MSA with Jackie Graham
The problem may be in the system.
This article is the first in a series in which
Jackie Graham, Ph.D., will explore measurement issues and measurement
systems analysis. Graham is director of PQ Systems Australia, Pty.
A critical aspect of managing processes is the ability to measure well. One needs to be confident in the integrity of the data collected so that appropriate decisions are made about acceptable product and process changes. Have you considered that product declared 'out-of-specification' may be the result of a defective measurement system rather than a poor process?
Costs associated with poor measurement systems include: defective product shipped to a customer; unnecessary process changes (over control); acceptance of poor product or rejection of good product, leading to unnecessary adjustments. The consequent negative effect on profits is obvious and avoidable. If you have ever re-sampled and re-tested because initial results were poor, or adjusted a production process based on bad results, only to find that the results got worse, you have likely been 'touched' by a poor measurement system. These symptoms are real indicators that measurement is the issue rather than the process.
When assessing any product, particularly when applying
the principles of statistical process control, we assume that the
data being collected and analyzed is accurate and reliable. We rely
on the measurement system to provide good
data. This assumption for many people is based on the fact that
they are collecting data in a logical manner using approved procedures
with an experienced tester and have an efficient calibration program
for their measurement devices. But is this enough? Frequently, the
answer is "no!"
Let's take some time to think about what constitutes a measurement system. It is important to look beyond the measuring instrument/equipment and consider the environment and personnel as well.
Have you wondered what impact different testers
can have on results? Over time, it is common for people to develop
bad habits when using measuring devices and test equipment. It is
part of human nature to look for short cuts. A good example is driving
a car. When we learn to drive, to avoid failing the driving test,
we are taught good techniques and learn to avoid bad driving habits.
Experienced drivers, however, often slip into sloppy habits, driving
one-handed, for example. In a similar fashion, it is easy for testers
to develop poor measuring habits. The result is that the accuracy
of the data is adversely affected. From tester to tester there is
no guarantee that everyone will achieve the same result. In measurement
systems analysis terms, the ability of different testers to achieve
the same result is known as reproducibility. It follows
that when poor measurement techniques are followed, it is likely
that individual testers will find it difficult to get the same results
consistently. The ability of one tester to achieve consistent results
is known as repeatability. A good reproducibility
and repeatability (R&R) statistic is an essential precursor
to the successful application of statistical process control and,
more importantly, the ability to consistently produce within specification.
Frequently, having good calibration programs for measurement equipment
is seen as sufficient, but there is no provision for calibrating
personnel or environmental factors. It's like checking a car's brakes
and assuming it will always stop safely without considering the
Our understanding of the principles of statistical process control tells us that every process contains a component of inherent or expected variation in output. It is important to understand that variation in output also contains a measurement system component. This can be thought of as an equation:.
Process variation + Measurement system variation
= Output measurement
Problems arising from a poor measurement system can be avoided by conducting a thorough assessment and implementing appropriate strategies to remedy any errors found and to ensure on-going accuracy. Such an assessment includes an examination of:
The methods employed, known as R&R analysis or measurement systems analysis (MSA), follow a thorough, statistically robust methodology. They accurately define the ability of the various components of measurement systems. Many measurement systems, when assessed using these techniques, are found to be inadequate and are adversely affecting output.
We will be talking more about measurement systems
analysis in future additions of Quality eLine, including
how to set up a measurement study and analyze the resulting data.
We recommend GAGEpack
for all your measurement assessment needs.
2006 PQ Systems.
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