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NWA Quality Analyst
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Using SQC to Monitor Analytical Methods
Using statistical quality control (SQC) analysis
to monitor analytical methods in the laboratory can help a manufacturer
improve the reliability of reporting and identify the source of problems
more quickly. This paper describes the application of NWA Quality Analyst
to monitoring analytical methods in the laboratory. The example used is
a composite of several NWA Quality Analyst users.
Analytical variation in the laboratory can have a large impact on a manufacturer's
bottom line. For example, when a manufacturer must certify that a batch
meets specifications before it can be shipped to the customer or stored
in tanks, accurate analysis for certification is critical. If an out-of-specification
batch is actually certified as within specification, the manufacturer faces
potential problems with customers, possibly even being removed from the
qualified purchasing list. Yet, if the laboratory finds out-of-specification
product, where is the problem? Is the lab wrong? Or, is there a problem
in production? The more confidence the plant has in the lab's analysis,
the quicker and easier the problem can be located and resolved. The more
credible the lab analysis, the more confidence customers can place in the
reports they receive.Statistical Quality Control (SQC) with its control
charts can be a powerful tool for monitoring analytical methods. The more
reliable the analytical methods, the more people can trust them to provide
valid results.The key to increasing the reliability of results is to monitor
the variation in the analytical methods themselves using a control chart.
Variation in analytical methods is usually caused by process-related phenomena
such as differences between technicians, instrument maintenance, or sampling
techniques. Developing a control chart allows the analyst to determine the
normal variation in a method. Then, if there is a problem with the method,
the control chart can signal the analyst to investigate it. Additionally,
control charts can help the analyst reduce the variability of the method
itself.The following example illustrates how a control lab uses SQC with
NWA Quality Analyst to monitor the variation in one of its analytical methods.Developing
a Control ChartA large, batch chemical manufacturer must provide a Certificate
of Analysis (C of A) with each batch before the material is shipped to a
customer or put into storage tanks for later shipment. The C of A shows
that the batch meets the customer's specifications for the product.This
lab has five technicians to handle all the testing required for the facility.
All technicians are cross-trained in all methods and any technician can
be assigned to any particular batch.In this example, the manufacturer must
show that three variables meet specifications. These three are Phenol (not
greater than 0.5%), Nonylphenol (not less than 95%) and Dinonylphenol (not
greater than 4%). The lab uses a gas chromatograph (GC) to test each of
these three variables.The first step in developing the control chart is
to choose a standard to test. In this example, the lab draws a 4-liter sample
of chemical from production to be used as an in-house reference lot. This
reference lot is selected because it represents the mid-range of the specifications
the manufacturer must meet. Assuming normal usage, the lab expects this
lot to last two years. The lab also calibrates its GC according to its in-house
operating procedures and certifies it. After the reference lot is certified
according to in-house operating procedures as meeting the specification
for all three variables, a sample is drawn and diluted to the necessary
0.5 grams per liter for testing.The next step in developing the control
chart is to generate data using the standard under the lab's normal operating
conditions. Since the lab can assign any technician to certify a batch,
it is important to have all of the analysts contribute data to the development
of the control chart. This will ensure that the lab accounts for the individual
differences between technicians as part of the analytical process. As a
result, the lab can test batches regardless of which technician is available.
Workloads can be smoothed, new employees can more easily be brought into
the lab, and vacations, sick leave, etc. do not influence the product-certification
process.Each of the five technicians tests the sample daily for eight consecutive
days. Thus, the testing performed on this sample yields 40 data points on
each of the three variables with the same GC. The data are entered into
NWA Quality Analyst for SQC analysis.The workhorse of SQC is the control
chart, which graphically shows the extent to which various measurable characteristics
(such as percentage of phenol) relevant to a process (in this case, the
analytical process itself) are in control (meaning "in statistical
control") or out of control (meaning "out of statistical control").
Each chart includes upper and lower "control limits," which are
calculated to distinguish between in-control points (inside the limits)
and out-of-control points (outside the limits). Note that in-control does
not mean the sample is within specification. In-control simply means the
process itself is predictable or stable.A common control chart is the Individual
and Range chart. This chart shows the central tendency of the process as
the measurement on each sample (Individual) and the short-term variation
of the process as the difference between successive measurements (Range).As
shown in Figure1, the control chart obtained from this
process includes the 40 data points. Because the lab is using NWA Quality
Analyst, specialized SQC software, the lab personnel need not concern themselves
with calculating the upper and lower control limits themselves. The software
does this automatically based on the data entered. The lab manager then
examines the chart to determine whether it shows a process in-control or
out-of-control.
Figure 1
Individual/Range chart depicts a process that is in statistical control.
If the analytical process is not statistically predictable, the initial
control charts will show data points falling above or below the upper and
lower control limits. (Only 3 out of 1,000 are expected to fall outside
of the limits due to chance.) When this is the case, the method is being
influenced by external or "special" causes such as different sampling
techniques, faulty calibration standards, or varying ambient temperatures.
If the analytical method is not predictable, the lab manager investigates
the external causes and ultimately corrects them.In this particular case,
there were no out-of-control points. The analytical method exhibited perfect
control.Since the 40 data points were found to be in control, the data can
be used to set the control limits for the analytical method. Note that SQC
in this case is being used to determine the variation in the testing results
that can be obtained predictability. SQC is not being used to determine
whether the process itself is producing acceptable results or whether any
individual is producing acceptable results. SQC is simply showing what variability
can be anticipated from the testing process on this sample and instrument.Using
the Control ChartNow that the lab has accounted for the normal method
variability, it must continue to monitor the method in order to determine
if the method is providing predictable results. To accomplish this, the
lab draws a sample from the control lot and tests it on the three variables
using the same GC three times per week. If the results fall within the control
limits on the control chart, the analytical process is stable. Therefore,
there is no need to investigate the situation further nor is there a need
to question the reliability of the GC.If the results do not fall within
the control limits, the method is not demonstrating predictability. The
lab has established a Standard Operating Procedure (SOP) for investigating
out-of-control situations. The first step is to check sample preparation.
Therefore, the technician immediately draws another sample, dilutes it,
and tests it. If the results still do not fall within the control limits
on the control chart, the SOP calls for the technician to assume there is
a problem with the GC.Step by step, according to the SOP, the lab examines
the GC to identify the problem. It might be the temperature sensors or a
faulty detector. Once the problem is identified and fixed, a technician
will prepare another sample and test it.Improving Test ReliabilityAssuming
that the results from the thrice weekly samples continue to fall within
the control limits on the control chart, the lab can test samples from production
batches using the GC and feel confident that the results are defensible.
As a result, if the lab analysis requires the GC and the testing shows a
batch does meet specifications, a C of A can be issued. If the results indicate
the production batch does not meet specifications, the lab takes another
sample and if the results still indicate the batch is out-of-specification,
the lab must assume the problem is in production.Repeated testing of a production
batch is not necessary. The lab can simply issue a C of A and feel confident
that the batch meets specification or let production know that a problem
has occurred in production with the batch. Turn-around time in the lab is
reduced.Furthermore, when the lab does issue a C of A, customers can depend
on it. They know that the lab monitors its analytical methods carefully.
If a result is obtained that could not be anticipated predictably, the lab
has an SOP for investigating and correcting the problem with the analytical
method. In fact, the reliability of its methods in this lab's case has been
an instrumental factor in improving customer relations.Using SQC To Effect
ChangeThe most stringent and sophisticated SQC analysis won't do any
good if the information gained isn't used to change the process. The real
challenge of SQC arises in problem management, such as when the data clearly
show that the test result falls outside the control limits. This is where
specialized SQC software and the clarity of graphic communication can make
a difference. With reliable calculations and good quality graphics, even
a non-technical person can easily see the result is not within control limits.Figure 2
provides an example of an out-of-control testing result. In this particular
case, the lab technician could immediately see that there was a problem.
Since a subsequent test showed a similar result, the lab knew the GC was
not providing predictable results and needed to be fixed.
Figure 2
Individual/Range chart depicts a process that is out of statistical control.
The lab has also found an unexpected benefit from using SQC with NWA Quality
Analyst to analyze its methods—morale among the technicians has improved
dramatically. Prior to using SQC to analyze its methods, production and
the lab were constantly pointing fingers at each other. If the testing results
showed the sample did not meet specifications, the production staff accused
the lab of errors. The lab, on the other hand, often faced repeated testing
to make sure it had not made an error. In the meantime, the batch waited.
Now the lab can cite the reliability of its testing confidently; if the
tests show the production batch does not meet specifications, the problem
is in production. The lab technicians now know their work is reliable.Production,
on the other hand, knows that they have a problem. Already, they are closer
to finding a solution and meeting the customer's order. |
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