Using innovative technology in QFD to improve marketing quality

Software design is a high value-added business, so the sequence decision of design requirements is a critical issue for the satisfaction of customer needs for improving marketing quality. On the other hand, data mining has been successfully applied in many fields. However, little research has been done in the quality function development of identifying the future sequence decision of design requirements, using data mining and grey theory. This study applied a time series-based data mining cycle and grey relational analysis, using sales questionnarie database, to identify the future sequence decision of design requirements for software designers. Certain advantages may be observed when the future sequence decision of design requirements was identified, using the data mining cycle and grey relational analysis. The future design requirement of each customer was found and satisfied in advance. The results of this study can provide an effective procedure of identifying the future design requirements to satisfy customer needs and enhance the marketing quality and competitiveness of software company in the marketplace.

[1]  Gary S. Wasserman,et al.  ON HOW TO PRIORITIZE DESIGN REQUIREMENTS DURING THE QFD PLANNING PROCESS , 1993 .

[2]  Xiaowen Wang,et al.  Development of Empirical Models for Surface Roughness Prediction in Finish Turning , 2002 .

[3]  Steven C. Wheelwright,et al.  Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency and Quality , 1992 .

[4]  Chen-Fu Chien,et al.  CONSTRUCTING SEMICONDUCTOR MANUFACTURING PERFORMANCE INDEXES AND APPLYING DATA MINING FOR MANUFACTURING DATA ANALYSIS , 2004 .

[5]  Susan Carlson Skalak.  House of Quality , 2002 .

[6]  Dennis H. O'Brien.  Software quality starts with the customer , 1991 .

[7]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[8]  A. Ansari,et al.  Quality Function Deployment: The Role of Suppliers , 1994 .

[9]  Hokey Min,et al.  Developing the profiles of truck drivers for their successful recruitment and retention , 2003 .

[10]  Andrew Harvey,et al.  Time Series Models. , 1983 .

[11]  Xiaoqing Frank Liu.  A quantitative approach for assessing the priorities of software quality requirements , 1996, J. Syst. Softw..

[12]  Chris Chatfield,et al.  The Analysis of Time Series: An Introduction , 1981 .

[13]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[14]  C. Fung.  Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis , 2003 .

[15]  Steven Walczak,et al.  Knowledge discovery techniques for predicting country investment risk , 2002 .

[16]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[17]  Dong Ha Lee,et al.  Data mining approach to policy analysis in a health insurance domain , 2001, Int. J. Medical Informatics.

[18]  L P Sullivan,et al.  QUALITY FUNCTION DEPLOYMENT , 1996 .

[19]  D. Y. Sha,et al.  Using Data Mining for Due Date Assignment in a Dynamic Job Shop Environment , 2005 .

[20]  Paolo Giudici,et al.  Applied Data Mining: Statistical Methods for Business and Industry , 2003 .

[21]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[22]  Qiang Yang,et al.  Mining Customer Value: From Association Rules to Direct Marketing , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[23]  Mourad Elloumi,et al.  A data mining approach based on machine learning techniques to classify biological sequences , 2002, Knowl. Based Syst..

[24]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[25]  A. Griffin.  Evaluating QFD's Use in US Firms as a Process for Developing Products , 1992 .