AI-Powered Sales Forecasting Engine
Developed machine learning models to predict sales trends and optimize inventory for retail chains across 100+ locations.
The Challenge
Retail chains faced significant losses due to overstocking and stockouts, with no reliable way to predict demand patterns.
Our Engineering Approach
Created an ML pipeline analyzing historical sales data, seasonal trends, and external factors to generate accurate demand forecasts with automated inventory recommendations.
Key Results & Business Impact
Achieved 92% prediction accuracy, reduced inventory costs by 28%, and decreased stockouts by 65% across 100+ retail locations.
92% prediction accuracy
Operational metrics collected after 30 days of standard staging deployment.
Technology Stack
Project Consultation
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