IDEAS home Printed from https://ideas.repec.org/a/eme/ijppmp/ijppm-07-2019-0317.html
   My bibliography  Save this article

Modelling the enablers of industry 4.0 in the Indian manufacturing industry

Author

Listed:
  • Vineet Jain
  • Puneeta Ajmera

Abstract

Purpose - The vision of Industry 4.0 concept is to create smart factories that will change the current processes of production and manufacturing system using smart machines to produce smart and intelligent products. The main aim of this research is to explore the enablers with regard to Industry 4.0 application in manufacturing industry in India as the available literature shows that manufacturing sector is still doubtful about the implementation of Industry 4.0. Design/methodology/approach - Seventeen enablers that can affect the adoption of Industry 4.0 in the manufacturing industry in India have been explored through an extensive review of available literature and viewpoints of industry and academic experts. Total Interpretive Structural Modelling methodology (TISM) has been used to evaluate the interrelationships among these factors. A TISM model has been developed to extract the key enablers influencing Industry 4.0 adoption. Findings - The result shows that Internet facility from government at reduced price, financial support and continued specialized skills training are the major enablers as they have strong driving power. Practical implications - Proper understanding of these enablers will help the managers and policymakers to explore the impact of each enabler on other enablers as well as the degree of relationships among them and to take concrete steps so that Industry 4.0 can be implemented successfully in the manufacturing sector in India. Originality/value - This study is pioneer in exploring the enablers Industry 4.0 which is the most advanced concept that has the capability to change the future of Indian manufacturing sector if implemented judiciously and cautiously.

Suggested Citation

  • Vineet Jain & Puneeta Ajmera, 2020. "Modelling the enablers of industry 4.0 in the Indian manufacturing industry," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 70(6), pages 1233-1262, June.
    • Handle: RePEc:eme:ijppmp:ijppm-07-2019-0317
    DOI: 10.1108/IJPPM-07-2019-0317
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJPPM-07-2019-0317/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJPPM-07-2019-0317/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/IJPPM-07-2019-0317?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. Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis & Thanos Papadopoulos, 2024. "Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support," Annals of Operations Research, Springer, vol. 339(1), pages 163-183, August.
    2. Govindan, Kannan & Jain, Preeti & Kr. Singh, Rajesh & Mishra, Ruchi, 2024. "Blockchain technology as a strategic weapon to bring procurement 4.0 truly alive: Literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(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:eme:ijppmp:ijppm-07-2019-0317. 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: Emerald Support (email available below). General contact details of provider: .

    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.