TY - JOUR
T1 - A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives
AU - Ng, Kam K.H.
AU - Chen, Chun Hsien
AU - Lee, C. K.M.
AU - Jiao, Jianxin (Roger)
AU - Yang, Zhi Xin
N1 - Funding Information:
The authors are grateful for the support from the editors of Advanced Engineering Informatics through the Call for Papers on emerging intelligent automation and optimisation methods for adaptive decision making with real-world applications. The authors would like to express their gratitude and appreciation to anonymous reviewers, the editor-in-chief, associate editors and editorial board members for providing valuable comments for the continuing improvement of this article. The research is supported by School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, USA and Department of Electromechanical Engineering, University of Macau, Macao SAR. Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering, the Hong Kong Polytechnic University for support of the project (ZVS9).
Publisher Copyright:
© 2021 Elsevier Ltd
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA's benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.
AB - With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA's benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.
KW - Adaptive decision making
KW - Artificial intelligence
KW - Innovative robotic process automation
KW - Intelligent automation
UR - http://www.scopus.com/inward/record.url?scp=85099458674&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2021.101246
DO - 10.1016/j.aei.2021.101246
M3 - Journal article
AN - SCOPUS:85099458674
SN - 1474-0346
VL - 47
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 101246
ER -