Paper Contents
Abstract
This project introduces a Disaster Resilience Network that combines a lightweight Python-based Electronic Manufacturing Execution System (EMES) with GIS-enabled transport logistics to support small-scale factories during natural disasters. The EMES efficiently manages production workflows, inventory control, and job scheduling, ensuring smooth operations even in crisis situations. Built using Python, the system follows a modular architecture inspired by the Model-View-Controller (MVC) pattern to maintain scalability and flexibility. The logistics module leverages the Google Maps API for route optimization, real-time vehicle tracking, and dynamic rerouting when disruptions occur. Vehicle movements are simulated through periodic coordinate updates using Pythons threading or asyncio modules, allowing a live visualization of transport activity. The system intelligently detects and avoids blocked or high-risk zones by recalculating routes during delivery operations, ensuring safe and efficient transport of essential supplies.Factory units can transmit real-time production and operational updates, enabling coordinators to adjust supply chain strategies as disaster conditions evolve. These continuous data streams enhance decision-making, resource prioritization, and resilience across the entire network. By integrating EMES and smart GIS logistics in Python, the system provides a cost-effective, scalable, and intelligent solution for maintaining manufacturing and supply operations during emergencies. It strengthens communication between factories and logistics coordinators, ensuring that vital goods reach affected areas quickly and safely. Overall, this Python-driven framework demonstrates how digital manufacturing systems and intelligent logistics can work together to improve disaster preparedness, responsiveness, and recovery efficiency.
Copyright
Copyright © 2025 Manoj R. This is an open access article distributed under the Creative Commons Attribution License.
