Provectus > 实例探究 > 机器学习驱动的需求预测帮助蓝瓶咖啡减少浪费并提高订购准确性

机器学习驱动的需求预测帮助蓝瓶咖啡减少浪费并提高订购准确性

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技术
  • 分析与建模 - 机器学习
  • 分析与建模 - 预测分析
适用行业
  • 食品与饮料
  • 回收与废物管理
适用功能
  • 采购
  • 销售与市场营销
用例
  • 补货预测
  • 减废预测
服务
  • 系统集成
  • 培训
关于客户
Blue Bottle Coffee 是一家咖啡烘焙商和零售商,在美国和亚洲设有咖啡馆。他们希望优化连锁咖啡馆的糕点销售流程。
挑战
Blue Bottle 咖啡馆的糕点订单不准确,导致售空和浪费。他们需要一个预测性订购解决方案来减少食物浪费。
解决方案
Blue Bottle 和 Provectus 构建了一个由机器学习驱动的预测订购系统,该系统根据库存数据、历史销售和增长预测建议每家咖啡馆应订购多少糕点。
运营影响
  • The implementation of the ML-powered predictive ordering system has brought significant operational benefits to Blue Bottle Coffee. The system has improved the accuracy of pastry ordering, leading to less under- or overstocking. This has resulted in happier customers who can enjoy a variety of pastries anytime, and cafe leaders who can better manage food utilization. The system has also saved cafe leaders' time, as they no longer have to spend hours estimating the quantity of pastries to order. Furthermore, the system aligns with BBC's zero waste goals and financial bottom line, as it has reduced overstock waste, leading to cost savings and a lower ecological footprint. The system has also paved the way for BBC's growth in new markets by enabling it to operate more efficiently and sustainably.

数量效益
  • Improved pastry ordering accuracy by 8% in July compared to the previous month

  • Reduced overstock waste, resulting in significant cost savings

  • Enabled efficient and accurate ordering of pastries, minimizing stockouts and waste

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