Quality Progress - September 2017 - 45

to evaluate how a process is currently functioning to how
it has functioned historically. In its SPC manual, the AIAG
has identified six rules for determining whether a process
is out of control:
+ Data points are beyond the control limits.
+ Seven consecutive data points are on one side of the
average.
+ Seven consecutive data points are constantly increasing or decreasing.
+ More than 90% of the plotted data points are in the
middle third of the control limit region.
+ Less than 40% of the plotted data points are in the
middle of the control limit region.
+ Obvious nonrandom patterns such as cycles.
2. The analysis of variance (ANOVA) method. Examines
what sources of variation have a significant impact on the
results.
3. The control chart method. Examines how a process
changes over time.
Discrete data, however, are difficult to evaluate
graphically because of the lack of measurement discrimination-for example, yes/no or accept/reject measurements.
The attribute agreement analysis is the common method
used for analyzing discrete or attribute data. This method is
used when the evaluation of items is subjective.

Causes of measurement variation

Observed process variation is caused by two things: actual
process variation or measurement variation (see Figure 1).
An MSA will quantify the amount of variation found in the
data that is caused by the measurement system.
Measurement variation can affect accuracy and precision

FIGURE 2

Accuracy vs. precision
Accurate
Precise

Imprecise

Inaccurate

FIGURE 3

Crossed GR&R study
Operator 1

Operator 2

Part
1

Operator 3

Part
2

GR&R = gage repeatability and reproducibility

(see Figure 2). Accuracy is the closeness of a measured value to the true value and is comprised of three
components:
1. Bias. The difference between the average measured
value and the true value of a reference standard.
2. Linearity. The change in bias over the normal operating
range.
3. Stability. Statistical stability of the measurement process with respect to its average and variation over time.
Precision, on the other hand, is the closeness of
repeated measurements to one another. Precision has two
components:
1. Repeatability (machine variation). Variation in the
measurement system itself. It is the variation that occurs
when successive measurements are made under the
same conditions:
+ Same person.
+ Same unit being measured.
+ Same characteristic.
+ Same measuring instrument.
+ Same setup and environmental conditions.
2. Reproducibility (operator-to-operator variation).
Variation in measurement when two or more people
measure the same unit using the same measuring gage
(or tool).
+ Different person.
+ Same unit being measured.
+ Same characteristic.
+ Same measuring instrument.
+ Same setup and environmental conditions.
Gage repeatability and reproducibility (GR&R) is one
MSA used to evaluate a measurement system's performance. It quantifies the capabilities and limitations of a
measurement instrument, often estimating its repeatability and reproducibility. A GR&R study can determine:
+ The degree of variability in the measurement system

qualityprogress.com ❘ September 2017

QP 45


http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - September 2017

Seen and Heard
Progress Report
Mr. Pareto Head
Career Coach
Expert Answers
Field Notes
Data Disruption
The Deal With Big Data
Better Intelligence
A Study in Measurement
Innovation Imperative
Statistics Spotlight
Standard Issues
ASQ's 2017 Quality Resource Guide
Marketplace
Footnotes
Back to Basics
Quality Progress - September 2017 - Intro
Quality Progress - September 2017 - cover1
Quality Progress - September 2017 - cover2
Quality Progress - September 2017 - 1
Quality Progress - September 2017 - 2
Quality Progress - September 2017 - 3
Quality Progress - September 2017 - 4
Quality Progress - September 2017 - 5
Quality Progress - September 2017 - Seen and Heard
Quality Progress - September 2017 - 7
Quality Progress - September 2017 - Progress Report
Quality Progress - September 2017 - 9
Quality Progress - September 2017 - Mr. Pareto Head
Quality Progress - September 2017 - 11
Quality Progress - September 2017 - Career Coach
Quality Progress - September 2017 - 13
Quality Progress - September 2017 - 14
Quality Progress - September 2017 - Expert Answers
Quality Progress - September 2017 - Field Notes
Quality Progress - September 2017 - 17
Quality Progress - September 2017 - 18
Quality Progress - September 2017 - 19
Quality Progress - September 2017 - Data Disruption
Quality Progress - September 2017 - 21
Quality Progress - September 2017 - 22
Quality Progress - September 2017 - 23
Quality Progress - September 2017 - 24
Quality Progress - September 2017 - 25
Quality Progress - September 2017 - The Deal With Big Data
Quality Progress - September 2017 - 27
Quality Progress - September 2017 - 28
Quality Progress - September 2017 - 29
Quality Progress - September 2017 - 30
Quality Progress - September 2017 - 31
Quality Progress - September 2017 - 32
Quality Progress - September 2017 - 33
Quality Progress - September 2017 - Better Intelligence
Quality Progress - September 2017 - 35
Quality Progress - September 2017 - 36
Quality Progress - September 2017 - 37
Quality Progress - September 2017 - 38
Quality Progress - September 2017 - 39
Quality Progress - September 2017 - 40
Quality Progress - September 2017 - 41
Quality Progress - September 2017 - A Study in Measurement
Quality Progress - September 2017 - 43
Quality Progress - September 2017 - 44
Quality Progress - September 2017 - 45
Quality Progress - September 2017 - 46
Quality Progress - September 2017 - 47
Quality Progress - September 2017 - Innovation Imperative
Quality Progress - September 2017 - 49
Quality Progress - September 2017 - 50
Quality Progress - September 2017 - Statistics Spotlight
Quality Progress - September 2017 - 52
Quality Progress - September 2017 - 53
Quality Progress - September 2017 - Standard Issues
Quality Progress - September 2017 - 55
Quality Progress - September 2017 - 56
Quality Progress - September 2017 - 57
Quality Progress - September 2017 - ASQ's 2017 Quality Resource Guide
Quality Progress - September 2017 - 59
Quality Progress - September 2017 - 60
Quality Progress - September 2017 - 61
Quality Progress - September 2017 - 62
Quality Progress - September 2017 - 63
Quality Progress - September 2017 - 64
Quality Progress - September 2017 - 65
Quality Progress - September 2017 - 66
Quality Progress - September 2017 - 67
Quality Progress - September 2017 - Marketplace
Quality Progress - September 2017 - 69
Quality Progress - September 2017 - Footnotes
Quality Progress - September 2017 - 71
Quality Progress - September 2017 - Back to Basics
Quality Progress - September 2017 - cover3
Quality Progress - September 2017 - cover4
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