Exploring Blockchain for Supply Chain Transparency: Enhancing Traceability and Reducing Fraud

The central theme of this research is the application of Blockchain coupled with machine learning to improve transparency, traceability and minimize cases of fraud in the supply chain. To support the findings of the study, empirical data are derived from Kaggle, and by applying real-time analysis of essential supply chain data, the study establishes the effectiveness of blockchain in securing and transforming the format of data. The findings establish that through the disintermediated and tamper-proof nature of blockchain, supply chain data is a better fit for feeding machine learning for rate of defect, lead time and other predictors. The integration of theories results in the efficient and accurate supply chain that minimizes fraudulent activities and enhances the decision-making process. The results indicate the significance of data precision and sophisticated analytics in the present supply chain networks and landmarks future research areas related to the applicability, comprehensibility and the assimilation of other innovative technologies such as IoT and AI.