The dynamic nature of today’s technology market requires new value-characteristic modeling methods; mainstream methods have limitations due to unrealistic assumptions, such as static customer preferences and no multicollinearity among product attributes. In particular, products with longer cycle times can suffer because the static model ignores changes in the market during the concept-to-customer lead time. This study proposes a dynamic, partial least squares path model for customer driven product design and development in order to reduce model uncertainty by formulating preference models to reflect market dynamics. The proposed dynamic model adopted partial least squares regression to handle the limited observations plagued by multicollinearity among product attributes. The main advantage of the proposed model is its ability to evaluate design alternatives during the front-end concept screening phase, using the overall product-value metric, customer-revealed value. A case study analyzing the US car market data for sedans from 1990 to 2010 showed the potential for the proposed method to be effective, with a 3.40 mean absolute percentage error.
Issue Section:
Special section: Strategies for Design Under Uncertainty
Keywords:
design evaluation,
design for market systems,
hierarchical modeling,
dynamic partial least squares path modeling,
partial least squares regression,
multivariate analysis,
customer revealed value
Topics:
Design,
Modeling,
Preferences,
Product design,
Time series,
Errors,
Dynamic models,
Data collection
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