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JULY 2019 I SMT007 MAGAZINE 117 near-real-time rates, will increase the availabil- ity of equipment parameter data to positively impact yield and quality. There are several challenges and potential solutions associated with these changes, including: • Connectivity architecture providing smart manufacturing functionality (Figure 1) • Data flow between manufacturing execution systems (MES)—fog or cloud— and machine interfaces (edge) • Security of information transmitted between and across the cloud to remote access points • Standardization/guidelines for data formats between edge devices (e.g., machine-to-machine (M2M) communication) • Preferred options for real-time monitoring and response time (e.g., M2M versus machine-to-MES, edge to fog) Printed Circuit Board Assembly (PCBA) The PCBA segment is in alignment with the semiconductor and OSAT verticals. The primary topics to address are data quality and incorporating subject-matter expertise in analytics to realize effective online manufac- turing solutions. The emergence of big data in electronics manufacturing operations should be discussed in terms of the "5Vs Framework," which is as follows and shown in Figure 2: 1. Volume 2. Velocity 3. Variety (data merging) 4. Veracity (data quality) 5. Value (application of analytics) The majority of subject-matter experts (semiconductor, OSAT, and PCBA) stress that robust and redundant architectures for data flow must be established to benefit from the 5Vs attributes. Data volumes in factories are growing at exponential rates. Pervasive data velocities measured as the data collection rates from equipment increased from less than 1 Hz in the 1990s to 10 Hz today, and are projected to be at 100 Hz in a few years. Data from equipment, maintenance, yield, inventory management, MES, and enterprise resource planning (ERP) existed for several years; however, analyt- ics tools have evolved to leverage and merge multiple data sources to explore relationships, detect anomalies, and predict events. The 5Vs are foundational to appreciate widespread adoption of big data analytics in the electron- ics industry. It is critical to address the iden- tified gaps—such as accuracy, completeness, context richness, availability, and archival length—to improve data quality to support advanced analytics for the electronics manu- facturing industry [1] . Quantified Key Attribute Needs The development of a scalable architecture that provides flexibility to expand; connect across the edge, fog, and cloud; and integrate a variety of devices and systems generating data flow streams is critical. Different indus- tries seeking to deploy smart manufacturing technologies should leverage architectures that provide the desired attributes, and data flow architecture is generally considered a prime candidate for leveraging and cross-industry collaboration to identify the optimum solution. Figure 2: "5Vs Framework" for big data.

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