Integrated Logistics System For Aircraft Development System Using Data Science ( ERP & MRO )

Purpose

This project aims to enhance the maintenance and operational efficiency of aircraft through a comprehensive integrated logistics system. By leveraging data science techniques, it focuses on anomaly detection, predictive maintenance scheduling, and inventory optimization.

Key Features

1. Anomaly Detection: Identifies irregular patterns in aircraft operations to preemptively address potential issues.
2. Predictive Maintenance: Utilizes historical data to forecast maintenance needs, minimizing unexpected downtimes.
3. Inventory Optimization: Analyzes inventory data to streamline parts management and reduce costs.
4. User-Friendly Interface: Intuitive design for easy navigation and access to critical information.

Data Collection Challenges

Obtaining data was especially challenging due to its highly secure nature. We initially struggled to find relevant information and mostly found stock data. However, we were able to establish a collaboration with EgyptAir to access essential data (excluding secure information). To complement this, we also created random data to simulate scenarios and fill gaps in our database. We integrated this data with our own to ensure it met our system requirements. We also implemented strict measures to ensure data integrity, security, consistency, and accuracy throughout.

Technologies Used

1. Database: Oracle , Oracle Miner , Oracle Warehouse
2. Programming Languages: HTML, CSS, JavaScript, PHP

Target Audience

This project is designed for aircraft maintenance teams, aviation companies, and data scientists interested in applying data analytics to improve aviation safety and efficiency.

Demo

Watch the demo video of the system here.

Interface

1.Login Page

2. Dashboard

3. Aircraft Page

4. Flight Table Page

Oracle Miner Interface

1. Linear Regression

2. Classification