A Study on Vehicle Service Management System with Artificial Intelligence Integration

Abstract

The automotive service industry continues to depend heavily on manual, paper-based workflows for managing vehicle service operations, resulting in inefficiencies such as delayed customer updates, billing inaccuracies, poor inventory visibility, and limited diagnostic capability. This paper presents the design and implementation of the Vehicle Service Management System (VSMS), a full-stack, web-based garage management platform that automates the complete vehicle service lifecycle. VSMS is developed using Python Flask for the backend REST API, SQLite with SQLAlchemy ORM for relational data management, and a Glassmorphism-styled HTML, CSS, and JavaScript frontend. The system incorporates role-based access control for Admin, Mechanic, and Receptionist roles; a five-stage Kanban job card pipeline; QR-based real-time customer service tracking; GST-compliant automated PDF invoice generation; AI-powered vehicle diagnostics and a conversational chatbot powered through the OpenRouter API; and real-time dashboard analytics using Chart.js. Security is enforced through bcrypt password hashing, JSON Web Tokens, and CSRF protection. Functional testing across all modules confirmed reliable system behaviour and sub-second response times for standard operations. The system offers a scalable and cost-effective alternative to expensive commercial garage management platforms.

Introduction

The text presents a Vehicle Service Management System (VSMS) designed to modernize and automate operations in automotive repair workshops, especially small and medium-sized garages that still rely on manual processes.

Core idea

Traditional garage management depends on paper job cards, manual billing, verbal updates, and spreadsheet-based tracking, leading to inefficiencies like billing errors, poor inventory control, and lack of real-time communication. Existing commercial tools are often expensive and not customizable.

Proposed system (VSMS)

VSMS is a web-based, full-stack platform that integrates multiple smart features into a unified system:

• AI-assisted vehicle diagnostics using OpenRouter API
• QR-based real-time service tracking for customers
• Automated GST-compliant PDF invoicing
• Job card workflow management (5-stage Kanban pipeline)
• Inventory tracking with low-stock alerts
• Appointment scheduling with conflict detection
• Mechanic attendance and performance tracking
• Expense and revenue analytics dashboards
• Vehicle inspection system with scoring
• Notification system for alerts and updates

System architecture

VSMS follows an MVC architecture:

• Frontend: Glassmorphism UI using HTML/CSS/JavaScript and Chart.js dashboards
• Backend: Python Flask REST API with modular Blueprint structure
• Database: SQLite using SQLAlchemy ORM (normalized design)
• AI integration: OpenRouter LLM-based diagnostic assistant
• Security: bcrypt hashing, JWT authentication, CSRF protection, and rate limiting

Key innovations

• QR-based public job tracking without login
• AI chatbot for vehicle fault diagnosis
• Automated billing with GST compliance
• Role-based access control (Admin, Mechanic, Receptionist)
• Real-time analytics and workflow automation

Performance results

• CRUD operations: 80–200 ms
• Invoice generation: ~650–900 ms
• AI diagnostics: 1.5–3.2 seconds
• QR status page: ~120 ms
• Dashboard rendering: 200–350 ms

Evaluation

• Successfully tested across full service workflow
• Role-based permissions work correctly
• AI assistant provides relevant diagnostic suggestions
• Users found the system intuitive, especially the Kanban workflow and QR tracking
• Responsive UI works across devices and browsers

Conclusion

This paper presented the design, implementation, and evaluation of the Vehicle Service Management System (VSMS), a full-stack web-based platform that automates the complete vehicle service lifecycle for automotive garages. By unifying role-based access control, OpenRouter-powered AI diagnostics, QR-based customer tracking, automated GST-compliant invoicing, real-time analytics, and comprehensive operational management within a single Python Flask and SQLite application, VSMS directly addresses the critical inefficiencies of manual garage management workflows. End-to-end functional testing confirmed reliable operation across all fourteen modules and three user roles. The system demonstrated sub-200-millisecond response times for standard operations, contextually accurate AI diagnostic responses via the OpenRouter API, and successful cloud deployment on the Render platform. The open-source technology stack ensures deployment accessibility for independent garages and academic institutions that cannot afford commercial alternatives. VSMS demonstrates that well-architected, AI-integrated web applications can meaningfully transform domain-specific operational management. Future work targeting PostgreSQL scalability, native mobile applications, local LLM inference, and OBD-II telematics integration will further advance the platform toward a comprehensive, production-grade garage management solution.

References

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Copyright

Copyright © 2026 Asst.Prof. Samikumar Waghmare, Dinbandhu Ajay Choudhary, Aditya Shripad Hardas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.