Intelligent Inventory Management

Overview

A 25-location restaurant chain was trapped between two problems: they over-ordered ingredients that spoiled before use ($40K/month in waste) while simultaneously running out of popular menu items during peak times (lost revenue and unhappy customers). Each location manager ordered based on gut feel. We built a per-location, per-item demand forecasting system that accounts for day of week, weather, local events, promotions, and seasonal trends to generate precise order quantities and prep lists.

The Challenge

Restaurant demand is highly variable and location-specific. A location near a stadium has completely different patterns on game days vs. off days. Weather affects dine-in vs. delivery mix. New menu promotions cannibalize existing items unpredictably. The system needed to handle all these factors while generating actionable output — not just forecasts, but specific order quantities respecting supplier minimums, shelf life, and delivery schedules.

Our Approach

We built a hierarchical forecasting model: a base model for chain-wide patterns, modified by location-specific factors, and further adjusted by external signals (weather, local events, school calendars). The output isn't a raw forecast — it's a specific order quantity for each ingredient from each supplier, accounting for current inventory, shelf life, delivery lead times, and minimum order quantities. Prep lists for each shift are generated automatically.

Key Features

• Per-item per-location demand forecasting
• Weather and event impact adjustment
• Automated supplier order generation
• Shift-level prep list generation
• Shelf life and waste tracking
• Location performance comparison
• Real-time inventory dashboards per location

Results

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