BLOCKCHAIN AND MACHINE LEARNING INTEGRATION: ENHANCING TRANSPARENCY AND TRACEABILITY IN SUPPLY CHAINS

Abstract

Purpose: This research paper explores the integration of blockchain and machine learning technologies to enhance transparency and traceability within supply chains. The primary purpose is to investigate how the synergy of these two cuttingedge technologies can address the challenges associated with supply chain management, particularly in ensuring accountability and traceability. Theoretical framework: The research paper employs a comprehensive theoretical framework that integrates principles of blockchain technology, machine learning algorithms, and supply chain management. This framework provides a solid foundation for understanding the potential impact of blockchain and machine learning integration on supply chain transparency and traceability. Findings: The findings of this research reveal that the integration of blockchain and machine learning indeed enhances transparency and traceability in supply chains. Through the immutability of blockchain and the predictive capabilities of machine learning, the paper demonstrates improvements in accountability, real-time tracking, fraud detection, and overall supply chain efficiency. Research, Practical & Social implications: This research holds significant implications for various stakeholders. From a research perspective, it contributes to the growing body of knowledge at the intersection of blockchain and machine learning technologies. Practically, it offers supply chain professionals a blueprint for implementing advanced solutions to address the challenges of accountability and transparency. On a broader social level, this integration has the potential to foster greater trust among consumers and regulators in supply chain operations. Originality/value: The originality of this research lies in its exploration of the convergence of blockchain and machine learning technologies in the context of supply chain management. While both technologies have been studied separately, this paper uniquely demonstrates the synergistic benefits of their integration. The research adds significant value by offering a novel approach to tackling longstanding issues in supply chain transparency.