下载PDF
WebFOCUS Drives Efficiency and Global Expansion at ClaimsPro
技术
- 分析与建模 - 实时分析
- 应用基础设施与中间件 - 数据交换与集成
适用功能
- 商业运营
用例
- 质量预测分析
- 根因分析与诊断
挑战
ClaimsPro, Canada’s leading independent adjusting company, was in the midst of rapid international expansion and wanted to enhance operations through improved measurement and monitoring of key activities. However, the company’s managers were forced to rely on the IT team to build reports for them from scratch. This process was time-consuming and inefficient. The company needed a business intelligence (BI) solution that would allow non-technical users to satisfy their own information needs and provide managers with the ability to track vital metrics, such as revenue, work in progress, and quality assurance.
关于客户
ClaimsPro is Canada’s leading independent adjusting company. Since 1986, the organization, a sub-division of SCM Insurance Services, has been managing claims on behalf of the country’s largest insurers and self-insured corporations. They help their clients to investigate incidents, assess the value of damages, detect and prevent fraud, and more. With more than 1,000 employees, ClaimsPro operates in more than 110 branches located across Canada. The company is in the midst of rapid international expansion and wanted to enhance operations through improved measurement and monitoring of key activities.
解决方案
ClaimsPro chose the WebFOCUS BI platform from Information Builders to create a comprehensive, yet user-friendly dashboard that provides managers with the ability to track vital metrics, such as revenue, work in progress, and quality assurance, and drill down to more detailed data to investigate problems. Information Builders solutions were leveraged to build and deploy a dashboard for in-depth claims reporting. First, iWay DataMigrator was employed to move information from the company’s SQL Server-based production systems into a Microsoft Analysis Service cube and a SQL Server repository. More than two million records are transferred to the repositories on a weekly basis, and over ten million records are migrated each month. WebFOCUS Developer Studio was then used to design and build the dashboard, which retrieves information from the repositories and displays it for end users. Information Builders’ Customer Education department provided training to Mudryk and his team, empowering them with the skills they needed to set up and roll out the new environment in a short timeframe.
运营影响
数量效益
相关案例.
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
IoT Data Analytics Case Study - Packaging Films Manufacturer
The company manufactures packaging films on made to order or configure to order basis. Every order has a different set of requirements from the product characteristics perspective and hence requires machine’s settings to be adjusted accordingly. If the film quality does not meet the required standards, the degraded quality impacts customer delivery causes customer dissatisfaction and results in lower margins. The biggest challenge was to identify the real root cause and devise a remedy for that.
Case Study
Large Oil Producer Leverages Advanced Analytics Platform
Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.
Case Study
The Internet of Trains
Train operators the world over are expected to work miracles, i.e. never to be late. So, with acute service and availability targets to meet, an efficient maintenance program is important. And data-enabled functionality is a must for Siemens. Reactive maintenance (after an incident) and routine, preventive maintenance with its visual inspections and scheduled exchange of components, are no longer enough. We’ve moved on to more cost-effective, condition-based, predictive maintenance. The actual condition of components is measured via the transfer and remote monitoring of diagnostic sensor data; data which is also used to analyse patterns and trends. This helps predict when a component is likely to fail, so it can be repaired before anything untoward happens. To ensure the commercial sustainability of this approach, Siemens needs to use and re-use existing data, creating a kind of ‘Internet of Trains’. Towards this end, they’re analysing sensor data in near real time, which means they can react very quickly, ensuring that customer transport services aren’t interrupted. “It is really difficult to define every issue before it impacts operations using only data from the trains”, Kress explains. However, recent success stories prove that everything is possible.
Case Study
FMCG Case Study – CPG Line Monitoring
The leading CPG company operates several warehouses, mostly closer to the last distribution point (Large retailers). Products in various categories are packaged in specific delivery or display cartons at these facilities. As most CPG businesses consist of high volume with low margins, optimizing every operation and effective utilization of resources add up to profit margins. The key problem at these warehouses is the lack of visibility into reasons for machine breakdowns or idle time, thereby delay in delivery. The sunrise meetings lead to post-mortem of delivery issues like delay or quality. The customer wanted to implement real-time line monitoring and alert system to gain control over downtime issues and implement improvement measures.
Case Study
Prevent Process Inefficiencies with Automated Root Cause Analysis
Manufacturers mostly rely on on-site expert knowledge for root cause analysis. When the defective product is sent to lab for analysis, it is laborious and always a post-mortem one. Manufacturers that collect data from IT and OT also need a comprehensive understanding of a variety of professionals to make sense of it. This is not only time consuming, but also inefficiencient.