| CPC G06Q 10/083 (2013.01) [G06Q 10/06316 (2013.01); G06Q 10/067 (2013.01); G06Q 10/087 (2013.01); G06F 3/0482 (2013.01)] | 36 Claims |
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1. A data processing method for pick-pack-ship operation of an enterprise application, the method comprising:
generating on the application UI, by at least one processor, a graphical user interface (GUI) that includes:
one or more graphical elements depicting one or more data points including one or more item data, one or more lot data for each of the one or more item data, one or more handling unit (HU) data for each of the one or more lot data, and one or more shipping data for each of the one or more handling unit (HU) data wherein the one or more data points generate a pick-pack-ship projection on the GUI through the graphical elements; and
one or more input data elements of the one or more graphical elements configured to receive at least one input data associated with the one or more data points in the pick-pack-ship operation projection;
receiving, by the at least one processor, the at least one input data through the one or more input data elements of the one or more graphical elements on the GUI for executing a task of the pick-pack-ship operation wherein the at least one input data includes one or more data attributes associated with the one or more data points;
generating, by the at least one processor, the one or more data attributes of the one or more data points on the interface wherein the one or more data attributes are modified depending on the task of the pick-pack-ship operation to be executed;
processing, by the at least one processor coupled to an Artificial intelligence (AI) engine, the at least one input data and the one or more modified data attributes of the one or more data points based on at least one data model;
processing, by the at least one processor, a historical dataset to generate and train the at least one data model by:
cleansing the historical dataset to obtain a normalized dataset,
filtering the normalized dataset;
dividing the normalized dataset into training and testing dataset to generate the at least one data model;
extracting a plurality of categories from the normalized dataset for creating taxonomy;
extracting a plurality of distinct words from the normalized dataset to create a list of variables;
transforming the normalized dataset into a training data matrix using the list of variables, and
creating the at least one trained data model from classification code vectors and the training data matrix by using a machine learning engine (MLE) and the AI engine; and
projecting, by the at least one processor, one or more stages in execution of the pick-pack-ship operation on the GUI using the processed at least one input data and the processed one or more modified data attributes.
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