Customer preference modelling using PLS-regression in mobile phone industry

Decision-making has always been a vital part in product development. Especially with the rapid technological advancement today, the competitive market demands designers to make tough choices while the product development cycle is becoming shorter and shorter. In order to capture the market,...

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Main Author: Chew, Kar Yeong.
Other Authors: School of Mechanical and Aerospace Engineering
Format: Final Year Project
Language:English
Published: 2010
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Online Access:http://hdl.handle.net/10356/40293
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-402932023-03-04T18:26:53Z Customer preference modelling using PLS-regression in mobile phone industry Chew, Kar Yeong. School of Mechanical and Aerospace Engineering Park Taezoon DRNTU::Engineering::Systems engineering Decision-making has always been a vital part in product development. Especially with the rapid technological advancement today, the competitive market demands designers to make tough choices while the product development cycle is becoming shorter and shorter. In order to capture the market, they have to be able to capture the customers’ wants and needs. Hence, this leads to the development of decision-support systems to aid designers. There are decision-support systems built based decision analysis and multiple-attribute decision making methods. Partial Least Squares regression (PLSR), a recently developed modeling technique, and Customer-revealed Value (CRV) are employed in this study in an effort to develop a more comprehensive decision making method. PLSR is able to construct predictive models using multiple collinear factors while CRV is able to capture customer preference by relating the demand of the product. In this research, the ability of PLSR to be a decision support system is investigated by testing this technique in the mobile phone industry. Predictive PLS models are constructed based on mobile phone attributes as input (X-matrix) and expert and consumer ratings for CRV as responses (Y-matrix). Using VIP, 10 influential attributes are first selected. Then, the PLS models are trimmed down to these 10 attributes before going through validation. The plots of each model are used to validate the PLS models. Then, the prediction ability of the models is examined to further check the validity and reliability of the models. The author proved the effectiveness of using PLSR to model customer preference for the mobile phone industry as the predictive models are valid and able to predict the responses of the validation sets accurately. Bachelor of Engineering (Mechanical Engineering) 2010-06-14T06:40:45Z 2010-06-14T06:40:45Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40293 en Nanyang Technological University 100 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Systems engineering
spellingShingle DRNTU::Engineering::Systems engineering
Chew, Kar Yeong.
Customer preference modelling using PLS-regression in mobile phone industry
description Decision-making has always been a vital part in product development. Especially with the rapid technological advancement today, the competitive market demands designers to make tough choices while the product development cycle is becoming shorter and shorter. In order to capture the market, they have to be able to capture the customers’ wants and needs. Hence, this leads to the development of decision-support systems to aid designers. There are decision-support systems built based decision analysis and multiple-attribute decision making methods. Partial Least Squares regression (PLSR), a recently developed modeling technique, and Customer-revealed Value (CRV) are employed in this study in an effort to develop a more comprehensive decision making method. PLSR is able to construct predictive models using multiple collinear factors while CRV is able to capture customer preference by relating the demand of the product. In this research, the ability of PLSR to be a decision support system is investigated by testing this technique in the mobile phone industry. Predictive PLS models are constructed based on mobile phone attributes as input (X-matrix) and expert and consumer ratings for CRV as responses (Y-matrix). Using VIP, 10 influential attributes are first selected. Then, the PLS models are trimmed down to these 10 attributes before going through validation. The plots of each model are used to validate the PLS models. Then, the prediction ability of the models is examined to further check the validity and reliability of the models. The author proved the effectiveness of using PLSR to model customer preference for the mobile phone industry as the predictive models are valid and able to predict the responses of the validation sets accurately.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Chew, Kar Yeong.
format Final Year Project
author Chew, Kar Yeong.
author_sort Chew, Kar Yeong.
title Customer preference modelling using PLS-regression in mobile phone industry
title_short Customer preference modelling using PLS-regression in mobile phone industry
title_full Customer preference modelling using PLS-regression in mobile phone industry
title_fullStr Customer preference modelling using PLS-regression in mobile phone industry
title_full_unstemmed Customer preference modelling using PLS-regression in mobile phone industry
title_sort customer preference modelling using pls-regression in mobile phone industry
publishDate 2010
url http://hdl.handle.net/10356/40293
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