ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses

Abstract

With the rapid expansion of global e-commerce, effectively managing supply chains and optimizing transportation costs has become a key challenge for businesses. This research proposed a new framework named Intelligent Supply Chain Cost Optimization (ISCCO). ISCCO integrates deep learning with advanced optimization algorithms. It focuses on minimizing transportation costs by accurately predicting customer behavior and dynamically allocating goods. ISCCO significantly enhanced supply chain efficiency by implementing an innovative customer segmentation system. This system combines autoencoders with random forests to categorize customers based on their sensitivity to discounts and likelihood of cancellations. Additionally, ISCCO optimized goods allocation using a genetic algorithm enhanced integer linear programming model. By integrating real-time demand data, ISCCO dynamically adjusts the allocation of resources to minimize transportation inefficiencies. Experimental results show that this framework increased the accuracy of user classification from 50% to 95.73%, and reduced the model loss value from 0.75 to 0.2. Furthermore, the framework significantly reduced order cancellation rates in practical applications by adjusting pre-shipment policies, thereby optimizing profits and customer satisfaction. Specifically, when the pre-shipment ratio was 25%, the optimized profit was approximately 7.5% higher than the actual profit, and the order cancellation rate was reduced from a baseline of 50.79% to 41.39%. These data confirm that the ISCCO framework enhances logistics distribution efficiency. It also improves transparency and responsiveness across the supply chain through precise data-driven decisions. This achieves maximum cost-effectiveness.