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NWA Quality Analyst
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Optimizing
Product Fill Weights
Using statistical quality control (SQC) analysis
in a program of continuous monitoring and improvement can help manufacturers
optimize product fill weights, allowing them to minimize overfill and
still comply with regulatory requirements. This paper describes the application
of NWA Quality Analyst to fill weight situations in various industries.
The examples used here are composites of several NWA Quality Analyst users.
Reducing variation in product fill weights can have a large impact on a
manufacturer's bottom line. Take, for example, a product for which a government
agency requires that the amount of product in the container, on average,
be at least as much as the label on the package states, a common requirement
with consumer products. Since under-filling the container puts the producer
at risk with government agencies (not to mention customers), producers tend
to avoid problems by over-filling. Yet, over-filling can create significant
cost problems. Using statistical quality control (SQC) analysis in a continuous
monitoring and improvement program, a company can reduce over-fill situations
and still meet the regulatory requirements. The resulting savings can be
huge.Another potential problem with product fill-weights can result when
manufacturers use materials pre-measured by their supplier. Often buyers
use the container as it comes from the supplier as a unit of measure. If
the supplier has under- or over-filled the container, the buyer may not
be using the correct amount of raw material. The result can be an end product
that does not meet specifications. Suppliers that provide unpredictable
product may be removed from the qualified purchasing list. Again, using
SQC in a continuous monitoring and improvement program, a company can optimize
its product fill to meet its customer requirements predictably and, potentially,
enhance its market position.The following examples illustrate how SQC with
NWA Quality Analyst can be used to optimize product fill weights.Example
1. Balancing Cost Concerns with Regulatory Requirements A large commercial
bakery uses three dough-mixing machines for its dinner roll line. When each
mixer has prepared its 1,200 pounds of dough, it dumps its batch into a
pouring machine, which then pours dough onto baking sheets. Dough for six
rolls is poured onto a baking sheet lane with each sheet holding 4 lanes
of rolls. The entire sheet is then baked after which the rolls are cut and
packaged in twelve-roll packs.The pouring machine keeps dispensing dough
onto baking sheets until it is empty. It then receives another 1,200 pounds
of dough from the next mixing machine in a fixed rotation and begins the
pouring process again.Since the bakery labels the package as weighing 454
grams (the bakery uses grams rather than ounces for its internal reporting),
the bakery requires that 100% of the packages weigh at least 454 grams in
order to avoid any problems with the Food and Drug Administration (FDA),
the governing regulatory agency. If the FDA finds the bakery is not meeting
the weight requirement (i.e., it is under-filling the packages), the bakery
can face product recalls or serious fines. On the other hand, excessive
over-filling can be expensive.In order to determine if it could reduce costs
in its dinner roll line and still meet the FDA weight requirement, the bakery's
process engineers conducted a study using SQC with NWA Quality Analyst.
The first step was to determine if the dinner roll production process was
predictable (this is called "in control"). To do this, samples
of baked dinner rolls from three lanes were taken every hour on both shifts
and weighed. This information was then analyzed using an X-Bar/Range chart.
As shown in Figure 1, the process was not in control
(note all of the points above and below the control limits). These results
indicated that external factors (i.e. factors that were not due to the inherent
variation in the pouring/filling process itself) were influencing the process.
Additional investigation revealed that there was little variation between
individual rolls in a lane or between lanes on a baking sheet. The engineers
also found little variation between shifts or operators. To understand the
problem more clearly, the engineers took samples every minute. This analysis
revealed a pattern - the excessive variation was occurring approximately
every 30 minutes, the time between dough batches from one of the three mixing
machines.Since each dough batch came from a different mixing machine, the
engineers analyzed the dough coming from each of the three machines. One
machine, Mixer #3, exhibited significantly higher variation in the texture
of the dough it produced. Further examination of the texture revealed that
the machine was not receiving flour and water in the proportions the bakery's
formula required. Adjustments were made to this machine and samples were
then taken. As shown in Figure 2, the X-Bar/Range
chart for the pouring/filling process indicates that the process was still
not in perfect control but exhibited much less variability.
At this point, the engineers examined the "capability" of the
process—i.e., they measured how predictably the process is producing
within specifications. Normally capability should not be examined before
a process is in-control because the analysis is unreliable. However, in
this case the engineers did the analysis to look at the capability of the
process relative to the specification because the variability was reduced
so significantly.The most commonly used statistic to evaluate the relationship
between the variation in the process and the specifications is Cpk, which
looks at the ratio of the spread of the specifications and the spread of
the process (the distance between the +/- 3 Standard Deviation lines). A
Cpk of 1.3 is usually considered adequate. This process, as shown in Figure 3,
shows a Cpk of 1.058, indicating that it will not be capable of consistently
producing within these specifications. However, this chart also shows that
the process is capable of producing rolls that meet the FDA weight requirement
predictably as long as the overfill averages 15 grams. (Note that virtually
all of the distribution is above the specification of 454 grams. Also, note
that the mean for this process is 468.74 grams).
Since the process is not in control, the engineers must first find other
external factors that are influencing the pouring/filling process. Once
the process is in control, they can continue to make improvements in the
process to reduce the over-fill. Despite the fact that the process is not
in control at this point, the changes the engineers were able to make during
the initial parts of the study to reduce the weight variability in the dinner
roll production line saved the bakery approximately $200,000 per year.Example
2. Multiple Head Filling Although the use of the X-bar/Range chart is
appropriate in many filling operations, it is not when there are multiple
fill heads. The problem is that an X-bar/Range chart computes the average
for the operation—i.e. averaging the fill for all the heads—rather
than computing the fill for each head. Yet, an analysis of the average for
the fill heads taken as a whole can mask the problem behaviors of each individual
fill head because each fill head is actually an individual process. The
proper analysis in a multiple fill head process is the Median and Individuals
Chart.In this example a milk filling line has twelve fill heads, each filling
a carton in the line. FDA regulations require each carton to weigh at least
8.604 pounds. In this milk-fill line, an X-bar/Range chart was produced
(as shown in Figure 4), showing that the process as
a whole was in control (meaning it predictably met the regulatory requirements).
Although the dairy had detected an occasional under-fill, the problem seemed
minor and did not seem to reflect a pattern. Besides, the SQC charts indicated
that the overall process was ok. Therefore,the dairy was surprised when
state regulators informed them that they had received several complaints
about underfilled cartons. The problem was that the X-bar chart reported
on the average fill weight, missing any problems with individual fill heads.
When the engineers re-examined the filling process, they used NWA Quality
Analyst's Median and Individual Chart. This allowed them to treat each head
as an individual process while still monitoring the median of the overall
process. This analysis immediately revealed a problem with Fill Head #8.
As shown in Figure 5, the variability on eleven of
the fill heads was in statistical control, but Fill Head #8 exhibited excessive
variation and was under-filling.
Now that the cause had been identified, Fill Head #8 could be repaired or
replaced. This change brought the entire process into statistical control
and the dairy was ready to determine if there were additional opportunities
for process improvements that could save the dairy money by reducing over-fill
(see Figure 6 showing that this process with an average
overfill of .02 pounds was capable of meeting the specifications) .
Example 3. Meeting Customer Requirements A titanium dioxide (TiO2)
producer supplies a paint manufacturer its product in 50lb bags, which are
used in paint batch operations as a unit of measure. Since any variation
in the bags affects the paint manufacturer's final product, cans of paint,
the customer specifies that variation in TiO2 bag weight be no more than
1lb.During process improvement studies using NWA Quality Analyst, the TiO2
producer discovered a 7 lb variation in the 50 lb fill weight. Since this
variation is well outside the buyer's accepted specifications, the manufacturer
needed to investigate this situation further. The first step was to determine
if the process was in statistical control. The analysis, as shown in Figure
7, showed that the process was in statistical control, suggesting
that the 7lb variation was a part of this process and not resulting from
an external influence (such as different raw materials).
As the TiO2 manufacturer analyzed this problem, it learned that
the primary source of the variation came from the bag-filling machine. After
adjusting the machine and providing more training to the operators, the
manufacturer reduced the fill weight variation to the necessary 1lb (see
the left half of Figure 7).Although the process was
now meeting the customer's specification, the TiO2 manufacturer
needed to look at the processes capability, that is how reliable was the
process for producing the fill weight within specification. Process capability
statistics are generally based on a ratio of the product's specifications
to the process variation (usually expressed as three standard deviations
based on the distribution). The most common way of denoting process capability
is the Cpk index, which is typically required to be at least 1.3. The higher
the Cpk, the more room there is between the process and its specifications.As
the histogram in Figure 7 shows, the Cpk for this
process is 0.7506. A Cpk this low does not necessarily mean the process
is "incapable" of meeting the required specification. However,
it is a warning to the manufacturer that it should investigate the process
further because additional problems with this process may arise. For example,
the manufacturer might experience new problems with its fill operations
if it needs to meet tighter customer specifications (to 0.5lb, for example,
from this customer or a new one) and/or it wants to make additional process
improvements.Answering Fill Weight Concerns Companies from a variety
of industries must meet fill weight specifications. The first step is getting
a process in control or predictable. The next step is determining how capable
the process is for meeting the required specifications. Finally comes improving
the process so that it optimizes fill-weights. These are all ideal uses
for NWA Quality Analyst. |
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