Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-powered IoE Services

Authors

  • Wei Lo School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China
  • Chun-Ming Yang School of Economics and Management, Dongguan University of Technology, Dongguan, Guangdong, China
  • Qiansha Zhang School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China
  • Mingyuan Li School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2313

Keywords:

Robotic process automation (RPA), generative AI (GAI), Internet of Everything (IoE), industrial productivity, waste management

Abstract

The convergence of robotic process automation (RPA) and generative AI (GAI) within the context of Internet of Everything (IoE) services represents a profound paradigm shift. This fusion of technologies not only streamlines routine tasks but also catalyzes innovation while harnessing the potential of interconnected devices. Such integration empowers organizations to achieve remarkable gains in efficiency and sustainability. This paper embarks on an exploration of these transformative services, designed to elevate productivity, and curtail wasteful practices in contemporary industries. By closely examining intricate case studies, we illuminate the multifaceted advantages of this integrated approach. Our investigation demonstrates how RPA accelerates the execution of repetitive processes, substantially diminishing the margin for human error and amplifying operational efficiency. In contrast, generative AI introduces a disruptive force, generating fresh ideas, designs, and solutions, thereby elevating the quality of products and services. The infusion of these cutting-edge technologies into the fabric of IoE services paves the way for organizations to attain unprecedented levels of automation, intelligence, and connectivity. Furthermore, this paper comprehensively addresses the intricate challenges and considerations associated with the proposed implementation. We delve into ethical concerns, security implications, and the necessary workforce adaptation to offer a balanced perspective on the adoption of these technologies. Additionally, we navigate through potential limitations and constraints, underscoring the imperative need for strategic planning and robust governance.

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Author Biographies

Wei Lo, School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China

Wei Lo earned a master’s degree in business administration from National Yunlin University of Science and Technology in Taiwan in 2003. In 2018, he obtained a Ph.D. in business administration from Fu Jen Catholic University in New Taipei City, Taiwan. Currently, he works as an Associate Professor at the School of Business Administration in Guangxi University of Finance and Economics, China. His research focuses on process improvement, technology innovation, and total quality management.

Chun-Ming Yang, School of Economics and Management, Dongguan University of Technology, Dongguan, Guangdong, China

Chun-Ming Yang received a Ph.D. in management sciences from Tamkang University in New Taipei City, Taiwan, in 2015. He worked at the Business School in Guilin University of Technology, China, from 2016 to 2019. In 2020, he joined the School of Economics and Management at Dongguan University of Technology, where he currently holds the position of Associate Professor. His research focuses on process capability analysis, quality management, and multi-criteria decision-making.

Qiansha Zhang, School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China

Qiansha Zhang earned a Ph.D. in agriculture from City University of Macau, China, in 2016. In 2018, she joined Guangxi University of Finance and Economics in China, where she is now an Associate Professor in the School of Business Administration. Her research focuses on innovation and management, with a particular emphasis on quality management.

Mingyuan Li, School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China

Mingyuan Li obtained a Ph.D. in Agriculture from Tottori University in Tottori City, Japan, in 2018. In the same year, she joined Guangxi University of Finance and Economics in China and is currently working as an Associate Professor in the School of Business Administration. Her research focuses on mathematical modeling and service quality management.

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Published

2024-03-27

How to Cite

Lo, W., Yang, C.-M., Zhang, Q., & Li, M. (2024). Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-powered IoE Services. Journal of Web Engineering, 23(01), 53–88. https://doi.org/10.13052/jwe1540-9589.2313

Issue

2024: Vol 23 Iss 1

Section

Advances, Risks, Solutions, and Ethics in Generative AI for Web Engineering