IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v53y2015i17p5310-5319.html
   My bibliography  Save this article

Practical information diffusion techniques to accelerate new product pilot runs

Author

Listed:
  • Der-Chiang Li
  • Wen-Chih Chen
  • Che-Jung Chang
  • Chien-Chih Chen
  • I-Hsiang Wen

Abstract

Under the increasing pressure of global competition, product life cycles are becoming shorter and shorter. This means that better methods are needed to analyse the limited information obtained at the trial stage in order to derive useful knowledge that can aid in mass production. Machine learning algorithms, such as data mining techniques, are widely applied to solve this problem. However, a certain amount of training samples is usually required to determine the validity of the information that is obtained. This study uses only a few data points to estimate the range of data attribute domains using a data diffusion method, in order to derive more useful information. Then, based on practical engineering experience, we generate virtual samples with a noise disturbance method to improve the robustness of the predictions derived from a multiple linear regression. One real data set obtained from a large TFT-LCD company is examined in the experiment, and the results show the proposed approach to be effective.

Suggested Citation

  • Der-Chiang Li & Wen-Chih Chen & Che-Jung Chang & Chien-Chih Chen & I-Hsiang Wen, 2015. "Practical information diffusion techniques to accelerate new product pilot runs," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5310-5319, September.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:17:p:5310-5319
    DOI: 10.1080/00207543.2015.1032437
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2015.1032437
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2015.1032437?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
    2. Der-Chiang Li & Wu-Kuo Lin & Liang-Sian Lin & Chien-Chih Chen & Wen-Ting Huang, 2017. "The attribute-trend-similarity method to improve learning performance for small datasets," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1898-1913, April.
    3. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.

    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:taf:tprsxx:v:53:y:2015:i:17:p:5310-5319. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.