Integrating machine learning and blockchain applications in business operations and supply chain management
Keywords:
blockchain technology, supply chain management, machine learning integration, supply chain optimization, transparency and traceabilityAbstract
This research investigates how combining blockchain technology with machine learning (ML) can enhance transparency, efficiency, and resilience in supply chain management. Using a mixed-methods approach, the study designed a blockchain framework and evaluated several ML models including LSTM, ARIMA, Isolation Forest, One-Class SVM, Q-Learning, and Deep Q-Networks for tasks such as demand forecasting, anomaly detection, and optimization. Results indicate that blockchain improves data integrity, traceability, and real-time visibility, especially in sectors like food and pharmaceuticals. Among the tested models, LSTM outperformed others in dynamic demand forecasting, Isolation Forest proved most effective for real-time anomaly detection, and Deep Q-Networks excelled in complex optimization challenges despite high computational requirements, while Q-Learning worked well for simpler optimization needs. The integrated blockchain and ML framework shows strong potential for boosting supply chain resilience by enabling secure, agile operations across various industries. However, challenges remain blockchain faces scalability limitations, and advanced ML models demand significant computational power. These constraints highlight opportunities for future research to develop more scalable blockchain solutions and computationally efficient ML techniques.
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