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Big Data Analytics Improve Manufacturing Performance Posted on : May 27 - 2015

The theme of the 2015 ARC European Forum in Amsterdam, “The Information Driven Enterprise in a Connected World,” was intended to help develop some clarity over emerging related concepts such as Industrie 4.0 and the Industrial Internet of Things (IIoT). At the forum, we heard many compelling use cases about how applying these concepts can deliver tangible benefits in real-world industrial production across a broad cross-section of industrial sectors. These included presentations about success stories at both Intel and Dow Chemical.

Mitsubishi Electric and Intel presented the results of three IoT pilot applications at one of Intel’s semiconductor fabrication facilities. Intel’s manufacturing equipment generates gigabytes of data per week, per unit, but much of it was not being put to good use. The data includes parameters, error logs, events from machines, and images from vision equipment.

Getting Industry to an Internet Frame of Mind

According to the presenters, Intel used Mitsubishi Electric C Language controllers of the Melsec-Q series, which offer robust network connectivity and high computational performance with high availability in potentially harsh environments. Cimsniper data acquisition and processing software was used to selectively transfer process data at rates on the order of mega-bytes per second via a CC-Link IE protocol for storage on a Cloudera Hadoop on-premise, private cloud-based Big Data Analytics Server (BDAS). Revolution R Enterprise from Revolution Analytics was used to analyze the data. The results are transformed into operational intelligence when presented on dashboards accessible through webservers. Not surprisingly, all the equipment used Intel’s high-performance processors.

The company evaluated three test cases. The first aimed to reduce incorrectly rejected units by automated test equipment. The analytics were able to predict 90 percent of potential tester failures to significantly reduce rejection of good units. A second case predicted issues in soldering related to process deviations, reducing equipment downtime and enabling proactive maintenance. A third case concerned image analytics and automating visual inspection of marginal quality units. The image analytics reduced the selection time by a factor of 10 compared with the manual method. Intel published a detailed white paper on the pilots. In the presentation, the consortium reported $9 million of savings during the pilots.

 

In their compelling presentation, Lloyd Colegrove and his team from the Dow Analytical Technology Center said that the company often underuses, misuses or misinterprets much of its data. Colegrove argued that when put in a much wider context, analyzed automatically, presented attractively, and enabled to act on weak signals, data provides “wisdom” that can guide the company. In this manner, the data can help “justify actions to fix, guide actions to improve, and prescribe actions to make breakthrough changes.”  View more