A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development

Abstract

Fuzzy regression methods have commonly been used to develop consumer preferences models which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models.In this paper, a novel fuzzy modelling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial which includes only significant regressors. Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least square regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly-used for new product development. Also, smaller-scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.

Data-driven product design toward intelligent manufacturing: A review

“…34 proposed a novel rough set-based QFD approach to manage the uncertain customer requirement data in product development. To evaluate the fuzzy requirement data in customers' perceptual preferences, Chan et al 35 proposed a novel fuzzy modeling method named fuzzy stepwise regression which is composed of an appropriate polynomial that only includes significant regressors. Based on the understanding and management of the complex relationship between customer requirements and technical requirements, the proposed method is used to manage inaccurate design information in product development.…”

Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

“…The output for hidden and output layers are expressed in Eqs. (14) and (15 When the error value or the number of training epoch reaches the network setting value, the training is stopped and the prediction result is obtained. Otherwise, network training continues from Eq.…”

“…Currently, researchers have utilized forward selection method, optimization subclass selection method, backward selection method and stepwise selection (SR) method to reduce the number of variables. Among them, the SR method is probably one of the most commonly used research practices in substantive and effectiveness studies [14]. It has the advantage of saving time for the individual.…”

“…The authors in [16] utilized the SR method to estimate the target parameters; they observed that inaccurate predictions are a result of missing and incomplete variables information. Meanwhile, the problem with this approach is that simply performing linear regression on the retained variables is not fully applicable to changes in the general aviation aircraft market, and the authors in [14] concluded that SR method itself has problems such as incorrect results and the inherent bias in the process itself.…”

A review on stability analysis of continuous-time fuzzy-model-based control systems: From membership-function-independent to membership-function-dependent analysis

“…With the support of fuzzy set theory and mathematics, a fuzzy logic system can perform reasoning according to the designated linguistic rules. Fuzzy logic systems were used successfully in a wide range of areas and applications [2,3,4] such as assessment [5], classification [6,7,8], control [9,10,11], decision making [12,13,14,15,16,17], evaluation [18,19], forecasting [20,21,22], learning [23,24,25], modeling [26,27] and etc.…”