IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v4y2017i1p56-86.html
   My bibliography  Save this article

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

Author

Listed:
  • Shivani K. Purohit

    (Manoharbhai Patel Institute of Engineering and Technology (MIET), Gondia, India)

  • Ashish K. Sharma

    (Manoharbhai Patel Institute of Engineering and Technology (MIET), Gondia, India)

Abstract

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

Suggested Citation

  • Shivani K. Purohit & Ashish K. Sharma, 2017. "Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment," International Journal of Business Analytics (IJBAN), IGI Global, vol. 4(1), pages 56-86, January.
    • Handle: RePEc:igg:jban00:v:4:y:2017:i:1:p:56-86
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2017010104
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Merve Dogruel & Seniye Umit, 2021. "Prediction of Innovation Values of Countries Using Data Mining Decision Trees and a Comparative Application with Linear Regression Model," Istanbul Business Research, Istanbul University Business School, vol. 50(2), pages 465-493, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:4:y:2017:i:1:p:56-86. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.