From the firm's own write-upReceipt no. 7875
What sucked
Conservative, static furnace profiles wasted energy across thousands of batch configurations, and the operation had no predictive capability to customize time-temperature curves per batch.
Two years of operational data sat unused in legacy systems amid rising energy costs and ESG pressure.
What they built
Sudolabs developed two systems: an ML-driven storage and production-planning engine that automates coil grouping and annealing batch scheduling under multi-step constraints, and predictive temperature-curve modeling that generates batch-specific optimization recommendations.
What changed
~5% energy cost reduction
Predictive control reduced energy and emissions costs by roughly 5% across annealing operations.
ROI in under 12 months
The project reached full ROI on investment in less than 12 months.
Millions in annual savings
For the $20B+ manufacturer, the improvement translated to millions in annual savings.
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