Authors : Yasmin Akter Bipasha; Md Razibul Islam; Md Faysal Ahmed

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2msr7dek

Scribd : https://tinyurl.com/59hrzr3p

DOI : https://doi.org/10.38124/ijisrt/26mar767

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Modern supply chains are complex systems involving multiple stakeholders such as manufacturers, logistics providers, distributors, and retailers. These systems frequently face challenges including lack of transparency, fraud, counterfeit products, and inefficient data sharing. Emerging technologies such as blockchain and machine learning offer promising solutions to address these issues. This study proposes an integrated framework that combines blockchain technology with machine learning algorithms to enhance transparency, security, and efficiency in supply chain operations. The blockchain layer provides immutable and decentralized transaction records that ensure data integrity and traceability across the supply chain network. Meanwhile, machine learning models analyze transactional and operational data to detect fraudulent activities and predict potential supply chain disruptions. Experimental simulation results demonstrate that the proposed hybrid framework achieves a fraud detection accuracy of 96.8% using the LSTM model, outperforming Random Forest (95.6%) and Support Vector Machine (93.1%). The system also significantly improves transaction verification efficiency and demand prediction performance. The proposed framework enhances supply chain visibility, strengthens trust among stakeholders, and supports intelligent decision-making. This research contributes to the development of secure and intelligent digital supply chain infrastructures capable of supporting modern industrial ecosystems.

Keywords : Blockchain, Supply Chain Management, Machine Learning, Fraud Detection, Data Analytics, Digital Supply Chains.

References :

1. M. Christopher, Logistics and Supply Chain Management, 5th ed. Harlow, U.K.: Pearson, 2016.
2. D. Ivanov and A. Dolgui, “Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability,” International Journal of Production Research, vol. 58, no. 10, pp. 2904–2915, 2020.
3. N. Kshetri, “Blockchain’s roles in meeting key supply chain management objectives,” International Journal of Information Management, vol. 39, pp. 80–89, 2018.
4. M. R. Islam, “Digital Leadership and Circular Economy Performance in Sustainable Supply Chains,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 3, pp. 503–508, 2025.
5. S. Saberi, M. Kouhizadeh, J. Sarkis, and L. Shen, “Blockchain technology and its relationships to sustainable supply chain management,” International Journal of Production Research, vol. 57, no. 7, pp. 2117–2135, 2019.
6. Y. A. Bipasha, “Stock market prediction using LSTM,” International Journal of Science and Research Archive, vol. 12, no. 2, pp. 3146–3153, Aug. 2024, doi: 10.30574/ijsra.2024.12.2.1542.
7. M. R. Islam, “System Dynamics of Leadership Influence in Sustainable Supply Chains,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 3, pp. 509–514, 2025
8. Shekh, J. (2025). Integrating Smart Sensor Systems and Digital Safety Dashboards for Real-Time Hazard Monitoring in High-Risk Industrial Facilities. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), 1533-1569.
9. Shekh, J., & Uddin, M. S. (2022). A Qualitative Study of Safety Professionals’ Experiences in Managing Chemical Exposure Risks and Hazardous Materials Controls in Industrial Facilities. Review of Applied Science and Technology, 1(04), 250-282.
10. Y. Wang, J. Han, and P. Beynon-Davies, “Understanding blockchain technology for future supply chains: a systematic literature review and research agenda,” Supply Chain Management: An International Journal, vol. 24, no. 1, pp. 62–84, 2019.
11. M. R. Islam, “Circular Economy Leadership for Sustainable Industrial Transformation: A Holistic Framework for Resilient and Resource-Efficient Growth,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 3, pp. 253–262, 2025.
12. M. Crosby, P. Pattanayak, S. Verma, and V. Kalyanaraman, “Blockchain technology: Beyond bitcoin,” Applied Innovation Review, no. 2, pp. 6–19, 2016.
13. Y. A. Bipasha, “Predicting fraud in credit card transactions,” International Journal of Science and Research Archive, vol. 15, no. 2, pp. 1167–1177, 2025.
14. K. Toyoda, P. T. Mathiopoulos, I. Sasase, and T. Ohtsuki, “A novel blockchain-based product ownership management system for anti-counterfeits in the post supply chain,” IEEE Access, vol. 5, pp. 17465–17477, 2017.
15. Y. A. Bipasha, “Blockchain technology in supply chain management: transparency, security, and efficiency challenges,” International Journal of Science and Research Archive, vol. 10, no. 1, pp. 1186–1196, 2023.
16. S. Choi, T. Wallace, and Y. Wang, “Big data analytics in operations management,” Production and Operations Management, vol. 27, no. 10, pp. 1868–1883, 2018.
17. H. Min, “Artificial intelligence in supply chain management: theory and applications,” International Journal of Logistics Research and Applications, vol. 23, no. 3, pp. 289–300, 2020.
18. Y. A. Bipasha, “Market efficiency, anomalies and behavioral finance: A review of theories and empirical evidence,” World Journal of Advanced Research and Reviews, vol. 15, no. 2, pp. 827–839, 2022.
19. Mobasher Hasan, Jubair Bin Sharif, Md. Kwosar, Md. Faysal Ahmed, Daniel Lucky Michael. Maximizing Business Performance through Artificial Intelligence. International Journal of Computer Applications. 186, 54 (Dec 2024), 9-15. DOI=10.5120/ijca2024924252
20. Uddin, M. B., Hossain, M., & Das, S. (2022). Advancing manufacturing sustainability with industry 4.0 technologies. International Journal of Science and Research Archive, 6(01), 358-366.
21. Hossain, M., & Uddin, M. B. (2024). Digital twins in additive manufacturing. World Journal of Advanced Engineering Technology and Sciences, 13(02), 909-918.