Special Issue on Transdisciplinary engineering research and practices focusing on digital transformations of complex systems Submission Date: 2023-03-31 Advanced engineering informatics calls for manuscript submissions for consideration in the Special Issue featuring “Transdisciplinary engineering research and practices focusing on digital transformations of complex systems.”

Digital transformation (DT) is the convergence of digital technology into various areas to bring fundamental changes, enhance critical system performance, and improve users’ or stakeholders’ satisfaction. The four essential elements of DT are (1) target entity (i.e., the organization that adopts DT); (2) scope and focus of the transformation; (3) technology adoption and manners; and (4) contexts and benefit goals of the expected change (Vial, 2019; Lee et al., 2021). Moreover, DT is the strategic adoption of digital intelligence, such as the deployment of artificial intelligence (AI), deep learning, big data analytics, cyber-physical systems (CPS), digital twins, cloud computing, edge computing, and immersive technologies (VR/AR/MR).

DT for any target entity will “trigger significant changes and effectiveness to its external-market and internal-organization strategies and tactics through combinations of information, computing, communication, and connectivity technologies” (Vial, 2019; Gimpel et al., 2018; Schallmo et al., 2017). To fully obtain the benefits of DT, an appropriate DT design and development strategy is critical for its success under complex and uncertain external and internal circumstances. Moreover, how to enable the connections of external and internal systems for exchanging and interrogating information and intelligence of each other is a challenging research issue for DT of complex systems.

A complex system is composed of many interacting parts, often called agents, which may exhibit collective and complex behaviors (Newman, 2011; Ladyman, 2013). Therefore, DT has great potential to drive changes for complex systems in their macro-, meso-, and/or micro-levels in various sectors, e.g., government, social, urban, industry, and enterprise realms where the wide spectrum and great challenges of advanced research in DT theories and practices are encountered (Lee et al., 2021).

Further, when going into the new era of DT, advanced theories must be explored to enable different complex systems operated and managed “digitally” with complete synchronization of external circumstances, humans, devices, and sub-systems reflecting both cybernetic and physical forms.

- Topics and subjects:

This Special Issue aims to explore the complex systems related to these DT challenges and solutions and, thus, open calls for research papers that present “Transdisciplinary engineering research and practices focusing on digital transformations of complex systems.” The types of complex systems suitable for DT practices have been outlined in six main themes in the recent DT special issue collection (Lee et al. 2022). These example types include Smart factory (I), Sustainability and product-service system (II), Construction (III), Techno-centric (IV), Public infrastructure centric (V), and Business model-centric (VI). Authors are encouraged to refer to these recent DT publications. Research scopes and topics of the special issue include, but are not limited to, the following:

New theories supporting future DT; Innovative DT models; DT industrialization and globalization; Practical DT design and implementation; Cutting edge DT knowledge management for complex systems; Intelligent technologies and applications for management or prediction of complex systems; Theoretical and practical performance models of DT for complex systems; Design and management for Macro, Middle or micro complex systems with DT models. Agent-based modelling for complex systems with DT models; The Computational complexity for complex systems with DT models; Information theory for complex systems with DT models; Adaptation and game theory for complex systems with DT models; Dynamical systems for complex systems with DT models; Social Network Analysis for complex systems with DT models; Explainable AI, responsible AI, trustworthy AI or ethical AI for DT-driven complex systems; Big data analytics-based DT for complex systems; Blockchain-based DT for complex systems; Cloud computing-based DT for complex systems; Digital twin-based DT for complex systems; Edge computing-based DT for complex systems; Metaverse-based DT for complex systems; Sustainable DT-driven complex systems; Managing risk or emergency of DT for complex systems; Complex systems with Urban DT models; Complex systems with Government DT models; Complex systems with Industry DT models; Complex systems with Enterprise DT models.