Purpose: Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.Design/methodology/approach: To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.Findings: The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.Originality/value: The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

Purpose: The purpose of this paper is to propose a valid and quantitative measurement method of knowledge diffusion efficiency for the knowledge collaboration networks (KCNs). Design/methodology/approach: This paper builds a weighted KCN model with the node and edge weights. Based on the weighted KCN, the factors of knowledge diffusion efficiency are proposed and analyzed. Then, the knowledge transfer effect between two nodes is proposed and measured by comprehensively integrating the above factors. Furthermore, the main metric of efficiency of knowledge diffusion is proposed by modifying Latora and Marchiori’s model of efficiency of network. Findings: A case is studied to illustrate the applicability of the proposed weighted network model and the knowledge diffusion efficiency measurement method. The results show the methods proposed in this paper can better measure and analyze the knowledge diffusion efficiency of KCNs than the traditional un-weighted methods and the subjective evaluation methods. Originality/value: The real KCNs are always weighted networks. The weighted model of KCN can better reflect the real networks than the un-weighted model. Based on the weighted networks, the measurement methods proposed in this paper can more efficiently and accurately measure and evaluate the knowledge diffusion efficiency than the traditional methods. This study can help researchers to better understand knowledge diffusion theoretically, and provide managers with a decision support for knowledge management in practice.

For the product R&D process, it is a challenge to effectively and reasonably assign tasks and estimate their execution time. This paper develops a method system for efficient task assignment in product R&D. The method system consists of three components: similar tasks identification, tasks' execution time calculation, and task assignment model. The similar tasks identification component entails the retrieval of a similar task model to identify similar tasks. From the knowledge-based view, the tasks' execution time calculation component uses the BP neural network to predict tasks' execution time according to the previous similar tasks and the Task-Knowledge-Person (TKP) network. When constructing the BP neural network, the satisfaction degree of knowledge and the execution time are set as the input and output, respectively. Considering the uncertain factors associated with the whole R&D process, the task assignment model component serves as a robust optimization model to assign tasks. Then, an improved genetic algorithm is developed to solve the task assignment model. Finally, the results of numerical experiment are reported to validate the effectiveness of the proposed methods.

Complex mechanical products are a priority area of advanced manufacturing strategy in the USA and Europe. Excellent models for design processes of complex mechanical products are essential for managers or designers to manage design processes and further improve design efficiency. Multiple elements and complicated relationships between elements in the design processes of complex mechanical products cause difficulties in systematically and quantitatively expressing the design processes. Therefore, we put forth a supernetwork-based model for designing complex mechanical products. First, we identified the key elements in the design processes of complex mechanical products. Next, based on this we analyzed sub-elements of the key elements and relationships between sub-elements. Then we built sub-networks with sub-elements as nodes and their relationships as edges and the supernetwork model for design processes of complex mechanical products based on the sub-networks and their relationships. Meanwhile, we also present a method for combining linguistic variables with the corresponding triangular fuzzy numbers, and a max/min synthesis method to accurately compute the edge weights. Finally, we suggest the practical applications of the proposed model and give a comparison of the proposed model and existing ones to verify the feasibility and validity of this study.

Customer requirements are constantly evolving, which will cause high change risks and redesign costs for the products. To control the change risks toward evolving customer requirements, margins are widely used in recent studies. Margins are the redundant values of design parameters, and they can absorb potential change risks of customer requirements. Nevertheless, the existing research does not well explore the relationship between margins of design parameters (MDPs) and change risks, i.e., how much each MDP should be predetermined for absorbing the potential change risks is unknown. Thus, to manage the evolving customer requirements, in this article, an optimization method considering the MDPs and change risks is proposed. First, the functional basis model of the product is established to obtain the relationships between design parameters. Second, change risks are analyzed based on the relationships between design parameters and the improved change prediction method. Third, the benefits and costs of MDPs in reducing change risk are analyzed, based on which, an optimization model for MDPs is established and solved. Finally, the optimization of MDPs for the hair dryer is implemented as a case study to illustrate the utility and effectiveness of the proposed method.