Abstract
Innovation and the marketplace have been pushing Statistical Process Control (SPC) outside its comfort zone, which requires a mature understanding of the product and process and a methodology for verifying the quality of each manufactured item. Especially when a new technology is proven to work and customer interest is high, companies want manufacturing to respond to the uncertainties. Process Monitoring for Quality (PMQ) is a strategy, based on the empirical learning and data gather capabilities of the Big Data environment, that addresses this challenge while verifiably producing quality product. PMQ offers opportunities for learning and quality improvement: it enhances the quality movement by addressing three quality problems that SPC or traditional quality control techniques cannot; and by illuminating future applications.
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