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基于模糊逻辑和拍卖理论的跨境电子商务供应链管理优化

Optimization of cross-border E-commerce (CBEC) supply chain management based on fuzzy logic and auction theory.

作者信息

Gong Zhi

机构信息

Winchester School of Art, University of Southampton, Winchester, UK.

出版信息

Sci Rep. 2024 Jun 18;14(1):14088. doi: 10.1038/s41598-024-64123-3.

DOI:10.1038/s41598-024-64123-3
PMID:38890347
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11189551/
Abstract

People have benefited enormously from e-commerce's explosive expansion in recent years. E-commerce, in contrast to the traditional business environment, is dynamic and complicated, which poses a number of challenges. The prediction market can create mixed intelligence for sales forecasting, which is essential for e-commerce enterprises, to handle this difficulty. Combining the usage of human analysts and machine learning algorithms can accomplish this. To accurately anticipate retailer volume and allot resources, a novel methodology for optimizing supply chain management at CBEC is proposed in this paper. The framework improves efficiency and profitability by using fuzzy logic and auction theory to make strategic decisions. Thanks to this creative strategy, managers can now make more informed decisions, ultimately enhancing the efficiency of CBEC's supply chain. The results of this paper reveal that our proposed method is superior to previous comparable methods, with RMSE and MAE values of 22.31 and 18.76, respectively. This approach offers a promising solution to the challenges faced by e-commerce businesses, and can help them achieve greater success in the dynamic and complex world of online commerce.

摘要

近年来，人们从电子商务的迅猛发展中受益匪浅。与传统商业环境相比，电子商务动态且复杂，带来了诸多挑战。预测市场可为销售预测生成混合智能，这对电子商务企业至关重要，以应对这一难题。将人类分析师的使用与机器学习算法相结合便可达成此目的。本文提出了一种在跨境电子商务中优化供应链管理的新方法，以准确预测零售商销量并分配资源。该框架通过使用模糊逻辑和拍卖理论进行战略决策来提高效率和盈利能力。得益于这种创新策略，管理者现在能够做出更明智的决策，最终提高跨境电子商务供应链的效率。本文结果表明，我们提出的方法优于先前的可比方法，均方根误差（RMSE）和平均绝对误差（MAE）值分别为22.31和18.76。这种方法为电子商务企业面临的挑战提供了一个有前景的解决方案，并能帮助它们在动态复杂的在线商业世界中取得更大成功。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/468ed67895b3/41598_2024_64123_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/39c24422305a/41598_2024_64123_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/9e14ca4ff59c/41598_2024_64123_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/5627c2d732e4/41598_2024_64123_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/c51e9f8cc0e6/41598_2024_64123_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/adc37e44c5ce/41598_2024_64123_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/1298ca37f82d/41598_2024_64123_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/809e/11189551/468ed67895b3/41598_2024_64123_Fig11_HTML.jpg

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