Revolutionising the production planning process in Rosenberger GmbH, by utilizing the QUINbridSteel AI model. We achieved optimal order configurations and improved efficiency in the manufacturing of high-quality steel parts.
noun /ˈbreɪk.θruː/
Elimination of high waste and high machine setup times hindering sustainable workflow.
Adapt to complex customer preferences by learning from previous orders and external data.
Significant cost savings and improved productivity by ensuring efficient machine setup and workflow.
Rosenberger GmbH has produced high-quality steel parts since 1982, but faced challenges in production planning, such as high waste, time-consuming machine setups, and unclear customer needs. This prevented a sustainable workflow. For increased productivity, an efficient planning system is crucial, but traditional rule-based methods struggle to manage the complexities of customer preferences and data from diverse sources, like previous orders and economic situations.
The QUINbridSteel AI model addresses these challenges by allowing users to create optimal order configurations for specific grade and thickness combinations while suggesting potential customers for parts. This enhancement in production planning reduces waste and boosts efficiency. The model's intelligent combination of five algorithms processes historical data, external information (such as economic conditions), and computes the probabilities of customers and items, to ensure efficient machine setup and seamless workflow. Ultimately, QUINbridSteel leads to substantial cost savings and improved productivity.
However here are a few common pain points that we often see, which can be solved through our programs and will lead to an AI breakthrough.
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