Normalized claim
Quantified impact: 87%
Enabled 87% risk prediction for crack formation, allowing earlier intervention.
Uddeholm, a historic Swedish steel manufacturer, partnered with CGI to enhance sustainability and profitability using artificial intelligence on Microsoft Azure. By analyzing vast amounts of production data, the company developed AI and machine learning models to predict and prevent the formation of cracks in steel. This enabled earlier detection and improved process efficiency, reducing material waste and energy consumption. Uddeholm established a data governance model and built a central AI-IT infrastructure hub to support ongoing and future data-driven initiatives. Additional AI projects have helped the company with predictive maintenance and real-time detection of leaks, contributing to operational efficiency and reduced costs. The transformation puts data-driven decision-making at the heart of Uddeholm’s operations, aligning business growth with ambitious sustainability objectives. The initiative showcases how collaborative efforts and enabling technology can support industry leaders tackling key environmental and economic challenges. The project featured a comprehensive approach including high-quality data gathering, interdisciplinary information sharing, and dedicated organizational structures such as an AI-IT hub. The partnership continues to identify new areas for AI applications within production and supply chains.
Reported outcomes
87%
quantified impactRisk, reliability & safety
Strategic outcomes
Normalized claim
Quantified impact: 87%
Enabled 87% risk prediction for crack formation, allowing earlier intervention.
Additional AI projects have helped the company with predictive maintenance and real-time detection of leaks, contributing to operational efficiency and reduced costs
Primary read
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Uddeholm collects production data, then integrates it into an Azure-based cloud platform for storage and analysis. Machine learning models are trained on this data to predict potential crack formation and other defects. The system delivers early warnings and recommended process changes to production teams, allowing real-time intervention. A governance model assures data quality and enables department-wide collaboration via an AI-IT infrastructure hub.
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