公司规模
Large Corporate
地区
- Asia
国家
- Singapore
产品
- DataRobot’s automated machine learning platform
- DataRobot’s Time Series functionality
技术栈
- Machine Learning
- Time Series Analysis
实施规模
- Enterprise-wide Deployment
影响指标
- Revenue Growth
- Customer Satisfaction
技术
- 分析与建模 - 预测分析
- 分析与建模 - 机器学习
适用功能
- 设施管理
用例
- 预测性维护
服务
- 数据科学服务
关于客户
星桥腾飞集团 (ASG) 是亚洲领先的可持续城市和商业空间解决方案提供商,其全球管理资产总额超过 200 亿美元。该集团总部位于新加坡,业务遍及亚洲、澳大利亚、欧洲和美国的 11 个国家。他们专注于提高新加坡和整个亚洲众多物业的停车场效率。该公司在澳大利亚、中国、印度、印度尼西亚和新加坡等 11 个国家的 28 个城市拥有价值超过 200 亿美元的管理资产 (AUM)。
挑战
星桥腾飞集团 (ASG) 是亚洲领先的可持续城市和商业空间解决方案提供商,其物业面临着停车容量方面的挑战。在新加坡等人口密集的城市,停车容量是一个主要问题。尽管高层建筑设有停车场或车库,但停车容量仍然是物业经理和司机面临的挑战。ASG 希望预测停车场容量,以优化停车服务,改善游客和司机的体验,并可能增加收入。他们之前曾使用过不同的平台来构建模型,但成本高昂,而且无法提供他们所需的准确预测。
解决方案
ASG 转向 DataRobot 的自动化机器学习平台,特别是其时间序列功能,以准确预测其停车场的容量。他们之前曾使用过不同的平台来构建模型,但成本高昂,而且无法提供所需的准确预测。在成功验证了 DataRobot 的概念后,他们发现该平台可以生成更准确的预测,而且更易于使用。最终目标是让司机通过应用程序获取停车场可用性信息。这将使司机能够找到周围哪些建筑物有按小时计费的停车位,为 ASG 提供新的收入来源,并为司机提供更好的停车体验。
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
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
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
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.