Birmingham City University and Hadley Group Holdings Limited KTP 24_25 R4

A multinational steel company is embedding artificial intelligence directly into its manufacturing lines to catch defects as they happen and eliminate waste entirely. Steel sections—the beams and girders that form the skeleton of buildings, bridges, and warehouses—are produced in vast quantities with tight tolerances. Even small flaws can compromise safety or require costly rework. Currently, many manufacturers rely on end-of-line inspections that catch problems too late, after energy and materials have already been wasted. This project tackles that gap by integrating AI and machine learning models with the company’s existing database systems, so production data flows in real time and defects are predicted or detected mid-process. If successful, the system could shift the plant toward zero-defect manufacturing—a goal that cuts material waste, reduces energy use, and improves profitability without sacrificing quality. The work also future-proofs the business by embedding competences in system integration and relational database management, making the company more adaptable to new digital tools. For the construction and infrastructure sectors that depend on steel, this means more reliable components and lower embodied carbon in the supply chain.