National Journal of System and Information Technology

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QFD and Data Mining: Analysis and Incorporation


     

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In today's fast-paced business environment, with floods of data available, decisionmaking has become a complex task. These data contains nuggets of valuable information in hidden form, which are often not effectively utilized due to lack of suitable analytic tools and techniques. Data Mining is a buzzword for the present era. Data Mining is the non-trivial process of identifying the valid, novel, potentially useful and ultimately understandable patterns in data. However, with the advent of some technology like Data Mining, the data can now be suitably analyzed and mined to yield valuable outcomes. Quality Function Deployment (QFD) is an extensive customer oriented product development process that strives for improving quality and gaining higher customer satisfaction. QFD contains voluminous data, which can be suitably mined to deduce important and pertinent information. As is the case with QFD - since the data happens to be voluminous, suitable mining of data may lead to product quality improvement and hence higher customer satisfaction. The paper thus aims to analyze the Data Mining in context of QFD process. In the light of above the paper talks about the QFD and Data Mining and then discusses the ways and means of incorporating Data Mining in the QFD.

Keywords

QFD, Data Mining, Data, Product Quality, Voice of Customer
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  • A. K. Sharma, I. C. Mehta, J. R. Sharma, ‘Analyzing Programming Tools For The Development Of Quality Function Deployment Software’, International Journal of Information and Decision Sciences, Inderscience Publishers, Vol. 2, No. 2, pp. 132- 146, 2010.

  • J. R. Sharma and A.M. Rawani, “Understanding Quality Function Deployment – A TQM Tool to Quantify Customers Needs”, National Conference on World Class Manufacturing, Coimbatore, 2003.

  • A. Yoji, “Quality Function Deployment: Integrating Customers Requirements into Product Design”, Productivity Press, Cambridge, MA, 1990.

  • A. Griffin, “Evaluating QFD´s use in US Firms as a Process for Developing Products” Journal of Product Innovation Management, Michigan, USA, 1992.

  • A. K. Sharma, I. C. Mehta, J. R. Sharma, ‘Development of Fuzzy Integrated Quality Function Deployment Software – A conceptual analysis’, I-manager’s Journal on Software Engineering, Vol. 3, No. 3, pp. 16-24, 2009.

  • A. K. Pujari, “Data Mining Techniques”, University press, 2001.

  • X. Liu, Y. Sun, G. Kane, “QFD Application in Software Process Management and Improvement Based on CMM”, International Conference on Software Engineering, 1 – 6.

  • T. Kivinen, “Applying QFD to improve the requirements and project management in small-scale project”, University of Tampere, Department of Computing Sciences, Computer sciences, May 2008.

  • T. N. Comstock, K. Dooley, “A Tale of Two QFDs”, University of Minnesota.

  • M. Lin, C.Tsai, C. Cheng, and A. C. Chang, “Using fuzzy QFD for Design of Low-end Digital Camera” International Journal of Applied Science and Engineering, 2, 3, 222- 233, 2004.

  • M. Berry M, G. Linoff, “Data Mining Techniques: for Marketing, Sales, and Customer Support”, Wiley, New York, 1997.

  • U. M. Fayyad, “Data Mining and Knowledge Discovery”, Editorial, 1: 5-10, 1997.

  • http://dms.irb.hr/tutorial/tut_prob_understand.php

  • C. H. Hsu, S. Y. Wang, and L. T. Lin, “Using Data Mining to Identify Customer Needs in Quality Function Deployment for Software Design”, Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases. Corfu Island, Greece, 170, 2007.

  • A. K. Sharma, I. C. Mehta, J. R. Sharma and S.A.Sharma, “A Futuristic Software Framework to Generate Actual Customer Needs for Quality Function Deployment”, National Journal of System and Information Technology, Vol. 2, No. 2, pp. 127-138, 2009.

  • S. Sharma, D. P. Goyal, and R. K. Mittal, “Data Mining research for customer relationship management systems: a framework and analysis”, International Journal – Inderscience.

  • M. A. Chowdhury, H. Sarwar, and S. Rafique, “A decision support system based on data mining technology, fuzzy logic and statistical time series model”.

  • A. Kusiak, “Data Mining: manufacturing and service applications, Intelligent Systems Laboratory, Mechanical and Industrial Engineering”, International Journal of Production Research, Iowa 52242–1527, USA. Vol. 44, Nos. 18–19, 4175 – 4191.

  • H.C. Harvey, Time series models. Halstead Press, New York, NY, USA, 1981.

  • K. Fu, D. Geng, ‘Determining the Target Customers Based on Data Mining’, The Sixth Wuhan International Conference on E-Business.

  • R. Menon, L. H. Tong, S. Sathiyakeerthi, A. Brombacher and C. Leong, The Needs and Benefits of Applying Textual Data Mining within the Product Development Process, Quality and reliability engineering international. Int. 2004; 20:1–15


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  • QFD and Data Mining: Analysis and Incorporation

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Authors

Abstract


In today's fast-paced business environment, with floods of data available, decisionmaking has become a complex task. These data contains nuggets of valuable information in hidden form, which are often not effectively utilized due to lack of suitable analytic tools and techniques. Data Mining is a buzzword for the present era. Data Mining is the non-trivial process of identifying the valid, novel, potentially useful and ultimately understandable patterns in data. However, with the advent of some technology like Data Mining, the data can now be suitably analyzed and mined to yield valuable outcomes. Quality Function Deployment (QFD) is an extensive customer oriented product development process that strives for improving quality and gaining higher customer satisfaction. QFD contains voluminous data, which can be suitably mined to deduce important and pertinent information. As is the case with QFD - since the data happens to be voluminous, suitable mining of data may lead to product quality improvement and hence higher customer satisfaction. The paper thus aims to analyze the Data Mining in context of QFD process. In the light of above the paper talks about the QFD and Data Mining and then discusses the ways and means of incorporating Data Mining in the QFD.

Keywords


QFD, Data Mining, Data, Product Quality, Voice of Customer

References