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Case / 02 · Predictive ML · Commodities
Predictive Procurement Engine
An ML-driven price-forecasting and hedge-trigger engine capturing 8% YoY cost reduction on a $300M+ global coal portfolio.
- YoY Cost ↓
- 8%
- Portfolio Scope
- $300M+
- Forecast Accuracy
- 94%
Context
Procurement was timing-blind: contracts were placed against rolling-average benchmarks, leaving meaningful basis on the table in volatile freight + index environments.
Approach
- Built a forward-curve model combining freight indices, FX, regional supply-demand, and weather priors.
- Backtested across seven years of monthly procurement decisions; tuned to a P50/P80 cone rather than a point forecast.
- Wrapped in a hedge-trigger rule set co-designed with the commercial desk — never autonomous, always advisory.
Outcome
- Trailing twelve-month landed cost reduced 8% vs benchmark.
- Hedge coverage at advantageous tenors rose 2.4x.
- Commercial team adopted the surface as their default morning read inside one quarter.