How AI Is Transforming the Factory Floor

Andrew Sherlock, Director of Data-Driven Manufacturing, National Manufacturing Institute Scotland (NMIS)

Artificial intelligence (AI) is quickly becoming one of the hottest topics in the manufacturing world. While a lot of the public chatter focuses on large language models (LLMs) like OpenAI’s ChatGPT, the impact of AI in industry goes far beyond just being a digital assistant or a tool for generating content.

We're now seeing a new wave of applications tailored specifically for different sectors, which are revolutionizing how manufacturers design their processes, make decisions, and tap into the wealth of data flowing through their operations.

This transformation is happening at a time when production systems are getting more complex, supply chains are becoming more demanding, and the bar for efficiency and quality is set higher than ever. Traditional tools are struggling to keep up, pushing manufacturers to look into how AI can take on tasks that have typically relied on human judgment or manual oversight.

Across the industry, various projects are already showcasing how specialized AI models can enhance accuracy, bolster process control, and provide clearer insights on the factory floor. As the momentum grows, the real question shifts from whether AI will impact manufacturing to how deeply it will integrate into engineering practices—and what groundwork is necessary to fully harness its potential.

Access to high-quality data is key to making this progress happen. At the National Manufacturing Institute Scotland, researchers are not just creating new AI capabilities; they’re also building the datasets needed to make those capabilities work effectively in real-world industrial environments. Even the most sophisticated software relies on solid training data; without it, its usefulness can quickly fade.

Practical examples are already starting to emerge. NMIS is teaming up with a major aerospace company to model how a component behaves during the forging process — a complex procedure where some of the underlying physics are still not fully understood. By blending machine learning with focused trials, the team is generating datasets that help predict changes in material properties. This provides engineers with the insights they need to design and optimize processes more accurately, which in turn reduces the number of trials, minimizes waste, and boosts overall efficiency.

Innovation is also picking up speed across the broader manufacturing landscape, with more organizations creating AI tools for design, simulation, and decision-making at the factory level. For instance, one UK software developer is helping engineers unlock more value from Computer-Aided Design (CAD) data through its HOOPS AI platform — a system that streamlines access, preparation, and training of machine-learning models using 3D geometry.

In another development, a new engineering platform is significantly cutting down traditional simulation times from days to mere seconds by using AI-powered models trained on existing simulation data. This allows engineers to refine designs in real time, speeding up development and reducing reliance on lengthy physical or computer-based testing.

NMIS is also working alongside academic partners, including colleagues from the University of Strathclyde and researchers at the University of Edinburgh, to implement AI in factory operations. By capturing raw data streams from equipment like forklifts, new algorithms can track parts, pinpoint inefficiencies and bottlenecks, and reveal opportunities to enhance workflow and reduce downtime in production settings.

Even though we're seeing some exciting advancements, the use of AI on the factory floor is still in its early days. Through the Data-Driven Design and Manufacturing Colab project—part of the Glasgow City Region Innovation Accelerator program and backed by Innovate UK on behalf of UK Research and Innovation—NMIS is teaming up with various organizations to connect the dots between manufacturing and digital tech. This initiative is empowering engineers to develop the skills and confidence they need to implement data-driven strategies in their own companies. So far, over 120 projects across sectors like aerospace, energy, food and drink, construction, and electronics have shown how data-focused methods can cut emissions, boost component accuracy, and improve reliability.

AI is already transforming how manufacturers design their processes and make decisions. As these specialized tools continue to evolve, the real opportunity lies in integrating them more thoroughly into engineering operations and ensuring that the workforce is equipped to use them effectively. This shift—from general conversations to real-world applications—is where AI is poised to make its most significant impact, helping manufacturers build greater resilience and extract more value from the data that drives modern production.

NMIS is run by the University of Strathclyde and is part of the High Value Manufacturing Catapult.