Abstract:With the rapid development of big data, 6G, Internet of Things (IoT) and other information and communication technologies (ICTs), artificial intelligence (AI) has ushered in new opportunities for more advancements. AI systems are changing the survival and development of human society. This study focuses on the research and application of AI systems within the scientific framework of systems thinking. From scientometrics, this study adopts cocitation analysis and keywords co‐occurrence analysis to sort existing… Show more
“…We also demonstrated that every internal implementation factor is either directly dependant on the IT infrastructure of the organisation or indirectly connected to it through a short causal loop. It is probable therefore that every organisation's attempt to move towards Industry 4.0 stands and falls with their ability to efficiently manage and scale their IT infrastructure, which may further help to explain why a great deal of Industry 4.0 literature is still focused on Industry 4.0 technologies (Ghobakhloo, 2020; Jiang et al, 2022; Nara et al, 2021). However, our findings also show that ‘IT Infrastructure’ has the second highest in‐degree, which is unusual for high out‐degree variables in a network and indicative of a dynamic and complex relationship to other factors.…”
Industry 4.0 provides companies with the technological and theoretical means to enhance data-driven decision-making procedures. To facilitate the transformation process, several studies have identified factors that need to be considered when implementing Industry 4.0 on a broader level. However, the dynamic relationship between these factors has yet to be understood to provide companies with the in-depth knowledge needed to effectively manage the transition. The principal aim of our research is therefore to map out the complex relationships between the identified factors, by adapting a novel approach that combines network analysis and causal loop diagrams. Results show that the roles of implementation factors are not static, and what role they play depends on their position in the network, complementing the findings of previous investigations about the drivers of change. Furthermore, our findings indicate that multiple intervention points exist, shedding more light on how to develop effective implementation strategies.
“…We also demonstrated that every internal implementation factor is either directly dependant on the IT infrastructure of the organisation or indirectly connected to it through a short causal loop. It is probable therefore that every organisation's attempt to move towards Industry 4.0 stands and falls with their ability to efficiently manage and scale their IT infrastructure, which may further help to explain why a great deal of Industry 4.0 literature is still focused on Industry 4.0 technologies (Ghobakhloo, 2020; Jiang et al, 2022; Nara et al, 2021). However, our findings also show that ‘IT Infrastructure’ has the second highest in‐degree, which is unusual for high out‐degree variables in a network and indicative of a dynamic and complex relationship to other factors.…”
Industry 4.0 provides companies with the technological and theoretical means to enhance data-driven decision-making procedures. To facilitate the transformation process, several studies have identified factors that need to be considered when implementing Industry 4.0 on a broader level. However, the dynamic relationship between these factors has yet to be understood to provide companies with the in-depth knowledge needed to effectively manage the transition. The principal aim of our research is therefore to map out the complex relationships between the identified factors, by adapting a novel approach that combines network analysis and causal loop diagrams. Results show that the roles of implementation factors are not static, and what role they play depends on their position in the network, complementing the findings of previous investigations about the drivers of change. Furthermore, our findings indicate that multiple intervention points exist, shedding more light on how to develop effective implementation strategies.
“…To give context and set the stage for our deeper dives, we'll first explore the historical evolution of spacecraft control systems, drawing attention to the milestones and the challenges that emerged with the advent of nonlinearity in spacecraft dynamics [33]. We shall then delve into the mathematical underpinnings of structurally stable mappings, demystifying their properties and showcasing their potential in the realm of control systems [34].…”
In recent developments within the domain of aerospace engineering, there is a burgeoning interest in the autonomous control of nonlinear spacecraft using advanced methodologies. The present research delves deep into the realm of self-organizing control systems tailored for such nonlinear spacecraft, emphasizing its application within the framework of structurally stable mappings. By harnessing the inherent characteristics of structurally stable mappingsoften renowned for their resilience to minor perturbations and local modificationsthis research endeavors to design a control mechanism that mitigates the challenges presented by the intrinsic nonlinearity of spacecraft dynamics. Initial findings suggest a commendable enhancement in spacecraft maneuverability and robustness against unforeseen disturbances. Furthermore, the employment of self-organization principles leads to an adaptive and resilient system that can reconfigure its control strategies in real-time, basing decisions on immediate environmental feedback. This adaptability, in essence, mimics biological systems that evolve and adapt in the face of challenges. Such a breakthrough in nonlinear spacecraft control not only widens the horizons for space exploration by making missions safer and more efficient but also contributes foundational knowledge to the broader field of nonlinear dynamic system controls. Researchers and practitioners are encouraged to explore this synergistic combination of self-organization and structurally stable mappings to further harness its potential in diverse arenas beyond aerospace.
“…30 However, existing studies on big data have mainly focused on technical debates, and research on smart technologies and smart supply chain management should improve operational performance. 35 Figure 1 illustrates the conceptual framework of this study.Figure 1.Conceptual framework.…”
The manufacturing industry plays a crucial role in the economy of many countries, but there is a lack of expertise in implementing smart supply chains and smart technologies. This study aims to investigate the relationship between smart supply chains, smart technologies, and operational performance in the manufacturing industry. A survey questionnaire was conducted among registered manufacturing industries, and the results were analyzed using Smart PLS to test 10 hypotheses. Four hypotheses were supported out of 119 responses received through simple random sampling. The study suggests implementing instrumented supply chains using smart technologies can enhance operational performance. The findings provide valuable insights for policymakers, academics, and industry practitioners to improve the competitiveness of the manufacturing industry. This research emphasizes the importance of smart supply chains and smart technologies in achieving operational excellence, and further studies are necessary to address the identified limitations and contribute to a deeper understanding of the role of smart technologies in the supply chain’s digitalization.
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