Bottleneck and Resource Analysis on IT Help Desk with Process Mining

Authors

  • Muhammad Cekas Permana Telkom University, Bandung
  • Anindya Prameswari Telkom University, Bandung
  • Agriva Detta Ginting Telkom University, Bandung
  • M. Rifadh Asjad Telkom University, Bandung
  • Barajati Syakurnia Telkom University, Bandung
  • Rachmadita Andreswari Telkom University, Bandung

DOI:

https://doi.org/10.25126/jitecs.202491581

Abstract

This study utilizes data from the help desk log of an Italian company to create a model of the company’s business processes. The primary objectives are to show the benefit of the implementation of process mining to identify bottleneck activites within the process and analyze the workload distribution among resources. The research reveals that the most common bottlenecks occur during the transition from ‘resolve tickets’ to ‘closed’ accounting for 99% of cases, and another activity from ‘assign seriousness’ towards ‘take in charge ticket’ experiences bottlenecks in 91% of cases. Furthermore, a decrease in the number of cases was discovered after October 2012. Prior to this period, the average number of cases per resource was high, leading to a high average number of active resources per day and average number of events per day. However, after October 2012, the average number of cases per resource decreased by approximately 74.6% to 47 cases per resource. The average number of active resources also decreased by 25% to 3 active resources per day. Additionally, the average number of events per resource decreased by 40% to 3 events per resource per day. Regarding the resource workload, the analysis reveals that ‘value 2’ has the highest workload, having worked on 4,235 events. This is followed by ‘value 5’ with 3,748 events, ‘value 1’ with 3,028 events, ‘value 9’ with 2,073 events, and ‘value 13’ with 1,420 events.

References

J. Sakchaikun, S. Tumswadi, P. Palangsantikul, P. Porouhan and W. Premchaiswadi, "IT Help Desk Service Workflow Relationship with Process Mining," in 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE), Bangkok, 2018.

H. O. Udoro, "Help Desk Operations Management and Service Quality of Telecommunication Companies in River State," International Journal of Information and Technology, vol. 5, no. 1, pp. 01-15, 2021.

K. Shanmugalingam, N. Chandrasekara, C. Hindle, G. Fernando and C. Gunawardhana, "Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach," in Digital Image Computing: Techniques and Applications (DICTA), Perth, 2019.

R. Dolak and J. Botlik, "Process Mining of Event Logs from Horde Helpdesk," in Advances in Science, Technology & Innovation, Springer, 2019, pp. 303-309.

L. Feng, J. Senapati and B. Liu, "TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems," arXiv, 2023.

J. v. Brocke, M. Jans, J. Mendling and H. A. Reijers, "A Five-Level Framework for Research on Process Mining," Business & Information Systems Engineering, vol. 63, no. 5, pp. 483-490, 2021.

W. v. d. Aalst, Process Mining : Data Science in Action Second Edition, Berlin: Springer, 2016.

D. Dakic, S. Sladojevic, T. Lolic and D. Stefanovic, "Process Mining Possibilities and Challenges: A Case Study," in 2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia, 2019.

R. Bemthuis, N. van Slooten, J. J. Arachchige, J. P. S. Piest and F. A. Bukhsh, "A Classification of Process Mining Bottleneck Analysis Technique Operational Support," 18th International Conference on e-Business (ICE-B 2021), pp. 127-135, 2021.

F. Veit, J. Geyer-Klingeberg, J. Madrzak, M. Haug and J. Thomson, "The Proactive Insights Engine: Process Mining meets Machine Learning and Artificial Intelligence," in International Conference on Business Process Management, Barcelona, 2017.

Downloads

Published

2024-04-03

How to Cite

Permana, M. C., Prameswari, A., Ginting, A. D., Asjad, M. R., Syakurnia, B., & Andreswari , R. (2024). Bottleneck and Resource Analysis on IT Help Desk with Process Mining. Journal of Information Technology and Computer Science, 9(1), 77–85. https://doi.org/10.25126/jitecs.202491581

Issue

Section

Articles