汽车制造商GWG设备维护系统
技术
- 功能应用 - 计算机化维护管理系统 (CMMS)
适用行业
- 汽车
适用功能
- 维护
用例
- 预测性维护
客户
未公开
关于客户
主要汽车生产商,为所有工厂和研发中心实施,以处理所有设备和测试工具。系统上的 800 多名工程师使用 SAP 进行集成
挑战
制造商需要对其所有设备和资产进行集中查看,以启用 RCM 和 TCM 等功能,以最大限度地延长正常运行时间和生产力。制造过程中有可能减少库存冗余、MRO 领域的浪费,并最大限度地利用工程师的不同技能组合,所有这些都是直接影响制造过程 OEE 的因素。
解决方案
GWG设备维护系统(以下简称GWG)提供全方位的维护过程支持,实现全生产性维护(TPM)、全生命周期管理、故障模式与影响分析(FMEA)等能力。 GWG 专注于工厂维护和设备运行状态管理,提供有关功能位置、设备、零件、FMEA 记录等的通用信息。GWG 旨在整合各种维护流程,并能够支持其数据库上的业务分析功能。 GWG 支持设备管理和工厂维护的全面生产维护 (TPM),从而在组织中实现精益制造。咨询服务有助于提高他们的设备性能,以实现零事故、零停机、零速度损失和零刮擦。 GWG 从根本上与机器测量工具集成,以及时记录所有故障、事件、维护实例、状态检查和其他相关信息。 GWG 可以在本地集中式系统中部署,也可以作为 SAAS 工具部署。
收集的数据
Asset Utilization, Inventory Levels, Maintenance Requirements, Overall Equipment Effectiveness, Product Lifecycle
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
Monitoring of Pressure Pumps in Automotive Industry
A large German/American producer of auto parts uses high-pressure pumps to deburr machined parts as a part of its production and quality check process. They decided to monitor these pumps to make sure they work properly and that they can see any indications leading to a potential failure before it affects their process.