Raw Material Price Forecasting For Capturing Customer Willingness To Pay 

At a chemical company, lack of visibility into supply-chain driven cost of raw materials fluctuations was preventing sales from negotiating smart deals and hampering their competitive ability.

Challenge

Improve deal win probability while maintaining margins at comm. products

To enable the sales teams of the heavily commoditized product group, the following information was needed:

  • 1 to 3-month price forecast of base commodities (in the value chain of products)
  • 1 to 3-month price forecast of main raw material input
  • 1 to 3-month predictive price recommendation for product incure raw material trend adjustment

Approach

Raw Material Price Forecast with Margin Recommendations

To start, elements in the value chain were identified, correlated, and analyzed for seasonal patterns and trends. Modeling techniques such as supervised machine learning methods and time-series analysis were used to Forecast Price movement of the raw material based on historical and forecast prices of linked base commodities.

On top of this, a margin model was built that gives a margin recommendation on top of the fluctuating base commodities and raw material as a guideline to sales on what to quote.

Methodologies 

  • Supervised Machine Learning

Results

  • Higher conversion probability of 32% on the monthly negotiated business
  • Ability to pass on increases and decreases in raw material price to customers swiftly
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