Retail Supply Chain Dashboard With Predictive Restocking

Added Jun 2025 3 design docs

Retailers lose money at both ends of inventory: stockouts send customers to competitors while overstock ties up capital and ends in markdowns. Most small and mid-size retailers still manage this balance with spreadsheets, reacting to yesterday's sales instead of anticipating next month's demand. The intern builds a supply chain platform that closes that loop. A Python backend with pandas processes sales and inventory history to forecast demand and compute predictive restocking recommendations per product and location, while supplier analytics track lead times and fulfillment reliability. PostgreSQL holds the transactional core of products, orders, suppliers, and stock levels, and a React dashboard presents real-time inventory positions, forecast curves, and reorder alerts that buyers can act on directly. Docker containerizes the stack for consistent deployment, and role-based multi-user access mirrors how merchandising teams actually share responsibility. The intern comes away able to build genuine decision-support software: time-series forecasting applied to a concrete business problem, dashboard design that drives action rather than decoration, and the secure, scalable architecture a retail operations tool requires.

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