Guest Columns

Perspective:
Dairy Technology

Data to decisions: a practical path to future of cheesemaking

Crystal Cristescu

Crystal Cristescu is a business development and sales enablement leader in automation and digital at Tetra Pak, where she partners with food and beverage manufacturers across the U.S. and Canada to advance smart factory and automation strategies.

Cheese manufacturers have more production data at their fingertips than ever before. The challenge is no longer simply collecting it. It’s knowing what matters, making that information accessible and turning it into decisions that improve the operation.

Across a plant, information may already be captured at numerous points, but often it lives in individual reports, equipment or systems, rather than providing a holistic view of production. Limited internal resources can make analysis difficult, while traditional reporting may mean waiting days for answers to questions that need to be addressed much sooner.

More accessible data, connected systems and intuitive dashboards can change that. A smarter, more connected operation does not require an overnight, plant-wide transformation. A practical path is to start small: Define the business problem, identify the information that matters and take the next step that delivers measurable value.

• Make the data tell a story

More data isn’t necessarily better data. Before investing heavily in artificial intelligence (AI), machine learning or sophisticated automation, manufacturers should determine which information matters to the decisions they are trying to make.

That means integrating information from different equipment and systems, establishing appropriate tolerances and looking for meaningful trends and outliers instead of reacting to individual numbers. It also means connecting data to larger business objectives.

Successful organizations have clearly defined goals, visibility into progress and the execution discipline to keep teams focused on the actions that move those goals forward. In a cheese plant, that could mean establishing an overall equipment effectiveness (OEE) goal that is visible across production, operations, maintenance and leadership.

Manufacturers can go further by identifying the leading measures individual teams can influence to improve that lagging OEE result. Instead of simply telling a production team that OEE needs to increase, data can identify factors influencing performance on specific lines, processes or shifts so teams can focus on the actions within their control.

Human expertise remains essential. Data expertise and cheese production knowledge need to work together to determine which patterns are meaningful. Consider raw milk reception: Looking at incoming milk composition provides one piece of information. Connecting it with downstream cheese yield begins to tell a story about how raw materials affect finished-product output.

The objective isn’t simply to know more. It’s to understand what the information is telling you, and what to do next.

• Replace assumptions with evidence

Better access to production data also allows manufacturers to test assumptions that may have guided operations for years.

Maintenance is one example. Rather than replacing an expensive component according to a predetermined schedule, historical and real-time equipment data may indicate when it needs replacement, helping manufacturers move toward a more predictive approach.

Clean-in-place (CIP) processes offer another opportunity. Instead of assuming that longer cleaning cycles or additional chemicals produce better results, manufacturers can examine actual cleaning data to identify opportunities to optimize time and chemical use while continuing to meet required standards.

The same thinking applies to cheese yield. Connecting variables such as moisture and pH with production outcomes can reveal opportunities to improve yield without compromising quality.

In each case, the value isn’t the data itself. It’s using that information to better identify root causes and determine the appropriate action. Think of tried-and-true problem-solving practices, such as the “Five Whys.”

• Start with the problem, not the technology

There is no one-size-fits-all starting point for digital transformation. Every cheese plant has different equipment, resources, pain points and priorities.

Rather than starting with technology, start with a question: What problem matters most to the business?
For one manufacturer, declining yield might be the clearest opportunity. Another may be focused on production time, unplanned downtime or maintenance. Identifying a focused, high-impact use case can demonstrate value and build momentum for broader initiatives.

Potential projects should ultimately be evaluated against their expected operational and financial impact. Technology should solve a business problem, not become a project in search of one.

• Move from understanding the past to predicting what comes next

As manufacturers build a stronger data foundation, they can begin answering increasingly sophisticated questions: What happened? Why did it happen? What is likely to happen if we change something?

Machine learning models can evaluate historical and current production runs to identify relationships that may be difficult to recognize manually. Manufacturers can then model potential process changes before implementing them on the plant floor.

The objective is tighter process variation and a more consistent finished product. Eventually, manufacturers can also connect operational decisions to financial outcomes, helping determine which improvements create the greatest economic value.

• Technology should empower people, not overwhelm them

Even sophisticated technology won’t create value if the people using it don’t understand or trust the information it provides. Digital transformation is as much a people and culture challenge as it is a technology challenge.

Building that understanding starts with giving people information relevant to their roles. Operators don’t need to see every data point collected across a plant. They need information that helps them make better decisions and an understanding of how those decisions contribute to larger organizational goals.

Shared visibility can help production teams, maintenance teams and leadership work toward the same objectives. It can also help employees better understand their individual contributions.

An operator working a particular line or shift may have little visibility into how that production run compares with others or contributes to plant performance. Making that information accessible creates opportunities for continuous improvement as well as recognition. If a line, shift or team makes measurable progress against an important indicator, manufacturers can recognize that achievement and align employee recognition programs with those results.

Training and change management must accompany technology investments. Employees need to understand not only how to use new tools, but why they’re being introduced, what goals they support and how their roles fit into the larger picture.

• The competitive advantage is better decision-making

With tight cheese margins, reducing process variability and extracting more value from existing operations is increasingly important.

The cheese plant of the future won’t necessarily be the one collecting the most data or deploying the most AI. It will be the one that can most effectively connect its data, people and technology around common goals and translate information into better decisions.

Manufacturers don’t need to arrive there tomorrow. They need to know where they’re going, identify the problems that matter most and take the next useful step.

CMN

The views expressed by CMN’s guest columnists are their own opinions and do not necessarily reflect those of Cheese Market News®.

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