When it comes to automating the factory and the manufacturing processes, big data has an increasing role to play.
No longer just the realm of Google, Facebook, and Amazon, big data is the new norm for enterprise analytics and is pervasive across many industries: drug discoveries enabled by genomic research, real-time consumer sentiment, and social interaction for retail represent a small sample of business innovation derived from big data technologies and analytics. Whether it is fine-tuning supply chains, monitoring shop floor operations, gauging consumer sentiment, or any number of other large-scale analytic challenges, big data promises to make a tremendous impact on the manufacturing enterprise.
The term “big data” describes a variety of data collection and analysis tools and techniques. The manufacturing shop floor, with its increasing automation, is ripe with data that can be collected and analyzed. Using the tools and techniques to collect and analyze the data to create “big data,” the information collected can be used to understand a plethora of factors that impact manufacturing and the factory floor.
Big data enables efficiently creating “mash-ups” of data from all these systems to answer specific questions, e.g., what was happening in the various automation systems when a certain manufacturing defect occurred; or tracing all the parts made that could be affected by a machine that was out of tolerance, or how an out-of-tolerance condition in a particular manufacturing cell would affect customer orders. Big data allows answering seemingly simple questions, such as “count how many times a certain machine breached a threshold,” that otherwise could not have been answered quickly or easily in the past.
Manufacturers are beginning to report substantial cost savings or new revenues resulting from big data insights. A leading chip manufacturing company pulls logs from their chip manufacturing automation controls, which can be up to five terabytes per hour. By using big data analytics, the chip manufacturer can identify which specific steps in one of their manufacturing processes deviate from normal tolerances. This early detection can save entire batches from being rejected.
This same manufacturer also runs tests on pre-production chips and analyzes the data. By using predictive analytics, they are able to reduce the number of tests they have to run during the production process, thus saving millions of dollars in manufacturing costs for just one product line.
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