AI USE CASE
Raw Material Price Forecasting
Forecast commodity prices using ML so procurement teams buy at the right time.
What it is
Machine learning models ingest commodity market data, supply/demand indicators, and geopolitical signals to predict raw material price movements 4–12 weeks ahead. Procurement teams use these forecasts to time purchasing decisions, hedge exposure, and negotiate contracts more effectively. The system continuously retrains on new market data to maintain forecast relevance.
Data you need
At least 2–3 years of historical commodity price data, internal purchasing volumes, and access to external market/geopolitical data feeds.
Required systems
- erp
- data warehouse
Why it works
- Combine internal procurement history with high-quality external data feeds (e.g. Bloomberg, Refinitiv, or equivalent).
- Involve procurement managers in model validation to build trust and embed forecasts into decision processes.
- Set up clear confidence intervals and communicate forecast uncertainty so buyers make risk-adjusted decisions.
- Schedule regular model retraining cycles (at least monthly) to capture changing market dynamics.
How this goes wrong
- Model accuracy degrades when unprecedented geopolitical shocks (e.g. new trade wars) fall outside historical training patterns.
- Procurement teams distrust model outputs and revert to intuition, especially after a single missed forecast.
- Insufficient historical data depth or poor data quality from legacy ERP systems undermines model training.
- Forecasts are produced but not integrated into actual purchasing workflows, leaving value unrealised.
When NOT to do this
Don't build a custom forecasting model if your procurement volume is too small to meaningfully act on price signals, the model cost will far exceed any purchasing savings.
Example vendors
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