Research on Optimization of Logistics Ordering and Crowdsourcing Delivery Models in the Digital Context: A Case Study of Dingdong Maicai Fresh Food Platform

Conggui Xie

doi:10.63313/EBM.2012

Authors

Conggui Xie School of Economics and Management of Southwest Petroleum University, Southwest Petroleum University, Chengdu 610500, China Author

DOI:

https://doi.org/10.63313/EBM.2012

Keywords:

Digital Logistics, Fresh Product Platforms, Crowdsourced Delivery, Supply Chain Optimization, Dingdong Maicai

Abstract

Fresh products are characterized by perishability, seasonality, and susceptibility to spoilage, making them highly sensitive to time and environmental factors, which often leads to safety risks during circulation. Although distribution models centered around chain supermarkets have flourished in China, significant safety concerns persist. At present, the transportation and delivery of fresh foods in China primarily rely on room-temperature logistics, resulting in substantial product losses during these processes. Therefore, key issues include how to reduce spoilage rates in logistics and distribution, ensure product freshness, and explore methods to optimize logistics ordering and crowdsourced delivery models. This study focuses on the logistics and distribution models of fresh products, taking Dingdong Maicai as a case study. By analyzing its current logistics framework and leveraging digital technologies such as big data, smart logistics, and artificial intelligence, the research aims to enhance supply chain efficiency, refine crowdsourced delivery systems, and propose practical recommendations and optimization strategies for its logistics model.

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