IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i11p3455-3487.html
   My bibliography  Save this article

Review of supplier diversification and pricing strategies under random supply and demand

Author

Listed:
  • Amirmohsen Golmohammadi
  • Elkafi Hassini

Abstract

Uncertainties of supply and demand are two major sources of risk in any supply chain. As a result, the companies are implementing different strategies to mitigate the effects of these risks. Supplier diversification and responsive pricing are two of the main strategies that are used to mitigate the supply and demand risks. In supplier diversification, a firm uses multiple channels of sourcing while in responsive pricing, a firm manipulates demand through pricing to mitigate supply and demand risks. In this paper, we review lot-sizing problems when supply and demand are random. We focus on studies that have considered supplier diversification or responsive pricing as a mitigation strategy. We classify the studies based on their main assumptions and summarise their major findings. Finally, we present some directions for future research. Part of what we have found is that most studies that use multiple decision makers have focused on cases where information is complete and non-cooperative. There is a need to consider more realistic situations when there is information asymmetry between the decision makers. In addition, we have found that there is a lack of studies that look at the impact of joint ordering and pricing in the existence of multiple suppliers.

Suggested Citation

  • Amirmohsen Golmohammadi & Elkafi Hassini, 2020. "Review of supplier diversification and pricing strategies under random supply and demand," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3455-3487, June.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:11:p:3455-3487
    DOI: 10.1080/00207543.2019.1705419
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1705419
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1705419?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. Thevenin, Simon & Ben-Ammar, Oussama & Brahimi, Nadjib, 2022. "Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1199-1215.
    2. Hasin Md. Muhtasim Taqi & Humaira Nafisa Ahmed & Sumit Paul & Maryam Garshasbi & Syed Mithun Ali & Golam Kabir & Sanjoy Kumar Paul, 2020. "Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    3. Xiaohong Chen & Xiaoyang Liu, 2024. "Mitigating Supply Disruption: The Interplay between Responsive Pricing and Information Sharing under Dual Sourcing," Sustainability, MDPI, vol. 16(13), pages 1-19, July.
    4. Slama, Ilhem & Ben-Ammar, Oussama & Thevenin, Simon & Dolgui, Alexandre & Masmoudi, Faouzi, 2022. "Stochastic program for disassembly lot-sizing under uncertain component refurbishing lead times," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1183-1198.
    5. Ajay Philip & Rahul R. Marathe, 2022. "A New Green Labeling Scheme for Agri-Food Supply Chains: Equilibrium and Information Sharing under Uncertainties," Sustainability, MDPI, vol. 14(23), pages 1-34, November.
    6. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    7. Yiyu Li & Qingjie Xu & Ying Wang & Bin Liu, 2024. "Genetic Algorithms Application for Pricing Optimization in Commodity Markets," Mathematics, MDPI, vol. 12(9), pages 1-16, 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:58:y:2020:i:11:p:3455-3487. 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.