Streamlining supply chains: An efficiency-driven permissioned blockchain framework for data reduction

Almaiah, M.; Ali, A.; Alkhdour, T.; Tin, T.; AlAli, R.; Aldahyani, T.

Growing Science » International Journal of Data and Network Science » Streamlining supply chains: An efficiency-driven permissioned blockchain framework for data reduction

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 8 Issue 4 pp. 2445-2458 , 2024

Streamlining supply chains: An efficiency-driven permissioned blockchain framework for data reduction Pages 2445-2458 Download PDF

Authors: Mohammed Amin Almaiah, Aitizaz Ali, Tayseer Alkhdour, Ting Tin Tin, Rommel AlAli, Theyazan Aldahyani

DOI: 10.5267/j.ijdns.2024.5.013

Keywords: Supply Chains, Efficiency, Permissioned Blockchain, Framework, Data Reduction, Supply Chain Optimization, Blockchain Technology

Abstract: In the ever-evolving landscape of supply chain management, the quest for efficiency has become paramount. This abstract explores a groundbreaking solution that combines the power of permissioned blockchain technology with innovative data reduction strategies to redefine how supply chains operate. Traditional supply chain systems often grapple with data overload, causing delays, inaccuracies, and operational inefficiencies. However, this abstract presents a promising approach that unleashes efficiency by harnessing the capabilities of a permissioned blockchain. Through data reduction techniques tailored to the needs of supply chain management, this approach streamlines the flow of information while maintaining security and trust among participants. This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative. Furthermore, it examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making. The impact of this innovative approach on supply chain stakeholders is profound. It reduces data related bottlenecks, enhances transparencies, and fosters collaboration among participants. Additionally, it provides a scalable framework adaptable to diverse supply chain ecosystems. As supply chain efficiency becomes increasingly important in our interconnected world, this permissioned blockchain-driven data reduction strategy offers a compelling vision for the future. It promises to unlock a new era of streamlined operations, cost savings, and improved customer satisfaction, ultimately shaping the next generation of supply chain management.

How to cite this paper

 Almaiah, M., Ali, A., Alkhdour, T., Tin, T., AlAli, R & Aldahyani, T. (2024). Streamlining supply chains: An efficiency-driven permissioned blockchain framework for data reduction.International Journal of Data and Network Science, 8(4), 2445-2458.

Refrences

Ali, A., Kundi, M., Ahmed, M., ur Rehman, A., Ali, H., & Misron, A. B. (2022a). A Novel Privacy-Preserving Framework Based on Blockchain Technology to Secure Industrial IoT Data. In Security, Trust and Privacy Models, and Architec-tures in IoT Environments (pp. 25-42). Cham: Springer International Publishing.
Ali, A., Almaiah, M. A., Hajjej, F., Pasha, M. F., Fang, O. H., Khan, R., ... & Zakarya, M. (2022b). An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural net-work. Sensors, 22(2), 572.
Al Hwaitat, A. K., Almaiah, M. A., Ali, A., Al-Otaibi, S., Shishakly, R., Lutfi, A., & Alrawad, M. (2023). A new block-chain-based authentication framework for secure IoT networks. Electronics, 12(17), 3618.
Almaiah, M. A., Hajjej, F., Ali, A., Pasha, M. F., & Almomani, O. (2022a). A novel hybrid trustworthy decentralized au-thentication and data preservation model for digital healthcare IoT based CPS. Sensors, 22(4), 1448.
Almaiah, M. A., Ali, A., Hajjej, F., Pasha, M. F., & Alohali, M. A. (2022b). A lightweight hybrid deep learning privacy preserving model for FC-based industrial internet of medical things. Sensors, 22(6), 2112.
Almaiah, M. A., Al-Zahrani, A., Almomani, O., & Alhwaitat, A. K. (2021). Classification of cyber security threats on mo-bile devices and applications. In Artificial Intelligence and Blockchain for Future Cybersecurity Applications (pp. 107-123). Cham: Springer International Publishing.
Almaiah, M. A., Almomani, O., Alsaaidah, A., Al-Otaibi, S., Bani-Hani, N., Hwaitat, A. K. A., ... & Aldhyani, T. H. (2022c). Performance investigation of principal component analysis for intrusion detection system using different support vector machine kernels. Electronics, 11(21), 3571.
Hastig, G. M., & Sodhi, M. S. (2020). Blockchain for supply chain traceability: Business requirements and critical success factors. Production and Operations Management, 29(4), 935-954.
Wan, P. K., Huang, L., & Holtskog, H. (2020). Blockchain-enabled information sharing within a supply chain: A systemat-ic literature review. IEEE access, 8, 49645-49656.
Wu, Y., & Zhang, Y. (2022). An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing. Advanced Engineering Informatics, 51, 101522.