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Effect of Big Data Analytics in Reverse Supply Chain: An Indian Context

Author

Listed:
  • Ajay Kumar Behera

    (ITER, Bhubaneswar, India)

  • Sasmita Mohapatra

    (ITER, Bhubaneswar, India)

  • Rabindra Mahapatra

    (National Institute of Technology, Meghalaya, India)

  • Harish Das

    (National Institute of Technology, Meghalaya, India)

Abstract

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasises on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 330 industries. Of the 330 surveys that were mailed, 125 completed surveys were returned, corresponding to a response rate of 37.87 percent, which was slightly greater than previous studies (Queiroz and Telles, 2018).The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized.

Suggested Citation

  • Ajay Kumar Behera & Sasmita Mohapatra & Rabindra Mahapatra & Harish Das, 2022. "Effect of Big Data Analytics in Reverse Supply Chain: An Indian Context," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(1), pages 1-14, January.
    • Handle: RePEc:igg:jisscm:v:15:y:2022:i:1:p:1-14
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    References listed on IDEAS

    as
    1. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    2. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    3. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
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