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An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade

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  • Qi, Bitian
  • Shen, Yanbo
  • Xu, Tieyu

Abstract

This work aims to improve the efficiency of logistics distribution in the international trade environment by optimizing the distribution path of the e-commerce supply chain model (SCM). First, this work analyzes the international trade and e-commerce backgrounds and unveils the existing problems in the sustainable supply chain model (SCM). Then, an experiment is designed to study sustainable SCM under the Business to Consumer (B2C) E-commerce business model and compare it to the Consumer to Consumer (C2C) business model. This research has important reference value for improving resource efficiency in the logistics field. The optimal experimental results of the model show that given an order quantity of 2000, the vehicle competition ratio (VCR) is only 1.1 when the vehicle capacity (VC) is 12. In contrast, when the VC is 72, the VCR is 1.45. Therefore, the greater that the load of a single vehicle in the logistics management system is, the higher the logistics distribution efficiency of the supply chain. The research content has practical application value for improving the efficiency of logistics distribution under the current e-commerce environment and promoting the digital and electronic development of international trade logistics.

Suggested Citation

  • Qi, Bitian & Shen, Yanbo & Xu, Tieyu, 2023. "An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:tefoso:v:191:y:2023:i:c:s0040162523001762
    DOI: 10.1016/j.techfore.2023.122491
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    1. Zhong, Yufei & Chen, Xuesheng & Wang, Zhixian & Lin, Regina Fang-Ying, 2024. "The nexus among artificial intelligence, supply chain and energy sustainability: A time-varying analysis," Energy Economics, Elsevier, vol. 132(C).
    2. Liu, Yang & Ying, Zhenzhou & Ying, Ying & Wang, Ding & Chen, Jin, 2024. "Artificial intelligence orientation and internationalization speed: A knowledge management perspective," Technological Forecasting and Social Change, Elsevier, vol. 205(C).

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