AI Retail Demand

The challenge

Before

The client struggled with frequent stockouts and overstock due to manual, intuition-based inventory planning across 50+ stores. Buyers were making purchasing decisions based on gut feel with no historical data analysis, seasonal trending, or promotion lift modeling — leading to empty shelves during peak periods and excess dead stock eating into working capital.

• Manual sales tracking in Excel with no trend analysis
• No predictive analytics — purchasing decisions based on buyer intuition
• Frequent stockouts during peak seasons — lost revenue and customer dissatisfaction
• Excess overstock tying up capital and driving markdown losses
• No centralized dashboard — buyers operated in silos across product categories
• Promotions planned without demand lift modeling

The solution

What we built

We built a machine learning demand forecasting platform that ingests historical sales data, seasonality patterns, promotional calendars, and external signals to generate SKU-level forecasts across all 50+ stores. Buyers receive automated replenishment recommendations with confidence intervals. A centralized analytics dashboard gives category managers visibility on forecast accuracy, stockout risk, and overstock exposure — updated daily.

How the system flows

1. Sales History + Seasonality Data→ML Model Training→SKU-level Demand Forecast
2. Forecast→Replenishment Recommendation→Buyer Review→Purchase Order
3. Promotion Calendar→Lift Modeling→Adjusted Forecast→Stock Pre-positioning
4. Dashboard→Stockout Risk Alert→Overstock Flag→Category Manager Action