IDEAS home Printed from https://ideas.repec.org/a/bao/ijimes/v2y2022i3p70-81id58.html
   My bibliography  Save this article

Big Data IoT-based Agile-Lean Logistic in Pharmaceutical Industries

Author

Listed:
  • Alireza Aliahmadi
  • Hamed Nozari
  • Javid Ghahremani-Nahr

Abstract

Purpose: In today’s world, with the presence of huge volumes of data, although organizations have faced many problems, using big data analysis has been able to significantly improve their efficiency and integrate information in the supply chain through the topic of computing. Cloud and big data achieve coordination between components and improve communication. On the other hand, Internet of Things (IoT) technology tools are one of the most important sources of big data production, and understanding and correct use of this data and their timely analysis using big data analysis techniques and technologies based on artificial intelligence can be effective steps to improve supply chain processes. Also, the use of these technologies can play an important role in process agility and, as a result, supply chain resilience. Methodology: In this study, the dimensions and key components of the use of large data obtained from the Internet of Things (IoT) in an industry's supply chain are investigated as a case study. Finally, a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented. Findings: This study shows that these technologies can be used as a powerful enabler, especially in the distribution of fast-acting pharmaceutical products. Originality/Value: In this paper a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented.

Suggested Citation

  • Alireza Aliahmadi & Hamed Nozari & Javid Ghahremani-Nahr, 2022. "Big Data IoT-based Agile-Lean Logistic in Pharmaceutical Industries," International Journal of Innovation in Management, Economics and Social Sciences, International Scientific Network (ISNet), vol. 2(3), pages 70-81.
  • Handle: RePEc:bao:ijimes:v:2:y:2022:i:3:p:70-81:id:58
    as

    Download full text from publisher

    File URL: https://ijimes.ir/index.php/ijimes/article/view/58/123
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shafique, Muhammad Noman & Yeo, Sook Fern & Tan, Cheng Ling, 2024. "Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

    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:bao:ijimes:v:2:y:2022:i:3:p:70-81:id:58. 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: International Scientific Network (ISNet) (email available below). General contact details of provider: https://ijimes.ir/index.php/ijimes/ .

    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.