IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v56y2018i13p4565-4584.html
   My bibliography  Save this article

A survey of semiconductor supply chain models part III: master planning, production planning, and demand fulfilment

Author

Listed:
  • Lars Mönch
  • Reha Uzsoy
  • John W. Fowler

Abstract

Part I of this three-part series described semiconductor supply chains from the decision-making and functional perspectives, using this as a framework to review the industrial engineering (IE) and operations research (OR) literature on the problems arising in these supply chains. Part I then reviewed the literature on Strategic Network Design, Supply Chain Coordination, Sustainability and Semiconductor Supply Chain Simulation, while Part II reviewed Demand Planning, Inventory Management, and Capacity Planning. This paper concludes the series, discussing Master Planning, Production Planning, Demand Fulfilment, and Available to Promise (ATP) in semiconductor supply chains.

Suggested Citation

  • Lars Mönch & Reha Uzsoy & John W. Fowler, 2018. "A survey of semiconductor supply chain models part III: master planning, production planning, and demand fulfilment," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4565-4584, July.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:13:p:4565-4584
    DOI: 10.1080/00207543.2017.1401234
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2017.1401234
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2017.1401234?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. Junliang Wang & Pengjie Gao & Zhe Li & Wei Bai, 2021. "Hierarchical Transfer Learning for Cycle Time Forecasting for Semiconductor Wafer Lot under Different Work in Process Levels," Mathematics, MDPI, vol. 9(17), pages 1-11, August.
    2. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    3. Vishal Kashav & Chandra Prakash Garg & Rupesh Kumar, 2023. "Ranking the strategies to overcome the barriers of the maritime supply chain (MSC) of containerized freight under fuzzy environment," Annals of Operations Research, Springer, vol. 324(1), pages 1223-1268, May.
    4. Wolfgang Albrecht & Martin Steinrücke, 2020. "Continuous-time scheduling of production, distribution and sales in photovoltaic supply chains with declining prices," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 629-667, September.
    5. Masaki Okada & Kunio Shirahada, 2022. "Organizational Learning for Sustainable Semiconductor Supply Chain Operation: A Case Study of a Japanese Company in Cross Border M&A," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    6. Hu, Man & Liu, Xue-Xin & Jia, Fu, 2024. "Optimal Emergency Order Policy for Supply Disruptions in the Semiconductor Industry," International Journal of Production Economics, Elsevier, vol. 272(C).
    7. Christ, Quentin & Dauzère-Pérès, Stéphane & Lepelletier, Guillaume, 2019. "An Iterated Min–Max procedure for practical workload balancing on non-identical parallel machines in manufacturing systems," European Journal of Operational Research, Elsevier, vol. 279(2), pages 419-428.
    8. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    9. Smirnov, Dina & van Jaarsveld, Willem & Atan, Zümbül & de Kok, Ton, 2021. "Long-term resource planning in the high-tech industry: Capacity or inventory?," European Journal of Operational Research, Elsevier, vol. 293(3), pages 926-940.
    10. Vasvári, Tamás & Hauck, Zsuzsanna & Longauer, Dóra, 2024. "Kiszervezési stratégiák és tanulási hatás a félvezetőiparban [Outsourcing strategies and the learning effect in the semiconductor industry]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 176-200.

    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:4565-4584. 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.