由 Everstream Analytics 提供支持的 Google 数据中心物流
技术
- 分析与建模 - 机器学习
- 传感器 - 全球定位系统
适用行业
- 水泥
- 运输
适用功能
- 人力资源
- 物流运输
用例
- 供应链可见性(SCV)
- 交通模拟
服务
- 系统集成
关于客户
谷歌是一家专门从事互联网相关服务和产品的跨国科技公司。其中包括搜索引擎、在线广告技术、云计算、软件和硬件。谷歌以其复杂的信息服务而闻名，这需要大量的硬件。为了满足这一需求，谷歌运营着一个由数百个设施组成的网络，从巨型数据中心到位于世界各地战略地点的小型终端设施。谷歌还以其复杂数据中心内部使用的大部分设备的设计和组装而闻名，这需要复杂的物理供应链。
挑战
谷歌运营着庞大的设施网络，包括超大规模数据中心和小型设施，以提供复杂的信息服务。该公司在内部设计和组装这些数据中心使用的大部分设备，这需要复杂的物理供应链。该供应链处理从完整服务器到组件、机架和网络设备的一切。 Google 每年管理着数千个始发目的地之间的数十万件货物。货物通常是高价值、专有且时间紧迫的。单个项目的丢失或延误可能会导致建设项目无法按计划进行，并面临敏感知识产权暴露的风险。因此，Google 需要一种解决方案来识别供应链风险，避免它们影响在途供应链。
解决方案
为了实现实时可见性和预测性异常处理，Google 的物流部门实施了一系列技术和基础设施解决方案。其中一项举措是在世界各地建立指挥和控制中心网络，其枢纽位于美国。这种可见性和控制基础设施的数字骨干是 Everstream Analytics，一个预测性供应链风险分析平台。谷歌的物流部门与 Everstream Analytics 合作，在系统中构建了完整的运输网络模型，并集成了多个现有遗留系统的网络数据。这提供了对其网络上所有货运的风险评估和分析。 Everstream Analytics 在其平台上覆盖了谷歌的运输网络和在途库存，该平台与来自数百万外部来源的数据源相关联。这些来源提供有关可能影响运输运营的情况和事件的近实时信息。该平台还利用物联网技术提供有关运输资产和特定物品位置的精确信息。
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
System 800xA at Indian Cement Plants
Chettinad Cement recognized that further efficiencies could be achieved in its cement manufacturing process. It looked to investing in comprehensive operational and control technologies to manage and derive productivity and energy efficiency gains from the assets on Line 2, their second plant in India.
Case Study
Airport SCADA Systems Improve Service Levels
Modern airports are one of the busiest environments on Earth and rely on process automation equipment to ensure service operators achieve their KPIs. Increasingly airport SCADA systems are being used to control all aspects of the operation and associated facilities. This is because unplanned system downtime can cost dearly, both in terms of reduced revenues and the associated loss of customer satisfaction due to inevitable travel inconvenience and disruption.
Case Study
IoT-based Fleet Intelligence Innovation
Speed to market is precious for DRVR, a rapidly growing start-up company. With a business model dependent on reliable mobile data, managers were spending their lives trying to negotiate data roaming deals with mobile network operators in different countries. And, even then, service quality was a constant concern.
Case Study
Digitize Railway with Deutsche Bahn
To reduce maintenance costs and delay-causing failures for Deutsche Bahn. They need manual measurements by a position measurement system based on custom-made MEMS sensor clusters, which allow autonomous and continuous monitoring with wireless data transmission and long battery. They were looking for data pre-processing solution in the sensor and machine learning algorithms in the cloud so as to detect critical wear.
Case Study
Cold Chain Transportation and Refrigerated Fleet Management System
1) Create a digital connected transportation solution to retrofit cold chain trailers with real-time tracking and controls. 2) Prevent multi-million dollar losses due to theft or spoilage. 3) Deliver a digital chain-of-custody solution for door to door load monitoring and security. 4) Provide a trusted multi-fleet solution in a single application with granular data and access controls.
Case Study
Vehicle Fleet Analytics
Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle.In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.
