Integration of Robotic Process Automation with Low-Code Development for Enhanced Productivity
Keywords:
Robotic Process Automation (RPA), Low-Code Development, Integration, Automation, Productivity, Digital Transformation, Business Processes, Application Development, SynergyAbstract
In the dynamic landscape of business process automation, organizations are increasingly leveraging the synergies between Robotic Process Automation (RPA) and Low-Code Development to achieve heightened levels of efficiency and productivity. This study explores the integration of RPA with low-code platforms, aiming to provide a comprehensive understanding of the collaborative impact on workflow automation and overall business productivity. The research delves into the unique strengths of RPA in automating rule-based, repetitive tasks and low-code development's ability to empower users with diverse technical backgrounds to contribute to application development. The research investigates the intersection of RPA and Low-Code Development, elucidating how the automation capabilities of RPA and the rapid application development features of Low-Code platforms can complement each other.
Downloads
References
A. C. Bock and U. Frank, "In search of the essence of low-code: an exploratory study of seven development platforms," in 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2021: IEEE, pp. 57-66.
A. Mukherjee, "Robotic process automation with Blue Prism to optimize inventory management," Technische Hochschule Ingolstadt, 2021.
A. Dey, "Automating Business Processes to Improve Efficiency Efficient Design of Building Automation Systems," 2021.
J. D. Castro, "Business Process Automation Using Intelligent Software Robots," Dissertação de Mestrado, Instituto Superior Técnico, Portugal). Retrieved …, 2018.
R. Sanchis, Ó. García-Perales, F. Fraile, and R. Poler, "Low-code as enabler of digital transformation in manufacturing industry," Applied Sciences, vol. 10, no. 1, p. 12, 2019.
D. Krejci, S. Iho, and S. Missonier, "Innovating with employees: an exploratory study of idea development on low-code development platforms," in ECIS, 2021.
S. Agostinelli, A. Marrella, and M. Mecella, "Towards intelligent robotic process automation for BPMers," arXiv preprint arXiv:2001.00804, 2020.
S. Agostinelli, A. Marrella, and M. Mecella, "Research challenges for intelligent robotic process automation," in Business Process Management Workshops: BPM 2019 International Workshops, Vienna, Austria, September 1–6, 2019, Revised Selected Papers 17, 2019: Springer, pp. 12-18.
G. Smith, M. Papadopoulos, J. Sanz, M. Grech, and H. Norris, "Unleashing innovation using low code/no code–The age of the citizen developer," ed: Arthur D. Little Prism, 2020.
D. Andrade, "Challenges of automated software testing with robotic process automation rpa-a comparative analysis of uipath and automation anywhere," Int. J. Intell. Comp. Res.(IJICR), vol. 11, no. 1, pp. 1066-1072, 2020.
Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
Raparthi, Mohan, Sarath Babu Dodda, and SriHari Maruthi. "Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks." European Economic Letters (EEL) 10.1 (2020).
Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
Vyas, Bhuman. "Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance." Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal 10.1 (2021): 59-62.
Rajendran, Rajashree Manjulalayam. "Scalability and Distributed Computing in NET for Large-Scale AI Workloads." Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal 10.2 (2021): 136-141.
Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
Raparthi, M., Dodda, S. B., & Maruthi, S. (2020). Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks. European Economic Letters (EEL), 10(1).
Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
Vyas, B. (2021). Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(1), 59-62.
Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
Rajendran, R. M. (2021). Scalability and Distributed Computing in NET for Large-Scale AI Workloads. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(2), 136-141.
Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
