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Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains

Author

Listed:
  • Vaibhav S. Narwane

    (K. J. Somaiya College of Engineering)

  • Rakesh D. Raut

    (National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE)

  • Sachin Kumar Mangla

    (O P Jindal Global University)

  • Manoj Dora

    (Brunel University London)

  • Balkrishna E. Narkhede

    (National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE)

Abstract

Supply chains (SCs) are susceptible to risks because of their dynamic and complex nature. Big data analytics (BDA) through blockchain technology (BCT) can significantly contribute to managing SC risks. However, to date, the combined effect of BDA-BCT for SC risks has not been investigated extensively in the literature. This paper aims to identify the risk factors of the BDA-BCT initiative for Indian manufacturing organisations. Through the literature and experts’ judgments, sixteen risk factors were identified. Data was collected from machine tool, automobile component, and electrical manufacturing organisations. Further interrelations between risk factors were evaluated using the grey DEMATEL approach. The results show that ‘supply chain visibility risks’, ‘infrastructure and development costs’, ‘demand forecasting and sensing risks’, ‘data privacy and security risks’, ‘policy and legality related risks’, and ‘supply chain resilience’ were identified as common factors in the adoption of BDA-BCT practices by the three organisations. The cause-effect relationship between risk factors can assist managers, suppliers, service providers, and policymakers in the significant adoption of BDA-BCT in the context of manufacturing organisations. The study provides a novel way to utilise BDA-BCT in minimising supply chain risks. Limitations of the study are that it was conducted only for Indian organizations. In the future, the findings of the study can be validated through empirical analysis.

Suggested Citation

  • Vaibhav S. Narwane & Rakesh D. Raut & Sachin Kumar Mangla & Manoj Dora & Balkrishna E. Narkhede, 2023. "Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains," Annals of Operations Research, Springer, vol. 327(1), pages 339-374, August.
    • Handle: RePEc:spr:annopr:v:327:y:2023:i:1:d:10.1007_s10479-021-04396-3
    DOI: 10.1007/s10479-021-04396-3
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Andreas Brinkhoff & Özalp Özer & Gökçe Sargut, 2015. "All You Need Is Trust? An Examination of Inter-organizational Supply Chain Projects," Production and Operations Management, Production and Operations Management Society, vol. 24(2), pages 181-200, February.
    3. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    4. Wu, Hsin-Hung & Chang, Shih-Yu, 2015. "A case study of using DEMATEL method to identify critical factors in green supply chain management," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 394-403.
    5. Jiri Chod & Nikolaos Trichakis & Gerry Tsoukalas & Henry Aspegren & Mark Weber, 2020. "On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption," Management Science, INFORMS, vol. 66(10), pages 4378-4396, October.
    6. Kamalahmadi, Masoud & Parast, Mahour Mellat, 2017. "An assessment of supply chain disruption mitigation strategies," International Journal of Production Economics, Elsevier, vol. 184(C), pages 210-230.
    7. Fu, Xiaoyong & Zhu, Qinghua & Sarkis, Joseph, 2012. "Evaluating green supplier development programs at a telecommunications systems provider," International Journal of Production Economics, Elsevier, vol. 140(1), pages 357-367.
    8. Baozhuang Niu & Zongbao Zou, 2017. "Better Demand Signal, Better Decisions? Evaluation of Big Data in a Licensed Remanufacturing Supply Chain with Environmental Risk Considerations," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1550-1565, August.
    9. Min, Hokey, 2019. "Blockchain technology for enhancing supply chain resilience," Business Horizons, Elsevier, vol. 62(1), pages 35-45.
    10. William Ho & Tian Zheng & Hakan Yildiz & Srinivas Talluri, 2015. "Supply chain risk management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5031-5069, August.
    11. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    12. Dou, Yijie & Zhu, Qinghua & Sarkis, Joseph, 2014. "Evaluating green supplier development programs with a grey-analytical network process-based methodology," European Journal of Operational Research, Elsevier, vol. 233(2), pages 420-431.
    13. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    14. He, Yuanjie, 2017. "Supply risk sharing in a closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 39-52.
    15. Chithambaranathan, P. & Subramanian, Nachiappan & Gunasekaran, Angappa & Palaniappan, PL.K., 2015. "Service supply chain environmental performance evaluation using grey based hybrid MCDM approach," International Journal of Production Economics, Elsevier, vol. 166(C), pages 163-176.
    16. Kevin P. Scheibe & Jennifer Blackhurst, 2018. "Supply chain disruption propagation: a systemic risk and normal accident theory perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 43-59, January.
    17. Shalini Talwar & Puneet Kaur & Samuel Fosso Wamba & Amandeep Dhir, 2021. "Big Data in operations and supply chain management: a systematic literature review and future research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3509-3534, June.
    18. Mihalis Giannakis & Michalis Louis, 2016. "A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility," Post-Print hal-01353916, HAL.
    19. Amin Vafadar Nikjoo & Mahdi Saeedpoor, 2014. "An intuitionistic fuzzy DEMATEL methodology for prioritising the components of SWOT matrix in the Iranian insurance industry," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 20(4), pages 439-452.
    20. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    21. Liang Liu & Futou Li & Ershi Qi, 2019. "Research on Risk Avoidance and Coordination of Supply Chain Subject Based on Blockchain Technology," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    22. 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.
    23. Dmitry Ivanov, 2017. "Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 11(1), pages 24-43.
    24. Angappa Gunasekaran & Nachiappan Subramanian & Shams Rahman, 2015. "Supply chain resilience: role of complexities and strategies," International Journal of Production Research, Taylor & Francis Journals, vol. 53(22), pages 6809-6819, November.
    25. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    26. Ray Y. Zhong & Chen Xu & Chao Chen & George Q. Huang, 2017. "Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2610-2621, May.
    27. Lawrence V. Snyder & Zümbül Atan & Peng Peng & Ying Rong & Amanda J. Schmitt & Burcu Sinsoysal, 2016. "OR/MS models for supply chain disruptions: a review," IISE Transactions, Taylor & Francis Journals, vol. 48(2), pages 89-109, February.
    28. Zhao, Na, 2019. "Managing interactive collaborative mega project supply chains under infectious risks," International Journal of Production Economics, Elsevier, vol. 218(C), pages 275-286.
    29. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    30. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    31. Armin Jabbarzadeh & Behnam Fahimnia & Fatemeh Sabouhi, 2018. "Resilient and sustainable supply chain design: sustainability analysis under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5945-5968, September.
    32. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    33. James G. March & Zur Shapira, 1987. "Managerial Perspectives on Risk and Risk Taking," Management Science, INFORMS, vol. 33(11), pages 1404-1418, November.
    34. Chen, Lujie & Zhao, Xiande & Tang, Ou & Price, Lydia & Zhang, Shanshan & Zhu, Wenwen, 2017. "Supply chain collaboration for sustainability: A literature review and future research agenda," International Journal of Production Economics, Elsevier, vol. 194(C), pages 73-87.
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    1. Ali Emrouznejad & Soumyadeb Chowdhury & Prasanta Kumar Dey, 2023. "Blockchain in operations and supply Chain Management," Annals of Operations Research, Springer, vol. 327(1), pages 1-6, August.

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