N-iX > 实例探究 > 通过工业机器学习提高物流效率

通过工业机器学习提高物流效率

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技术
  • 分析与建模 - 数据挖掘
  • 分析与建模 - 机器学习
适用行业
  • 消费品
  • 运输
适用功能
  • 物流运输
  • 仓库和库存管理
用例
  • 计算机视觉
  • 库存管理
服务
  • 数据科学服务
  • 培训
关于客户
我们的客户是一家总部位于德国的全球财富 100 强跨国工程技术公司。他们在全球 60 多个国家开展业务,拥有四个业务领域:移动、工业技术、消费品以及能源和建筑技术。
挑战
客户需要简化更多仓库的库存管理,但现有的物流解决方案难以扩展。
解决方案
N-iX 专家通过引入微服务架构、DevOps 最佳实践以及实施包括 ML、AI、NLP、计算机视觉等先进技术,帮助客户改造其物流平台。
运营影响
  • The modernized and scalable logistics platform significantly improved the efficiency of internal logistics. The cloud-native microservices architecture enabled the solution to scale fast to more than 400 warehouses in over 60 countries, ensuring better performance and responsiveness. The redesigned architecture allowed data streaming without delays and provided high load-carrying capacity. The solution could be easily deployed on any cloud provider such as AWS, GCP, Azure, or even on-premise, providing the client with the flexibility to control expenses by selecting the cloud provider with the best offering. The embedded computer vision solution for cameras installed in warehouses allowed the client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. The multiplatform mobile application allowed warehouse staff to scan barcodes and allocate the boxes efficiently in a warehouse.

数量效益
  • Streamlined inventory management for 400+ warehouses around the globe.

  • Real-time tracking of packages, effectively managing the delivery statuses of boxes, and predicting warehouse load.

  • Automated manual work and reduced paperwork for warehouse staff.

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