, new roles are emerging such as AI ethicists, data engineers and digital twin managers.
Importantly, existing roles are becoming more strategic and less repetitive, which allows us, the human workers, to focus on what we’re really good at: innovation, problem solving and collaboration. What is exciting is that GenAI allows us to imagine what we can achieve tomorrow − a manufacturing ecosystem that is hyper-efficient, agile and sustainable. A space where man and machine can work side by side to solve some of the most pressing challenges of our time, from energy use to supply chain resilience.
Smart factories
At Schneider Electric, our Smart Factory programme, powered by AVEVA’s industrial software, is a testament to the power of technology. Today, our smart factories use digital twins, real-time analytics and AI-driven insights to enhance everything from production efficiency to predictive maintenance.
Our industrial software solutions are used to achieve real-time visibility of production states, ensuring consistent standards across multiple sites. This integration has enabled us to reduce unplanned downtime, improve operational visibility and enhance workforce productivity across our smart factories.
As testament to this, our smart factories in France, Indonesia, China and Mexico have all been recognised as Advanced Manufacturing Lighthouses by the World Economic Forum.
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