Package Quality Control

Net Content Control

Billions of packages of all kinds are filled around the world every day. “Package” can mean bottle, jar, tube, box, or can: in essence, any container filled with product. All prepackaged goods display by law net weight or volume and number of pieces. Today the value of a product includes more than its function. Saleable elements also include safety and image. Even simple products include these elements, and can both influence their perceived compliance with regulatory requirements and consumer acceptance.

Thorough product inspection checks, including ensuring that packages contain what they state in legal amounts, are required for a successful product. Statistical Quality Control (SQC) can help. A quality assurance system based on SQC provides (among other attributes) the following core quality data:

Production (period) mean value
Number of violations of the legally defined tolerance limits T1- and T2-
Mean standard deviation of the production (period)
Other quality or safety-relevant attributes (Critical Control Points)

Based on the legal requirements and test plans, this information allows real-time assessment and control of production quality and safety parameters.

A suitable control system requires an up-front investment. It must be fast, simple to operate, reliable, and objective. The right system can provide ROI within 12 months or less through:

Minimized product giveaway caused by excessive overfilling
Prevention of government obstacles to product distribution
Better end-user product acceptance
Optimized production and packaging
Streamlined QA procedures/personnel
Prevent legal conflicts
Minimize customer complaints
Predictable quality

This paper addresses the aspects and benefits of implementing robust quality data management solutions and systems, such as METTLER TOLEDO’s FreeWeigh.Net®, to ensure overall product quality and safety improvement.

1 Cost control through minimized overfill

Filling is subject to a large number of influences that can cause fluctuations in packaged goods’ weight. Yet, weight fluctuations must not cause the net weight of even a single package to fall appreciably below the stated net weight. Government regulations generally specify permissible underfill amounts.

Some manufacturers systematically overfill to eliminate the risk of consumer and legal complaints. Such general overfills can be costly and lower the revenue considerably. Even with the modest output rate of smaller companies, corresponding product giveaway costs are striking.

Accurate monitoring and quality data management provide better results. Giving the process closely controlled limits can help reduce expensive product giveaways.

2 Available methods – Random Sampling and 100% Inspection

In many countries static scales must be used to verify compliance with net content legislation, and produce package tare weight verification reports. Product-specific parameters and processes, in combination with financial and economic factors, usually dictate which method is beneficial to a production line.

An in-depth understanding of filling machine scatter and package parameters is essential to select the right sampling method, random sampling on static scales or 100% checks of all packages using dynamic Checkweighers.

Random sampling control with static scales	100% inspection control with dynamic checkweighers
Random sampling, Rapid product change (size, weight) Low space requirements Low system costs Tare weights, component weighing and filling head control Optimum regulation to the nominal fill quantity Allows collecting and analyzing additional quality and safety attributes Higher accuracy and repeatability	All packages are checked (100%) Tolerance infringements are automatically sorted out Use in filling processes in which access to the product is difficult Less control personnel Operator errors less probable Slightly higher deviations

Process and economic factors to consider when choosing static or dynamic checkweighers include:

weight fluctuation potential, filling machine repeatability/scatter
product characteristics (package weight, package size, shape, …)
production line throughput
tradeoff between sampling speed and measurement precision
initial investment budget
running costs of ownership
manual efficiency and personnel costs

How SQC helps

To truly quantify and control product fill, an understanding of Statistical Quality Control (SQC) is required. SQC takes random sample data and creates comprehensive quality control information. This information helps to ensure that a batch meets legal requirements.

The optimum or lowest, possible fill quantity can be answered irrespective of the control system used. The goal of the filling process is to attain the optimum mean filling quantity while fulfilling net content legislation requirements.

3 Systems considerations

Ideally, a solution should address any needs for quality data acquisition points throughout the factory and test labs. It should be highly configurable and expandable to ensure an enhanced degree of control, with no need for software engineering during implementation or daily routine. System design considerations include:

System usability

Intuitive user interfaces allow increased setup flexibility, ease of operation and more precise control during filling and packaging.

Data connectivity

Industry-standard data communication interfaces such as Ethernet with TCP/IP protocol, help keep infrastructure costs low when adding and networking instrumentation such as balances and scales, check-weighers, metal detectors, terminals, and sensors to a comprehensive quality control system.

Easy data access

Easy access to production parameters is crucial. A key parameter in most cases is fill quantity. However, increasingly other parameters such as foreign body detection, ingredient analysis data (e.g. pH, moisture…), results from visual inspection or any results from other critical control points.

Gaining quality information can be broken down into five simple steps with a well-designed solution.

Step 1: Product specification

Define declared net content, applicable tolerances, tare management, and other Q attributes

Step 2: Catalog definition

Adding product data and test item information to define the workflow

Step 3: Product selection

Product is selected on the test scale or terminal in direct dialog with the system

Step 4: Sampling/data acquisition

Samples are taken, guided by the system according to test plan

Step 5: Monitoring and reporting

Results are automatically analyzed by the system and process deviations lead to immediate, alarm messages to operators and supervisors.

Printed reports in addition to electronic records can be produced based on documentation requirements.

Enhanced Compliance

If the process begins deviating from the target, the chosen solution should ensure that appropriate corrective measures can be taken for both enhanced compliance and optimized production. For compliance tracking, traceability of all quality and safety relevant data is critical over the entire life of ingredients.

Increasing regulatory requirements require food industries such as infant formula or nutraceuticals to adapt more to Pharma-like practices such as ‘audit trail’ or electronic record keeping.

The US FDA has implemented 21 CFR Part 11 in such a way that electronic audit documents become the original, while paper printouts are non-binding copies. Companies wishing to comply with 21 CFR Part 11 must therefore implement systems that support it.

Overall, a well-implemented quality data management solution or system reduces user error and subsequent loss of product information. The resulting improved product quality helps a manufacturer reach important operating targets.

4 Summary

Overfill cost is directly related to raw material costs. Yet safe-margin overfills are an effective way to ensure compliance with net content legislation.

Overfills are costly, even with the modest output rate of small companies. Calculated, minimized overfilling can be very effective at controlling giveaways and their resulting expense without increasing personnel costs.

Various solutions are available such as static scales with built-in SQC intelligence for random sampling of net content data or inline CheckWeighers for 100% data checks.

A state-of-the-art quality data management system, such as METTLER TOLEDO’s FreeWeigh.Net® offers multiple benefits to food manufacturers. It allows data collection for important quality attributes from static scales, in-line Checkweighers, Foreign Body Detectors, pH meters, and sensory test panels. It alerts operators to required adjustments almost immediately, thus helping prevent failed production batches. Furthermore, centralized test planning and decentralized data acquisition at individual workstations account for unique company structure and expansion. They also integrate easily with MES or ERP systems.

An integrated quality data management system is an excellent way to achieve better quality control and real cost savings. METTLER TOLEDO offers solutions and systems that pay for themselves and provide a full ROI within 12 months or less.

5 Additional resources

WELMEC (European Cooperation in Legal Metrology) – www.welmec.org

Additional information on METTLER TOLEDO solutions can be found under the following links:

General SQC information – www.mt.com/sqc
SQC Application Overview – www.mt.com/sqc-application
Quality data management solution FreeWeigh.Net® – METTLER TOLEDO Freeweigh.net
Request your free copy of the comprehensive SQC guide – METTLER TOLEDO SQC Guide
Access your potential savings through reduction of overfilling – ROI Calculator – METTLER TOLEDO SQC Cost Savings Calculator

This article was taken from the METTLER TOLEDO Food Regulatory Guide. To read additional articles on how food safety and compliance can be achieved with high performance weighing and inspection equipment, click here.