A rule-based method to effectively adopt robotic process automation

被引:0
|
作者
Bedard, Maxime [1 ]
Leshob, Abderrahmane [1 ]
Benzarti, Imen [2 ]
Mili, Hafedh [3 ]
Rab, Raqeebir [4 ]
Hussain, Omar [5 ]
机构
[1] Univ Quebec Montreal, Analyt Operat & IT, Montreal, PQ, Canada
[2] Ecole Technol Super, Software & IT Engn, Montreal, PQ, Canada
[3] Univ Quebec Montreal, Comp Sci, Montreal, PQ, Canada
[4] Ahsanullah Univ Sci & Technol, Dept CSE, Dhaka, Bangladesh
[5] Univ New South Wales, Sch Business, Canberra, Australia
基金
加拿大自然科学与工程研究理事会;
关键词
business process modeling; business rules; goal-oriented requirements language; robotic process automation; BUSINESS RULES;
D O I
10.1002/smr.2709
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error-prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule-based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%. image
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页数:22
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