📦 Logix: AI-Driven Inventory Management System
Logix is a high-performance warehouse management system (WMS) tailored for the Warehouse. It integrates a modern React/Next.js frontend, a robust Node.js API, and a specialized Python AI module to deliver real-time stock optimization and predictive demand forecasting.
🚀 System Architecture
Frontend (React/Next.js 14):
A responsive dashboard featuring real-time data visualization, state management via Context API, and SEO-optimized metadata with OpenGraph support for professional social sharing.
Backend (Node.js/Express):
An orchestrator handling RESTful API requests, secure authentication, and atomic MySQL transactions to ensure data integrity.
AI Engine (Python 3):
A dedicated worker utilizing RandomForestRegressor to transform historical sales_history into actionable demand insights.
🛠️ Setup & Installation
1. Database Configuration
ai_inventory_system
Initialize Schema:
Import the provided Create_Table_Queries.sql to create database with all the tables.
Populate Data:
Import InsertQueries.sql to populate initial categories and sample products
2. Backend Setup
cd backend
npm install
Configure Environment: Create a .env file in the backend/ directory:
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=yourpassword
DB_NAME=ai_inventory_system
JWT_SECRET=your_super_secret_key
PORT=3001
Run Server:
npm run dev
3. AI Module Setup
cd ai-module
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
Configure Environment: Create a .env file in the ai-module/ directory:
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=yourpassword
DB_NAME=ai_inventory_system
JWT_SECRET=your_super_secret_key
PORT=3001
4. Client Setup
cd client
npm install
Configure Environment: Create a .env.local file in the client/ directory:
NEXT_PUBLIC_API_URL=http://localhost:3001/api
Run app:
npx next dev
⚡ Key Operations & Scripts
Historical Data Migration
To sync your historical CSV datasets with the live MySQL environment for AI training, run the migration script:
For historical data generation
cd ai-module
python historical_data_generation.py
For migrating the generated data to mysql db sales_history table.
cd backend
node scripts/migrate-history.js
This script populates the sales_history table used by the Random Forest model to identify seasonal trends and demand patterns.
