Data-driven logistics brokerage platform for managing shipment operations, truck assignments, commission-based workflows, and route-level analytics.
🌐 Live Demo: https://freightflow-puce.vercel.app
🎥 Demo Video: https://youtu.be/V2yE2vdnJWg
- Quick Demo access for recruiters and public showcase
- Role-based access control (admin, demo admin, client)
- Create, edit, and manage shipment requests
- Assign, reassign, and unassign trucks
- Shipment workflow tracking (Pending, Assigned, In Transit, Completed)
- Truck availability management
- Dashboard with operational KPIs
- Route pricing benchmark insights during shipment creation
- Analytics page with revenue, commission, fleet, and workflow insights
- Python + Pandas-powered route profitability and margin analysis
- Demo reset flow to restore seeded public demo data
- Frontend: React (Vite), Tailwind CSS, React Router
- Backend: Node.js, Express
- Database: PostgreSQL
- Analytics: Python, Pandas
- Visualization: Recharts
- Auth: JWT, bcryptjs
- Deployment: Vercel (frontend), Render (backend + PostgreSQL)
git clone https://github.com/mahir-alam/Freightflow.git
cd Freightflow
Backend
cd server
npm install
pip install -r requirements.txt
npm run dev
Frontend
cd client
npm install
npm run dev
🔐 Environment Variables
Create a .env file in server:
DB_HOST=your_postgres_host
DB_PORT=5432
DB_NAME=your_postgres_database
DB_USER=your_postgres_user
DB_PASSWORD=your_postgres_password
JWT_SECRET=your_secret_key
CLIENT_URL=http://localhost:5173
Create a .env file in client:
VITE_API_BASE_URL=http://localhost:5000- React Frontend → Express API → PostgreSQL + Python/Pandas Analytics
- Frontend sends shipment, truck, and analytics requests
- Backend handles authentication, business logic, and database operations
- PostgreSQL stores users, shipments, trucks, and workflow data
- Python + Pandas process shipment and truck data for route-level analytics
- Dashboard and analytics pages surface operational and profitability insights
- First request may be slower on Render free tier
- Demo mode uses seeded synthetic logistics data
- Public demo includes a reset flow to restore original demo data
- Pandas analytics require requirements.txt dependencies during backend setup
- Toast notifications for major user actions
- Custom confirmation modals instead of browser confirm dialogs
- Exportable analytics and operational reports
- Activity log / audit trail for shipment actions
- Advanced client and company management workflows
Mahir Alam (University of Calgary)