Multi-phase consolidation optimization tool

One or more non-transitory computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• subsequent to training a machine learning model:
• extracting a set of features from a plurality of purchase orders, wherein each purchase order of the plurality of purchase orders comprises a feature of a good that is to be received for importing;
• converting the set of features into one or more feature vectors;
• processing the one or more feature vectors using the machine learning model to generate a prediction for a set of goods associated with the plurality of purchase orders of being inspected by a customs clearance agent, wherein the machine learning model generates the prediction based at least in part on determining a distance, in a vector space, between the one or more feature vectors and one or more representative feature vectors, and training the machine learning model comprises:
• extracting a set of historical features from historical data comprising past purchase orders that have been labeled as inspected or not inspected;
• converting the set of historical features into the one or more representative feature vectors;
• and
• processing the one or more representative feature vectors using the machine learning model to identify the vector space representative of the set of historical features;
• accessing at least one rule data set associated with constraints controlling how the plurality of purchase orders is to be consolidated to an entry, wherein the entry corresponds to a customs clearance document identifying at least one of the goods that is to be received for importing;
• based at least in part on the prediction and the at least one rule data set, automatically generating, for each good of the set of goods, an estimated risk for inspection by the customs clearance agent;
• based at least in part on the estimated risk for each good of the set of goods, automatically consolidating the plurality of purchase orders to a single entry for importing the set of goods;
• in response to automatically consolidating the plurality of purchase orders, storing the single entry to a storage device;
• and
• based at least in part on consolidating the plurality of purchase orders, causing a user interface to be generated that renders information associated with consolidating the plurality of purchase orders.

A system comprising:

• one or more processors;
• and
• one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• training a machine learning model by:
• extracting a set of historical features from historical data comprising past purchase orders that have been labeled as inspected or not inspected;
• converting the set of historical features into one or more representative feature vectors;
• and
• processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features;
• subsequent to training the machine learning model:
• extracting, by at least one computer processor, a set of features from a plurality of purchase orders, wherein each purchase order of the plurality of purchase orders comprises a feature of a good that is to be received for importing;
• converting, by the at least one computer processor, the set of features into one or more feature vectors;
• processing, by the at least one computer processor, the one or more feature vectors using the machine learning model to generate a prediction for each good of a set of goods associated with the plurality of purchase orders of being inspected by a customs clearance agent, wherein the machine learning model generates the prediction based at least in part on determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors;
• accessing at least one rule data set associated with constraints controlling how the plurality of purchase orders is to be consolidated to an entry, wherein the entry correspond to a customs clearance document identifying at least one of the goods that is to be received for importing;
• based at least in part on the prediction and the at least one rule data set, generating, for each good of the set of goods, an estimated risk for inspection by the customs clearance agent;
• based at least in part on the estimated risk for each good of the set of goods, automatically consolidating the plurality of purchase orders to a single entry for importing the set of goods;
• storing the single entry to a storage device;
• and
• based at least in part on consolidating the plurality of purchase orders, causing a user interface to be generated that renders information associated with consolidating the plurality of purchase orders.