IDEAS home Printed from https://ideas.repec.org/a/sae/vision/v25y2021i3p300-311.html
   My bibliography  Save this article

An Empirical Investigation of Industry 4.0 Preparedness in India

Author

Listed:
  • Neeraj Singhal

Abstract

The term ‘Industry 4.0’ or the fourth Industrial Revolution refers to the addition of smart technology to traditional manufacturing and industrial practices. This practice was first introduced by the German government in the year 2011 to empower its economy. The trend steadily gained roots and spread to other countries across the globe. This study explores the preparedness of select Indian industries to implement Industry 4.0, or in other words, build smart factories using innovative technologies. It also throws light on the benefits, challenges, drivers and barriers to Industry 4.0 in the Indian context. Based on data from corporate executives, it also highlights issues and challenges related to investment cost, skills gap and data security in implementing Industry 4.0. The study also provides a framework for a mapping application to map the qualities, benefits and challenges faced by selected Indian industries at two levels: concept implementation and the full implementation.

Suggested Citation

  • Neeraj Singhal, 2021. "An Empirical Investigation of Industry 4.0 Preparedness in India," Vision, , vol. 25(3), pages 300-311, September.
    • Handle: RePEc:sae:vision:v:25:y:2021:i:3:p:300-311
    DOI: 10.1177/0972262920950066
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0972262920950066
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0972262920950066?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
    ---><---

    References listed on IDEAS

    as
    1. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Frank Werner & Marina Ivanova, 2016. "A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 386-402, January.
    2. Sakgasem RAMINGWONG & Wapee MANOPINIWES & Varattaya JANGKRAJARNG, 2019. "Human Factors Of Thailand Toward Industry 4.0," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 11(1), pages 15-25, March.
    3. Shaohua Yi & Jie Xie, 2017. "A study on the dynamic comparison of logistics industry’s correlation effects in China," China Finance and Economic Review, Springer, vol. 5(1), pages 1-26, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Harshad Sonar & Vivek Khanzode & Milind Akarte, 2022. "Additive Manufacturing Enabled Supply Chain Management: A Review and Research Directions," Vision, , vol. 26(2), pages 147-162, June.
    2. B. Deepthi & Vikram Bansal, 2024. "Industry 4.0 in Textile and Apparel Industry: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends," Vision, , vol. 28(2), pages 157-170, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sony, Michael & Naik, Subhash, 2020. "Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model," Technology in Society, Elsevier, vol. 61(C).
    2. Kuang-Sheng Liu & Ming-Hung Lin, 2021. "Performance Assessment on the Application of Artificial Intelligence to Sustainable Supply Chain Management in the Construction Material Industry," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Rihab Khemiri & Khaoula Elbedoui-Maktouf & Bernard Grabot & Belhassen Zouari, 2017. "A fuzzy multi-criteria decision making approach for managing performance and risk in integrated procurement-production planning," Post-Print hal-01758604, HAL.
    5. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    6. Yuran Jin & Cheng Gao, 2023. "Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    7. Hubert Wojciechowski & Michał Adamczak, 2021. "Influence of the Characteristics of Young Logisticians on the Level of Acceptance of Work in an Automated and Robotic Environment – A Survey Study," European Research Studies Journal, European Research Studies Journal, vol. 0(2B), pages 893-903.
    8. Zhitao Xu & Adel Elomri & Roberto Baldacci & Laoucine Kerbache & Zhenyong Wu, 2024. "Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective," Annals of Operations Research, Springer, vol. 338(2), pages 1359-1401, July.
    9. De Giovanni, Pietro, 2021. "Smart Supply Chains with vendor managed inventory, coordination, and environmental performance," European Journal of Operational Research, Elsevier, vol. 292(2), pages 515-531.
    10. Tygran Dzhuguryan & Agnieszka Deja, 2021. "Sustainable Waste Management for a City Multifloor Manufacturing Cluster: A Framework for Designing a Smart Supply Chain," Sustainability, MDPI, vol. 13(3), pages 1-25, February.
    11. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    12. Cuesta-Valiño, Pedro & Gutiérrez-Rodríguez, Pablo & Núnez-Barriopedro, Estela & García-Henche, Blanca, 2023. "Strategic orientation towards digitization to improve supermarket loyalty in an omnichannel context," Journal of Business Research, Elsevier, vol. 156(C).
    13. Kazancoglu, Yigit & Sezer, Muruvvet Deniz & Ozkan-Ozen, Yesim Deniz & Mangla, Sachin Kumar & Kumar, Ajay, 2021. "Industry 4.0 impacts on responsible environmental and societal management in the family business," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    14. Tan, Jianhua & Wang, Xiongyuan & Chan, Kam C., 2020. "Does a national reform of a logistics system matter in corporate cash management? Evidence from logistics service standardization in China," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
    15. Wen-Hsien Tsai & Po-Yuan Chu & Hsiu-Li Lee, 2019. "Green Activity-Based Costing Production Planning and Scenario Analysis for the Aluminum-Alloy Wheel Industry under Industry 4.0," Sustainability, MDPI, vol. 11(3), pages 1-20, February.
    16. Frank, Alejandro G. & Mendes, Glauco H.S. & Ayala, Néstor F. & Ghezzi, Antonio, 2019. "Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 341-351.
    17. Jang Hee Lee & Olga A. Shvetsova, 2019. "The Impact of VR Application on Student’s Competency Development: A Comparative Study of Regular and VR Engineering Classes with Similar Competency Scopes," Sustainability, MDPI, vol. 11(8), pages 1-26, April.
    18. Marco Savastano & Carlo Amendola & Francesco Bellini & Fabrizio D’Ascenzo, 2019. "Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 11(3), pages 1-38, February.
    19. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    20. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.

    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:sae:vision:v:25:y:2021:i:3:p:300-311. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (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.