Customer description‍

A fruit and vegetable exporter active in Europe, the Middle East and Asia, with a dynamic purchasing and sales process based on global market prices. The company trades in rapidly perishable products and is highly dependent on season, weather and global trade.

Challenge‍

Market prices for products such as tomatoes, cucumbers and peppers fluctuated sharply. Due to poor forecasts, purchases were regularly made too early or too late, leading to missed margins or surpluses. Intuition and experience were leading, but insufficiently predictive.

Solution‍

A solution was developed that included an AI-driven price forecasting model that combined historical price data, import/export statistics, weather forecasts and international trade flows to accurately predict the expected price development per product.

Approach

1. Collection of historical market data
   Data from price developments at auctions, global markets and trade organizations was collected and enriched.
2. Integrating external factors
   Weather data, seasonality, geopolitical trends and supply-demand data were added to the model.
3. Model development and validation
   A time series model with machine learning was trained and validated for accuracy by product category.
4. Building a Purchasing Dashboard
   The forecasts were made clear via a dashboard that buyers use every day to align their decisions.

Results

• +6% average margin improvement on seasonal products
• More peace of mind in the purchasing process thanks to data-driven choices
• Faster turnaround time in decision-making
• Higher reliability towards customers through more stable prices

Learnings‍

By using AI smartly at the cutting edge of market knowledge and data, the agri-exporter was able to significantly increase its commercial strength. Intuition was supported by facts, with a direct impact on results.

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