SGER GOALI: Improving conjoint-based consumer preference models for use in engineering design optimization
SGER GOALI: Improving conjoint-based consumer preference models for use in engineering design optimization
批准号:
0541610
负责人:
Fred M. Feinberg
金额:
$0.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-08-01 至 2007-01-31
中文摘要
SGER GOALI项目旨在通过开发消费者对新产品设计需求的准确分析模型来改进设计优化的实践。探索性活动将探讨使用基于现有市场研究的联合分析方法来开发可集成到设计优化中的消费者偏好模型的可行性。风险在于这些模型是否可以扩展到工程领域,除了最简单的建模。复杂的产品设计需要创建可验证的估计算法。只有在增加建模属性数量的同时减少估计时间的情况下,这才可行。是否能够开发出一种健壮的方法,从而满足SGER所需的高风险成分,这是非常值得怀疑的。更广泛的影响来自于实现汽车和计算机等消费品设计优化改进的高潜力。如果这种方法被证明是可行的,它就开辟了新的研究途径,在这些途径中,相对于客户需求的产品改进可以得到满足,同时预测产品实现的验证方法也可以得到推进。这个SGER是一个目标ali项目,通用汽车作为一个直接的合作伙伴,通过智力参与和资助。通用汽车将为这一探索性研究带来消费者偏好建模的贝叶斯估计方法方面的专业知识以及验证目的的数据
英文摘要
This SGER GOALI project aims to improve the practice of design optimization by developing accurate analytical models of consumer demand for new product designs. The exploratory activity will address the feasibility of using an existing market research based method of conjoint analysis to develop consumer preference models that can be integrated into design optimization. The risk is whether such models can be extended into the engineering domain for anything but the most simple modeling. Complex product design requires the creation of estimation algorithms that can be validated. This will only be feasible if the estimation time can be reduced while the number of attributes being modeled is increased. It is highly questionable if a robust method can be developed, thus satisfying the high-risk component necessary for an SGER.The broader impacts come from the high potential of attaining improvements in design optimization of consumer products such as automobiles and computers. If this methodology proves feasible, it opens up new research avenues where improvements to the product relative to customer needs can be met, while the validation methodology for predictive product realization can also be advanced. This SGER is a GOALI project with GM as a direct partner, through both intellectual participation and funding. GM will bring expertise in Bayesian estimation methods of consumer preference modeling as well as data for validation purposes for this exploratory research
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