This slide shows EID use cases for which there is supporting material, either in this presentation or in field presentations. Use cases in red are supported by slides in this presentation. Use cases in gray are supported by field presentations. Insurance use cases are per business process, per industry, with some commonality across industries. Major insurance areas include: Property Casualty (auto) Employment (health, retirement, social security, workers comp) Financial (fraud, risk mitigation, securities insurance) Utilization modeling product portfolio management – create better new offerings, tightly manage their life cycle Premium optimization – tie premiums to risk, consumer behavior Increase customer loyalty/reduce flight risk Claims management Impact of the uninsured Distributed workforce management
After 减少80%的分析时间 节省10万小时工程师工作时间 帮助证明加速问题非电子原因。 合并20个数据源数据，包括结构化数据（整车仓
库数据，质量检测数据等等）和非结构数据（内部保修数据，外部投诉记录数据），以及内部和外部的信息。
容易收索、浏缆和聚合 企业级规模内存分析
演示者
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COMPANY OVERVIEW Toyota Motor Corporation is a Japanese multinational automaker. Industry: Automotive Employees: 300,000 Revenue: $220B US CHALLENGES/OPPORTUNITIES Toyota is another great example of Discovery for Warranty and Quality. In 2010 they had the biggest recall in their history. Every day they were criticized on the front page of the NY Times for unintended acceleration problems with the Camry, among others. But the real issue for them was that they didn't know if they had a real problem or if they were just being accused. Clearly, there was no report to answer this question. SOLUTION We helped them combine data from the vehicle warehouse, quality touch point apps (Oracle apps) that capture data from the manufacturing floor, in-house warranty claims and more -- about 20 sources in all, both structured (vehicle warehouse data, QA data, etc) and unstructured (internal warranty claims, externally filed complaints, etc). But then they realized they didn't have enough claims in-house to do the analysis. They had to the claims filed with the National Highway Transportation Safety Authority (NHTSA). So, it was multiple sources all with their own structures plus unstructured text in the claims descriptions, full of misspellings and bad grammar. Then, they had to make this data available to the quality engineers. These are people with the expertise to ask questions about vehicles, suppliers, manufacturing processes and facilities. But they don't have the expertise to write the queries. That's what our discovery app did. It combined these diverse, changing data sources so it could be easily searched, browsed, and aggregated by people with expertise in the business, but not in writing queries. And it does this with in-memory performance at enterprise scale BENEFITS This app helped the quality engineers get fast answers to new questions. It played a pivotal role in helping Toyota prove that there were no problems with its electrical systems or pedal assemblies. And the app did this in a timely fashion. Toyota estimates that if they had to solve this problem with BI tools -- a new warehouse, with reports and dashboards on top -- it would have taken over a year. With EID it took just 12 weeks.
而宝马公司用此工具来降低保修成本。 主要有两个目的， 1。用来发现一些欺诈性的索赔项目，比如说一些汽车并不存在的索赔项目。 2。发现用户索赔和供应商供货质量相关关系，找出不合格的产品供应商和产品批次。 那他们现有的分析系统和方法效率很慢， 查找一个问题的原因需要几天甚至几个月的时间。另外，质量相关的数据存在多个数据源中，很难聚合在一起。 在实施过程中， 他们讲项目分成三个阶段，第一阶段，导入保修数据、故障数据，车辆数据， 供应商记录数据。 实施周期是8个星期，部署到十个用户。 那在第一阶段上线最初几个月，就已经为他们省了220万欧元的费用。 第二阶段，他们扩充数据源， 导入外部记录，包括一些网站上面的调研报告和社交媒体记录，用户数扩充到3百个用户。 第三阶段，导入工程数据，CAD图表等等，持续改善产品质量。 用户反馈的声音是，他不需要为一个分析依赖于专业的分析人员， 并且提高了工作效率，节省了保修成本。 BMW, the world’s leading provider of premium products and services for individual mobility, significantly reduces annual warranty costs and improves warranty response times with Oracle Endeca Information Discovery. They can now find field performance issues in minutes rather than days. In the first few months alone, identified 2,2 eur million in savings. COMPANY OVERVIEW World’s leading provider of premium products and services for individual mobility Industry: Automotive Employees: 100,000 Revenue: 68 eur billion CHALLENGES/OPPORTUNITIES Objective: reduce warranty costs, Identify incorrect or fraudulent claims (example: Identify claims for repairs for options that the vehicle doesn‘t have), understand correlations between customer system usage and warranty claims and identify trends in supplier quality and associated claims Challenges: Existing analysis solutions are too slow. A complex analysis may take several days to complete in several stages - or not be possible at all Quality related data is stored in numerous data bases and warehouses – difficult to combine all the data needed for an investigation SOLUTION The first application (pilot) was deployed in 8 weeks to 10 users. In the first few months alone, identified 2,2 eur million in savings. This first application combined 5 disparate data sources, including Warranty Data, Problem Management Data, Supplier Data, Vehicle Data and Breakdowns The second phase added several additional data sources and is deployed to about 300 users. Data sources include JD Power market research, social media, websites. TUV vehicle inspection data, etc. Phase 3 plans to combine additional data sources from engineering data, CAD views, continuous improvement data, etc. With OEID, BMW can now more quickly investigate problems to understand how to fix them. As well, they can be pro active in product development and rollout to understand what kinds of problems could occur and avoid building them into the design. The application supports the identification and realization of significant Warranty cost reductions. BENEFITS: Quickly combine the data: deployed in 8 weeks with 5 disparate data sources. Were able for the first time to included total costs by supplier in analysis Quickly explore the data: Analyses can be conducted rapidly and interactively in one sitting, rather than being spread over multiple steps and hours or days. Tasks previously requiring days of work are now conducted in minutes Quickly evolve the application: From 5 initial sources, added several more, and deployed to 300 users. CUSTOMER PERSPECTIVE “At last I‘m not dependent on a specialist for every analysis!” “Monitoring of warranty costs for the TOP Panorama Roof problems takes 30 mins with ISS compared to 2 days previously.” “Time saving with ISS for the Analysis of top warranty costs for seats is 1:40 [Hours].” “In the PLT for lights we were able to identify that LED-Number Plate Lights were being claimed under warranty.” “For our meeting with McKinsey we were able to quickly pull together all important data using ISS.”
福特每年采购成本占到销售收入3/2，超过600亿美金， 他们有144个小的团队进行全球范围内配件采购，最初他们认为他们面临一个流程管理方面的问题，所以耗费大量的人力、物力，财力制造相应的报表去监控他们的供应商，用户的需求，合同等等， 以便很好的管理他们的采购费用。 其实这是一个信息可见性问题， 因为静态的报表并不能估算实际的需求，评估供应商，甚至不能作为合同谈判的素材。 福特需要很快发现周围市场的价格， 在不同区域内有哪些供应商，他们的产品信息，从而增加合同谈判能力。其二，他要知道，够成一台车中所有零配件的采购价格，从而知道一台车的真实成本。 那这套系统上线以后，福特知道如何来制造他的汽车，成本是最低的。另外一方面，设计工程师在设计产品时候， 可以选择合适的配件，从而降低整车制造成本。 COMPANY OVERVIEW Ford Motor Company is an American multinational automaker Industry: Automotive Employees: 164,000 Revenue: $136B US CHALLENGES/OPPORTUNITIES Material costs (including commodity costs) make up the largest portion of Ford’s Automotive total costs and expenses, representing in 2011 about two-thirds of the total amount. So conservatively, $60-70B a year. Ford initially thought they had a process problem around their commodity management. With 144 commodity teams on a global basis: brakes, window accessories, etc, each of those teams had to create a report analyzing supply, demand, suppliers, contracts, etc. These reports took 6 months to prepare and, should they be printed would be about 2 inches thick. And out of date before they were done. This was not a process problem, it was an information visibility problem. A discovery problem. Static ppt reports could not provide the insight needed to estimate demand, review suppliers, rationalize spend, negotiate contracts, etc. Ford needed the ability to conduct discovery very quickly around spot prices, contract prices, negotiated contracts, what suppliers have contracts, for what parts, what region of the world, what markets they service, what brands are represented, what models are part of that mix. Second, Ford needed the ability to conduct discovery to view all the sub assemblies and components that go into a car in order to build out a bill of material and understand the true cost of building a car. SOLUTION OEID was able to bring together global regional systems and so Ford could understand, both from a commodity perspective and an engineering perspective, how they’re building cars now and how they will be building in the future. It also improved their ability to do “should” costing. It’s not that Ford didn’t have projections previously, it’s just that they couldn’t provide visibility across all the data needed to make better design & purchasing decisions. For instance, knowing the “should cost” price allows a designer to see the full picture around price impact when specifying a part which will be used for at least the next 5 years. Also a commodity manager can see how pricing is trending based on where they are today vs. where they wanted to be when the “should cost” pricing was established. Part rationalization They can look up a particular part where used, across all brands and models. This allows design engineers to find the right part to use and avoid introducing a new part. They can also find “similar” parts or “like” parts. They save 30k for every part they avoid introducing. BENEFITS For the initial POC, ingested40 spreadsheets of part technical attributing around brakes and windows, melded it together with hierarchy of parts, subcomponent and component assemblies, as well as model and brand info, and costs of all those parts. They had never been able to do this before. Deal was done in 4 months for over $3M. They won’t tell us how much they’ve saved, but if even 1% of their $60B spend on material costs… Ford has 18,000 named users of which 8000 use Endeca at least daily.
BMW Identify opportunities for warranty cost reductions 售后维修服务
Ford Sharing vehicle architectures, components and best practices from around the world, and taking full advantage of global economies of scale, reduce operating costs and realign capacity
供应链管理
GM Decrease warranty costs and choose the right parts to put into components (design)
The main lesson to draw from this story is that BI and Information Discovery are peers. They solve different problems and create different kinds of value. BI provides proven answers to known questions. Information Discovery provides fast answers to new questions. And the KPIs, reports and dashboards on the left drive the need for the exploration and discovery on the right. For example, when the report says that warranty claims on the top-selling product went up 15% last month, the new questions are “What changed? What’s the root cause? What are customers saying about this? That exploration happens in a discovery app. The relationship goes both ways. Information Discovery creates new KPIs for the BI stack to deliver. For example, a consumer packaged goods company learned that preference for seemingly unrelated brands was highly correlated in certain customer segments. This came from a social media discovery app and suggests new KPIs they should track.
• ProductID = 506 • Amount = $499.99 • Saddle = Bontrager SSR • Mountain Accessories = Fork and shock sag meter • Mountain Accessories = Water Bottle • Review = A great bike for off road. Smooth ride over the bumps • ReviewSentiment = Positive • ReviewTerm = Great • ReviewTerm = Off Road • ReviewTerm = Smooth • ReviewTerm = Bumps
So that’s the server, now let’s take a tour of the tools provided to enable the Endeca Server to do it’s work… The Endeca Integrator and Endeca Integration Suite.
We’ve enabled advanced search, contextual navigation and visual analysis through a set of Java-based discovery components. These components are available through a portal framework based on the LifeRay Open Source edition. This implementation delivers on the capabilities of secure communities of interest, across multiple discovery applications with shareable aspects including EID data sources, portal pages, bookmarked filtered states, and common views of the data including shared metrics. Simple visualization and discovery applications can be assembled through a drag-and-drop approach in a matters of minutes and the most complex applications can be built out in days as opposed to months. Let’s take a look at the interaction model when assembling an application…
[build] Oracle Big Data Discovery is a set of visual analytic capabilities, built natively on Hadoop to transform raw data into business insight in minutes, without the need to learn complex products or rely only on highly skilled resources. It’s the visual face of Hadoop.
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