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Fleet Management System Modernization and Enhancement

celadonsoft.com

Business services

Logistics

Telecommunications

Challenges with Legacy Fleet Management System

The client is experiencing difficulties with an outdated and poorly architected fleet management system. This legacy system suffers from architectural flaws and poor quality, leading to instability and hindering efficient remote management of vehicles and workers. The existing system lacks modern features like sophisticated routing optimization and data analytics, impacting operational costs and hindering data-driven decision-making.

About the Client

A business management firm seeking to optimize its fleet operations, improve worker tracking, and enhance routing efficiency.

Project Goals

Modernize and enhance the existing fleet management system architecture.
Develop a robust and scalable cloud-based platform for remote fleet management.
Improve routing optimization and load planning capabilities.
Implement comprehensive GPS tracking and real-time vehicle monitoring.
Enhance data analytics and reporting for operational insights.
Improve user experience for web, iOS, and Android applications.

System Functionality

Real-time GPS tracking of vehicles and workers.
Route optimization based on factors like traffic, distance, and delivery schedules.
Load planning and zone management.
Role-based access control and permission management.
Google Maps integration for navigation and mapping.
Data analytics and reporting dashboards.
Order distribution and management.
Alerts and notifications for critical events (e.g., speeding, unauthorized use).

Technology Stack

Nest.js

React Native

Sanity

Cloud Server (AWS, Azure, or GCP)

iOS

Android

Web

External Integrations

Google Maps API
Potentially other third-party logistics or dispatch systems

Non-Functional Requirements

Scalability to accommodate future growth.
High performance and responsiveness.
Robust security measures to protect sensitive data.
Reliability and availability.
Cross-platform compatibility (iOS, Android, Web).

Expected Business Impact

This project is expected to result in a significant reduction in operational costs (estimated at 30%), improved fleet efficiency, enhanced worker productivity, better data-driven decision-making, and increased customer satisfaction. The ability to leverage operational data for machine learning model development will further drive automation and system improvements.