IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v164y2023ics0148296323003387.html
   My bibliography  Save this article

An Artificial-Intelligence-Based omnichannel blood supply chain: A pathway for sustainable development

Author

Listed:
  • Ghouri, Arsalan Mujahid
  • Khan, Haseeb R.
  • Mani, Venkatesh
  • Haq, Mirza Amin ul
  • Lopes de Sousa Jabbour, Ana Beatriz

Abstract

We formulated and tested an innovative omnichannel blood supply chain (OBSC) model based on artificial intelligence (AI) using inputs raised in semi structured interviews conducted with heath care practitioners in a blood supply chain. The proposed AI-based OBSC model addresses the supply and demand imbalance in crucial situations for blood supply chains. A resource dependence theory bottom-up approach was applied to underpin the OBSC model. This model consists of two parts: (a) helping to find the closest and fastest available blood supply (omnichannel perspective) and (b) raising a blood supply request among university students (with the help of the university’s IT system) through SMS messaging in case of emergencies or blood shortages (AI perspective). This OBSC model is significant because it contributes to the United Nations’ Sustainable Development Goals, specifically the goal 3 to “ensure healthy lives and promote well-being for all at all ages.”

Suggested Citation

  • Ghouri, Arsalan Mujahid & Khan, Haseeb R. & Mani, Venkatesh & Haq, Mirza Amin ul & Lopes de Sousa Jabbour, Ana Beatriz, 2023. "An Artificial-Intelligence-Based omnichannel blood supply chain: A pathway for sustainable development," Journal of Business Research, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:jbrese:v:164:y:2023:i:c:s0148296323003387
    DOI: 10.1016/j.jbusres.2023.113980
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323003387
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2023.113980?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.

    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:eee:jbrese:v:164:y:2023:i:c:s0148296323003387. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.