How to better control net content fills in your packages

When a consumer purchases a 500-milliliter bottle of a soft drink, how do they know if they get the amount of product that they paid for? Fortunately, in most—if not all—developed nations, there are legislations that enforce acceptable levels of variation between declared net content and the actual weight or volume within a package. For instance, in the United States, there is the U.S. Department of Commerce’s Maximum Allowable Variation (MAV). The United Kingdom has the Weights and Measures (Packaged Goods) Regulations, which sets tolerable negative error (TNE).

While each country has its own rules, regulations, and methods of measurement and reporting, such legislations are all designed to protect consumers from buying underfilled packages. Food and beverage manufacturers, packagers and bottlers must thus take care to prevent underfilling and avoid possible fines or legal action—not to mention reputational risks. After all, with how fast news travels today, unhappy consumers with underfilled packages can quickly create a viral firestorm, making it hard to regain their trust and business. While such a firestorm may be unlikely for low-value products, such as bottled water, for more premium products, it may be much more likely.

To avoid underfilling, many organizations resort to some degree of overfilling within permissible levels of variation. However, overfilling simply provides a band-aid to the problem and can prove costly. Imagine overfilling by a small fraction on every product on a single bottling line. Those small fractions can quickly accumulate over time. Then, imagine that happening on an exponential scale across multiple lines in a single plant or several plants. It all adds up to significant financial costs—not only in product giveaway, but also wasted resources consumed to produce that additional content (across work hours, materials and machinery use). It’s the modern equivalent of the baker’s dozen.

For those who opt to absorb these costs, overfilling may seem like the only option. But a more data-driven approach is possible now, leveraging the latest in automation, digital sensors, process control and quality monitoring to effectively control net content. The result is a more refined packaging process—and much less underfilling or overfilling.


It all starts with data

Data provides the foundation for net content control. This includes process-related data collected from packaging and filling equipment or product-specific data, such as inline checkweighers or optical fill-level sensors. Namely, with timely access to these data, you can get insight into how the filling process is performing in real time. You can thereby proactively catch issues in net content variation and make informed process improvements.

Such levels of visibility provide benefits to individuals in various roles. Plant operators can identify trends and anomalies to ensure packages are consistently filled as close to target as possible. Maintenance engineers can identify equipment that requires attention before it negatively impacts net content performance. Facility managers and other executives can monitor overall performance over time—across multiple products, processes, lines or shifts—and prioritize improvement efforts.


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None of this could be easily achieved with paper-based data collection and calculations, as these files are often either overlooked or only reviewed at the end of a shift or batch, the latter of which is too late if corrective action is necessary. Instead, there are three main methods of data collection that can empower operators and other quality personnel with timely information on their packaging processes:

Manual data collection: Here, operators manually enter data directly into a data collection interface using a computer or mobile device. As data is entered, an operator can receive immediate feedback as to where the entered values fit against the target fill value.

• Semi-automated data collection: Operators are supported by wired or wireless digital gauges that collect and send data into a centralized repository. A user interface will prompt an operator to select the corresponding value relevant to a product or process. Notably, semi-automated data collection increases operator efficiency and data accuracy by circumventing manual data entry.

• Fully automated data collection: This requires no operator action or intervention. It relies solely on Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, and other inline process equipment to capture and collect all data automatically.

No matter the data collection method, operators and other quality professionals then need a way to act on these values, as well as to monitor and predict variability over time, to optimize net content control. This is where advanced process and quality monitoring are essential.


Catch problems now, rather than later

Advanced process and quality monitoring enable operators and quality personnel to leverage their collected data to monitor net content performance in real time. And rather than require personnel to decipher densely populated data reports, there are systems available now with intuitive, user-friendly interfaces that make it visually easy to understand how packaging processes are performing.

Personnel also don’t need to continuously stare at a screen, waiting for a problem to occur. An effective system should include built-in alerts to notify the relevant, responsible team members when an event falls outside of defined parameters or when the data shows concerning statistical trends. This enables timely discovery and quick remedial action. Better yet, some solutions can now automatically prioritize a queue of issues for users so they can decide what to attend to first, that is, the areas that present the most impact or risk to net content control.

Beyond addressing plant-level problems, identifying root causes and learning from those issues are just as important to net content control. Such information can be logged and then easily analyzed to support continuous improvement initiatives and mitigate future risks associated with net content on not just the plant floor, but also across an entire organization.


From the plant floor to the top floor

The same real-time data that alerts plant-floor personnel can serve a higher purpose when aggregated and rolled up to the executive level, giving executives, managers and other quality professionals heightened visibility into the performance of multiple packing or filling lines—across individual plants and different sites and regions. This gives them the ability to compare and monitor net content performance across multiple dimensions.

Suddenly, the answers to some of their biggest questions become clear:
Which products or product groups have greater unpredictability and variability in net content?
Is a specific part of the packaging process underperforming?
Which filling lines are performing better than others?
What best practices can we standardize across all lines and plants?

The derived actionable intelligence can direct executives in how to prioritize continuous improvement initiatives and optimize net content performance on an enterprise scale.

To elevate data to the executive level for comparative analysis though, two factors become critical. One is standardization, which involves consistency in data. All lines and plants need to follow standardized ways in how and when data collections are performed, the format of collected data, relevant metadata, units of measurement, naming conventions and more. Standardization is necessary to do any sort of apples-to-apples comparisons and effective analysis.

The second critical factor is centralization. If data resides in disparate or localized systems, significant effort, information technology (IT) knowledge, and costs are required to integrate and bring those data together in a central repository. A unified cloud-based data repository simplifies this immensely, storing data from all lines and locations and making them readily available for analysis.

Using such a data-driven approach, you can do more than put a band-aid on net content control. You can intelligently catch problems, implement preventive measures and finetune the packaging process on every single line. Significant underfilling and overfilling, along with their associated risks and costs, become things of the past. Both the organization and consumer know—beyond a shadow of a doubt—that there is definitely 500 milliliters worth of product in that soft drink bottle and with only a negligible variation, if at all.


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