Overview:
The Out-of-Stock Prediction System is a data-driven web application designed to help retailers and inventory managers predict when products are likely to run out, allowing them to restock in advance. It uses historical sales data, seasonal trends, promotions, and external factors (like holidays or events) to forecast future demand and prevent stockouts.

Key Features:

1. Sales Data Analysis – Processes historical sales records to understand demand patterns.

2. Real-Time Inventory Tracking – Monitors current stock levels from the store’s inventory system.

3. Demand Forecasting Model – Uses predictive algorithms (e.g., regression, time-series forecasting) to estimate when stock will run out.

4. Seasonality & Event Awareness – Adjusts predictions based on seasonal trends, festivals, or promotions.

5. Low Stock Alerts – Sends notifications to procurement teams before critical items run out.

6. Supplier Lead Time Integration – Factors in delivery times from suppliers to avoid replenishment delays.

7. Visual Dashboard – Displays stock health indicators, upcoming out-of-stock risks, and restocking priorities.

Technology Stack:

• Backend: PHP, Java, or Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript (with charting libraries like Chart.js or D3.js)

• Database: MySQL or PostgreSQL

• Data Processing & ML: Python (via API), integrated with machine learning models for prediction

Use Cases:

• Retail Stores: Maintain constant stock availability for high-demand products.

• E-commerce Platforms: Prevent product unavailability during peak sales.

• Pharmaceutical Chains: Ensure critical medicines are always available.