Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.
Complex product design, manufacturing, and service are the key elements of a product’s life cycle. However, the traditional manufacturing processes of design, manufacturing, and service are independent of each other, so lack deep integration. The emergence of digital twins offers an opportunity to accelerate the integration of complex product design, manufacturing, and services. For intelligent manufacturing, physical entity and virtual entity transformation can be realized through digital information. A collaborative framework for complex product design, manufacturing, and service integration based on digital twin technology was proposed. The solutions of process integration, data flow, modeling and simulation, and information fusion were analyzed. The core characteristics and key technologies of service-oriented manufacturing, design for service and manufacturing, and manufacturing monitoring based on the deep integration of the digital twin were discussed. Finally, the feasibility of the framework was verified by a self-balancing multistage pump manufacturing case. The performance of the upgraded pump under the framework was tested, and the test results proved the effectiveness of the integrated framework.
As an important path to enhance the innovation performance of enterprises, knowledge management has received much attention in recent years. However, most of the existing literature on knowledge management and innovation performance is based on a static perspective, and ignores the influence of dynamic changes in the environment. This study intends to explore the relationship between dynamic knowledge management capability and innovation performance as well as examine the moderating effect of environmental dynamism. The questionnaire survey approach is used in this study and the data is collected from 253 sample enterprises in China. To estimate the proposed relationships in the theoretical model, this study adopts hierarchical Multiple Regression (MR) and Moderated Multiple Regression (MMR) methods. The results show that all dimensions of dynamic knowledge management capability have different degrees of positive influence on innovation performance. Moreover, it was also confirmed that there are different moderating effects of environmental dynamism on the relationship between the dimensions of knowledge management capability and innovation performance. This study can contribute to enriching the theoretical research of dynamic knowledge management capability and innovation performance, and offer scientific guidance for decision making to efficiently enhance the enterprise’s knowledge management level and innovation performance. Moreover, the findings can also provide valuable insights for enterprises to make use of KM capabilities to enhance innovation performance in practice.
In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, located in the western part of Henan Province, China was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93 cm; the average relative error of the verified simulated water depth was 15.27%, the average absolute error was 7.54 cm, and the simulation result was good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management.
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