Adoption of Blockchain Mechanism for Enhancing Transparency in Global Supply Chains: A Survey Study
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P120Keywords:
Blockchain Technology, Decentralization, Supply Chain Management, Financial Transactions, Transparency, Security, Scalability, Artificial Intelligence, Fraud Detection, ImmutabilityAbstract
Global supply chains are become increasingly intricate, dispersed, and vulnerable to problems including fraud, data silos, counterfeit goods, traceability, and lack of transparency. Conventional centralized supply chain models may not offer real-time visibility and credible information exchange between various stakeholders. A decentralized, immutable, transparent, and cryptographically secured system called blockchain has recently emerged as a viable option for circumventing these limitations. The paper provides an overview of blockchain technology as well as its potential applications in international supply chain management to increase trust and transparency. After outlining the fundamental blockchain architecture, key consensus features, and different kinds of blockchains, the paper delves deeply into supply chain transparency-related topics. Several industries, such as the pharmaceutical, food and agricultural, electronics, and luxury goods markets, have benefited from blockchain technology's efficiency, traceability, and fraud prevention capabilities, as noted in the study's brief summary of real-world applications. Lastly, the essay skims over the present state of scalability, interoperability, and data privacy issues, then moves on to potential areas for future study, such as the integration of blockchain with emerging technologies like the IoT and AI. The results show that blockchain-based supply chains should cause a substantial improvement in accountability, data integrity, and sustainability under the condition of proper technical and regulatory frameworks.
References
[1] K. Francisco and D. Swanson, “The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency,” Logistics, vol. 2, no. 1, p. 2, Jan. 2018, doi: 10.3390/logistics2010002.
[2] M. M. Queiroz and S. Fosso Wamba, “Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA,” Int. J. Inf. Manage., vol. 46, pp. 70–82, Jun. 2019, doi: 10.1016/j.ijinfomgt.2018.11.021.
[3] R. Manoja, R. Mekala, and B. Bhuvaneswari, “A Survey of Blockchain Technology used in Supply Chain Management,” Int. J. Comput. Sci. Inf. Technol., vol. 10, no. 6, pp. 52–58, 2019.
[4] P. Gonczol, P. Katsikouli, L. Herskind, and N. Dragoni, “Blockchain Implementations and Use Cases for Supply Chains-A Survey,” IEEE Access, vol. 4, pp. 1–16, 2016, doi: 10.1109/ACCESS.2019.
[5] C. Bai and J. Sarkis, “A Supply Chain Transparency and Sustainability Technology Appraisal Model for Blockchain Technology,” Acad. Manag. Proc., vol. 2019, no. 1, p. 16069, Aug. 2019, doi: 10.5465/AMBPP.2019.16069abstract.
[6] S. gouri aruna Sri and L. Bhaskari, “A study on blockchain technology,” Int. J. Eng. Technol., vol. 7, no. 2.7, p. 418, Mar. 2018, doi: 10.14419/ijet.v7i2.7.10757.
[7] Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,” in 2017 IEEE International Congress on Big Data (BigData Congress), IEEE, Jun. 2017, pp. 557–564. doi: 10.1109/BigDataCongress.2017.85.
[8] W. Viriyasitavat and D. Hoonsopon, “Blockchain characteristics and consensus in modern business processes,” J. Ind. Inf. Integr., vol. 13, pp. 32–39, Mar. 2019, doi: 10.1016/j.jii.2018.07.004.
[9] Y. Wang, J. H. Han, and P. Beynon-Davies, “Understanding blockchain technology for future supply chains: a systematic literature review and research agenda,” Supply Chain Manag. An Int. J., vol. 24, no. 1, pp. 62–84, Jan. 2019, doi: 10.1108/SCM-03-2018-0148.
[10] J. K. Namabira, K. Adalbertus, and P. Uugwanga, “WhatsApp in Action: An Exploration of Time Use by Academics,” TEXILA Int. J. Acad. Res., no. December 2018, pp. 20–26, Dec. 2019, doi: 10.21522/TIJAR.2014.SE.19.02.Art003.
[11] Y. Tribis, A. El Bouchti, and H. Bouayad, “Supply chain management based on blockchain: A systematic mapping study,” MATEC Web Conf., 2018, doi: 10.1051/matecconf/201820000020.
[12] Y. Wang, S. W. Wallace, B. Shen, and T.-M. Choi, “Service supply chain management: A review of operational models,” Eur. J. Oper. Res., vol. 247, no. 3, pp. 685–698, Dec. 2015, doi: 10.1016/j.ejor.2015.05.053.
[13] K. Bala, “Supply Chain Management : Some Issues and Challenges - A Review,” Int. J. Curr. Eng. Technol. E-ISSN, vol. 4, no. 2, pp. 946–953, 2014.
[14] B. Marchi and S. Zanoni, “Supply Chain Management for Improved Energy Efficiency: Review and Opportunities,” Energies, vol. 10, no. 10, p. 1618, Oct. 2017, doi: 10.3390/en10101618.
[15] A. Kamilaris, A. Fonts, and F. Prenafeta Boldú, The Rise of Blockchain Technology in Agriculture and Food Supply Chains. 2019. doi: 10.48550/arXiv.1908.07391.
[16] E. Koberg and A. Longoni, “A systematic review of sustainable supply chain management in global supply chains,” J. Clean. Prod., vol. 207, pp. 1084–1098, Jan. 2019, doi: 10.1016/j.jclepro.2018.10.033.
[17] S. Yousuf and D. Svetinovic, “Blockchain Technology in Supply Chain Management: Preliminary Study,” in 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), IEEE, Oct. 2019, pp. 537–538. doi: 10.1109/IOTSMS48152.2019.8939222.
[18] S. Aich, S. Chakraborty, M. Sain, H. Lee, and H.-C. Kim, “A Review on Benefits of IoT Integrated Blockchain based Supply Chain Management Implementations across Different Sectors with Case Study,” in 2019 21st International Conference on Advanced Communication Technology (ICACT), IEEE, Feb. 2019, pp. 138–141. doi: 10.23919/ICACT.2019.8701910.
[19] A. E. C. Mondragon, C. E. Coronado, and E. S. Coronado, “Investigating the Applicability of Distributed Ledger/Blockchain Technology in Manufacturing and Perishable Goods Supply Chains,” in 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), IEEE, Apr. 2019, pp. 728–732. doi: 10.1109/IEA.2019.8715005.
[20] D. Kaid and M. M. Eljazzar, “Applying Blockchain to Automate Installments Payment between Supply Chain Parties,” in 2018 14th International Computer Engineering Conference (ICENCO), IEEE, Dec. 2018, pp. 231–235. doi: 10.1109/ICENCO.2018.8636131.
[21] V. Naidu, K. Mudliar, A. Naik, and P. Bhavathankar, “A Fully Observable Supply Chain Management System Using Block Chain and IOT,” in 2018 3rd International Conference for Convergence in Technology (I2CT), IEEE, Apr. 2018, pp. 1–4. doi: 10.1109/I2CT.2018.8529725.
[22] D. Tse, B. Zhang, Y. Yang, C. Cheng, and H. Mu, “Blockchain application in food supply information security,” in 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2017, pp. 1357–1361. doi: 10.1109/IEEM.2017.8290114.
[23] Mamidala, J. V., Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., & Kurma, J. (2023). Machine Learning Models Powered by Big Data for Health Insurance Expense Forecasting. International Research Journal of Economics and Management Studies IRJEMS, 2(1).
[24] Nadella, V. M. (2023). Zero Trust Architecture for Telecom Operations. International Journal of Emerging Research in Engineering and Technology, 4(3), 115-129.
[25] Bitkuri, V., Kendyala, R., Kurma, J., Enokkaren, S. J., & Mamidala, J. V. (2023). Forecasting Stock Price Movements With Deep Learning Models for time Series Data Analysis. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-531. DOI: doi. org/10.47363/JAICC/2023 (2), 489, 2-9.
[26] Nadella, V. M. (2023). Anomaly Detection and Fault Prediction using ML in Telecom Operations. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 134-143.
[27] Kosaraju, P., & Nadella, V. M. (2022). Security and Privacy in IoT Ecosystems. Universal Library of Engineering Technology, (Issue).
[28] Singh, A. A. S. S., Mania, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D. N., & Tamilmani, V. (2023). Exploration of Java-Based Big Data Frameworks: Architecture, Challenges, and Opportunities. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-8.
[29] Routhu, K. K. (2023). AI-driven succession planning in Oracle HCM Cloud: Building resilient leadership pipelines through predictive analytics. International Journal of Science, Engineering and Technology, 11(5).
[30] Tamilmani, V., Namburi, V. D., Singh Singh, A. A., Maniar, V., Kothamaram, R. R., & Rajendran, D. (2023). Real-Time Identification of Phishing Websites Using Advanced Machine Learning Methods. Available at SSRN 5837142.
[31] Routhu, K. K. (2023). AI-driven succession planning in Oracle HCM Cloud: Building resilient leadership pipelines through predictive analytics. International Journal of Science, Engineering and Technology, 11(5). https://doi.org/10.5281/zenodo.17292018
[32] From Fragmentation to Focus: The Benefits of Centralizing Procurement. (2023). International Journal of Research and Applied Innovations, 6(6), 9820-9833. https://doi.org/10.15662/
[33] Routhu, K. K. (2023). Embedding fairness into the digital enterprise, data driven DEI strategies with Oracle HCM Analytics. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(8), 266-274.
[34] Routhu, K. K. (2023). AI-driven skills forecasting in Oracle HCM Cloud: From static competencies to predictive workforce design. International Journal of Science, Engineering and Technology, 11(1).
[35] Padur, S. K. R. (2023). AI-Augmented Enterprise ERP Modernization: Zero-Downtime Strategies for Oracle E-Business Suite R12. 2 and Beyond. Available at SSRN 5605510.
[36] Routhu, K. K. (2022). From Case Management to Conversational HR: Redefining Help Desks with Oracle’s AI and NLP Framework. International Journal of Science, Engineering and Technology, 10(6).
[37] Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 72-80.
[38] Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., & KURMA, J. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. Available at SSRN 5741263.
[39] Padur, S. K. R. (2022). Intelligent resource management: AI methods for predictive workload forecasting in cloud data centers. J. Artif. Intell. Mach. Learn. & Data Sci, 1(1), 2936-2941.
[40] Nadella, V. M. (2022). Digital Twins for Predictive Network Management and System Simulation. International Journal of AI, BigData, Computational and Management Studies, 3(3), 100-111.
[41] Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart Time Tracking in Oracle HCM Cloud. International Journal of Science, Engineering and Technology, 10(4).
[42] Nadella, V. (2019). Extracting road traffic data through video analysis using automatic camera calibration and deep neural networks.
[43] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2022). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 31-41.
[44] Padur, S. K. R. (2022). AI augmented platform engineering, transforming developer experience through intelligent automation and self optimizing internal platforms. International Journal of Science, Engineering and Technology, 10(5), 10-5281.
[45] Kosaraju, P. , & Nadella, V. M. (2021). Quality of Experience (QoE) and Network Performance Modelling for Multimedia Traffic. Journal of Artificial Intelligence and Big Data, 1(1), 1-13. https://doi.org/10.31586/jaibd.2021.1358.
