Maple Leaf Foods needed a way to improve performance across batches — not by analyzing more data, but by running each batch in alignment with its current conditions.
By changing how the process is run during production, they turned batch-to-batch variability into consistent yield, quality, and efficiency.
Key outcomes:
- ROI achieved in 3 months
- +12% yield improvement across multiple lines
- 100% golden batches achieved consistently
- Reduced giveaway and improved resource utilization
- More consistent performance across shifts and conditions
When Visibility Doesn’t Improve Performance
Maple Leaf Foods is one of Canada’s leading protein producers. At the Heritage Plant, production was already supported by strong systems.
MES provided visibility into operations. A historian captured detailed process data. But performance gaps remained.
Variations in raw materials affected slicing consistency. Cooking conditions were set conservatively, reducing yield.
Equipment settings didn’t always reflect how the process behaved in the moment.
The information was there — but it didn’t translate into a consistent way to run the process.
When Every Batch Behaves Differently
Each batch introduced new conditions. Small variations in inputs, equipment behavior, and environment changed how the process performed.
But the process was still being run using fixed approaches and historical assumptions.
Adjustments were made, but they didn’t hold as conditions changed.
There was no consistent way to run each batch based on what it needed.
Running Each Bach Under Current Conditions
To address this, Maple Leaf Foods introduced a new way of running the process — one that stays aligned with current conditions as each batch runs.
Instead of relying on fixed settings, the process began running in alignment with how each batch behaved.
This made it possible to maintain performance without overcorrecting or relying on conservative settings.
Applying This Across the Line
Maple Leaf Foods first applied this approach to slicing, where small variations had a direct impact on yield.
With the process aligned to current conditions, teams could maintain more consistent output while reducing waste.
The same approach was extended across production:
- In log preparation, more consistent dimensions improved downstream performance
- In cooking, conditions were adjusted to balance safety and yield
- Across the line, processes ran based on current conditions rather than fixed targets
- As these changes took hold, performance became more consistent across batches, shifts, and lines.
Results
By running each batch in alignment with its current conditions using Real-Time Process Optimization, Maple Leaf Foods translated variability into measurable operational and financial impact:
ROI achieved in 3 months
Faster reduction of inefficiencies drove immediate cost savings
+12% yield improvement across multiple lines
Better alignment reduced giveaway and increased usable output
100% golden batches achieved consistently
Each batch ran under its optimal conditions
Reduced resource consumption
More precise operation lowered unnecessary energy and material use
A More Consistent Way to Run Production
What began as a way to improve specific processes became a new way of operating.
Real-Time Process Optimization is now embedded across Maple Leaf Foods’ production lines, providing a consistent way to run each batch as conditions change.
Instead of relying on fixed recipes, teams run each batch based on how it behaves in the moment.
