Identification and Response Strategies for Supply Chain Logistics Risks of E-Commerce Enterprises During Major Promotional Campaigns

Ye He; Mengbo Liu

doi:10.63313/EBM.2044

Authors

Ye He School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China Author
Mengbo Liu School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China Author

DOI:

https://doi.org/10.63313/EBM.2044

Keywords:

E-Commerce, Major Promotional Campaigns, Supply Chain Logistics Risk, Supply Chain Resilience, Genetic Algorithm, Last-Mile Delivery

Abstract

Major promotional campaigns generate pulse-like surges in e-commerce orders and place exceptional pressure on procurement, warehousing, line-haul transportation, last-mile delivery, and reverse logistics. This study identifies the major logistics risks arising across the end-to-end supply chain during promotional peaks and examines their underlying causes. Drawing on supply chain resilience theory, supply chain risk management theory, the SCOR framework, and the principles of genetic algorithms, the study qualitatively identifies last-mile route-planning failure as a critical operational bottleneck. The Double 11 practices of JD Logistics are then used as a case to examine digital forecasting, intelligent warehousing, flexible transport capacity, multimodal transport, and end-to-end monitoring. To address the identified bottleneck, a vehicle routing problem with time windows is formulated conceptually and a genetic-algorithm-based solution procedure is proposed. On this basis, an integrated risk-response framework is developed, covering data-driven inventory management, flexible multi-level logistics networks, last-mile route optimization, reverse-logistics control, and emergency coordination. The study concludes that algorithmic optimization is most effective when combined with flexible capacity allocation and coordinated management mechanisms. The proposed framework provides a practical reference for improving the responsiveness and resilience of e-commerce logistics systems under peak promotional demand, while recognizing the need for future validation using large-scale operational data.

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