DB Cargo 的 Camunda BPM
公司规模
1,000+
地区
- Europe
国家
- Germany
产品
- Camunda BPM
- Camunda Process Engine
- Camunda Cockpit application
- Camunda Enterprise Platform
技术栈
- BPMN
- Cloud Computing
实施规模
- Enterprise-wide Deployment
影响指标
- Digital Expertise
- Productivity Improvements
技术
- 应用基础设施与中间件 - API 集成与管理
- 平台即服务 (PaaS) - 连接平台
适用行业
- 铁路与地铁
适用功能
- 物流运输
用例
- 过程控制与优化
- 服务备件管理
服务
- 软件设计与工程服务
- 系统集成
关于客户
DB Cargo AG 是欧洲领先的货运铁路公司，每天运营 4,520 趟列车。该公司总部位于法兰克福，为各行各业的商业客户提供全欧洲范围的铁路运输。长途跨境运输面临的最大挑战之一是确保货物运输可靠且准时。为了解决这一问题，DB Cargo 加入了欧洲生产联盟 Xrail，该联盟旨在通过中央经纪人和商定的流程模型优化跨境互操作性。IT 系统运输链和服务管理 (TSM) 是将 DB Cargo 连接到 Xrail 容量预订的平台。
挑战
DB Cargo 是欧洲生产效率最高的货运铁路公司，它在长途跨境运输方面面临重大挑战——确保货物运输可靠且准时。作为欧洲生产联盟 Xrail 的一部分，DB Cargo 旨在通过中央代理和商定的流程模型来优化跨境互操作性。然而，将 DB Cargo 连接到 Xrail 生产联盟的初始架构方法被证明效率低下。Xrail 流程所需的业务逻辑和技术功能必须分布在所有相关系统中，这增加了服务编排和转换功能的复杂性。
解决方案
为了降低复杂性并确保 Xrail 业务逻辑高效集成到 DB Cargo 系统中，通过新的运输链和服务管理 (TSM) 系统实施了一个中央编排组件。Camunda 平台构成了该系统的基础，可灵活集成旧系统并快速适应不断变化的条件。Camunda 平台还确保符合高可用性和响应时间行为 SLA。工作流引擎嵌入在 TSM 应用程序中，使 Camunda 成为可与常见开发工具集成的轻量级 BPMN 解决方案。TSM 系统在云中运行，充分利用了可扩展性、高可用性和按使用付费模式的优势。
运营影响
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
Building Smart IoT-Connected Railways
• Difficult environment. Communications equipment on trains must function properly in harsh conditions, such as environment temperatures ranging from -25°C to +85°C, according to the EU standard EN50155.• Railway regulations. All products in a train must adhere to strict standards, relating to working vibration, power consumption, and lifetime.• Lengthy process. Time to market in the railway industry can take years from concept to mass production, so product design requires a solid long term vision.
Case Study
Connected Transportation: A Smarter Brain for Your Train with Intel
A modern locomotive, for example, has as many as 200 sensors generating more than a billion data points per second. Vibration sensors surround critical components, video cameras scan the track and cab, while other sensors monitor RPM, power, temperature, the fuel mix, exhaust characteristics, and more.Most of today’s locomotives lack sufficient on-board processing power to make full use of all this data. To make matters worse, the data from different subsystems, such as the brakes, fuel system, and engine, remain separate, stored in isolated “boxes” that prevent unified analysis. The data is available, but the technology needed to process it in the most effective manner is not. As new sensors are added to the machine, the problem escalates.
Case Study
Using LonWorks to Keep Acela Trains Zip Along
Canadian transportation company, Bombardier was tasked with building a bullet train system on rails that were designed for lower speed trains. In addition, they had to ensure safe and optimal operation at high speeds, maximize train uptime and enhance communication with passengers.
Case Study
Delhi NCR Metro: A Mobile App Revolutionizing Public Transportation
The Delhi NCR Metro, a major public transportation system in India, was facing a challenge in providing accurate and comprehensive information to its daily commuters and tourists. The lack of a centralized platform for information about metro station details, train schedules, fare details, parking, elevators, and tourist locations was causing inconvenience to the users. The challenge was to develop a mobile app that could provide all this information accurately and conveniently. The app needed to be equipped with GPS services to help users find the nearest metro and renowned locations. An interactive map was also required to assist travelers who were familiar with the metro lines. The goal was to provide maximum information with minimum input.
Case Study
Cooperation with VR FleetCare for predictive analytics
Bogies are the most significant components of the rail fleet in terms of lifecycle costs and traffic safety. In addition to creating significant cost savings for the rail fleet owners, data-driven maintenance will enhance safety and the usability of the rolling stock. The predictive maintenance capability will improve reliability of the trains, cost-efficiency and passenger comfort. Train traffic will operate more reliably when it is possible to predict rolling stock malfunctions before they cause disruptions in traffic.
