Adjusting a master build plan based on events using machine learning

A server comprising:

• one or more processors;
• and one or more non-transitory computer readable media to store instructions executable by the one or more processors to perform operations comprising:
• obtaining a set of news items associated with entities stored in an entity database, wherein the plurality of entities are extracted from an enterprise resourcing planning (ERP) system, wherein the set of news items is obtained from at least one website via the Internet, wherein at least a portion of the set of news items is pushed from the at least one website to a machine learning system, wherein an intelligent web scraper associated with the machine learning system identifies and pulls the at least a portion of the set of news items associated with the entities stored in the entity database, and wherein natural language processing provided by a natural processing language system applying one or more of support vector machines, Bayesian networks, maximum entropy, conditional random field, neural networks, and deep learning to the at least a portion of the set of news items to extract news features that are associated with the entities stored in the entity database;
• processing the extracted news features to identify critical features of the news items that is are predicted to affect one or more supply chains used to supply components to manufacture at least one product;
• determining a confidence score associated with each news item in the subset of news items;
• modifying a replenishment plan based at least in part on the predicted affect to the one or more supply chains to create a modified replenishment plan;
• and
• performing a modification to a master build plan that includes a build plan associated with the at least one product based at least in part on the modified replenishment plan to modify an impact to the master build plan taking into account the critical features of the news items.

One or more non-transitory computer readable media storing executable by one or more processors to perform operations comprising:

• obtaining, by one or more processors, a set of news items associated with entities stored in an entity database, wherein the set of news items is obtained from at least one website via the Internet, wherein at least a portion of the set of news items is pushed from the at least one website to a machine learning system, wherein an intelligent web scraper associated with the machine learning system identifies and pulls the at least a portion of the set of news items associated with the entities stored in the entity database, and wherein natural language processing provided by a natural processing language system applying one or more of support vector machines, Bayesian networks, maximum entropy, conditional random field, neural networks, and deep learning to the at least a portion of the set of news items to extract news features that are associated with the entities stored in the entity database;
• processing, by the one or more processors, the extracted news features to identify critical features of the news items that is are predicted to affect one or more supply chains used to supply components to manufacture at least one product;
• determining a confidence score associated with each news item in the subset of news items;
• modifying a replenishment plan based at least in part on the predicted affect to the one or more supply chains to create a modified replenishment plan;
• and
• performing a modification to a master build plan that includes a build plan associated with the at least one product based at least in part on the modified replenishment plan to modify an impact to the master build plan taking into account the critical features of the news items.