Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Abstract

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.