Product modular analysis with design structure matrix using a hybrid approach based on MDS and clustering

Abstract

ABSTRACTModular analysis using the Design Structure Matrix (DSM) identifies the interactions between groups of components, and clusters them into modules in order to achieve competitive advantages in the product design processes. In this paper, a hybrid approach, based on multidimensional scaling (MDS) and clustering methods, is applied to component DSM for product architecting. The motivation is to facilitate better modularizations that enhance different product attributes in various product lifecycle stages. An experimental framework is developed to evaluate the performance of MDS clustering. Three MDS methods and four ubiquitous clustering methods are compared to reveal the most suitable for DSMs. The experimental results with several examples demonstrate that the effectiveness of MDS clustering, and show the superiority of non-metric MDS, SMACOF (Scaling by MAjorizing a Complicated Function) and hierarchical/cosine methods. These methods are capable of partitioning the product architecture into a set of modules and outperform the Newman-Girven algorithm, which has been extensively applied to DSM clustering. The proposed method is capable of partitioning the product architecture into reasonable modules. In addition, it can produce the optimal modules for any number of clusters, which is favourable especially when the cluster number is a higher managerial decision.