AI-Driven Demand Forecasting for Retail Chain

The Challenge

A major electronics retail chain with 45 stores across Dubai and Northern Emirates was struggling with inventory optimization during peak seasons like Ramadan, Dubai Shopping Festival, and Back-to-School periods. Stockouts during high-demand periods resulted in AED 12M lost revenue annually, while overstock of seasonal items led to AED 8M in clearance losses. Traditional forecasting methods couldn't account for local cultural events and weather patterns.

Our Solution

Our machine learning platform combines historical sales data from all 45 stores with external factors unique to the UAE market: Ramadan timing effects, Dubai Shopping Festival impact, tourist arrival data from Dubai Tourism, weather patterns affecting electronics sales (AC units, fans during summer), and economic indicators like oil prices affecting consumer spending. The system processes over 2.5M transactions monthly and generates SKU-level forecasts for 15,000+ products.

Implementation Steps

• Integrated POS data from 45 retail locations across Dubai and Northern Emirates
• Built ensemble models combining XGBoost, LSTM, and Prophet for different product categories
• Incorporated UAE-specific external data: cultural calendar, weather, tourism statistics
• Created automated replenishment recommendations integrated with existing WMS
• Developed mobile dashboard for store managers with real-time inventory insights
• Implemented A/B testing framework comparing AI vs traditional forecasting methods

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