Enterprise recommendation and inventory optimization system
Prediction and automation platform for procurement optimization, loss reduction and operational efficiency in retail environments.
Context
Enterprise retailer with a very large product portfolio, strong seasonal variations and procurement processes dependent on manual estimations. Lack of predictability led to overstock, stockouts and recurring operational losses.
Challenge
• excessive inventory in low-rotation categories • stockouts on high-demand products • decisions based on manual estimates and incomplete data • recurring operational losses and limited control • low visibility into demand and consumption dynamics
Solution
We designed and implemented an end-to-end prediction and automation system, fully integrated into operational workflows, with focus on accuracy, traceability and deterministic decision-making: • advanced analysis of sales history and seasonality • automatic identification of consumption patterns • demand and rotation prediction models • automated procurement recommendations • real-time inventory level optimization • direct integration with inventory management and procurement systems The architecture was built for scalability, observability and long-term maintainability.
Technologies
Python, TensorFlow, Node.js, PostgreSQL, Azure (cloud infrastructure, scaling, monitoring)
Impact
• significant reduction of dead stock • increased product turnover • elimination of reactive decision-making • reduction of recurring operational losses • high predictability in procurement processes • increased confidence of operations and management teams in the system
Our Role
AI/ML architecture, prediction model development, system integration and technical leadership
Project Timeline
20 weeks (from architecture to production)