Author
Listed:
- Nikolaos Spanoudakis
(Technical University of Crete)
- Nikolaos Batakis
(Technical University of Crete)
- Nikolaos F. Matsatsinis
(Technical University of Crete)
Abstract
Robotic Process Automation is a modern field of Information Technology that enables the automation of mechanically repeated tasks by humans when they use their computer. It is a field that is currently trending, but many RPA projects fail. This paper aims to aid decision makers who want to select one or more Robotic Process Automation projects for implementation among several candidates. To achieve our goal we developed two tools, one for filtering proposals, leaving out those that are not good candidates, and another for ranking the remaining proposals to aid the decision maker in deciding which ones to implement. To develop the tools, firstly we discovered the criteria required for a successful Robotic Process Automation project proposal assessment by interviewing six experts in the field using the Coding method. Our findings led to the development of a first tool, i.e. the Process Assessment Model tool, which filters the proposals. Then, we asked two of the experts to rank sample RPA project proposals—many of which were real-world proposals. Subsequently we applied the UTA* multi-criteria method that provided weights for the criteria. Using them, we developed the Process Assessment Formula, a tool which calculates the complexity of the process as well as the expected value it will provide and shows this information in a priority table. Our tools can assist organizations in deciding effectively which processes can be automated, and in ranking them, so that the best candidates among them can be selected for automation.
Suggested Citation
Nikolaos Spanoudakis & Nikolaos Batakis & Nikolaos F. Matsatsinis, 2023.
"Utility-Based Robotic Process Automation Candidate Projects Ranking,"
Springer Proceedings in Business and Economics, in: Nikolaos F. Matsatsinis & Fotis C. Kitsios & Michael A. Madas & Maria I. Kamariotou (ed.), Operational Research in the Era of Digital Transformation and Business Analytics, pages 89-96,
Springer.
Handle:
RePEc:spr:prbchp:978-3-031-24294-6_9
DOI: 10.1007/978-3-031-24294-6_9
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