바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Using an Evaluative Criteria Software of Optimal Solutions for Enterprise Products' Sale

Using an Evaluative Criteria Software of Optimal Solutions for Enterprise Products' Sale

The Journal of Distribution Science(JDS) / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2015, v.13 no.4, pp.9-19
https://doi.org/https://doi.org/10.15722/jds.13.4.201504.9
Liao, Shih Chung (Department of Visual Communication Design, Technology of Taoyuan Innovation Institute)
Lin, Bing Yi (Chung Yuan Christian University)
  • 다운로드 수
  • 조회수

Abstract

Purpose - This study focuses on the use of evaluative criteria software for imprecise market information, and product mapping relationships between design parameters and customer requirements. Research design, data, and methodology - This study involved using the product predicted value method, synthesizing design alternatives through a morphological analysis and plan, realizing the synthesis in multi-criteria decision-making (MCDM), and using its searching software capacity to obtain optimal solutions. Results - The establishment of product designs conforms to the customer demand, and promotes the optimization of several designs. In this study, the construction level analytic method and the simple multi attribute comment, or the quantity analytic method are used. Conclusions - This study provides a solution for enterprise products' multi-goals decision-making, because the product design lacks determinism, complexity, risk, conflict, and so on. In addition, the changeable factor renders the entire decision-making process more difficult. It uses Fuzzy deduction and the correlation technology for appraising the feasible method and multi-goals decision-making, to solve situations of the products' multi-goals and limited resources, and assigns resources for the best product design.

keywords
Multi-Criterion Decision-Making (MCDM), Evaluative Criteria Software, Product Design, Synthesizing Design Alternative, Products Sale

참고문헌

1.

Jason, P. (2009). Introduction Creative industries & Innovation policy, Innovation Managemen Policy and Practice. 2(11), 138–147.

2.

John, H. (2007). Csiro: Partnering for the future, Innovation Managemen Policy and Practice. 2(9), 146–158.

3.

Judith, L. (2008). SECTION 1: Innovation and the food industry volume. Innovation Managemen Policy and Practice, 1(10). 2-3.

4.

Narelle, K. (2007). CSIRO and Australian innovation: a business commentary, Innovation Managemen Policy and Practice, 2(9), 203-214.

5.

Paul, K., Ian, M., and David, R. (2008). Lost in translation? Building science and innovation city strategies in Australia and the UK. Innovation Managemen Policy and Practice, 2(10), 211–223.

6.

Rogers, E. (1995). Diffusions of innovations. New York : The Free Press.

7.

Stefan, H., and Mann, H. (2010). The role of promoters in effecting innovation in higher education institutions. Innovation Managemen Policy and Practice, 2(12), 180-191.

8.

Tzeng, G., Chiang, C., and Li, C. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with Applications, 32(4), 1028-1044.

9.

Yun, Y., Nakayama, H., and Arakawa, M. (2004). Multiple criteria making with generalized DEA and an aspiration level method. European Journal of Operational Research, 158(3), 697-706.

The Journal of Distribution Science(JDS)