IDEAS home Printed from https://ideas.repec.org/a/igg/jfsa00/v11y2022i3p1-19.html
   My bibliography  Save this article

IoT Analytics and ERP Interoperability in Automotive SCM: ANN-Fuzzy Logic Technique for Designing Decision Support Systems

Author

Listed:
  • Paul Jayender

    (Vellore Institute of Technology, India)

  • Goutam Kumar Kundu

    (Vellore Institute of Technology, India)

Abstract

Objective – The objective of this paper is to understand the potential of Interoperability between ERP and IOT Analytics in enabling the agile performance in Automotive supply chain by exploring the influence between Interoperability, SC Visibility and SCM agile performance and propose design for decision making system using Artificial neural network integration with fuzzy logic technique. Design/methodology/approach – TOE view was used to develop theoretical framework in addition to the elaborate literature review. Empirical analysis on the collected data from professionals in the automotive industry used to conclude on the findings. Findings – The IOT-Analytics and ERP interoperability identified as an enabler of SCM agile performance. Originality/value – The research article provides theoretical and empirical evidence over the IOT analytics and ERP interoperability potential impact in the Automotive SCM with novel approach towards designing effective decision support system using artificial neural network-fuzzy logic integration technique.

Suggested Citation

  • Paul Jayender & Goutam Kumar Kundu, 2022. "IoT Analytics and ERP Interoperability in Automotive SCM: ANN-Fuzzy Logic Technique for Designing Decision Support Systems," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(3), pages 1-19, July.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:3:p:1-19
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJFSA.306282
    Download Restriction: no
    ---><---

    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:igg:jfsa00:v:11:y:2022:i:3:p:1-19. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.