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This invention describes a software system that helps manage production resources in a supply chain. It automatically predicts how changes, like using a production machine differently, will affect overall supply chain performance. The system then shows a user a visual interface, perhaps with a drag-and-drop feature, where they can see scheduled production runs and a 'value at risk' associated with each. When the user makes a change, the system instantly updates the predicted performance and risk, even communicating this to other planning systems.
Why it matters: Filed before significant advancements in machine learning for predictive analytics and causal inference, which now make the automatic generation of metrics and inference of causal factors more robust and scalable. Cloud-based AI platforms have also simplified the deployment of such complex optimization systems since 2019.
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