Process selection for RPA projects with MDCM: The case of Izmir Bakircay University

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dc.contributor.authorErdoğan, Ali Mert
dc.contributor.authorDoğan, Onur
dc.date.accessioned2025-03-20T09:44:54Z
dc.date.available2025-03-20T09:44:54Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023 -- 9 November 2023 through 10 November 2023 -- Lübeck -- 309359
dc.description.abstractRobotic Process Automation (RPA) has emerged as a powerful technology for streamlining business operations by automating repetitive tasks. It is important for public universities as it helps streamline administrative processes, improve operational efficiency, and free up staff resources, allowing the institutions to focus more on delivering quality education and enhancing the overall student experience. However, selecting the right processes for RPA implementation poses a challenge due to the multitude of criteria involved. To address this issue, this paper proposes a multi-criteria decision-making (MCDM) approach for RPA process selection. The objective of this research is to develop a systematic methodology that enables decision-makers to evaluate and prioritize RPA processes based on multiple criteria, such as process complexity, ROI, and strategic importance. The proposed methodology incorporates two MCDM techniques, including the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assist decision-makers in effectively assessing and ranking alternative RPA processes. AHP helps determine the relative weights of criteria, while TOPSIS ranks alternatives based on their similarity to an ideal solution. A case study was conducted to validate the effectiveness of the proposed methodology. Empirical results showed that “Campus Event Management” is the most suitable alternative for RPA implementation, followed by “Campus Facility Management” and “Library Management”. In the study, sensitivity analysis was also performed by changing the weight values given for three different experts. The findings of this research contribute to the field of RPA process selection by providing a structured framework that facilitates the evaluation and prioritization of RPA processes. The proposed methodology empowers organizations to maximize the benefits of RPA implementation by selecting processes that align with strategic goals, enhance operational efficiency, and optimize resource utilization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.doi10.1007/978-3-031-55486-5_2
dc.identifier.endpage28
dc.identifier.isbn978-303155485-8
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85188674170
dc.identifier.scopusqualityQ3
dc.identifier.startpage15
dc.identifier.urihttps://doi.org/10.1007/978-3-031-55486-5_2
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2046
dc.identifier.volume2047 CCIS
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectChatGPT Expert
dc.subjectMulti-criteria decision making (MCDM)
dc.subjectProcess selection
dc.subjectRobotic Process Automation (RPA)
dc.subjectSmart campus
dc.titleProcess selection for RPA projects with MDCM: The case of Izmir Bakircay University
dc.typeConference Object

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