Research On Clustering Product Development Project Organization Based On Complex Network And Design Structure Matrix

Research and development(R&D)is the foundation of enterprises in the marketplace.The research and development project is a complex network composed by interdependent elements in different domains such as the requirement,product,process and organization domain.Organizational design is one of the key contents of R&D project management.Effective organizational design has a significant impact on the success or failure of product development projects.Organizational modular design is an effective way to reduce management complexity and improve innovation efficiency.To realize the modular design of R&D project organization,the interdependent between teams should be quantified.We mainly measure dependency strength among teams from three perspectives:team similarity,multi domain mapping and trust.Firstly,similarity can reflect the degree of dependence between teams,promote coordination and enhance organizational knowledge integration.Therefore,it is necessary to propose an organization modularity method based on team similarity.Secondly,the R&D design process is closely integrated with customer requirements,quantifying the "implicit" dependencies between customer requirements,products,and organizational domains,and then achieving modular design of the organization,which is conducive to the product development project organization to better adapt to changes in external requirements and improve products market recognition.Finally,the construction of trust dependency relationship can also effectively reduce conflicts,promote communication between teams and improve team knowledge sharing.It is essential to propose an organization modularity design method based on trust relationship.This thesis starts from the dependency relationship between teams in R&D projects,and expands it to the similarity,the multi domain mapping relationship driven by key customer requirements,and the trust dependency relationship.It adopts complex network theory,design structure matrix(DSM)and other methods to achieve organizational modular design research,focusing on the following three aspects of research:(1)We take a more sophisticated approach than prior methodologies to improve coordination via organizational clustering,by accounting for both team structural and attribute similarity from the perspective of social network analysis(SNA).We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams,which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix(DSM).To evaluate the organization network,we propose social embeddedness-related centrality indices within(intra-cluster)and across(inter-cluster)team groupings.To facilitate knowledge sharing,we base team attribute similarity on product-and process-related expertise among teams.We integrate the modularity index and an improved silhouette index to find an optimal number of clusters,which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm.An industrial example illustrates the proposed model.The clustering results reinforce several managerial practices but also yield new insights,such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.(2)We propose a clustering optimization method for product development organization based on the key customer requirements and the core teams.Organization modularity is not only affected by team similarity,but also driven by diversified customer requirements.We first build a PD project organizational network oriented to the key customer needs.Then,we examined how the presence of a common third party in the organizational network affects the trust transferability between interdependent teams,which is the root cause of social embeddedness.To identify the core teams,which typically have high importance as well as diverse knowledge in the organizational network,we improved the LeaderRank centrality with trust transferability related to common recipients and sources to evaluate the importance of teams and present the team attributes(i.e.,expertise)diversity.To build the group around core teams,we used the core teams as the input parameter(i.e.,the initial clustering seeds)of the K-means clustering algorithms.The clustering results reinforce several managerial practices in an industrial example,including how trust transferability impacts the optimal organizational network structure and how to build an organizational network structure based on core teams.(3)We propose a clustering optimization method for product development organization based on trust relationship.The purpose of this part is to advance understanding of trust in a PD organizational context,its dimensions,its propagation and its impact on organizational structure and tacit knowledge which is a significant strategic source to the sustainable development of PD organizations.First,we find two dimensions of trustworthiness,competence and goodwill,predict tie formation for tacit knowledge sharing.We use the concept of possibility theory to quantify the competence trust in overlapping processes and calculate the goodwill trust from the participation structure,the participation ratio and the shared value perspectives.Second,the benefit of trust propagation emerged.We build a complete PD organizational network by considering the effect of small world theory(i.e.average path length and clustering coefficient).Third,we examine how the trust dimensions and trust propagation influence the organization modularity and tacit knowledge sharing which is measured by entropy.Finally,a manufacturing sample is given to demonstrate the proposed models.The results provide an integrated managerial insight for estimating trust relationships,reducing total coordination cost and enhancing tacit knowledge sharing.