RHI Magnesita aimed to improve parts management and asset performance for industrial customers while increasing maintenance efficiency and reducing costs. Managing refractory material stock for over 1,000 machines worldwide posed significant challenges. By deploying AVEVA PI System and CONNECT, they enabled predictive maintenance that alleviated supply bottlenecks and minimized downtime. Machine learning models accurately forecast refractory consumption six months ahead with over 80% precision, contributing to an expected annual reduction of more than 4,200 tons of carbon dioxide emissions, showcasing AVEVA’s impact on sustainability and efficiency.

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