使用人工智能来检测恐慌购买并改善流行病期间的产品分销
Yossiri Adulyasak1, Omar Benomar2, Ahmed Chaouachi2
1HEC Montreal, 3000, Chemin de La Cote-Sainte-Catherine, Montreal, QC H3T 2A7 Canada.
概括
零售商现在可以通过人工智能驱动的框架来检测和应对恐慌购买. 该系统增强了对需求异常的检测,并改善了危机期间必需产品的可用性.
科学领域:
- 供应链管理 供应链管理
- 人工智能的人工智能
- 消费者行为 消费者行为
背景情况:
- 随着COVID-19的流行,人们大范围的购买恐慌,导致必需品库存不足.
- 零售商缺乏技术基础设施,以有效地管理突然的需求激增.
研究的目的:
- 开发一个人工智能驱动的框架来检测需求异常并改善产品分销.
- 加强零售商在供应链中断期间的战略响应能力.
主要方法:
- 使用数据驱动框架,整合内部和外部数据源.
- 应用AI模型用于异常检测,并开发了一种规范分析模拟工具.
- 经过验证的模型,在三个产品类别中进行了超过1500万次的观察.
主要成果:
- 异常检测模型成功识别了与恐慌购买相关的需求激增.
- 在2020年3月的恐慌购买事件中,处方工具显示基本产品可访问性增加了56.74%.
- 外部数据集成提高了模型的可预测性和可解释性.
结论:
- 开发的AI框架为管理需求波动提供了系统的解决方案.
- 零售商可以在不确定的时期战略性地提高基本产品的分销和可用性.
- 该研究强调了数据集成和人工智能在现代供应链弹性中的重要性.
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