Modern supply chains require decision-support systems that integrate operational efficiency, financial performance, and environmental sustainability. However, existing studies often examine blockchain transparency, machine learning forecasting, and optimization models separately, with limited integration for real-time supply chain decision-making. This study proposes an integrated framework combining machine learning prediction, multi-objective optimization, and blockchain-enabled traceability to improve supply chain performance and sustainability. The methodology includes data preprocessing, predictive modeling using a Random Forest algorithm, and multi-objective optimization based on Linear Programming to simultaneously minimize operational costs and carbon emissions. A blockchain-based transaction layer is incorporated to enhance supply chain transparency and data integrity. Empirical analysis using supply chain operational and sustainability indicators demonstrates strong predictive performance (R 2 = 0.893; RMSE = 0.503) and reveals a measurable trade-off between cost efficiency and carbon emissions across supplier–buyer allocations. The proposed framework provides a structured approach for integrating predictive analytics, sustainability-oriented optimization, and blockchain-supported traceability, offering insights for improving transparency, efficiency, and sustainability in modern supply chains.
Karami et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: