🏪 Ranchi Dukaan Pro — Predicting Stock Demand with TabPFN-TS

Описание и применение

Ranchi Dukaan Pro использует TabPFN-TS для прогнозирования спроса на семь дней и генерирует рекомендации по пополнению запасов для небольших магазинов, демонстрируя улучшенные метрики MAE и WAPE по сравнению с простым базовым подходом, но результаты основаны на синтетических данных.

Задача

Small retailers need to decide what to reorder, how much stock to keep, and when to place the next order, but lack tools to forecast demand and generate inventory recommendations.

Результат

In a synthetic test with three products, TabPFN-TS achieved MAE 1.29 and WAPE 9.18%, outperforming a simple weekday‑one‑week‑earlier baseline (MAE 1.62, WAPE 11.56%). The model did not beat the baseline for Bread.

Рабочий процесс

• Enter daily sales history for each product
• Use TabPFN-TS to generate a seven‑day demand forecast
• Check expected demand during the supplier’s lead time
• Combine that estimate with current stock, incoming stock, and safety stock
• Display a suggested reorder quantity and its estimated cost

Доказательства

I generated synthetic daily sales data for three example products: Milk 500 ml, Bread, and Rice 1 kg.

Источник

The results from this single test window were: Mean Absolute Error (MAE) 1.29 units for TabPFN-TS vs 1.62 units for baseline.

Источник

Important limitation: these results come from synthetic data and one seven-day holdout.

Источник