The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

Abstract

When a company decides to automate its business processes by means of RPA (Robotic Process Automation), there are two fundamental questions that need to be answered. Firstly, what activities should the company automate and what characteristics make them suitable for RPA. The aim of the presented research is to design and demonstrate a data-driven performance framework assessing the impact of RPA implementation using process mining (PPAFR). Firstly, we comment on and summarise existing trends in process mining and RPA. Secondly, we describe research objectives and methods following the Design Science Research Methodology. Then, we identify critical factors for RPA implementation and design process stages of PPAFR. We demonstrate the design on real data from a loan application process. The demonstration consists of a process discovery using process mining methods, process analysis, and process simulation with assessment of RPA candidates. Based on the research results, a redesign of the process is proposed with emphasis on RPA implementation. Finally, we discuss the usefulness of PPAFR by helping companies to identify potentially suitable activities for RPA implementation and not overestimating potential gains. Obtained results show that within the loan application process, waiting times are the main causes of extended cases. If the waiting times are generated internally, it will be much easier for the company to address them. If the automation is focused mainly on processing times, the impact of automation on the overall performance of the process is insignificant or very low. Moreover, the research identified several characteristics which have to be considered when implementing RPA due to the impact on the overall performance of the process.