Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

Jacky Chen (), Chee Peng Lim, Kim Hua Tan, Kannan Govindan and Ajay Kumar
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Jacky Chen: Deakin University
Chee Peng Lim: Deakin University
Kim Hua Tan: Nottingham University Business School
Kannan Govindan: University of Southern Denmark
Ajay Kumar: EMLYON Business School

Annals of Operations Research, 2025, vol. 350, issue 2, No 6, 493-516

Abstract: Abstract Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

Keywords: Artificial Intelligence; Decision support; Small and medium enterprises; Predictive maintenance; Asset management; Pandemic preparedness (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-021-04373-w

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