Any manufacturing packaging process where the product is being filled into individual packages has the potential of overfilling, underfilling, or having excessive uncontrolled fill variation. Companies can use Lean Six Sigma and statistical process controls to better manage package weight and volume. Proper utilization of these processes controls fill weight variation, minimizing occurrences of overfilling or underfilling, both of which can subject the manufacturer to unnecessary expenses, risk of litigation or brand damage. Companies that optimize filling can generate substantial savings without any capital investment and achieve a competitive advantage.

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Substantial
savings with no capital
investment required
Manufacturing Packaging:
Controlling Fill Variation
Increases Yield, Reduces Costs
By Bruce Everett
Curtis Gavin
Lily Ho
Abstract
Any manufacturing packaging process where the product is being filled into individual
packages has the potential of overfilling, underfilling, or having excessive uncontrolled
fill variation. Companies can use Lean Six Sigma and statistical process controls to
better manage package weight and volume. Proper utilization of these processes
controls fill weight variation, minimizing occurrences of overfilling or underfilling, both
of which can subject the manufacturer to unnecessary expenses, risk of litigation or
brand damage. Companies that optimize filling can generate substantial savings
without any capital investment and achieve a competitive advantage.

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Introduction
Today’s highly competitive market has forced companies to look at all alternatives to reduce waste and control fill variation.
Whereas overfill gives away product, costing many companies tens of thousands of dollars annually, underfill can subject
companies to litigation, fines or damage to the brand. For these reasons, it is critical that companies optimize their filling
operation to ensure that the proper amount of product is in each and every package.
Understanding Fill Variation
In any packaging process where product is being filled into individual packages that use a target or nominal weight label, there is
a risk of improper filling. This problem impacts a variety of manufacturing facilities. For this reason, it is crucial that manufacturers
direct their attention and efforts to controlling fill variation.
Underfilled packages
Underfilled packages pose significant risk to manufacturers, as the Federal Trade Commission (FTC) oversees and enforces the
Fair Packaging and Labeling Act. Under this Act, manufacturers are required to disclose net contents of the package. The FTC
may conduct random sampling to ensure that companies are properly filling their packaging, weighing packaging contents during
announced or unannounced site audits. The enforcement of this Act is designed to investigate and stop consumer deception such
as slack fill. Underfill can result in temporary discontinuation of a production line or facility, causing loss of productivity, rework or
yield loss. Underfilling can also result in consumer complaints that can destroy brand equity or diminish customer loyalty.
Overfilled packages
Overfilling results in unnecessary product “giveaway,” which reduces revenue and margin. For example, consider a product with
a one-pound nominally labeled weight per package, which holds a product worth $1.00 per pound, and is processed at a rate
of 50,000 packages per week, 50 weeks per year. The savings generated by a 1% reduction in overfill could save 25,000 pounds
annually, or $25,000 per year.
By minimizing underfill/overfill and controlling fill weight variation, companies can create a competitive advantage, not only by
reducing the amount of product that is given away but also by reducing material costs in packaging, storage and transportation.
Additionally, companies can package product that was previously wasted in overfill as additional output.
Methodology for Minimizing Fill Variation
In any packaging process where product is being filled into individual packages that use a target or nominal weight label, there is
a risk of improper filling. This problem impacts a variety of manufacturing facilities. For this reason, it is crucial that manufacturers
direct their attention and efforts to controlling fill variation.
Myrtle Consulting has developed a methodology using Lean Six Sigma and statistical process capability analysis, available in most
statistical software packages, to drastically reduce weight/volume fill variation. Myrtle Consulting’s process of controlling package
weight/volume can be broken down into six steps:

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Step 1: Analyze current data – Analyzing current data from the manufacturing filling line is the first step to
determining the amount of underfill or overfill that is taking place. Continuous data is required for a fill analysis using
statistical process control and process capability methods. This type of data is information that can be measured on
a continuum or scale. The data is normally captured in the operating system that governs the fill process. Typically,
within that fill process there is a target or nominal weight. There are also tolerance limits: UCL, which represents
the upper control limit, and LCL, which represents the lower control limit. This information is generally displayed
graphically on a control chart.
A control chart (see Figure 1) is a line graph that displays a continuous picture of what is happening in the production
process over time. It is an important tool for statistical process control. The UCL and LCL on a control chart indicate
whether the observed variation in the process is within tolerance and therefore acceptable, or whether the variation is
caused by an abnormal event that must be investigated.
Control Chart Example
Below is just one example of a control chart that contains continuous process data over time. This particular chart
measures the size of marbles being manufactured on a line. To be within specification, the marble has a nominal size of
26mm but must be at least 25mm (LCL), but no bigger than 27mm (UCL). If the size of each marble in the manufacturing
line is measured and recorded (i.e. 25.2mm, 26.1mm, 27.5mm, etc.) over a significant period of time, then it is easier to
see variation or one-time events impacting the marble manufacturing process.
Marble Sizes
Size
27.0
26.5
26.0
25.0
25.5
0 5 10 15 20
LCL = 25 mm
NOM = 26 mm
UCL = 27 mm

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Step 2: Understand legalities and company policies – Understanding legalities and company policies for the
filling operation is crucial when tackling underfilling and overfilling. Most company policies are centered around the FTC
requirements for filling and labeling a particular product. Most packaged goods are pre-labeled with a nominal weight at the
processor, and the regulatory requirements in most states come from the National Institute of Standards and Technology
Handbook 133 (NIST HB133). Two aspects of FTC packaging requirements are relevant to overfill: the Maximum Allowable
Variation (MAV) and the Average Error (AE).
The weight/volume of each individual package in an inspection lot cannot be below an absolute lower limit known as
the Maximum Allowable Variation (MAV). NIST HB133 defines MAV as: “…a deficiency in the weight, or measure, of an
individual package beyond which the deficiency is considered to be an unreasonable error.” This requirement is designed to
keep manufacturers from intentionally shorting customers’ product in order to cut costs or increase profits.
Average Error has to do with the overall distribution of weights/volumes in an entire sample lot or run. The AE requirement
stipulates that the average of all sampled units cannot be lower than a predetermined amount below the nominal labeled
weight. In other words, if an auditor pulled a random sample of a particular product off the shelf, then the average weight of
all of those packages should not fall below the nominal weight on the label.
By understanding these legalities, companies can focus their efforts on measuring MAV and AE to ensure that they
consistently comply with company policies and FTC regulations..
Step 3: Determine fill weight targets – In order to maximize profitability while complying with government regulations,
manufacturers must achieve a precise balance when setting the optimal fill rate. Consistent overfilling to minimize risk is
inefficient and sacrifices profitability, while underfilling results in significant risks of non-compliance with regulations and
related punitive actions. Effective root cause analysis, statistical process control and process capability methods can be used to
determine optimal targets for product fill for a given process. Focused efforts to minimize fill variation will allow the target to be
further optimized, resulting in less waste without increasing risk of non-compliance.
A commonly used statistical method for minimizing risk is to calculate the Z-score. The Z-score takes the current state data and
estimates targets to reduce giveaway of product, and is known as the shifted target. This estimation process is completed with
extensive customer input and collaboration to provide a realistic check on the statistical data. Even though the shifted target is
based on actual data, it does require monitoring to ensure the desired results are achieved.
Step 4: Implement control and tracking charts – It is important to post control charts in filling operations and have
the operations group chart the process status as a short interval control on an hourly basis. Once the shifted targets have
been determined by the customer, implementation of these targets is communicated and tracked on the production floor by
means of a tracking chart. As demonstrated with the marble example, a control chart is a visual management tool designed
to chronologically track the overfill status for each filling machine. This helps to monitor machine performance with respect
to hourly fill weights.
The tracking chart is constructed by identifying the y-axis as overfill amount and the x-axis as time (see Figure 2). The tracking
chart is divided into three zones depicted by the colors green, yellow and red, from bottom to top respectively. The green
zone ranges from zero overfill (LCL) to the green zone upper control limit. The green zone upper control limit is the desired
overfill shifted target, as determined earlier, to allow a percentage of samples below label claim.
The yellow zone ranges from the green zone upper control limit to the yellow zone upper limit (UCL). The yellow zone is
the amount of overfill allowed above the overfill target and should result in actions by the operator to bring fill rates back
into the green zone.
The red zone ranges from the yellow control upper limit to the red zone upper limit. The red zone is the amount of
overfill not allowed and must trigger immediate action by the operator to bring back into the green or yellow zone.
Additionally, there is a red zone that is below the label claim and requires machine adjustment to increase the fill
weight into the green zone.

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The tracking chart helps the operations team identify, in real-time, the adjustments required to optimize fill rates.
Manufacturers should carefully consider how changes in formulation may impact their operations prior to pursuing this strategy.
Step 5: Install shiftly and monthly meetings – Data without action is not sufficient to change behaviors. It is
important that companies establish meetings to evaluate report data and identify action items as a team. By establishing
regular meetings by shift and again on a monthly basis, it is possible to review control and tracking charts, understand
variances and set goals. Shiftly reviews are used to determine common and special causes of any outliers in the process.
Based on this information, the team can determine what actions need to take place to minimize variation in the process. All
organizations’ management operating systems contain feedback and control loops to promote continuous improvement of
processes as illustrated by the prior tracking chart. The current shift tracking results are aggregated and discussed in the pre-
shift meetings, highlighting assignable causes for data points in the red zones if known. If each shift is managed properly with
respect to overfills, then the sum of the shifts will result in favorable weekly, monthly and yearly performance. The valued
outcome from this frequent meeting is to identify causes and assign actions to prevent recurrence of the red zone data
points. Succinct identification of the causes within the organization’s problem-solving framework will aid ultimate overfill
control. Monthly meetings are held to track overfill results to ensure progress is occurring over time.
1ǥǦ ShiŌ 2ⁿǖ ShiŌ 3Ǥǖ ShiŌ
1ǥǦ
Hour
2ⁿǖ
Hour
3Ǥǖ
Hour
4Ǧǚ
Hour
5Ǧǚ
Hour
6Ǧǚ
Hour
7Ǧǚ
Hour
8Ǧǚ
Hour
Date
Grams
Batch Number(s)
+6.0
+5.5
+5.0
+4.5
+4.0 UCL
+3.5
+3.0
+2.5
+2.0
+1.5
+1.0
+0.5
-0.5
-1.0
-1.5
-2.0
Label Claim LCL
AVG.FillWt.(+/-)LabelClaim(Grams)
Line 4: Fill Weight Tracking

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Step 6: Review results and current targets – The final step of the process involves conducting a regular review of the fill
weight targets every six months to determine if set targets are still valid and if any further adjustments are required. A periodic
management review meeting is held to illustrate overfill trend, projected annual savings and determine future overfill targets
based on the data from the period. This is normally completed semi-annually but is customer-defined and can be quarterly.
Highlights of this meeting are the actions to prevent recurrence and their results on overfill actuals. This places the importance
on identification of improvement directives and not solely on the projected annual savings. Perpetuation of the assignable cause
activity, identification of deviations, and a process to find causes and actions to prevent recurrence are crucial to the continuous
improvement process and require significant visibility.
Conclusion
Controlling packaging weight/volume allows companies to reduce and control variation of products, thereby decreasing costs
and increasing overall profits. By incorporating processes such as tracking charts on the manufacturing plant floor, all levels of
the organization have the ability to see the process capability of the filling operation. Continually resolving issues with the
outliers in the process ultimately results in a significant productivity improvement for most companies. Companies are able to
use previously wasted materials to package more product, giving them a competitive advantage in their respective markets.
Using the Myrtle Consulting methodology to reduce fill variation, one healthcare company was able to save over $250K in baby
formula production per year. In a little under four weeks, savings of over $24K were realized instantly to the bottom line. This
demonstrates that a tiny shift in fill weights can equate to substantial savings – without any capital investment. Proper analysis of
the data, identifying the causes of variation in the process, and developing the right actions to minimize this variation will result in
reducing overfill and thereby improve productivity.
About Bruce Everett - Bruce Everett is a consultant with Myrtle Consulting Group. He has worked in a range of industries
including automotive, appliances and metals. He has a degree in Industrial Engineering and several years of engineering
experience. Bruce has delivered operational improvements to clients in a variety of industries through the use of Toyota Production
System and Lean Manufacturing techniques.
About Curtis Gavin - Curtis Gavin is a consultant with Myrtle Consulting Group. He is a process improvement and operational
effectiveness professional with the ability to assess process flow, identify bottlenecks, create metrics for improvement, and train
supervisors and staff to execute recommendations. He has more than a decade of operational and change management experience
and four years of experience in quality/process improvement. He consistently achieves project savings and deliverables, saving time
for clients and adding revenue to their bottom line. His communication skills are complemented by analytical ability, knowledge of
industrial products, manufacturing, distribution and retail industries.
About Lily Ho - Lily Ho is a seasoned Manager with Myrtle Consulting Group helping clients to reduce costs, improve
processes and manage project execution. She began her career as a Six Sigma Black Belt in the heavy machinery industry working at
Caterpillar. During her nine years of consulting experience, she has undertaken a wide range of local and international assignments
across multiple industries and functional areas. Lily has experience in Lean Six Sigma, Lean implementation, cost reduction, process
redesign, organization design, project management, change management, project prioritization and selection, operations strategy,
asset optimization, supply chain management, analysis, and cross team collaboration. Her industry experience includes consumer
packaged goods, chemicals & energy, mining, oil & gas, heavy equipment, utilities, electronics, and technology solutions.