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Creating the Optimal Product Business Management System for Social and Enterprise Development

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2013, v.11 no.6, pp.21-30
https://doi.org/https://doi.org/10.13106/jds.2013.vol11.no6.21
Liao, Shih-chung

Abstract

Purpose - This paper examines product design management, the current design focus of which has shifted to the need to produce innovation applications that can effectively respond to the market's consumption changes in a timely manner. Research design, data, methodology - This study discusses several methodologies that are widely used in experimental processes, such as fuzzy theory, multi-criteria decision-making theory, and managing decision making. The designers will better understand their customers by applying these methodologies. This study examines how the current trend in product innovation design observes customer needs, controls innovation, and stimulates design ability. Results - This paper takes innovative telephone design as an experimental case to investigate how to create a product using market-oriented and customized management concepts and creative design abilities. Conclusions - If accompanied by an innovative product value chain, a product can further the development of enterprise management, now the main element of every developed country's social and economic development.

keywords
Fuzzy Theory, Innovation Design, A Decision Making Technology System, Multi Criteria Decision Making (MCDM), Product Design Managing

Reference

1.

Carlos, R. (2004), "A general structure of achievement functions for a goal programming model", European Journal of Operation Research, 3(153), 675-686.

2.

Collins, L. (2010), "Recession Pushing Innovation in New Directions", Research Technology Management, 2 (52), 35-39.

3.

Collins, L. (2010), "Nokia to Give Away Ideas and Innovations", Research Technology Management, 5(52), 19-32.

4.

Hirotaka, N., Yun, Y., Asada, T. & Yoon, M., (2005), "MOP/GP models for machine learning", European Journal of Operation Research, 3(166), 756-768.

5.

Hu, J., Tan, B., Shabanov, N., Crean, K.A.,Martonchik, J.V., Diner, D.J., Knyazikhin, Y. & Myneni, R.B. (2003), "Performance of the MISR LAI and FPAR algorithm: a case study in Africa", Remote Sensing of Environment, 88, 324–340.

6.

Igartua, J. I., Garrigos, J. A. & Jose, H.O. (2010), "How Innovation Management Techniques Support an Open Innovation Strategy", Research Technology Management, 3(53),41-52.

7.

Tseng, F.M. & Tzeng, G.H. (2002), "A Fuzzy seasonal ARIMA model for forecasting", Fuzzy Sets and Systems, 3(126), 367–376.

8.

Wang, C.H., Chin, Y.C. & Tzeng, G.H. (2010), "Mining the R&D innovation performance processes for high-tech firms based on rough set theory", Technovation, 7(30), 447-458.

The Journal of Distribution Science