Aided analysis for quality function deployment with an Apriori-based data mining approach

Abstract

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.

Keywords:

• quality function deployment
• data mining
• Apriori approach
• association rule

Acknowledgements

The project was supported by Shanghai Technology Innovative Activity Planning Program (09dz1124600, 10dz1121600), National High-Tech. RandD Program for CIMS, China (No. 2007AA04Z140), Research Fund for Doctoral Program of Higher Education, China (No. 20070248020) and Shanghai Leading Academic Discipline, China (No. Y0102). The authors express sincere appreciation to the anonymous referees for their helpful comments to improve the quality of the paper. Thanks also go to Dr. Li Yu for invaluable advice and encouragement.