Multi‐objective decision‐making methodology to create an optimal design chain partner combination

In today’s highly competitive business environment, many companies adopt the time‐to‐market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far‐reaching effect on the results of product development. With this perspective, this paper develops an integrated decision‐making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight‐restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi‐objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi‐objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology.