A working dashboard of the AI system that cut food waste from 8% to 2.5% for a 30-store grocery chain. Try the tabs, switch stores, watch the ML recommendations update.
Temperature forecast: 34–37°C. Historical data shows +45% beverage sales, +28% ice cream, +15% salad ingredients during similar waves. Current stock level: 60% below recommended for beverages.
Every sale, per SKU, per store, for the last 3 years minimum.
Historical + 14-day forecast. Temperature, precipitation, heat waves.
National holidays, religious observances, local festivals, school calendars.
Active campaigns, competitor prices, historical promotion lift patterns.
Store-specific traffic patterns, neighborhood demographics, seasonality.
Granular forecasts for 10,000+ products per store per day.
Direct integration with procurement — orders placed without human intervention where confidence is high.
Markdown recommendations for near-expiry items. Captures margin that would otherwise rot.
Warehouse-to-store allocation balanced for demand, transport cost, expiry.
Store managers get actionable recommendations, not endless dashboards.
€40k–65k implementation. 10–14 weeks to production. Typical payback in 3–5 months from waste reduction alone.
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