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Real-Time Shipment Tracking Implementation at Magnus Logistics
技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 应用开发平台
适用行业
- 运输
适用功能
- 物流运输
- 销售与市场营销
用例
- 供应链可见性(SCV)
- 交通模拟
服务
- 数据科学服务
挑战
Magnus Logistics 是波罗的海地区领先的物流服务提供商,其运营面临着重大挑战。随着客户对供应链和物流领域的期望不断变化,Magnus 认识到了进一步实现运营数字化以提高服务水平的机会。他们的运输业务、自有车队以及分包服务都存在可见性差距。这使得我们很难预测延误,也无法提前警告客户。如果没有这些信息,客户满意度可能会受到损害并产生经济处罚。此外,许多大型客户需要在 RFP 中进行某种发货跟踪,从而产生了与缺乏可见性相关的机会成本。
关于客户
Magnus Logistics 成立于 2007 年,是波罗的海地区领先的物流服务提供商,目前管理着一支由 1000 多辆卡车组成的车队,每天运送超过 200 批货物,业务遍及整个欧盟。 2015 年,这家总部位于立陶宛的公司还将业务扩展到波兰,在华沙开设了分部。该公司2020年销售收入超过5000万欧元,货运量为61,000辆。 Magnus 的管理体系已获得 ISO9001、ISO14001 和 AEO、SQAS 等国际认证,并受到一些世界上最知名品牌的信赖。该物流提供商的可靠性和经济稳定性也名列立陶宛前 6% 的公司之列。
解决方案
为了应对这一挑战,Magnus Logistics 实施了实时运输可视性平台 Shippeo 平台。该平台为 Magnus 提供了一种填补可见性差距并将整体能力提升到新水平的方法。收集的实时位置数据使 Magnus 能够全面了解其运输业务(包括分包商),从而无需手动追踪交货。因此,这种自动化货运控制流程也更具成本效益。该平台分析收集的位置数据,并使用由内部数据科学团队创建的复杂机器学习算法,以市场领先的准确性和可靠性提供预测预计到达时间。这些信息会在 Magnus 品牌通知中自动与客户共享,确保他们及时了解有关交付的最新信息。
运营影响
数量效益
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