IDEAS home Printed from https://ideas.repec.org/a/ids/ijpqma/v30y2020i2p252-277.html
   My bibliography  Save this article

Statistical investigation of Lean Six Sigma for waste reduction in Indian SMES by identify rank define analyse improve control model

Author

Listed:
  • P.N. Ramkumar
  • K.P. Satish

Abstract

Lean manufacturing is capable of reducing the existing rates of production to a reasonable amount and also to raise the productivity by improving the quality of the material, and hence to maximise the number of customers. In this research paper, the LSS is practiced in the small and medium scale industries for improving the productivity of the firm. For that, a new statistical method named identify rank define analyse improve control (IRDAIC) model is actualised in the Indian SMEs for handling the survey. For optimising the industrial parameters multiple nonlinear regression analysis and enhanced grey wolf optimisation is used in this paper. The proposed hybrid statistical assessment and optimisation process is executed and evaluated in the working platform of MATLAB in terms of production cost. Ultimately, from the evaluation the proposed novel statistical optimisation technique is optimal for improving the productivity of the Lean Six Sigma manufacturing firm respectively.

Suggested Citation

  • P.N. Ramkumar & K.P. Satish, 2020. "Statistical investigation of Lean Six Sigma for waste reduction in Indian SMES by identify rank define analyse improve control model," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 30(2), pages 252-277.
  • Handle: RePEc:ids:ijpqma:v:30:y:2020:i:2:p:252-277
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=107815
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijpqma:v:30:y:2020:i:2:p:252-277. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=177 .

    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.