Excerpt: The Printed Circuit Assembler’s Guide to… Smart Data, Chapter 2


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The following excerpt is from The Printed Circuit Assembler's Guide to...™ Smart Data: Using Data to Improve Manufacturing book.

Chapter 2: The Difference Between Data and Analytics
Companies have been collecting data in large volumes. Highly varied data from manufacturing operations comes in quickly that needs to be validated, and its value prioritized so that it can be turned into something useful—transformed from big data to smart data. The amount of data available has grown exponentially into big data. Twenty years ago, a PCB work order resulted in 100 data records, megabytes of data; today, it is 10 billion records, terabytes of data. The investment in collecting this data and storing it is high. However, without a way to analyze the data, without analytics, it will not result in ROI.

Using analytics provides the best results with high-quality data. However, with bad or non-validated, and inappropriately prioritized data, the most advanced analytics still can be misleading and a waste of time. What is high-quality data? It is data that has been normalized, formatted, and validated so that it can be translated and read to provide insights and foresight. It is data that can be understood at the point of consumption and be immediately acted on.

Data analytics is the process of examining data sets to draw conclusions about the information they contain. Manufacturing intelligence means taking big data, applying advanced analytics, and putting it back into manufacturing processes.

Analytics for manufacturing can be used in:
• Asset management for accurate, real-time utilization, and OEE
• Traceability for capturing and investigating complete material and process traceability data for individual PCBs, as well as full system assemblies, using high-availability big-data storage
• Operation and labor management to measure and analyze how resources are spent and track WIP in real-time
• Quality control to identify and analyze process and material failures and drive continuous improvement
• Design-to-manufacturing flow to detect factors affecting yield and point out areas for improvement

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