Purpose
The 1.5 sigma shift and drift takes into account the natural behavior of most processes to vary with time. Based upon analysis, it is recognized as the approximate value of variation over time.
Anatomy
Understanding The Six Sigma 1.5 Sigma (STD DEV) Shift
Reference: Mikel J. Harry, The Vision of Six Sigma
Terminology
A. Long term central tendency of the process, ( i.e. overall mean)
B. Sample distribution (typical)
C. Sample mean (typical)
D. Dynamic shift between consecutive means (typical)
E. Sample number
F. Equivalent short term distribution
G. Short term standard deviation short-term (ST), result of common-cause variation
H. Short term mean centered (i.e. the best you may expect)
I. ±1.5 sigma ST band in which the short term means are expected to lie, result of dynamic variation in the process mean over time only due to special-cause variation.
J. Long term distribution illustrating the total variation due to both common and special-cause variation.
Purpose
To illustrate the effect of shifts –static and dynamic, on process output distribution.
Anatomy
Static And Dynamic Process Shift
Reference: Mikel J. Harry, The Vision of Six Sigma
Terminology
A. Data points (typical)
B. Subgroup (size n=5, typical)
C. Subgroup mean (typical)
D. Grand average : for subgroups 1 to 10
E. Grand average : for subgroups 11 to 20
F. Static shift between grand averages and overall average (i.e. offset of the mean)
G. Dynamic shift between consecutive subgroup averages
H. Overall average
The changes between consecutive grand means (i.e. static shift) may be small but still indicates clearly that the process central tendency is shifting. This shift may be the result of process improvement or unintentional process change.
Note: The occurence of variation over time is real and can be clearly seen in graphical charts. In Six Sigma the 1.5 standard deviation (sigma) shift is an estimate of this variation over time. It is based upon studies performed by Motorola.
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