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GKN Land System: Business intelligence proactively manages supply chain performance
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 实时分析
适用行业
- 汽车
适用功能
- 物流运输
- 采购
用例
- 库存管理
- 供应链可见性(SCV)
服务
- 云规划/设计/实施服务
- 数据科学服务
挑战
GKN Land Systems 是一家全球集成动力系统部件、系统、解决方案和服务供应商，在通过收购实现增长的过程中，该公司面临着挑战。其 ERP 系统的复杂性不断增加，难以维持对供应链管理、库存管理、采购和客户满意度的关键绩效指标 (KPI) 的高层监督。该公司需要一种解决方案，能够全面了解其运营情况，并帮助其根据 KPI 监控其绩效。
关于客户
GKN Land Systems 是一家全球集成动力系统组件、系统、解决方案和服务供应商。该公司在全球拥有 39 家制造工厂。随着公司通过收购不断发展，它面临着管理日益复杂的 ERP 系统的挑战。这种复杂性使公司难以对供应链管理、库存管理、采购和客户满意度的关键绩效指标 (KPI) 进行高水平监督。
解决方案
GKN Land Systems 实施了数据仓库和商业智能解决方案，使公司能够监控其及其制造设施如何满足 KPI。当出现偏差时，该解决方案会触发自动警报。例如，如果客户满意度分数下降，GKN Land Systems 可以使用 BI 解决方案从多个维度深入研究设施和业务流程数据，以找到根本原因并采取措施快速解决问题。该解决方案组件包括 IBM Netezza 1000、IBM Cognos Business Intelligence V10、IBM InfoSphere DataStage 和 IBM Global Business Services—Business Consulting Services。
运营影响
数量效益
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.
