Itil V3 Based Service Desk System With Incident Classification and Technician Assignment
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Date
9/1/2016
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Addis Ababa University
Abstract
Ethiopian Airlines is a company with abundant IT assets and services. It is the responsibility of the IT support department to resolve any issue related to IT services as soon as possible and to greatly minimize the impact it has on the business. Currently, the department is facing issues such as incidents not being assigned in time or assigned to the wrong teams, incidents not being correctly logged, incidents having no association with assets etc. In addition to this, other systems in the company follow international standards while the support team barely uses the in-house application developed to assist it.
The aim of this project is to design, develop and implement a system to improve the service management process of the IT support department. After collecting requirement and data from the company, several literature was reviewed to find an appropriate solution. Several data mining techniques were also examined to see which algorithms gave the best result. This has also helped in better understanding the current system and identifying important features that need to be developed.
We proposed a service desk system that complies with ITIL standards (IT Infrastructure Library), the most widely adopted approach for IT service management, with the added capability to automatically assign technicians just from the incident description.
The service desk system is implemented with selected processes that fit the current workflow of Ethiopian Airlines. In addition to that, data mining techniques are applied to the data collected from their current system in order to classify incidents (based on their description) to support teams. This will enable the system to automatically assign technicians without delay. The system was tested at the company and was found to accurately capture their workflow as well as introduce helpful ways to improve the service management process.
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Keywords
Itil, Itsm, Service Desk, Data Mining, Text Classification