Overview
Developed an AI-powered inventory and sales analytics platform for retail businesses to automate stock management, sales forecasting, and purchase planning across multiple stores.
The Challenge
A multi-store retailer was losing money to both overstocking and stockouts because purchasing decisions were based on gut feel rather than data, and nobody had a single view of inventory across locations. The client needed forecasting it could actually act on, plus alerts before a shortage became a lost sale.
The Solution
We built a Python/TensorFlow forecasting model integrated into a React/Node platform, backed by PostgreSQL for transactional data and MongoDB for flexible catalog data. Automated low-stock alerts and purchase-order automation act on the forecasts directly, and a multi-store dashboard rolls sales and inventory data into one view with exportable reports.
Business Objectives
- Reduce inventory wastage and stock shortages
- Improve sales forecasting accuracy
- Centralize multi-store inventory management
- Generate business intelligence reports
Key Features
- AI-driven demand forecasting
- Inventory tracking dashboard
- Automated low-stock alerts
- Multi-store product management
- Sales and revenue analytics
- Purchase order automation
- Role-based admin panel
- Exportable reports and insights
What We Delivered
- Web application / platform
- Admin & operations dashboard
- Backend APIs & data layer
- Analytics, reporting & insights
- Deployment, monitoring & operational setup
Implementation Highlights
- TensorFlow-based demand forecasting driving automated low-stock alerts, not just historical charts
- Purchase order automation triggered directly off forecast output
- Multi-store inventory rolled into a single dashboard instead of per-location spreadsheets
- Dual-database design — PostgreSQL for transactions, MongoDB for catalog flexibility — on AWS RDS
Reliability & Security
- Role-based admin access separating store-level and head-office views
- Redis caching to keep the analytics dashboard responsive as store count grew
- Dockerized deployment on AWS EC2 for consistent scaling across peak retail periods
Results & Impact
- Reduced inventory losses
- Improved purchase planning efficiency
- Increased operational visibility
- Enhanced business decision-making
Tech Stack
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