Analyzing a helpdesk process through the lens of actor handoff patterns

Abstract

In this study, we analyze the activity logs of fully resolved incident management tickets in the Volvo IT department to understand the handoff patterns i.e., how actors pass work from one to another using sequence analytics, an approach for studying activity patterns from event log sequences. In this process the process model itself is rather simple, but a large amount of variety is present in it in terms of the handoff patterns that arise. Hence, process modeling is not so helpful to gain a deeper understanding of the performance of the process. We offer an alternative approach to analyze such processes through the lens of organizational routines. A generic actor pattern here describes the sequence in which actors participate in the resolution of an incident. We characterize actor handoff patterns in terms of canonical sub-patterns like straight, sub- and full-loop, and ping-pong. Then, we predict incident resolution duration with machine learning methods to understand how actor patterns affect duration. Finally, the evolution of patterns over time is analyzed. Our results shed light on emergence of collaboration and have implications for resource allocation in organizations. They suggest that handoff patterns should be another factor to be considered while allocating work to actors along with position, role, experience, etc.