AI for Raw Materials Procurement

Procurement decisions relied heavily on recent history and individual experience. Without a consistent view of demand, prices, and supplier lead times, the business faced both excess inventory and shortages.

• Demand swings were difficult to anticipate.
• Price volatility complicated purchasing decisions.
• Supplier delays could leave production without key materials.
• Safety stock tied up cash and warehouse capacity.

Historical demand, cost, and supplier data offered a basis for forward-looking forecasts. Combining that history with current market signals could give buyers quantified decision support.

• Forecast demand using historical patterns and seasonality.
• Model supplier lead-time variability and supply risk.
• Assess expected cost trends before placing orders.
• Use scenarios to compare purchasing choices under uncertainty.

We built a forecasting and decision-support tool for purchase timing and volume. Buyers can compare scenarios and combine model guidance with their knowledge of suppliers and business context.

• Model demand, costs, and supplier-side risk together.
• Recommend purchasing timing and quantities.
• Test the impact of price changes or supplier delays.
• Keep procurement judgment central to the final decision.