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Edge intelligence‐enabled supply chain financial model based on Business‐to‐Business e‐business platforms

Edge intelligence‐enabled supply chain financial model based on Business‐to‐Business e‐business platforms

Yin, Feng, Yang, Rongjun, Yu, Hongxin, Zhou, Wei, Zhao, Yuanjun and Zhang, Shuai ORCID: 0000-0002-9796-058X (2021) Edge intelligence‐enabled supply chain financial model based on Business‐to‐Business e‐business platforms. Concurrency and Computation: Practice and Experience. ISSN 1532-0626 (Print), 1532-0634 (Online) (In Press) (doi:https://doi.org/10.1002/cpe.6353)

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Abstract

Based on the analysis of the existing traditional supply chain financial model, this article proposes an edge intelligence‐enabled supply chain financial model based on Business‐to‐Business (B2B) platforms, combines the operation mechanism of the model and the quantitative analysis thinking of the traditional supply chain financing. This article uses the model to construct and evaluates the cost–benefit model of dealers, manufacturers, and B2B e‐business platforms in the perspective of the supply chain finance for B2B platforms. In order to further explore the operation strategy of supply chain member enterprises, this article also explores the selection of financing objects of B2B platforms, the optimization of financing cost rate formulated by the loan amount of financing enterprises, and the optimization of product order quantity. Numerical analysis shows that the proposed model can directly reflect the changing trend of the relevant parameters of B2B platforms' supply chain financial model and provide suggestions for the cooperation mechanism between supply chain and commercial banks.

Item Type: Article
Uncontrolled Keywords: Business-to-Business e-business, commercial bank, edge intelligence, numerical model, supply chain finance
Subjects: H Social Sciences > HB Economic Theory
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Last Modified: 19 Jun 2021 23:04
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32716

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