Scrap is the largest variable cost for metal manufacturers, yet conventional decision-making, traditional recipes, and “business as usual” purchase decisions leave big gaps in margins and quality.
Machine Learning models prioritize math and data in scrap mix optimization, providing reduced scrap costs, reduced energy costs, improved throughput and improved yield.
Scrap- and heat-level characterization of chemistry, combined with heat-level predictions of yield, energy consumption, and density, provide an indispensable foundation for commodity analyses.
Run what-if scenario analyses for recipe generation and procurement guidance based on user input mix, constraints, etc.
Actionable recipe and purchasing recommendations to optimize scrap mix profitability, given throughput and yield requirements as well as energy and scrap costs.