AI & Data: Intelligent Demand Forecasting

U.M was bleeding money in two ways: holding excess inventory of out-of-season clothes (markdown losses) and missing sales on trending items (stockouts). Their purchasing decisions were made on Excel sheets using simple 'Last Year + 10%' logic, which failed to account for micro-trends, weather, or social media sentiment.

GTEMAS provided a squad of 3 ML Engineers to work alongside the client's internal IT. We implemented a Time-Series Forecasting model using LSTM (Long Short-Term Memory) networks. Intelligent Reporting: The model ingests data from 5 sources, including Google Trends and local weather forecasts. It generates a daily 'Buying Recommendation Report' for category managers, highlighting exactly which SKUs to reorder and which to discount immediately.

The AI system shifted the buying culture from intuition-based to evidence-based. Category managers now trust the data, leading to a leaner, more profitable supply chain.