IT Help Desk Service Workflow Relationship with Process Mining

In this paper, the data was initially collected from an IT service department which aimed to handle the computer equipment/server problems and requests of customers whom contacted the company. The IT company has developed a help-desk service in which anyone who requests for any IT service will have to come to this service for help, and the system will automatically generate a ticket for each of the request (i.e., registration number, type of the problem, etc.) and then the system will arrange and assign the work between the a group of IT staff including 5 people in order to address the mentioned customer’s problem. The order and sequence of the IT staff to handle the problems is alternatively changed one by one. For example, if the first problem is addressed by IT Expert #1, the second problem is handled by IT Expert #2, and so on until the IT Expert #5, which one cycle is completed and then the forthcoming tasks will be started from IT Expert #1 again. In order to increase the level of the customer satisfaction, the company has set a guideline for each IT Expert in such a way that they need to finish every request (assigned task) within a maximum of 4 hours during the working hours (i.e., 9-12 AM and 1-4 PM). However, the problem that currently the company is facing is that, for some tasks it takes more than 4 hours to handle the customers’ requests. In order to discover and investigate what are the main reasons of such delays, and in order to solve the problem, a process discovery Process Mining technique so-called Fuzzy Miner —in terms of both Time Performance and Frequency-Based Analysis metrics— were applied on the collected event logs. Quite surprisingly, the results of the Fuzzy Miner models (based on Time Performance metric) showed that the average time gap between the opening ticket and closing ticket is 4 days, rather than the 4 hours, which is much longer than the targeted guideline. In addition, the results of the Fuzzy Miner models (based on Frequency-Based) could reveal on the sequence and order of the way the activities have been executed and performed while addressing the customers’ requests. However, using the Fuzzy Miner techniques did not shed light on the main reasons of the long delays throughout the repairing/customer service process. Accordingly, another type of process mining technique so-called Social Network Miner (based on Handover of Task metric) was used in order to better study the relationships and communicational dependencies amongst the experts. According to the resulting social network graphs, it was understood that out the 5 IT Experts, only 4 of them has really handled most of the workload, while 1 of them performed only 5 tasks per year. By further zooming on this guy, it was realized that not only this guy has performed and accomplished very few number of tasks per year but he has transferred almost all of his assigned tasks to others as well, playing absolutely an inactive and idle role throughout the year. Eventually, the results of the study could help the company to improve the quality of their customer service leading to increased customer satisfaction and improved efficiency.