How Cargoways Accelerated Race Car Shipments with Advanced Visibility
公司规模
SME
地区
- Europe
国家
- Austria
- Finland
- Germany
- Italy
- Norway
- Sweden
产品
- project44’s Advanced Visibility Platform
技术栈
- Telematics Systems
- GPS Systems
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Productivity Improvements
技术
- 功能应用 - 运输管理系统 (TMS)
- 传感器 - 全球定位系统
适用行业
- 运输
- 汽车
适用功能
- 物流运输
用例
- 车队管理
- 资产跟踪
服务
- 系统集成
关于客户
Cargoways Logistik & Transport GmbH is a forwarding company that has recently ventured into the business of transporting race cars to various venues. Their first major contract involved transporting Formula E equipment using 35 trucks from London to Paris, which was a success and led to inquiries from Formula One. Besides race cars, Cargoways specializes in transporting goods to islands, with its core business operations between Italy and England. The company is now expanding its opportunities in Sweden, Norway, Finland, and Germany. The main office is located in Kufstein, Austria, from where all transport operations are controlled.
挑战
To reduce the amount of paid idle time, Cargoways needed a TMS integration to access the location and availability of carriers’ trucks.
解决方案
Cargoways found an efficient solution in project44’s Advanced Visibility Platform, which integrates various telematics systems into a single viewport. This platform allows Cargoways to obtain accurate vehicle information, even if the vehicle uses an unknown GPS system, as project44 can establish a new interface within a day. The platform incurs no upfront costs for Cargoways, as they are charged a fixed monthly rate per truck. Additionally, Cargoways’ customers can access status updates for their shipments but cannot see the trucks after their shipment has ended.
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
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
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.
