Reducing overfill costs through statistical quality control

Mettler-Toledo Ltd
Monday, 22 October, 2012



Billions of packages of all kinds are filled around the world every day. ‘Package’ can mean bottle, jar, tube, box or can - any container filled with product. All prepackaged goods by law display net weight or volume and number of pieces.

Today, the value of a product includes more than its function. Saleable elements include safety and image as well. Even simple products include these elements and can influence their perceived compliance with regulatory requirements and enhance consumer acceptance.

Thorough product inspection checks - including ensuring 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

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

  • minimised product giveaway caused by excessive overfilling
  • prevention of government obstacles to product distribution
  • better end-user product acceptance
  • optimised production and packaging
  • streamlined QA procedures/personnel
  • prevention of legal conflicts
  • minimised customer complaints
  • predictable quality

Cost control through minimised overfill

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

Some manufacturers systematically overfill to eliminate risk of consumer and legal complaints. But 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 provides better results. Giving the process closely controlled limits can help reduce expensive product giveaway.

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 on a production line.

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 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 analysing 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
  • Fewer control personnel
  • Operator errors less probable
  • Slightly higher deviations

Table: Random sampling versus 100% inspection.

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 etc)
  • production line throughput
  • trade-off 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 ensure that a batch meets legal requirements.

The question of 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 optimum mean filling quantity while fulfilling net content legislation requirements.

SQC spot-checks determine batch acceptability

SQC spot-checks determine batch acceptability.

System 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 set-up flexibility, ease of operation and more precise control during filling and packaging.
  • Data connectivity: Industry standard data communication interfaces such as ethernet and TCP/IP help keep infrastructure costs low when adding and networking instrumentation such as balances and scales, checkweighers, metal detectors, terminals and sensors to a comprehensive quality control system.
  • Easy data access: Easy access to production parameters is crucial, and while a key parameter in most cases is fill quantity, increasingly other parameters are important, such as foreign body detection, ingredient analysis data (such as pH and moisture) and results from visual inspection or results from other critical control points.
  • Enhanced compliance: If the process begins deviating from the target, the chosen solution should ensure that appropriate corrective measures can be taken for enhanced compliance as well as optimised production. For compliance tracking, traceability of all quality and safety relevant data is critical over the entire life of ingredients as well as final products.

Increasing regulatory requirements require food industries such as infant formula or nutraceuticals to adopt practices as found in the pharmaceutical industry, such as audit trails and electronic record keeping.

For example, 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.

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

Summary

Overfills are costly, even with the modest output rate of small companies. Calculated, minimised overfilling can be very effective at controlling giveaway and its 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, can offer multiple benefits to food manufacturers. They allow data collection for important quality attributes from static scales, inline checkweighers, foreign body detectors, pH meters and sensory test panels. They can alert operators to required adjustments almost immediately, thus helping to prevent failed production batches. Further, centralised test planning and decentralised data acquisition at individual workstations can account for unique company structure and expansion. It also helps If the system integrates 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. For more information, go to www.mt.com/ind-food-productivity-guide2.

Source

This article is based on a recent white paper by Mettler Toledo titled Package Quality Control - Net Content Control’, the original of which can be downloaded at www.mt.com/ind-wp-package-quality-control.

Additional resources

Additional information on Mettler Toledo solutions can be found under the following links:

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