Identifying And Prioritizing Suitable RPA Candidates in ITSM Using Process Mining Techniques: Developing the PLOST Framework

Abstract

One of the main challenges for a successful implementation of the emerging technology RPA is which candidate tasks should be automated. While different methods exist to identify RPA candidate tasks, they lack in providing objective evidence on why to automate that candidate. Such objective evidence can be gathered by applying process mining techniques to gain insights into the performance of a process or task. Although this has multiple advantages, it can be time-consuming to analyze all potential processes. By adding a qualitative check before the analysis, time and effort are saved because process mining is only applied to relevant processes. A literature review of existing methods was conducted to identify relevant criteria and method components. I synthesized these into a framework for identifying and prioritizing suitable RPA candidate tasks: the Prioritized List of Suitable Tasks (PLOST) Framework. The framework includes both qualitative and quantitative criteria. It focuses on high- as well as low-level processes, while also taking into account a customized automation strategy. A case study was conducted to evaluate the applicability and effectiveness of the PLOST Framework, and thinking-aloud experiments to evaluate the usability, practicality, and completeness. This resulted in adjustments to the framework that were subsequently incorporated into an enhanced PLOST+ Framework.