Amazon Data Complexity: Sales, inventory, and shipment data needed to be synced for real-time AI recommendations.
Inventory Volatility: Rapid changes in FBA stock levels and delays in Amazon reporting required predictive buffer strategies.
Demand Forecasting Accuracy: Seasonality and product lifecycle trends needed to be accounted for to avoid overstock or stockouts.
Procurement Logic: Mapping forecasted demand to vendor-specific purchase rules and MOQ constraints was non-trivial.
Scalability: The system had to support thousands of SKUs across multiple Amazon marketplaces without performance degradation.
SaaS Platform with Subscriptions: Built a secure multi-tenant app with support for monthly and yearly Stripe-based billing.
AI Recommendation Engine: Developed a AI/ML powered model to suggest optimal restocking quantities from Amazon sales data.
SP-API Integration: Integrated Amazon’s Selling Partner API to fetch and sync inventory, sales, and FBA Shipment data.
Custom Procurement Rules Engine: Developed a configurable rule engine to recommend order quantities aligned with supplier MOQ and restock windows.
Custom ReactJS UI: Crafted responsive UI from Word mockups using React and Bootstrap with clear UX patterns.
Admin Dashboard: Built a super admin panel to manage users, plans, usage, and error logs.
CI/CD on Private Server: Deployed using Git, Docker, and PM2 on a private Ubuntu server with nightly backups.
Anonymous - Owner, Third-Party Amazon Seller
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