Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume09Issue11-03
BLOCKCHAIN APPLICATIONS IN BUSINESS OPERATIONS AND SUPPLY CHAIN MANAGEMENT BY MACHINE LEARNING
Tauhedur Rahman , Dahlkemper School of Business, Gannon University, USA Md Kafil Uddin , Dahlkemper School of Business, Gannon University, USA Md Monir Hosen , MS in Business Analytics, St.Francis college, USA Biswanath Bhattacharjee , Department of Management Science and Quantitative Methods, Gannon University, USA Md Siam Taluckder , Phillip M. Drayer Department of Electrical Engineering Lamar University, USA Sanjida Nowshin Mou , Department of Management Science and Quantitative Methods, Gannon University, USA Pinky Akter , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Shakhaowat Hossain , Department of Management Science and Quantitative Methods, Gannon University, USA Md Rashel Miah , Department of Digital Communication and Media/Multimedia, Westcliff University, USA Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USAAbstract
This study explores the integration of blockchain technology and machine learning (ML) models to improve transparency, efficiency, and resilience in supply chain management. Utilizing a mixed-methods approach, we developed a blockchain framework and tested ML models, including LSTM, ARIMA, Isolation Forest, One-Class SVM, Q-Learning, and Deep Q-Networks, to address demand forecasting, anomaly detection, and optimization. Our findings demonstrate that blockchain significantly enhances data integrity, traceability, and real-time monitoring across supply chains, particularly in industries like food and pharmaceuticals. Among ML models, LSTM showed superior performance for dynamic demand forecasting, while Isolation Forest was highly effective for real-time anomaly detection. Deep Q-Networks excelled in complex optimization tasks but required high computational resources, whereas Q-Learning proved efficient for simpler scenarios. This blockchain-ML integration presents a promising framework for advancing supply chain resilience, enabling secure and agile operations across diverse industrial applications. Limitations include blockchain’s scalability challenges and ML’s computational demands, suggesting areas for future research.
Keywords
Blockchain Technology, Supply Chain Management, Machine Learning Integration
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Copyright (c) 2024 Tauhedur Rahman, Md Kafil Uddin, Biswanath Bhattacharjee, Md Siam Taluckder, Sanjida Nowshin Mou, Pinky Akter, Md Shakhaowat Hossain, Md Rashel Miah, Md Mohibur Rahman
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