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Special section: Strategies for Design Under Uncertainty

Dynamic Partial Least Square Path Modeling for the Front-end Product Design and Development

[+] Author and Article Information
Chathura Withanage

School of Mechanical & Aerospace Engineering,  Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798with0003@e.ntu.edu.sg

Taezoon Park1

School of Mechanical & Aerospace Engineering,  Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798tzpark@ntu.edu.sg

Truong Ton Hien Duc

 Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075thdtruong@simtech.a-star.edu.sg

Hae-Jin Choi

Department of Mechanical Engineering,  Chung-Ang University, Seoul 156-756, South Koreahjchoi@cau.ac.kr

1

Corresponding author.

J. Mech. Des 134(10), 100907 (Sep 28, 2012) (14 pages) doi:10.1115/1.4007448 History: Received January 22, 2012; Revised August 01, 2012; Published September 21, 2012; Online September 28, 2012

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.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 4

Overall procedure of the proposed, dynamic PLSPM method

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Figure 5

The PLSPM structure used for the initial static model formulation

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Figure 6

CRV time series of car models with CRV forecasts for the years 2009 and 2010

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Figure 7

Coefficient time series and forecasts from year 2008. Forecasts are given by hollow markers, connected by dotted lines.

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Figure 1

Dynamic value-attribute modeling for the front-end decision support phase

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Figure 2

The PLSPM estimation algorithm. PLSR is used for the model parameter estimation.

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Figure 3

The overall methodology from the initial data collection to the future model formulation

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