Abstract: Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.

Abstract: Methods and systems to predict a supply chain performance are described. A system receives supply chain data for delivery of a product. The supply chain data includes input signals comprising operational plans and observed supply chain operational metrics. The input signals include a delivery date of the product. The system automatically generating predicted supply chain operational metrics across including a value at risk that is predicted for the product. The system automatically infers causal factors that impact the predicted supply chain operational metrics including impacting the value at risk that is predicted for the product. The system automatically generates action recommendations for the supply chain. An action recommendation includes a first predicted value impact and a sequence of actions impacting the product the delivery date of the product and the value at risk that is predicted for the product.

Abstract: Disclosed herein are systems and methods for automating production intelligence across value streams using interconnected machine-learning models. An embodiment of a system includes an upstream machine-learning model corresponding to each of one or more upstream entity in a production value stream of a product; a final-assembly machine-learning model corresponding to a final-assembly process in the production value stream of the product; a causal-analysis machine-learning model for the production value stream of the product; an action-and-alert process for the production value stream of the product; and an implementation interface for the production value stream of the product. The upstream machine-learning models and the final-assembly machine-learning model are interconnected to provide product-throughput prediction for the product.

Abstract: Methods and systems for controlling inventory in a supply chain are described. The system receives supply chain data including input signals comprising operational plans and observed supply chain operational metrics. The system automatically generates predicted supply chain operational metrics including a value at risk that is predicted for a product. The system automatically infers causal factors including a shipment of the product. The causal factors impact the predicted supply chain operational metrics. The system communicates a user interface for shipments of the product and the system receives input causing a change to a shipment of the product impacting the predicted supply chain operational metrics including the value at risk for the first product.

Abstract: Methods and systems to predict a supply chain performance are described. A system receives supply chain data for delivery of a product. The supply chain data includes input signals comprising operational plans and observed supply chain operational metrics. The input signals include a delivery date of the product. The system automatically generating predicted supply chain operational metrics across including a value at risk that is predicted for the product. The system automatically infers causal factors that impact the predicted supply chain operational metrics including impacting the value at risk that is predicted for the product. The system automatically generates action recommendations for the supply chain. An action recommendation includes a first predicted value impact and a sequence of actions impacting the product the delivery date of the product and the value at risk that is predicted for the product.

Abstract: Methods and systems for controlling inventory in a supply chain are described. The system receives supply chain data including input signals comprising operational plans and observed supply chain operational metrics. The system automatically generates predicted supply chain operational metrics including a value at risk that is predicted for a product. The system automatically infers causal factors including a shipment of the product. The causal factors impact the predicted supply chain operational metrics. The system communicates a user interface for shipments of the product and the system receives input causing a change to a shipment of the product impacting the predicted supply chain operational metrics including the value at risk for the first product.

Abstract: Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The predicted supply chain operational metrics include a value at risk associated with a scheduling of a production run including scheduling a production of a product with a production resource. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with the value at risk. The system receives input causing a change to the utilization of the production resource.