人工智能驱动跨境电商柔性供应链管理——以“Prophet销量预测”为例

Artificial Intelligence-Driven Flexible Supply Chain Management in Cross-Border E-Commerce—A Case Study of “Prophet Sales Forecasting”

摘要: 在当前全球贸易变革与消费需求个性化的时代背景下，构建高效敏捷的供应链体系成为企业核心竞争力的关键。然而，跨境电商供应链因其高度动态性、复杂性和不确定性，常面临预测失准与响应迟滞等问题。为此，本文提出一种人工智能驱动的柔性供应链管理框架，依托数据驱动决策、人机协同与动态调整三大机制，重点以Prophet时间序列预测模型为例，阐述其如何通过精准销量预测推动供应链的敏捷响应与整体优化。基于某跨境电商企业真实出口数据进行研究，并对模型历史预测的准确性进行性能评估以及可视化。结果表明，该Prophet模型在平均绝对百分比误差(MAPE)等关键指标上表现优异，验证了该框架提升供应链柔性的有效性。本研究为电商企业柔性供应链构建提供了实践路径，也拓展了人工智能在特定商业场景中的应用深度。

Abstract: Against the backdrop of current global trade transformation and increasingly personalized consumer demand, establishing an efficient and agile supply chain system has become pivotal to a company’s core competitiveness. However, cross-border e-commerce supply chains frequently encounter challenges such as inaccurate forecasting and delayed responses due to their inherent dynamism, complexity, and uncertainty. To address this, this paper proposes an AI-driven flexible supply chain management framework. Relying on three core mechanisms—data-driven decision-making, human-machine collaboration, and dynamic adjustment—it focuses on the Prophet time series forecasting model as an exemplar. The framework elucidates how precise sales forecasting drives agile supply chain responses and overall optimization. Research was conducted using authentic export data from a cross-border e-commerce enterprise, with performance evaluation and visualization of historical forecasting accuracy. Results demonstrate the Prophet model’s superior performance on key metrics such as Mean Absolute Percentage Error (MAPE), validating the framework’s effectiveness in enhancing supply chain flexibility. This study provides a practical pathway for e-commerce enterprises to construct flexible supply chains while expanding the depth of artificial intelligence applications within specific commercial contexts.