A Supply Chain Oriented Product Design Optimization Decision Method Based on Improved CUR Matrix Decomposition

Abstract

At present, product family design has become an important link in enterprise development and manufacturing. Optimization ideas and technologies are important foundations and core frameworks in product family design. Previous research on product family design has mainly been limited to optimization problems within the product domain. As an important influencing factor in the product family design process, the supply chain not only affects the cost level of the product family in the back-end of the design process, but also affects the modular structure layout of the product family in the front-end of the design process. Therefore, the optimization of the correlation between supply chain and product family design process is a crucial issue that determines the success or failure of product families. However, when researching the personalized needs of users in product family design and configuring product modules, there is very little consideration given to the optimization of supply chain correlation. To address the aforementioned issues, this article develops supply chain oriented product design optimization decision-making method based on improved CUR matrix decomposition. Firstly, based on the customer’s functional requirements C matrix and module relationship R matrix, perform customer clustering and corresponding product configuration. Then, utilizing the numerical stability of orthogonal trigonometric decomposition (QR), U matrix is constructed, which represents the inherent relationship between functional requirements and module relationships. Secondly, based on quality/character requirements, functional module levels division and initial supplier configuration are carried out. Finally, determine the supplier configuration for each module with the goal of maximizing total profit. Analyze the customer selection, classification, and product configuration process of a contractor as a case study. The research results indicate that the optimization decision method based on improved CUR matrix decomposition can effectively obtain the optimal solution of the decision problem.