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Revolutionizing Retail Operations with AI/ML: A Canadian Retail Leader's Journey
技术
- 分析与建模 - 机器学习
- 功能应用 - 库存管理系统
适用行业
- 汽车
- 零售
适用功能
- 物流运输
- 仓库和库存管理
用例
- 需求计划与预测
- 库存管理
挑战
一家横跨汽车、硬件、体育和休闲行业的加拿大领先零售商正在努力应对准确预测消费者需求并在其网络中有效分配库存的挑战。该零售商的需求预测因缺乏整合各种外部需求驱动因素(例如天气、人口统计、定价、促销、产品分类和位置)的能力而受到阻碍。这对于时尚和季节性商品来说尤其成问题。此外,分配过程高度人工化,并且依赖于回顾性信息,没有考虑对商店进行量身定制的分配。商店的容量也超负荷,没有利用智能来帮助优先分配新的和有利可图的款式。
关于客户
该客户是一家领先的加拿大零售商,业务涉及汽车、硬件、体育和休闲等多个领域。该公司拥有由 220 家实体店和电子商务渠道组成的庞大网络,管理着 50 万个 SKU。该零售商在准确预测消费者需求和在其网络中有效分配库存方面面临挑战。手动和向后看的分配过程,以及管理商店容量方面缺乏智能,进一步加剧了问题。该公司寻求利用先进技术来增强其需求预测、库存分配和商店容量管理流程。
解决方案
该零售商与人工智能平台 o9 合作,增强其需求预测和库存分配流程。借助 o9,该公司可以在一个平台下引入各种需求约束并对其进行建模,从而推动对 220 家实体店以及电子商务渠道的 50 万个 SKU 进行基于 AI/ML 的增强预测。分配流程实现了自动化并通过异常进行管理,从而显着提高了生产力,并为企业腾出了时间来专注于战略、分析和库存政策。该流程利用基于机器学习的预测、库存策略和商店特定的尺寸配置文件,以确保向商店补充正确的商品。此外,零售商可以通过全面了解预计容量利用率并应用自动更正来管理商店容量。这是通过优先考虑有利可图的款式并将其流向商店以及缓解库存问题来实现的。
运营影响
数量效益
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