🚚 FlowChain AI Intelligent Supply Chain Management Platform (v1)
FlowChain AI is a full-stack intelligent supply chain management system designed to manage orders, inventory, warehouses, and shipments, while providing rule-based intelligence, analytics, and real-time alerts — all without using machine learning.
This project focuses on system design, backend logic, clean UI/UX, and real-world workflows, making it suitable for enterprise-style applications.
🧠 Why FlowChain AI?
Most student projects stop at CRUD apps or basic dashboards. FlowChain AI goes further by introducing:
Intelligent alert systems
Rule-based forecasting and insights
Multi-role access control
Real-time operational visibility
It is built to simulate how real supply chain software works in production.
✨ Key Features 🔐 Authentication & Authorization
Email & password authentication
JWT-based secure sessions
Role-based access control (RBAC)
Protected routes (frontend + backend)
📦 Inventory & Warehouse Management
Multiple warehouses support
Product-warehouse inventory tracking
Stock movement logging (inbound/outbound)
Configurable low-stock thresholds
Inventory health indicators
🛒 Order Management
Customer order placement
Complete order lifecycle:
Created → Confirmed → Packed → Shipped → Delivered
Automatic inventory deduction
Order history and tracking
🚚 Shipment Tracking
Shipment creation linked to orders
Delivery status timeline
Estimated delivery time (rule-based)
Delay detection using historical averages
Map-based shipment visualization
Low stock alerts
Shipment delay alerts
High demand alerts
Alert severity levels:
Info
Warning
Critical
Alert resolution workflow
📊 Analytics & Insights
Order volume trends
Demand moving averages
Fulfillment time analysis
Inventory health scoring
Auto-generated operational insights:
“Product X may run out in 4 days”
“Warehouse B has slower fulfillment time”
🔔 Notifications
In-app notifications
Triggered by:
Order status changes
Alerts
Shipment updates
👥 User Roles Role Access Admin System overview, analytics, user & threshold management Operations Manager Shipments, delays, fulfillment analytics Warehouse Manager Inventory, stock updates, warehouse alerts Customer Orders, tracking, notifications
Each role sees only relevant data.
🖥️ UI / UX Design
Dark mode by default
Clean, card-based layout
Data-dense dashboards
Consistent spacing and typography
Subtle animations for better UX
Designed for enterprise dashboards
🧱 Tech Stack Frontend
React (Vite)
JavaScript
Tailwind CSS
Chart.js (analytics & graphs)
Leaflet (maps)
Framer Motion (light animations – optional)
Backend
Node.js
Express.js
MongoDB
Mongoose
JWT Authentication
REST APIs
node-cron (scheduled tasks)
🗄️ Database Design (MongoDB) Core Collections
users
warehouses
products
inventory
orders
orderItems
shipments
alerts
notifications
Relationships
User → Orders (1-to-many)
Order → OrderItems (1-to-many)
Order → Shipment (1-to-1)
Warehouse → Inventory (1-to-many)
Product → Inventory (1-to-many)
Alerts → Orders / Products / Shipments (polymorphic)
The schema is designed to be scalable and extensible.
🧠 Intelligence Without Machine Learning
FlowChain AI uses rule-based intelligence instead of ML:
Moving averages for demand trends
Threshold-based alert generation
Historical averages for ETA prediction
Heuristic scoring for risk detection
This ensures:
Full explainability
No black-box logic
Easy future ML integration
🚀 Future Enhancements
Machine learning-based demand forecasting
Predictive delay analysis
Python microservice for advanced analytics
Redis-based background jobs
Dockerized deployment
External logistics API integration
🎯 What This Project Demonstrates
Full-stack development skills
Clean backend architecture
Real-world system design
Role-based access control
Data-driven dashboards
Production-style workflows
Strong UI/UX sense
📌 Ideal Use Cases
D2C brands
Small logistics companies
Warehouse operations teams
Supply chain analytics prototypes
🧑💻 Author
Divyansh Choudhary B.Tech (AI & ML) – 2nd Year Focused on building real-world, system-driven applications
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