IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v56y2018i13p4629-4643.html
   My bibliography  Save this article

An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

Author

Listed:
  • Ying-Jen Chen
  • Chen-Fu Chien

Abstract

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.

Suggested Citation

  • Ying-Jen Chen & Chen-Fu Chien, 2018. "An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4629-4643, July.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:13:p:4629-4643
    DOI: 10.1080/00207543.2017.1421783
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2017.1421783
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2017.1421783?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. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    2. Hsieh, Chung-Chi & Lathifah, Artya, 2022. "Ordering and waste reuse decisions in a make-to-order system under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1290-1303.
    3. Shui Ming Li & Felix T. S. Chan & Yung Po Tsang & Hoi Yan Lam, 2021. "New Product Idea Selection in the Fuzzy Front End of Innovation: A Fuzzy Best-Worst Method and Group Decision-Making Process," Mathematics, MDPI, vol. 9(4), pages 1-18, February.
    4. Wang, Mengyue & Huang, Hongxuan, 2019. "The design of a flexible capital-constrained global supply chain by integrating operational and financial strategies," Omega, Elsevier, vol. 88(C), pages 40-62.
    5. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.
    6. Marketa Kubickova, 2022. "Revenue management in manufacturing: systematic review of literature," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 147-152, April.

    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:56:y:2018:i:13:p:4629-4643. 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.