Case Study: Large Global Cement Manufacturer achieves $13.5M savings and lower CO2 emissions with Tredence AI/ML

AI/ML-Driven Cement Strength Prediction and Design Mix Optimization for a Large Global Cement Manufacturer

Large Global Cement Manufacturer worked with Tredence to tackle the challenge of maintaining cement quality while controlling clinker and energy costs, reducing CO2 emissions, and improving decision-making across its operations. The manufacturer used its Cement Quality Monitoring System (CQMS) to forecast short- and long-term cement strength, but needed a more scalable, AI-driven approach to support quality managers and plant operators across a global plant network.

Tredence implemented an AI/ML solution on AWS to predict cement strength in near real time for 1-day, 2-day, 7-day, and 28-day horizons, using laboratory and process data integrated into the Factory Information System (FIS). The solution helped reduce clinker factor and grinding fineness, delivering over $13.5 million in savings across 50 plants, along with significant sustainability gains including approximately 150,000 tons of CO2 savings across the client network.