Food Manufacturers Are Changing the Way They Use Data
For years, food manufacturers have been looking for ways to improve yield in food manufacturing, often by adding more data, more systems, and more visibility. This strategy worked for some time, giving teams a much clearer view of what was happening on the production floor.
Now, most operations face a different challenge. They already have MES, historians, control systems, and dashboards. There’s no shortage of data. Yet when yield drops or waste rises, the same question remains: what actually caused it?
This is where the industry is changing. It’s not about collecting more data anymore. Now, it’s about understanding how the data relates to what’s really happening in the process.
Maple Leaf Foods set out to address this shift. At one of their production sites, the team aimed to improve yield while keeping product consistency. Like many manufacturers, their first thought was to collect more data and increase visibility into the process.
Instead, they took a step back and asked a different question: what if the answer is already in the data we have?
That decision changed the direction of the project. Rather than expanding their systems’ footprint, the team focused on building on what was already in place. By connecting their existing production data and applying advanced analytics, they were able to better understand how their process behaved and how different conditions influenced outcomes.
What they found wasn’t one major issue that needed to be fixed. It was a series of smaller, interconnected factors quietly impacting results across the process. Product dimensions influenced downstream yield, cooking conditions affected moisture levels, and equipment settings introduced small but consistent losses.
None of these stood out as a major problem individually. But together, they were shaping the outcome of every production run. Once those relationships became clear, the team could move away from reacting to issues and start making more intentional adjustments based on what was actually driving results.
Over time, that shift led to a more controlled and repeatable process, which the team described as a “golden batch.” Instead of relying solely on trial and error or individual experience, they applied consistent operating conditions that produced better outcomes across shifts and runs.
The results followed quickly. The team saw a 10–12% increase in gross profit driven by improved yield and reduced waste, along with a return on investment in just three months. These weren’t isolated improvements. They reflected a bigger change in how the process was understood and managed on the plant floor.
That’s what it all comes down to. It’s not about adding something new just to have more. It’s about getting more value from the systems and data you already use every day. Yield doesn’t change by chance, and waste doesn’t just show up. There’s always a reason behind it.
The real difference is whether you can actually see that reason.
Read The Full Success Story
The full case study breaks down exactly how they uncovered hidden sources of yield loss across their process, identified the operating conditions that had the biggest impact, and turned that understanding into consistent, repeatable results on the plant floor.