We have secured Innovate UK funding to lead AI-STREAMS, a feasibility project focused on establishing an AI Manufacturing Test Centre at the University of Derby. The project will evaluate AI solutions in a manufacturing environment, exploring how they can help manufacturing SMEs unlock greater value from the growing volumes of data generated across their operations.

Working alongside our project partners, the University of Derby and AluCast, we will investigate how connecting fragmented data sources can provide deeper insights into manufacturing processes and support more informed operational decision-making.

Manufacturers today have access to more information than ever before. Machine monitoring systems, production schedules, energy networks, sensors, and operational technologies continuously generate data designed to improve visibility and control. Yet despite significant investment in data collection, many organisations still struggle to translate that information into meaningful business improvements.

The challenge we’re looking to address isn’t the volume of data available. It’s the ability to connect information from multiple sources to understand how operational events influence one another. For example, production data may sit separately from machine performance records, energy consumption can be monitored independently of manufacturing activity, and process information is often distributed across different systems and teams.

This fragmentation makes it difficult for manufacturers to identify the root causes of performance variation, detect emerging issues early and gain a clear, connected understanding of what is happening across their operations.

As part of AI-STREAMS, we will enhance blocHub, our business data and knowledge platform, to bring together enterprise systems, production technologies, and operational data within a single secure environment. By bringing together data from across an organisation, we aim to demonstrate how blocHub can help manufacturers move beyond isolated datasets and establish the foundations needed for advanced analytics, AI, digital twins, and continuous operational improvement.

Drawing on our expertise in digital transformation and data visualisation, we’ll explore how production schedules, machine data, operational information, and energy consumption can be analysed together to reveal patterns that would otherwise remain hidden. Through controlled machining trials and industrial testing, we aim to understand how changes in tooling, machine condition, materials, and production activity appear within connected datasets. This will help us explore how manufacturers can recognise early indicators of process drift before they contribute to downtime, waste, or reduced productivity.

Our blocHub platform will play a central role in bringing these insights together. By capturing, orchestrating, and contextualising information from multiple sources, we will use blocHub to analyse manufacturing activity as a connected process rather than a collection of individual data streams.

Through this approach, we aim to build a clearer understanding of the relationships between operational events, process behaviour and the factors influencing manufacturing performance. Ultimately, we want to explore how these connected insights could support more effective condition monitoring, helping manufacturers identify potential issues earlier, reduce operational risk and improve productivity.

For us, the opportunity extends well beyond a single project. As manufacturers continue to adopt connected technologies, we recognise the growing importance of turning operational data into early warnings, actionable insights, and more informed decisions.

Through AI-STREAMS, we’re bringing together our expertise in data integration, visualisation and digital transformation to explore how AI, supported by connected and contextualised data, can help manufacturers identify issues sooner, reduce the likelihood of unplanned downtime and unlock greater value from the information they already possess.