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Transforming Demand Planning in Beauty Retail with IoT
技术
- 平台即服务 (PaaS) - 应用开发平台
- 传感器 - 液位传感器
适用行业
- 电子商务
- 零售
适用功能
- 物流运输
- 销售与市场营销
用例
- 需求计划与预测
- 最后一英里交付
挑战
这家美容零售商通过各种分销渠道营销 15 个品牌和 2000 个 SKU,由于 COVID-19 大流行,面临着从实体零售渠道向在线渠道的重大转变。这种转变需要一种面向未来的规划工具来支持其雄心勃勃的增长计划并更深入地了解需求驱动因素。该公司严重依赖 Excel,这导致了延迟和孤立的流程。他们缺乏对其五个渠道的主要需求驱动因素的全面了解。该公司还无法按照所需的粒度进行计划,从而导致短缺和交付延迟。需求规划人员大部分时间都花在处理 Excel 电子表格上,无法专注于更高级别的战略任务。经常需要手动干预,特别是在评估新产品推出的效果时。
关于客户
该客户是一家领先的美容零售商,共销售 15 个品牌,涵盖 2000 个 SKU,并使用多种分销渠道:大众市场、美发美容院以及各个品牌的选择性分销。主要由于 COVID-19 大流行,该公司的交付方式从实体零售渠道转向在线渠道。这种转变需要一个面向未来的规划工具来支持其雄心勃勃的增长计划,并帮助更深入地了解需求驱动因素。该公司严重依赖 Excel,这导致了延迟和孤立的流程。
解决方案
该公司部署了 o9 的需求感知功能,以了解电子商务、现代贸易和一般贸易渠道不断增长的渠道转变背后的驱动因素。这使他们能够确定正确的预测水平,并为所有 2000 个 SKU 执行高粒度的需求规划。该公司还使用 o9 企业知识图来构建需求模型,使其能够在单个集成平台上运行所有产品线的关键需求规划流程。利用 o9 的开放架构来确保与 Matelabs.ai 的数据集成。该解决方案通过启用数据驱动的异常工作流程和基于机器学习的新产品推出预测,消除了低价值的手动工作。该公司用 o9 取代了 Excel,获得了拥有平台而不是产品的灵活性,从而实现了建模灵活性。
运营影响
数量效益
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