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aracı teknoloji
TR
20255-branch restaurant chain based in Istanbul

AI Demand Forecasting for a Restaurant Chain.

AIPythonData Analytics

Challenge

Weekly order fluctuations made inventory management and waste rates impossible to control.

Solution

We built an AI-powered demand forecasting system that analyzes historical order data, weather, and special occasions.

results

Waste rate down 40%

Inventory costs down 25%

Customer satisfaction up 35%

A restaurant chain with 5 branches in Istanbul was struggling with serious inventory management problems caused by weekly order fluctuations. Some weeks excess stock went to waste; other weeks popular items ran out.


Problem


The restaurant's existing inventory management relied entirely on branch managers' intuition. Demand drivers such as weather, special occasions, and school holidays were never factored in systematically. The result: a 22% waste rate and a rising number of customer complaints.


Approach


We started by collecting two years of historical order data, then combined it with a weather API, the official holiday calendar, and regional event data.


The machine learning model we developed (XGBoost-based) produces 7-day demand forecasts and is retrained every day with fresh data.


Solution


  • Feature extraction from historical order data
  • Weather, holiday, and event data integration
  • Branch-level XGBoost forecasting models
  • Web-based dashboard (React + Flask)
  • Mobile approval and manual override capability

    • Results


    After a 3-month pilot:


    Waste rate: dropped from 22% to 13% (40% improvement)

    Inventory costs: down 25% per month on average

    Customer satisfaction: "item out of stock" complaints down 35%

    ROI: The system paid for itself in 4 months

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