Logix Warehouse Management System

📦 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.