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Aided analysis for quality function deployment with an Apriori-based data mining approach
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DOI：10.1080/0951192X.2010.492840.png)
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Quality function deployment (QFD) is a proven useful methodology in new product development to satisfy customer requirements (CRs). House of quality (HoQ), the general implementing mode of QFD, is aimed to identify the variables of engineering characteristics (ECs) based on the relationships between CRs and ECs. Traditionally, the establishment of these relationships is mainly dependent on the designers' experience and then the HoQ included many items difficult to handle. For aiding the designers on the HoQ analysis, the paper proposes an Apriori-based data mining approach to extract knowledge from historical data. The approach is mainly focused on mining potential useful association rules (including positive and negative rules) that reflect the relationships according to three objectives: support, confidence, and interestingness. For ensuring the availability and conciseness of these extracted rules, the definitions and calculations of rule conflict and redundancy are proposed and processing procedures are also developed to unite or delete unnecessary rules. The reserved rules are clustered in order to facilitate rule management and reuse. Furthermore, a reuse procedure is also developed for new HoQ analysis. Computational experiments of an electrically powered bicycle are used to illustrate the proposed approach and its capability of extracting useful knowledge.
Keyword:
quality function deployment
data mining
Apriori approach
association rule
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