Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Prioritising engineering characteristics based on customer online reviews for quality function deployment

Jin, Jian, Ji, Ping and Liu, Ying 2014. Prioritising engineering characteristics based on customer online reviews for quality function deployment. Journal of Engineering Design 25 (7-9) , pp. 303-324. 10.1080/09544828.2014.984665

[img]
Preview
PDF - Accepted Post-Print Version
Download (2MB) | Preview

Abstract

In market-driven product design, customer requirements (CRs) are usually obtained from consumer surveys. However, valuable CRs can also be found in a large number of online reviews. Largely due to their free text nature and the quantity, these reviews are often neglected and are seldom utilised directly by designers. In this work, one important question in quality function deployment on how to prioritise engineering characteristics (ECs) is investigated. Customer opinions concerning ECs are extracted from online reviews. By taking advantage of such opinion information, an ordinal classification approach is proposed to prioritise ECs. In a pairwise manner, in which customer opinions are deemed as features and the overall customer satisfactions are regarded as the target values, the weights of ECs are derived. Furthermore, an integer linear programming model is implemented to convert the pairwise results into the original customer satisfaction ratings. Finally, an exploratory case study is presented using reviews of four branded printers collected from Amazon and their analysis was conducted by two experienced design engineers. The experimental study reveals the merits of the proposed approach.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Additional Information: Published online 12.12.14.
Publisher: Taylor & Francis
ISSN: 0954-4828
Date of First Compliant Deposit: 30 March 2016
Last Modified: 28 Jun 2019 14:13
URI: http://orca.cf.ac.uk/id/eprint/68390

Citation Data

Cited 17 times in Google Scholar. View in Google Scholar

Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item