Systems and methods for automating production intelligence across value streams using interconnected machine-learning models

A system comprising:

• 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, wherein:
• each upstream machine-learning model is configured to:
• receive operational metric corresponding to the respective upstream entity;
• generate, based on at least the received operational metric corresponding to the respective upstream entity, upstream delay predictions corresponding to the respective upstream entity;
• and
• provide the upstream delay predictions to both the final-assembly machine-learning model and the causal-analysis machine-learning model;
• the final-assembly machine-learning model is configured to:
• receive operational metric corresponding to the final-assembly process;
• receive the upstream delay predictions from the respective upstream machine-learning models;
• generate, based on at least the received operational metric corresponding to the final-assembly process and the upstream delay predictions from the respective upstream machine-learning models, product-throughput prediction for the product;
• and
• provide the product-throughput prediction to the causal-analysis machine-learning model;
• the causal-analysis machine-learning model is configured to:
• receive the upstream delay predictions from the respective upstream machine-learning models;
• receive the product-throughput prediction from the final-assembly machine-learning model;
• identify, based on at least the received upstream delay predictions from the respective upstream machine-learning models and the product-throughput prediction from the final-assembly machine-learning model, causal factor for one or both of the upstream delay predictions and the product-throughput prediction;
• and
• provide the identified causal factor to the action-and-alert process;
• the action-and-alert process is configured to:
• receive the identified causal factor from the causal-analysis machine-learning model;
• generate, based on at least the identified causal factor, one or both of one or more alerts and one or more recommended actions;
• and
• providing the one or both of one or more alerts and one or more recommended actions to the implementation interface;
• the implementation interface is configured to:
• receive the one or both of one or more alerts and one or more recommended actions from the action-and-alert process;
• obtain and process response to the one or both of one or more alerts and one or more recommended actions;
• and
• provide data reflective of the response to one or more of one or more of the upstream entity, the final-assembly process, one or more of the upstream machine-learning models, and the final-assembly machine-learning model.