Quality Progress - September 2017 - 44

F E AT U R E

MEASUREMENT
automated, human factors
A measurement system is evaluated to idenalso can contribute to process
tify how accurate and precise its data outputs
Measuring data correctly
variation.
are and to identify any potential variation in the
is important because
Regardless of whether you
measurement system.
incorrectly measured
observe it, a process still produces
Two methods can be used to gauge a meadata will lead to an
variation. Measurement adds an
surement system: define, measure, analyze,
incorrectly identified root
extra component of variation.
improve and control (DMAIC) and design for Six
cause of variation.
There is inherent variation in a
Sigma (DFSS), which is also known as define,
process, which must be identified
measure, analyze, design and verify (DMADV)
before collecting and using data
or identify, define, develop, optimize and verify
for analysis.
(IDDOV).
Knowing variation in the meaDMAIC refers to a data-driven quality
surement system is important to:
strategy for improving processes and is an
+ Differentiate between good and bad products as
integral part of an organization's Six Sigma quality initiative. It
early as possible.
is a roadmap for process improvement and reducing variation
+ Determine whether continuous improvement initiain performance. Organizations typically start with DMAIC and
tives are working.
gradually proceed to DFSS, DMADV or IDDOV as they mature in
+ Understand process stability.
terms of culture and experience.
If the measurement system can't be trusted, neither
The DMAIC process is:
can the data it produces. It makes a measurement
+ Define the problem and improvement required.
system analysis (MSA) a key component of establish+ Measure the process performance.
ing, improving and maintaining quality systems.
+ Analyze the cause of the variation.
+ Improve the process performance by eliminating the root
cause.
MSA
+ Control the improved process and future process
"Measure" is an important step in the DMAIC process.
performance.
Organizations use measurements as the basis for process improvements because you can't control what
you can't measure.
Why measuring matters
Measuring data correctly is important because
All measurements have some degree of uncertainty associated
incorrectly measured data will lead to an incorrectly
with them, which is expressed as a standard error of measureidentified root cause of variation. This, in turn, leads
ment. When the measurement process is manual or partially
to ineffective improvement efforts-one of the
major reasons improvement initiatives fail. Ideally,
FIGURE 1
measuring instruments are evaluated in a controlled
environment. But this isn't always possible. So, to
measure accurately, the instruments must be carefully
constructed and calibrated.
Observed variation
MSA can be generated for continuous and discrete
data. For continuous data, process output data are
measured and remeasured to compare the measureMeasurement
Process variation
variation
ment variation to the overall process variation. This
within and between subgroup variation can be shown
graphically using control charts.
Accuracy
Precision
The three common methods used to analyze variable or continuous data are:
Bias
Repeatability
1. The Automotive Industry Action Group (AIAG)
method. The AIAG is a global organization that
provides an open forum for organizations from
Linearity
Reproducibility
around the world to develop and share information
that contributes to the automotive industry. StatisStability
tical process control (SPC) uses statistical methods

Observed process variation

44 QP

September 2017 ❘ qualityprogress.com


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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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