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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | THUSHANTHINI, S | - |
| dc.date.accessioned | 2026-07-13T04:36:54Z | - |
| dc.date.available | 2026-07-13T04:36:54Z | - |
| dc.date.issued | 2025-06-15 | - |
| dc.identifier.uri | https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4988 | - |
| dc.description.abstract | Abstract The Inland Revenue Department (IRD) identified the need to improve the efficiency and responsiveness of its IT support services. To address this, a comprehensive IT help desk ticketing system was developed and implemented. The system is designed to provide a structured and automated approach to managing IT support requests across all IRD branches. The primary goal of the project is to streamline the process of issue reporting, tracking, and resolution. Key features of the system include automated ticket generation, categorization based on issue type, prioritization by urgency and impact, real-time status tracking, email notifications, and a complete history of reported issues. The system also incorporates advanced reporting and analytics tools to support performance monitoring and decision-making. As a result, the IRD has significantly improved its response times, reduced manual workload, and enhanced user satisfaction. IT management can now identify recurring issues more effectively and allocate resources based on data-driven insights. The system ensures higher operational efficiency and promotes continuous improvement in IT service delivery. This project uses modern web technologies and follows best practices in IT service management to build a reliable and scalable support system for the IRD. It helps improve how IT issues are handled and creates a strong foundation for future improvements and digital upgrades. | en_US |
| dc.language.iso | en | en_US |
| dc.title | IT Help Desk Ticketing System for Inland Revenue Department | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | 2024 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022 MIT 083.pdf | 3.27 MB | Adobe PDF | View/Open |
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