Why Some Digital Projects Fail to Deliver - and How to Ensure Yours Succeed

Steel and chemical operations are proving that AI and digital transformation can work. See how explainable machine learning and focused deployment help rebuild trust and deliver real results in production.

This is Where Your Steel Mill Is Bleeding >$1.5M Annually

Steel mills are losing millions annually through inefficient alloy optimization systems that over-alloy by 15-20%. Although most plants have existing alloy optimization initiatives, traditional statistical methods and internal machine learning projects fail due to data complexity, model degradation, and lack of transparency. Advanced white-box ML systems with real-time adaptation have proven to reduce alloy costs by $3+ per ton while improving quality consistency by 15% and reducing environmental impact. Successful implementation requires gradual deployment, operator training, and change management strategies documented in an award-winning technical research paper by Gerdau and their partner Fero Labs, showing sustained results across thousands of production heats. Key Benefits: $3/ton alloy cost reduction • 15% quality improvement • 66% process capability enhancement • 500K+ lbs material waste elimination • Real-time mechanical property prediction • Automated model maintenance • White-box transparency for operator trust

Linear vs. Non-Linear Regression in Steel: Why In-House Linear Models Leave Money on the Table (How Fero Labs Closes the Gap)

Discover why legacy linear models underperform in steelmaking—and how Fero Labs’ explainable, non-linear AI analyzes 3+ years of data in seconds to boost yield, reduce cost, and optimize operations.

Good Moves Don't Come From Thinking Too Long