Case Study: a agriculture industry leader achieves better commodity price forecasting with Descartes Labs

Forecasting Commodity Prices with Geospatial Insights

The agriculture industry leader faced the challenge of needing to incorporate new technologies and data sources to maintain a competitive edge in commodity trading.

Descartes Labs provided a solution using remote sensing, machine learning, and statistics to combine spatial and non-spatial data, offering a holistic view of the supply chain. This enabled the customer to quantify various market factors into profitable actions. As a result, they now rely on Descartes Labs' predictive models to optimize trading strategies, achieving an ROI of up to 5x on sugar and soybean meal price forecasts while significantly reducing the internal cost and skill required for advanced analytics.