Tarragon Systems provides a demand forecasting solution for multi-unit restaurants using machine learning algorithms that analyze external datasets and historical sales data to optimize inventory ordering. This technology helps restaurant managers reduce food cost variances and improve profitability by offering precise ordering recommendations tailored to individual store needs.
Funding
Funding not disclosed
Founders
Product
Problem
Multi-unit restaurants often struggle with inaccurate demand forecasting, leading to over- or under-ordering of inventory. This results in increased food cost variances, waste, and reduced profitability due to spoilage or lost sales opportunities.
Solution
Tarragon Systems offers a demand forecasting solution tailored for multi-unit restaurants, leveraging machine learning algorithms to analyze both external datasets and historical sales data at the individual store level. The platform provides restaurant managers with precise ordering recommendations, enabling them to optimize inventory levels and minimize food cost variances. By integrating with existing POS systems, Tarragon Systems streamlines the inventory replenishment process, allowing managers to efficiently manage their stock and prepare for upcoming demand. The system also provides insights into the factors driving each prediction, enhancing transparency and trust in the recommendations.
Target Audience
The primary target audience is multi-unit restaurants seeking to improve demand forecasting accuracy, reduce food waste, and enhance profitability through data-driven inventory management.
Features
- Demand forecasting using machine learning algorithms trained on external datasets (e.g., weather, holidays, school schedules) and historical sales data.
- Recommended ordering quantities with explanations of the underlying factors influencing each prediction.
- Integration with POS systems for streamlined inventory replenishment.
- Easy-to-use ordering guides with suggested ordering quantities.