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云 Hortonworks 是领导者。阅读 Forrester Wave。



Manufacturing is all about efficiency. Using connected devices, internet of things (IoT), predictive analytics and machine learning, manufacturers organizations can now leverage big data in manufacturing to create efficiencies and be first to market with better products while reducing costs and improving customer satisfaction to make better products and be first to market.



Now relatively inexpensive sensors and IoT devices can gather and frequently transmit data along many steps in the manufacturing supply chain: design shops, supply chain, production line and warranty operations. Hortonworks DataFlow (HDF™) securely collects real-time sensor data-in-motion, allowing manufacturers to quickly identify problems as they occur, wherever they occur in the connected factory. Hortonworks Data Platform enables historical analytics on data that just doesn't fit into legacy platforms, helping engineers move beyond reactive error avoidance to proactive process improvement.



Manufacturers want to minimize the inventory that they keep on hand and prefer just-in-time delivery of raw materials. On the other hand, stock-outs can cause harmful production delays. Sensors, and RFID tags and IoT in manufacturing reduce the cost of capturing supply chain data, but this creates a large, ongoing flow of data. Hadoop can store this unstructured data at a relatively low cost. That means that manufacturers have more visibility into the history of their supply chains and they are able to see large patterns that might be invisible in only a few months of data. This intelligence can give manufacturers greater lead-time to adjust to supply chain disruptions. It also allows them the connected factory to reduce supply chain costs and improve margins on the finished product.


High-tech manufacturers use sensors to capture data at critical steps in the manufacturing process. This data is useful at the time of manufacture, to detect problems while they are occurring. However, some subtle problems—the “unknown unknowns”—may not be detected at time of manufacture. Nevertheless, those may lead to higher rates of malfunction after the product is purchased. When a product is returned with problems, the manufacturer can do forensic tests on the product and combine the forensic data with the original sensor data from when the product was manufactured. This big data in manufacturing adds added visibility, across a large number of products, helps the manufacturer improve the process and products to levels not possible in a data-scarce environment.


Today’s manufacturing workflows involve sophisticated machines coordinated across pre-defined, precise steps. One machine malfunction can stop the production line. Premature maintenance has a cost; there is an optimal schedule for maintenance and repairs: not too early, not too late. Machine learning algorithms can compare maintenance events and machine data for each piece of equipment to its history of malfunctions. These algorithms can derive optimal maintenance schedules, based on real-time information and historical data. This The use of manufacturing predictive analytics can help maximize equipment utilization, minimize P&E expense, and avoid surprise work stoppages.


生物医药制造需要仔细监视和控制环境条件。任何生产运行的目标是最大限度提高首次产出率 (FTY),这是产品首次通过产品线时正确制造的产品数量。FTP 每增加百分之一都表示生产成本的降低。对操作的洞察力不佳通常会阻碍 FTY 提升。如果传感器数据能够与其他现有数据存储器集成,传感器可以提供原始数据来提高这种洞察力。Hadoop Data Lake 简化了这种集成,因为 Hadoop 在提取数据之前不需要先验模式。此外,Hadoop 的更低成本存储意味着群集可以存储更多数据、更多格式、更长时间,以探索数据中的新型关系。阅读了解默克研究实验室如何通过 Hortonworks Data Platform 来优化生物医药制造。


彻底测试过的产品仍会有售后问题。客户可能不会向制造商报告问题,但仍会在社交媒体上向朋友和家人抱怨产品。这种关于产品问题的社交数据流可能增加来自传统支持渠道的产品反馈。Hadoop 存储海量的社交媒体情绪数据。制造商可以挖掘该数据以发现有关产品在其生命周期中如何延误的早期信号。能够快速了解问题并采取提前操作来保护产品的声誉,这对于赢得和保持客户忠诚度很重要。


Western Digital
Western Digital

全球一半的硬盘是由西部数据制造的。 借助 Hadoop 和 Hortonworks Data Platform,西部数据工程师可以更快速地看到制造数据,更长时间的保存并可将其与更多团队成员共享。这意味着持续改进其制造流程,从而降低成本,提高客户满意度。在采用 Hortonworks Data Platform 之前西部数据遇到了制造方面的难题,西部数据…