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To produce products with consistent high quality, it is critical to maintain the stability of a manufacturing process. However, the traditional statistical process control (SPC) do not use process adjustments as potential tools for continuous quality improvement. This should be reconsidered because many assumptions used by SPC are no longer valid in a modern manufacturing environment. For example, customized short-run productions require frequent process setups, which may cause a high possibility of process setup error and drastically deteriorate product quality if the process is not quickly and properly adjusted. This monograph aims to provide several process adjustment strategies, which are derived from statistical theory, for maintaining a manufacturing process on its stable and desirable level.

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Rong Pan is an Assistant Professor of Industrial Engineering at Arizona State University, United States. He received his doctoral degree in Industrial Engineering from the Pennsylvania State University in 2002. His research intererst is quality and reliability engineering, and more recently, reliability modeling and data analysis.

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