Fleet Management System

Industry:                  Business management

 

Platforms:                Android, iOS, System management web app

 

Duration:                  5 months

 

 

Technologies

Challenge

 

We’re pleased to tell you about an amazing business management app system that we've worked on recently.

It is the biggest fleet management solution we had the chance to be a part of and one of the biggest challenges Celadon has faced: we had to take over the project with the unfinished legacy code.

We conducted a comprehensive analysis of the code and performed up-to-date works. We came to the conclusion that the selected approach had architectural issues and its quality was below the standard required for such a project to perform smoothly on the scale.

 

Solution

 

We started the work on the fleet management software with legacy code refactoring and improving the UX/UI. We knew that gaps and mistakes in design can lead to disastrous wasting of time and nothing less than software architecture approach errors. We quickly managed to implement an alternative architectural solution and reuse the biggest part of the written backend code. However, most of the frontend had to be rewritten because the wrong UX approach had been chosen at the beginning.

Once we had a clear vision of where we stood with the project (it took less than one month to reach this), we started the development of a new functional.

The system comprises mobile apps with GPS, orders, navigation and complex web interface with flexible permission-based role managing systems.

The interactive cross-platform map with navigation, information overlay and special modes for different roles deserves some special attention.

Due to our Business Intelligence and Machine Learning background we’ve integrated the right approach to capture and store the operations data for further analysis and system automation.

As a result of our ongoing engagement, the client managed to reach the savings on the operational cost up to 30%. At the same time, the client accumulated valuable data that we’ve already started to use for building the ML models for fleet management automation features for further enhancement.

 

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