A Comprehensive Framework for Blockchain and Machine Learning Integration in Supply Chain Management

This paper recommends the combination of blockchain technology and machine learning algorithms to enhance supply chain management (SCM), to counteract past issues including high costs, opaqueness, and vulnerability to fraud. This study aims to examine how these advanced technologies work harmoniously and contribute positively to SCM processes. Blockchain will maintain data integrity and transparency due to the distributed and tamper-proof ledger, while ML algorithms support various supply chain functions to enhance their predictive value. The performance parameters for the proposed system included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), and execution time and were tested with a sample set containing supply chain transactions. Computational results show that the integrated system produces substantial improvement compared to state-of-the-art approaches with an acquired MAE of 2.45, RMSE of 3.78, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.89 seconds, and an execution time of 12.5 seconds. Besides, the investigations of epoch-wise and batch size-wise performance enable the identification of the most effective configurations and enhance the overall performance of the model. Nevertheless, according to the results of the study, a wide range of problems associated with the implementation of SCM within the framework of blockchain and machine learning can be considered scalable successes. This research establishes a theoretical framework for the use of these technologies to enhance the delivery of increased transparency, accuracy, and efficiency in supply chain management processes.