JOURNAL ARTICLE
Smart Parcel Consolidation at Cainiao.
Published In: INFORMS Journal on Applied Analytics, 2024, v. 54, n. 5. P. 417 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Chen, Yujie; Yuan, Biao; Zhou, Yinzhi; Chen, Yuwei; Hu, Haoyuan 3 of 3
Abstract
The article focuses on a novel parcel fulfillment business model proposed by Cainiao Network, the logistics arm of Alibaba Group, which consolidates parcels ordered by the same consumer from multiple merchants during the fulfillment process to increase delivery speed without additional costs. To support this model, three analytics methods were developed: (1) a two-phase online optimization algorithm that dynamically determines parcel consolidation and shipping methods while satisfying delivery time constraints, (2) a statistical method to compute delivery time distributions for on-time delivery (OTD) rate estimation, and (3) a simulation-based optimization method to help managers set appropriate OTD target values. The online optimization algorithm is proven to have sublinear bounds on expected optimality gap and constraint violation, and its effectiveness is validated with real-world data. Since 2020, Cainiao’s system has consolidated millions of parcels shipped from China to over 50 countries, achieving at least a 50% reduction in delivery time and saving tens of millions of dollars annually, benefiting Cainiao, merchants, e-commerce platforms, and consumers.
Additional Information
- Source:INFORMS Journal on Applied Analytics. 2024/09, Vol. 54, Issue 5, p417
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:2644-0865
- DOI:10.1287/inte.2024.0124
- Accession Number:180147929
- Copyright Statement:Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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