PDF(1067 KB)
Research on Product Optimization Design Method to Respond Rapidly to Customer Requirements
Rupeng Li, Wei Wei, Feng Zhou, Cheng Zou
Strategic Study of CAE ›› 2018, Vol. 20 ›› Issue (2) : 33-41.
PDF(1067 KB)
PDF(1067 KB)
Research on Product Optimization Design Method to Respond Rapidly to Customer Requirements
In the current highly competitive market environment, a critical success factor for enterprises is the ability to rapidly respond to customer requirements (CRs). This paper proposes a novel method for rapid response to CRs in product optimization design via fuzzy clustering and conjoint analysis-quality function deployment (CA-QFD).The proposed approach has two key characteristics. The first is classifying original complex CR data as a standard CR dataset by fuzzy clustering method. The second is the new CA-QFD transformation method integrating conjoint analysis and traditional QFD, which can accurately transform CRs into product design attributes. Finally, an example for product optimization design by forging a machine's main hydraulic cylinder is carried out to demonstrate the validity of the proposed method.
rapid response to customer requirements / fuzzy clustering / requirements transformation / CA-QFD method / product optimization design
| [1] |
Luo X G, Kwong C K, Tang J F, et al. QFD-based product planning with consumer choice analysis [J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2015, 45(3): 454–461.
|
| [2] |
Sheng Z, Wang Y, Song J, et al. Customer requirement modeling and mapping of numerical control machine [J]. Advances in Mechanical Engineering, 2015, 7(10): 1–11.
|
| [3] |
Violante M G, Vezzetti E, Alemanni M. An integrated approach to support the Requirement Management (RM) tool customization for a collaborative scenario [J]. International Journal on Interactive Design & Manufacturing, 2017, 11(2): 191–204.
|
| [4] |
He L, Ming X, Li M, et al. Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model [J]. Journal of Engineering Manufacture, 2015, 231(4): 699–712.
|
| [5] |
Wang Y, Tseng M M. A Naïve Bayes approach to map customer requirements to product variants [J]. Journal of Intelligent Manufacturing, 2015, 26(3): 1–9.
|
| [6] |
Zaim S, Sevkli M, Camgöz-Akdağ H, et al. Use of ANP weighted crisp and fuzzy QFD for product development [J]. Expert Systems with Applications, 2014, 41(9): 4464–4474.
|
| [7] |
Li Y L, Tang J F, Chin K S, et al. Rough set-based approach for modeling relationship measures in product planning [J]. Information Sciences an International Journal, 2012, 193(11): 199–217.
|
| [8] |
Chowdhury M M H, Quaddus M A. A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh [J]. Omega, 2015 (57): 5–21.
|
| [9] |
Wang Y, Tseng M M. Integrating comprehensive customer requirements into product design [J]. CIRP Annals- Manufacturing Technology, 2011, 60(1): 175–178.
|
| [10] |
Wang Y, Tseng M M. Incorporating tolerances of customers’ requirements for customized products [J]. CIRP Annals- Manufacturing Technology, 2014, 63(1): 129–132.
|
| [11] |
Dong Y Y. Fuzzy cluster analysis [J]. Journal of Jinggangshan University, 2006, 62(10): 750–754.
|
| [12] |
Wei W, Feng Y, Tan J, et al. Product platform two-stage quality optimization design based on multiobjective genetic algorithm [J]. Computers & Mathematics with Applications, 2009, 57(11): 1929–1937.
|
| [13] |
Chen Y L, Chu W L, Xu Y S. Adaptability-oriented parametric product platform design [J]. Computer Integrated Manufacturing Systems, 2007, 13(5): 877–884.
|
| [14] |
赵升吨, 王军, 白振岳, 等. 法兰支承液压缸结构的遗传算法优化设计 [J]. 锻压技术, 2008, 33(2): 95–99.
|
/
| 〈 |
|
〉 |
AI Summary
