IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v54y2016i17p5169-5181.html
   My bibliography  Save this article

Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers

Author

Listed:
  • Chih-Hsuan Wang
  • Yu-Wei Chien

Abstract

Light emitting diode (LED) is a popular component to replace the traditional lighting source or advertising sign display. In 2014, high-brightness LED has a strong growth in backlight display, mobile appliances, automotive devices and outdoor illumination. However, emerging technologies in compound materials, epitaxying, packaging and new entrants result in a scale-based economy and intensively competitive environment. Inspired by the concept of business analytics, this paper proposes a novel framework to conduct corporate diagnosis for Taiwanese LED manufacturers: (1) balanced scorecard is fused with data envelopment analysis to address the impact of operational efficiency on performance outcomes, (2) financial and non-financial indicators are incorporated into the process of performance measurement, (3) the intricate causalities between key performance indicators (KPIs) and multiple outcomes (i.e. earnings per share and return on equity) are captured and (4) managerial insights are provided to indicate adaptive adjustment on significant KPIs. More importantly, a data-set comprising representative Taiwanese LED companies spanned from 2010 to 2014 is used to justify the validity of the proposed framework.

Suggested Citation

  • Chih-Hsuan Wang & Yu-Wei Chien, 2016. "Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5169-5181, September.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:17:p:5169-5181
    DOI: 10.1080/00207543.2016.1156780
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2016.1156780
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2016.1156780?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. Ehsan Pourjavad & Rene V. Mayorga, 2019. "A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1085-1097, March.
    2. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.

    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:54:y:2016:i:17:p:5169-5181. 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.