直观的货运跟踪提高了印度物流公司预计到达时间的准确性
技术
- 分析与建模 - 机器学习
- 机器人 - 自动导引车 (AGV)
适用行业
- 汽车
- 运输
适用功能
- 物流运输
- 仓库和库存管理
用例
- 最后一英里交付
- 车辆性能监测
服务
- 云规划/设计/实施服务
- 系统集成
关于客户
该客户是一家领先的印度物流科技公司，被誉为该国最大的中立货运网络。他们在一个复杂的行业中运营，不同层面的技术采用率较低。该公司管理每次旅程的数千个数据点，包括跨高速公路、乡村道路和仓库位置的准确路线、跨城市和州的交通数据、设置上下文 POI 以及轮胎磨损、车辆利用率等附加数据、通行费和许可证。他们还必须考虑当地的细微差别，例如车辆限制和驾驶行为，这些差异在印度各邦之间存在巨大差异。
挑战
一家领先的印度物流科技公司被称为该国最大的中立货运网络，在为客户提供直观的货运跟踪体验方面面临着挑战。该公司所处行业复杂，不同层面的技术采用率较低。主要挑战包括跟踪每次旅程的数千个数据点，例如跨高速公路、乡村道路和仓库位置的准确路线、跨城市和州的交通数据、设置上下文 POI 以及轮胎磨损等附加数据、车辆使用、通行费和许可证。这些数据点由不同的利益相关者管理，带来了重大的运营挑战。第二个挑战是考虑当地的细微差别，例如车辆限制和驾驶行为，这些细微差别在印度各邦之间存在巨大差异。其他复杂因素包括车辆类型、地形变化、驾驶模式以及取决于货物的速度限制。
解决方案
该解决方案涉及分析公司的历史货运和导航数据，并通过机器学习模型运行它。这个过程为了解问题的细微差别提供了宝贵的见解。使用混合人工智能方法创建了定制解决方案，可以应对物流行业的挑战，同时帮助客户蓬勃发展。该解决方案有效地解决了当地条件、非结构化地址、不同车辆类型和独特驾驶行为的问题，帮助他们的解决方案对驾驶员可能采取的路线和相应的预计到达时间做出明智的预测。制定了一个 5 步方法，其中包括地图数据管理、人工智能推理、路线和预计到达时间建模、影子测试，以及最终采用新解决方案。自定义地图堆栈是利用 Kubernetes 在客户的云上设置的。
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