ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses
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 advanc...
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2537.pdf |
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| author | Yangyan Li Tingting Chen |
| author_facet | Yangyan Li Tingting Chen |
| author_sort | Yangyan Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e4ec7c63cf9746f98d509a80dc2ed876 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e4ec7c63cf9746f98d509a80dc2ed8762025-08-20T02:39:16ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e253710.7717/peerj-cs.2537ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost lossesYangyan Li0Tingting Chen1School of Accounting, Xijing University, Xi ’an, Shaanxi, ChinaDepartment of Basic Faulty, Engineering University of PAP, Xi ’an, Shaanxi, ChinaWith 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.https://peerj.com/articles/cs-2537.pdfDeep learningTransportation cost optimizationPre-shipment policiesData-Driven decisionsIntelligent supply chain cost optimization |
| spellingShingle | Yangyan Li Tingting Chen ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses PeerJ Computer Science Deep learning Transportation cost optimization Pre-shipment policies Data-Driven decisions Intelligent supply chain cost optimization |
| title | ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses |
| title_full | ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses |
| title_fullStr | ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses |
| title_full_unstemmed | ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses |
| title_short | ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses |
| title_sort | iscco a deep learning feature extraction based strategy framework for dynamic minimization of supply chain transportation cost losses |
| topic | Deep learning Transportation cost optimization Pre-shipment policies Data-Driven decisions Intelligent supply chain cost optimization |
| url | https://peerj.com/articles/cs-2537.pdf |
| work_keys_str_mv | AT yangyanli isccoadeeplearningfeatureextractionbasedstrategyframeworkfordynamicminimizationofsupplychaintransportationcostlosses AT tingtingchen isccoadeeplearningfeatureextractionbasedstrategyframeworkfordynamicminimizationofsupplychaintransportationcostlosses |