Inventory Optimization Platform
Project by:
Devansh Gaur
- Project Objectives
Optimize Inventory Levels: Streamline stock management, reduce costs, and enhance supply chain efficiency.
Predictive Analytics for Demand Forecasting: Utilize real-time data to optimize stock levels, automate restocking, and track inventory seamlessly.
Scalability and Data Security: Ensure high availability, secure data management, and efficient deployment using Vultr’s services.
- Key Algorithms and Data Structures
Hash Tables: For efficient retrieval of product data.
Queues, Trees, Heaps: Manage inventory levels, prioritize order processing, and categorize items.
Graph Algorithms (Dijkstra’s Algorithm): Calculate optimal routes within warehouse networks for efficient retrieval and delivery.
- Future Use Cases
Enhanced Demand Forecasting: Incorporate advanced AI models to predict seasonal demand variations.
Automated Warehouse Routing: Enable real-time route optimization for in-warehouse transportation.
Dynamic Pricing and Inventory Reordering: Suggest pricing adjustments based on stock levels and demand predictions.
- Architecture and Components
Backend: Developed in Java with PostgreSQL for robust and reliable data management.
Frontend: A responsive interface designed using HTML, CSS, and JavaScript.
Deployment: Scalable and efficient setup on Vultr for high performance and real-time processing.
Security: Implements data encryption and restricted access protocols to safeguard inventory management systems.