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Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corrective action only after variations occur. Learn how the Stream of Variation (SoV) methodology helps reduce or even eliminate variations throughout the entire MMP in Jianjun Shi's Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. The unified methodology outlined in this book addresses all aspects of variation reduction in a MMP, which consists of state space modeling, design analysis and synthesis, engineering-driven statistical methods for process monitoring and root-cause diagnosis, and quick failure recovery and defect prevention. Coverage falls into five sections, beginning with a review of matrix theory and multivariate statistics followed by variation propagation modeling with applications in assembly and machining processes. The third section focuses on diagnosing the sources of variation while the fourth section explains design methods to reduce variability. The final section assembles advanced SoV-related topics and the integration of quality and reliability. Introducing a powerful and industry-proven method, this book fuses statistical knowledge with the engineering knowledge of product quality and unifies the design of processes and products to achieve more predictable and reliable manufacturing processes.