By Kim C., Kim S., Park M.
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Timeline of selected statistical methods The realities of mass production led W. html). The specific “X-Bar and R” charts he developed are also called “Shewhart” charts. These methods discourage process tinkering unless statistical evidence of unusual occurrences accrues. Shewhart also clarified the common and harmful role that variation plays in manufacturing, causing a small fraction of unit characteristics to wander outside their specification limits. The implementation of Shewhart charts also exposed many unskilled workers to statistical methods.
The financial problem with the wait-and-see attitude based on tinkering and not upfront formal experimentation is that the costs of changing the design grow exponentially with time. This follows because design changes early in the process mainly cost the time of a small number of engineers. Changes later in the process cause the need for more changes, with many of these late-in-the-process changes requiring expensive retooling and coordination costs. Also, as changes cause the need for more changes, the product development time can increase dramatically, reducing the company’s “agility” in the marketplace.
This includes methods for design of experiments (DOE) and optimization. In this book, DOE and optimization methods have been separated out mainly because they are the most complicated quality methods to apply and understand. 2, we preview some of the SQC methods described more fully later in this book. 3 relates these techniques to possible job descriptions and functions in a highly formalized organization. 4 discusses the possible roles the different methods can play in the six sigma problem-solving method.
A bias reducing technique in kernel distribution function estimation by Kim C., Kim S., Park M.