Activating dynamic production master data by identifying golden batch sizes


A leading tire manufacturing company covering 85% of their business in tires and 15% in the diversified products segment.

Facing a lot pressure on their service level, caused high inventory levels and cost. Also, they were experiencing a continuously increasing production complexity, whilst running at full plant capacity, which resulted in decreasing reliability.

This is why optimizing factory asset performance, whilst balancing service level and costs was identified as one of the key challenges.


They aimed to determine accurate and dynamic product batch sizes – especially for low volume SKU’s – to perform continuously at the optimum level within each factory.

By combining three years of historical production data and advanced LOP algorithms, they identified the demonstrated throughput and product mix.

This enabled them to predict the golden batch sizes and trigger both stock and service level improvements in a continuous way.


Improved scheduling accuracy of 8%

Reduced batch sizes of 3%

Increased reliability & factory output

Increased reliability & factory output

Alignment between operations and planning

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