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Revolutionizing Retail Operations with IoT: A Case Study of a Major Canadian Retailer
技术
- 传感器 - 流量计
- 传感器 - 液体检测传感器
适用行业
- 电子产品
- 运输
适用功能
- 物流运输
用例
- 需求计划与预测
- 交通模拟
服务
- 系统集成
挑战
加拿大最大的零售商之一拥有超过 400 家商店的网络，其供应链运营面临着重大挑战。该公司正在努力应对网络流量波动的问题，这造成了瓶颈，并给劳动力和运输规划带来了问题。目标是通过平滑需求并调整劳动力和运输能力来提高货架可用性。该公司还面临着如何在响应商家请求的同时高效运输货物的挑战。他们需要评估各种选项，例如调整需求、在配送中心 (DC) 增加另一个班次、使用临时劳动力库或使用灵活的运输能力。此外，公司需要每天制定第二天的计划，考虑到近期的产能问题。根据修改后的产能和商家优先事项，使产能与需求保持一致并阻止过剩需求的流动存在挑战。
关于客户
该客户是加拿大最大的零售商之一，提供各种商品，包括服装、家居用品、小家电、电子产品、硬件和杂货。该公司还提供药房、园艺中心和视力中心等专业服务。该零售商拥有超过 400 家商店的网络，在全国拥有重要的影响力。该公司在供应链运营方面面临挑战，特别是在网络流量波动、劳动力和运输规划以及近期产能问题的日常规划方面。目标是提高货架上的可用性和运输货物的效率，同时响应商家的请求。
解决方案
该公司与人工智能供应链解决方案领先提供商 o9 合作，应对这些挑战。 o9 通过获取前一年的数据并应用根据商家计划的未来事件丰富的机器学习算法，帮助该公司创建物流预测。这种方法可以进行情景规划，并帮助公司更有效地预测和管理需求。使用 o9，根据当前商店需求创建了每周计划，以商品/商店/天的粒度进行操作。此工作流程在根据商家团队定义的容量限制和流量优先级平滑流量之前推动了战术容量规划的更改。每天执行一次的日计划是根据已确认的商店订单投放来执行的，并且粒度为商品/商店/天。该工作流程还根据商家团队定义的容量限制和流量优先级，在阻止流量之前推动第二天容量计划的更改。 o9知识图谱用于构建完全集成的端到端流程规划模型和基于机器学习的物流预测。
运营影响
数量效益
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