Consortium of Agricultural Producers
Demand forecasting for sales price optimization
We developed an artificial intelligence solution to forecast demand by crop variety, optimize inventory turnover, and maximize spot market sales opportunities through predictive models and risk scenario analysis.
- Industry
- Capabilities
- Regions
- 🇺🇸 United States
- Published
- July 2026
The Challenge
The company needed to improve the accuracy of demand forecasting across different crop varieties to optimize inventory management. Overestimating contracted deliveries resulted in excess inventory that had to be sold on the spot market, increasing operational costs and the risk of inventory aging.
The Solution
We implemented an artificial intelligence solution based on advanced time-series forecasting models to predict weekly demand by crop variety. The solution combined algorithms such as ARIMA, LSTM, and Prophet to evaluate performance and generate forecasts with different confidence levels. It also incorporated scenario simulation and risk analysis to optimize decisions related to contracts, spot market sales, and inventory turnover.
Benefits
- 160% increase in hop sales through the spot market.
- 95% forecast confidence for demand predictions.
- Reduced costs through more efficient inventory management.
- Improved commercial decision-making with scenario and risk analysis.
- Scalable model adaptable to changing demand patterns and new crop varieties.
- +160%
- Spot market sales
- 95%
- Contract compliance confidence