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Winklix built a Golden Batch analytics system that identifies the ideal process conditions from historical production data and guides operators to repeat them.
Contact UsA leading tobacco manufacturer ran high-volume production lines where small variations in temperature, moisture and blend timing affected the quality of the finished product.
Process knowledge sat with experienced operators, and quality was checked after production rather than controlled during it:
The manufacturer wanted a data-driven way to make every batch as good as its best batches.
Winklix delivered a Golden Batch analytics system that learns from historical batches and gives operators real-time guidance.
Process data from PLCs and historians was collected through OPC UA and combined with lab quality results in a central data lake.
Machine learning models identified the parameter profiles of the highest-quality batches and the variables that most influenced quality.
Live dashboards compare running batches with the Golden Batch profile and alert operators when parameters drift.
As new batches complete, models are retrained so the Golden Batch keeps improving.
The engagement delivered clear, practical results for the client:
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