Towards Discovering Erratic Behavior in Robotic Process Automation with Statistical Process Control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F24%3A00010288" target="_blank" >RIV/46747885:24310/24:00010288 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10257-024-00686-y" target="_blank" >https://link.springer.com/article/10.1007/s10257-024-00686-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10257-024-00686-y" target="_blank" >10.1007/s10257-024-00686-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Discovering Erratic Behavior in Robotic Process Automation with Statistical Process Control

Popis výsledku v původním jazyce

Companies that use robotic process automation very often deal with problems maintaining the bots in their RPA portfolio. Current key performance indicators do not track the behavior of RPA bots or processes. For better maintainability of RPA bots, it is crucial to easily identify problematic behavior in RPA bots. Therefore, we propose a strategy that tracks and measures the behavior of processes to increase the maintainability of RPA bots. We selected indicators of statistical dispersion for measuring variability to analyze the behavior of RPA bots. We analyzed how well statistical dispersion can describe the behavior of RPA bots on 12 processes. The results provide evidence that, by using statistical dispersion for behavioral analysis, the unwanted behavior of RPA bots can be described. Our results showed that statistical dispersion can describe the success rate with a correlation of -0.91 and outliers in the data with a correlation of 0.42. This research implies that we can describe the behavior of RPA bots with variable analysis. Furthermore, with high probability, it can also be used for analyzing other processes, as a tool for gaining insights into performance and as a benchmark tool for comparing or selecting a process to rework.

Název v anglickém jazyce

Towards Discovering Erratic Behavior in Robotic Process Automation with Statistical Process Control

Popis výsledku anglicky

Companies that use robotic process automation very often deal with problems maintaining the bots in their RPA portfolio. Current key performance indicators do not track the behavior of RPA bots or processes. For better maintainability of RPA bots, it is crucial to easily identify problematic behavior in RPA bots. Therefore, we propose a strategy that tracks and measures the behavior of processes to increase the maintainability of RPA bots. We selected indicators of statistical dispersion for measuring variability to analyze the behavior of RPA bots. We analyzed how well statistical dispersion can describe the behavior of RPA bots on 12 processes. The results provide evidence that, by using statistical dispersion for behavioral analysis, the unwanted behavior of RPA bots can be described. Our results showed that statistical dispersion can describe the success rate with a correlation of -0.91 and outliers in the data with a correlation of 0.42. This research implies that we can describe the behavior of RPA bots with variable analysis. Furthermore, with high probability, it can also be used for analyzing other processes, as a tool for gaining insights into performance and as a benchmark tool for comparing or selecting a process to rework.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Information Systems and e-Business Management

ISSN

1617-9846

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

18

Strana od-do

741-758

Kód UT WoS článku

001302270100001

EID výsledku v databázi Scopus

2-s2.0-85202747335