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

https://doi.org/10.1016/j.cie.2012.06.002Get rights and content

Abstract

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.

Highlights

► We establish the evaluation criteria and methods for different roles of design chain partners. ► We create a weight-restricted DEA model to evaluate the performance of candidates. ► We develop a multi-objective evaluation model for design chain partner combinations.

Introduction

Because companies face fierce business competition and the product life cycle is becoming shorter, no enterprises are exempt from the requirement to accelerate product development and innovation. However, the current complexity of product design has significantly increased, and product development involves a wide variety of expertise and professional domain knowledge. Thus, for a single company, it is very difficult to complete all product development activities with limited enterprise resources in a short time. Because of the flourishing development of information technology and the significantly improved network infrastructure, it is possible to establish a virtual product development team to develop a new product collaboratively. Therefore, enterprises can effectively employ the product development and innovation capacity of the design chain members to deliver a low-cost, high-quality, and customer-oriented new product in a short amount of time. It is obvious that a design chain partner combination formed by members of different organizations across geographical barriers will have far-reaching effects on the market competitiveness of a new product and the profitability of a company in the future. However, this new pattern of product development also generates some problems in design chain management: rapidly and effectively evaluating and selecting effective design chain members, considering different partner roles and forming the best design chain partner combination when a company senses new market opportunities. Short product life cycles cause enterprises to evaluate design chain partner combinations constantly. Moreover, to shorten the time to market, companies often adopt the pattern of derivative product development to respond quickly to changes in market requirements. Based on the investigation by Crawford and Di Benedetto (2010), new-to-the-world products only account for approximately 10% of all new products. Therefore, most new products belong to the class of derivative new products which are built around improved preexisting or established technologies, such as consumer electronics, software, airplanes, and cars. If the current product development activity is similar to that of a previous project, the performance of the product development activity can be derived from historical data for the product development partner (Ulrich & Eppinger, 2011). Therefore, this study aims to develop an integrated decision-making methodology to assist companies as they create an optimal design chain partner combination for derivative product development.

The organization of this paper is as follows. Section 2 reviews the related papers of partner evaluation and selection. Section 3 describes an integrated decision-making to create an optimal design chain partner combination using weight-restricted DEA and a multi-objective genetic algorithm (GA). Section 4 employs a digital TV box development project as a case to demonstrate the significant contribution of the methodology presented in this paper. Finally, Section 5 makes some conclusions of this study.

Section snippets

Literature review

This section reviews the related research for evaluation criteria and methods of partner selection. Wang, Huang, and Dismukes (2004) developed an integrated analytic hierarchy process (AHP) and goal programming (GP) based on a multi-criteria decision-making methodology, which takes into account delivery reliability, flexibility and responsiveness, purchasing cost and assets in supplier selection. Xia and Wu (2007) integrated AHP with multi-objective mixed integer programming to support supplier

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

Fig. 1 shows the analytical procedure of the proposed methodology for creating an optimal design chain partner combination. The analytical procedure can be divided into three parts. The first part includes the frameworks and evaluation models of criteria for design chain partners. The second part is the performance analysis of design chain partners. The last part is the synthesized evaluation of design chain partner combinations. In the first part, the concept of a product architecture is used

Case Study

Fig. 8 shows the structure of a design chain network for the development of a digital TV box (modified from Chuang et al., 2009). In the system integration part, one partner (P1) is responsible for establishing the product specification and integrating all functional modules of a new product. With regard to the functional module development, three partners are required to develop the software module (P2), the motherboard module (P3), and the mechanism module (P4). For the software development,

Conclusion

Given the significant increase in the complexity of product design, product development requires expertise in a variety of fields and involves specialized facilities for product development. Currently, an enterprise cannot complete the product development tasks by itself with limited product development resources. Therefore, most companies outsource the product development activities to other enterprises and form a design chain. Because many design chain partners are available, an outsourcer

Acknowledgments

The author would like to thank the anonymous referees for their helpful comments and suggestions to improve the quality of this paper. In addition, this work was supported by Taiwan National Science Council for financial support under Contract No. NSC 97-2221-E-251-006 and 98-2221-E-251-006.

References (39)

  • C.A. Weber et al.

    Non-cooperative negotiation strategies for vendor selection

    European Journal of Operational Research

    (1998)
  • W. Xia et al.

    Supplier selection with multiple criteria in volume discount environment

    OMEGA – The International Journal of Management Science

    (2007)
  • J. Yue et al.

    Selecting sourcing partners for a make-to-order supply chain

    OMEGA - The International Journal of Management Science

    (2010)
  • R.D. Banker et al.

    Some models for estimating technical and scale inefficiencies in data envelopment analysis

    Management Science

    (1984)
  • H.Y. Cao et al.

    A simulation based genetic algorithm for risk-based partner selection in new product development

    International Journal of Industrial Engineering: Theory, Applications and Practice

    (2003)
  • F.T.S. Chan

    Interactive selection model for supplier selection process: An analytical hierarchy process approach

    International Journal of Production Research

    (2003)
  • F.T.S. Chan et al.

    Global supplier selection: A fuzzy-AHP approach

    International Journal of Production Research

    (2008)
  • A. Charnes et al.

    Measuring the efficiency of decision making units

    European Journal of Operational Research

    (1978)
  • T.-A. Chiang et al.

    A benchmark-based hybrid evaluation methodology for selecting the best design chain partners

    Journal of Quality

    (2011)
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