Multi-objective decision-making methodology to create an optimal design chain partner combination☆
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)
Fuzzy hierarchical analysis
Fuzzy Sets and Systems
(1985)- et al.
An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information
Expert Systems with Applications
(2008) - et al.
A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly
Advances in Engineering Software
(2010) - et al.
A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA
Expert Systems with Applications
(2010) - et al.
Journal bearing design using multi-objective genetic algorithm and axiomatic design approaches
Tribology International
(2005) - et al.
Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise
Computers and Operations Research
(2003) A fuzzy DEA/AR approach to the selection of flexible manufacturing systems
Computers & Industrial Engineering
(2008)Fuzzy analytical approach to partnership selection information of virtual enterprises
OMEGA – The International Journal of Management Science
(2002)- et al.
An integrated model for supplier selection decisions in configuration changes
Expert Systems with Applications
(2007) - et al.
Product-driven supply chain selection using integrated multi-criteria decision-making methodology
International Journal of Production Economics
(2004)
Non-cooperative negotiation strategies for vendor selection
European Journal of Operational Research
Supplier selection with multiple criteria in volume discount environment
OMEGA – The International Journal of Management Science
Selecting sourcing partners for a make-to-order supply chain
OMEGA - The International Journal of Management Science
Some models for estimating technical and scale inefficiencies in data envelopment analysis
Management Science
A simulation based genetic algorithm for risk-based partner selection in new product development
International Journal of Industrial Engineering: Theory, Applications and Practice
Interactive selection model for supplier selection process: An analytical hierarchy process approach
International Journal of Production Research
Global supplier selection: A fuzzy-AHP approach
International Journal of Production Research
Measuring the efficiency of decision making units
European Journal of Operational Research
A benchmark-based hybrid evaluation methodology for selecting the best design chain partners
Journal of Quality
Cited by (7)
Member combination selection for product collaborative design under the open innovation model
2023, Advanced Engineering InformaticsCitation Excerpt :Chen et al. [34] constructed a crowdsourcing MCS model considering the QoS attributes of the candidate members. Chiang et al. [35] proposed a design chain partner combination selection method for derivative product development by analysing the synthesized performance of different design chain combinations. He et al. [36] presented the selection of the optimal partner combination of joint distribution alliance considering the portfolio performance of different team combinations in terms of four aspects: economic, social, environmental, and flexibility.
A novel platform for designing and evaluating Dynamic Manufacturing Networks
2013, CIRP Annals - Manufacturing TechnologyCitation Excerpt :Conventional Supply Chain Management (SCM) strategies have long attempted to deal with the problem of setting up long-term, static collaboration schemes for satisfying the market demand on an extended time horizon. The problem of carrying out the initial configuration of the supply chain, selecting, at the same time, the partners who will produce specific parts, has been mainly addressed by employing knowledge-based approaches [3] and multi-objective techniques, such as the analytic hierarchy process [4] and the data envelopment analysis [5]. Their purpose has been in principle to select the most suitable supply chain composition of partners, without taking into account short-term constraints or near real-time information, related to the available capacity and inventory levels of the networked partners.
Modeling and Simulation Study of Two-Phase Collaborative Behaviors Oriented to Open Source Design Process
2018, Mathematical Problems in EngineeringSupply chain configuration: Concepts, solutions, and applications, second edition
2016, Supply Chain Configuration: Concepts, Solutions, and Applications, Second EditionA review of the use of multicriteria and multi-objective models in maintenance and reliability
2015, IMA Journal of Management MathematicsOther risk, reliability and maintenance decision problems
2015, International Series in Operations Research and Management Science
- ☆
This manuscript was processed by Area Editor Imed Kacem.