Impacts of product structural characteristics on modular performance with virtual DSM data mining

ABSTRACT

Increasingly fierce market competition and rapidly changing customer demand has led many manufacturing enterprises to actively adopt modular architecture for product design and development. This helps them to effectively control their production costs while maintaining the diversity of product variants. However, there are considerable differences between the structure of different products; the degree to which each product is suitable for adopting a modular architecture also varies. Here, we explored the impact of product structural features (the number of parts and the average number of connected nodes) on its modular performance, based on virtual product design structure matrix (DSM) data. We first propose a method for constructing a DSM of a virtual product and randomly generate a database of DSMs with different structural parameters. We then carry out hierarchical clustering and modularity evaluation on a DSM model to obtain the optimal modularity scheme and corresponding Q value. Finally, based on numerical simulation data, the relationship between the structural features of virtual products and their modular performance was analyzed. In addition, eight actual products were selected to verify the simulation results and ensure the credibility of these results.

Abbreviations: ANCN, Average number of connected nodes; CE, Clustering efficiency index; DSM, Design structure matrix; HC, Hierarchical clustering; IC, Integrative complexity; LCA, Life cycle assessment; MDL, Minimum description length; MDS, Multi-dimensional scaling; MI, Modular index; MSI, Module strength indicator; PC(c), Partition coefficient c; PDM, Product data management; PLM, Product lifecycle management.

KEYWORDS:

• Topology structure
• Modularity assessment
• Big data
• Modular design
• Design structure matrix

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The support of this work by Fundamental Research Funds for the Central Universities of China (2017XKQY040), and Priority Academic Program Development of Jiangsu Education Institutions of China (PAPD) are gratefully acknowledged. Sincere appreciation is extended to the reviewers of this paper for their helpful comments.