All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

The Performance Assessment Framework (PPAFR) for RPA Implementation in a Loan Application Process using Process Mining

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F23%3AA0000382" target="_blank" >RIV/47813059:19520/23:A0000382 - isvavai.cz</a>

  • Result on the web

    <a href="http://link.springer.com/article/10.1007/s10257-022-00602-2#citeas" target="_blank" >http://link.springer.com/article/10.1007/s10257-022-00602-2#citeas</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10257-022-00602-2" target="_blank" >10.1007/s10257-022-00602-2</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    The Performance Assessment Framework (PPAFR) for RPA Implementation in a Loan Application Process using Process Mining

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Information Systems and E-Business Management

  • ISSN

    1617-9846

  • e-ISSN

    1617-9854

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    DE - GERMANY

  • Number of pages

    45

  • Pages from-to

    277-321

  • UT code for WoS article

    000906086700001

  • EID of the result in the Scopus database

    2-s2.0-85145169355