Turn dairy production data into quality intelligence

Thanks to modern automated data collection, dairy producers can now capture and store data around the clock for comprehensive metrics on their manufacturing activities. This is in stark contrast to the days when paper-based, manual data collection was the only option, which left most companies with only a small snapshot of process and product quality. But the ease of data collection has created a new problem for data-hungry dairy producers: They now have too much data on their hands.

The truth is that having data, simply for the sake of having it, doesn’t benefit quality improvement efforts. What manufacturers need is real-time process control and data interrogation.

This starts at the individual plant level, enabling organizations to preemptively catch quality issues. It would be an impossible task to react to everything, so operators and quality professionals can get proactive and perform statistical analyses to find opportunities for improvement. Then when data from multiple sites are rolled up to the enterprise level, executives can get a big picture view of their manufacturing operations and enact wide-scale transformations.

Quality begins with consistency

Effective process control in dairy production is all about consistency, not only in taste profile and packaging, but also in how everyone works on the plant floor. Critical to achieving this consistency is making sure everyone is on the “same page” when it comes to a quality assurance program’s overall goals and objectives.

This means standardization in one quality intelligence system, overall production processes and even naming conventions for parts, features and processes. A good quality intelligence system will provide real-time alerts if there is a process out of specification, a missed or late data collection, or a machine down.

The right people need to get the right information at the right time — otherwise, too many opportunities for improvement will be lost. To aid in consistency, workflows can provide prescriptive steps to walk users through timely response. That way, in the event of an issue, plant personnel always follow the same best practices. 

Perhaps most importantly, dairy producers should have a single centralized data repository in the cloud, from which users — at every site — store and pull data for analysis. Consistency in quality management and data storage enables plant personnel to compare performance between products, processes and lines, and allows prioritization of where they will spend their time for any improvement initiatives. With all quality data together in the cloud, dairy producers can attain enterprise visibility and conduct cross-plant analysis for quality intelligence that leads to global improvements, not just localized improvements.

Make data interrogation a priority

With unified data, it is much easier for producers to take the time to interrogate their data. Plant quality professionals and C-suite executives should make it a priority to hold regular interrogation sessions, during which they can uncover new ways to improve production, reduce costs and fine-tune processes.

Sometimes the most valuable insight can come from data that didn’t even indicate an issue, but was actually “in spec.” Finding clarity in the noise, people can begin to take a statistical look at then prioritizing improvement initiatives according to the levels of effort and amount of return.

Lessons from the frontlines of quality

To ensure consistent product quality for every pint of ice cream, Burlington, Vt.-based Ben & Jerry’s uses automated data collection and real-time monitoring of variabilities across each of its production lines. By measuring, monitoring and controlling four main product attributes — weight, volume, air addition and inclusion amounts — as the pints come off the lines, Ben & Jerry’s quality teams can work with production to quickly make adjustments in real time before products or processes exceed specification limits.

By taking the time to thereafter perform comparative analyses of the data, the quality teams are able to identify sources of variability, as well as opportunities to improve its run capabilities and raw material usage. By fine-tuning its processes with more precise specification limits, Ben & Jerry’s has realized less raw material variation, increased cost savings, and consistently higher-quality products for consumers.

Similarly, Michael Foods Group Inc. is an international producer and distributor of egg products, refrigerated potato products, cheese and other products. By centralizing data in the cloud, Michael Foods has unified data on product weight, dimensions, line speeds, packaging and machine performance across 75% of its plants. Michael Foods now has scheduled alerts for quality checks and data collection reminders, as well as real-time feedback for operators to make data-driven decisions.

At the corporate level, Michael Foods’ operations and quality executives can easily identify variation trends and use data to determine the root cause of variations. Desktop dashboards display exactly what is happening on the plant floor — or across multiple plants — in real time, so supervisors and executives can see whether a particular line or plant is running efficiently and consistently. This enables the company to proactively monitor and easily correct any processes that are out of specification.

It’s no secret that there is valuable insight within quality data — or else manufacturers wouldn’t be so keen to collect as much as they can. When dairy producers work toward consistent quality improvements, centralize their quality data in the cloud and take measures to interrogate the data, they can proactively address plant-floor issues and find ways to optimize their processes, reduce cost and raise profits. Mountains of data suddenly turn into treasure troves of intelligence.