A Complete MCDM Model for NPD Performance Assessment in an LED-Based Lighting Plant Factory

Wen-Chin Chen, Yen-Fu Lin, Kai-Ping Liu, Hui-Pin Chang, Li-Yi Wang and Pei-Hao Tai

Mathematical Problems in Engineering, 2018, vol. 2018, 1-24

Abstract:

Globally, industries and economies have undergone rapid development and expansion over the last several decades. As a result, global warming and environmental contaminations have resulted in climate change and jeopardized food security. In many developing countries, already decreasing crop yields are threatened by extreme weather and soil damaged by genetically modified food, making environmental problems worse and increasing food and organic product prices. For these reasons, this study proposes a hybrid multicriteria decision-making (MCDM) model for new product development (NPD) in the light-emitting diode- (LED-) based lighting plant factory. First, literature reviews and expert interviews are employed in constructing a list of decision-making objectives and criteria for new product development. Then, a fuzzy Delphi method (FDM) is used to screen the elements of the objectives and criteria, while a fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to determine the relationships among the objectives and criteria. Finally, a fuzzy analytic network process (FANP) and a composite priority vector (CPV) are manipulated to determine the relative importance weights of the critical objectives and criteria. Results show that the proposed method can create a useful and assessable MCDM model for decision-making applications in new product development, and a case study is herein performed to validate the feasibility of the proposed model in a Taiwanese LED-based lighting plant factory, which not only provides the decision-makers with a feasible hierarchical data structure for decision-making guidance but also increases the competitive advantages of trade-offs on developing novel products.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7049208

DOI: 10.1155/2018/7049208

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