Large steel manufacturer cuts production planning time 99% with AI schedule optimization

A national steel manufacturer operating 30+ mills produced over 400 distinct steel products across seven cast sizes on two-week production cycles. Scheduling relied on subject matter experts pulling from seven data sources across five systems, using manual spreadsheets, and took 5-7 days per cycle. Plans were rigid, produced excess inventory, and often failed to meet customer demand due to uncodified transition rules, yield calculations, and the difficulty of generating what-if scenarios.

Over 26 weeks, C3 AI deployed its Production Schedule Optimization application for a $1 billion steel mill. Three years of historical data from seven source systems were ingested and unified into a federated data image. An AI optimization algorithm was built with 300+ variables and constraints to sequence caster production, minimize scrap, and balance product, process, and supply chain factors. A workflow-driven multi-screen UI was configured so planners can visualize optimization results and create scheduling scenarios in real time.

The optimizer was validated against 46 historical cycle schedules and matched or exceeded expert-generated schedules in all cases. Across one year of production, it improved net output by ~1%, yielding 1,000+ additional tons of finished steel worth approximately $1 million at a single mill and up to $4 million across three mills. Planning cycle time dropped from 5-7 days to 1 hour — a 99% reduction.

• Codifying decades of tacit expert knowledge (transition rules, yield calculations) into a machine-readable constraint model is a prerequisite for AI scheduling to outperform manual processes.
• Unifying data from multiple disparate source systems into a single federated image unlocks not just scheduling optimization but also demand forecasting and inventory optimization on the same foundation.
• Even a ~1% throughput improvement in high-volume continuous manufacturing translates to seven-figure annual value, making the ROI case for AI scheduling straightforward in capital-intensive industries.