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Frontiers in Statistical Quality Control 6 (Paperback, Softcover reprint of the original 1st ed. 2001)
Loot Price: R3,118
Discovery Miles 31 180
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Frontiers in Statistical Quality Control 6 (Paperback, Softcover reprint of the original 1st ed. 2001)
Series: Frontiers in Statistical Quality Control, 6
Expected to ship within 10 - 15 working days
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In the 1920's, Walter Shewhart visualized that the marriage of
statistical methods and manufacturing processes would produce
reliable and consistent quality products. Shewhart (1931) conceived
the idea of statistical process control (SPC) and developed the
well-known and appropriately named Shewhart control chart. However,
from the 1930s to the 1990s, literature on SPC schemes have been
"captured" by the Shewhart paradigm of normality, independence and
homogeneous variance. When in fact, the problems facing today's
industries are more inconsistent than those faced by Shewhart in
the 1930s. As a result of the advances in machine and sensor
technology, process data can often be collected on-line. In this
situation, the process observations that result from data
collection activities will frequently not be serially independent,
but autocorrelated. Autocorrelation has a significant impact on a
control chart: the process may not exhibit a state of statistical
control when in fact, it is in control. As the prevalence of this
type of data is expected to increase in industry (Hahn 1989), so
does the need to control and monitor it. Equivalently, literature
has reflected this trend, and research in the area of SPC with
autocorrelated data continues so that effective methods of handling
correlated data are available. This type of data regularly occurs
in the chemical and process industries, and is pervasive in
computer-integrated manufacturing environments, clinical laboratory
settings and in the majority of SPC applications across various
manufacturing and service industries (Alwan 1991).
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