Systems, apparatuses, methods, and computer program products for initiating performance of one or more item reconfiguration actions
A method provided herein includes receiving item feature data representative of a plurality of item configuration features associated with a plurality of items. In some embodiments, the method includes generating a field item feature structure. In some embodiments, the method includes identifying an item of the plurality of items using the field item feature structure and an item reconfiguration candidate machine learning component of a composite machine learning model. In some embodiments, the method includes generating item reconfiguration data for the item of the plurality of items using the field item feature structure and at least one of a plurality of reconfiguration machine learning components of the composite machine learning model. In some embodiments, the method includes initiating performance of one or more item reconfiguration actions based on the item reconfiguration data.
1 . A method comprising:
receiving item feature data representative of a plurality of item configuration features associated with a plurality of items, wherein a first part of the item feature data is received from an internal item feature database and a second part of the item feature data is received from an external item feature database;
generating a field item feature structure, wherein the field item feature structure comprises at least a portion of the item feature data and one or more field item predictions;
identifying an item of the plurality of items using the field item feature structure and an item reconfiguration candidate machine learning component of a composite machine learning model, wherein the item of the plurality of items is a candidate for reconfiguration;
generating item reconfiguration data for the item of the plurality of items using the field item feature structure and at least one of a plurality of reconfiguration machine learning components of the composite machine learning model; and
initiating performance of one or more item reconfiguration actions based on the item reconfiguration data.
2 . The method of claim 1 , further comprising:
determining the one or more field item predictions by applying the item feature data to an item data hub machine learning component of the composite machine learning model.
3 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises a first item material reconfiguration machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises replacing a first component of the item with a second component, wherein the first component is a resin component of the item.
4 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises a second item material reconfiguration machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises replacing a first component of the item with a second component, wherein the first component is a metal component of the item.
5 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises an item component standardization machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises standardizing one or more components of the item.
6 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises an item matching machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises standardizing one or more dimensions of the item.
7 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises an item formation machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises altering one or more manufacturing operations associated with the item.
8 . The method of claim 1 , wherein the plurality of reconfiguration machine learning components comprises a static item component machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises removing a static component of the item.
9 . The method of claim 1 , wherein initiating performance of the one or more item reconfiguration actions comprises:
generating an item reconfiguration interface component, wherein the item reconfiguration interface component comprises one or more of the item feature data, the item reconfiguration data, or a visual representation of the item; and
causing the item reconfiguration interface component to be rendered to an item reconfiguration interface.
10 . The method of claim 1 , wherein initiating performance of the one or more item reconfiguration actions comprises:
causing an item inventory record to be modified.
11 . The method of claim 1 , wherein initiating performance of the one or more item reconfiguration actions comprises:
causing an item manufacturing procedure to be modified.
12 . An apparatus comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
receive item feature data representative of a plurality of item configuration features associated with a plurality of items, wherein a first part of the item feature data is received from an internal item feature database and a second part of the item feature data is received from an external item feature database;
generate a field item feature structure, wherein the field item feature structure comprises at least a portion of the item feature data and one or more field item predictions;
identify an item of the plurality of items using the field item feature structure and an item reconfiguration candidate machine learning component of a composite machine learning model, wherein the item of the plurality of items is a candidate for reconfiguration;
generate item reconfiguration data for the item of the plurality of items using the field item feature structure and at least one of a plurality of reconfiguration machine learning components of the composite machine learning model; and
initiate performance of one or more item reconfiguration actions based on the item reconfiguration data.
13 . The apparatus of claim 12 , whether the one or more processors are further configured to:
determine the one or more field item predictions by applying the item feature data to an item data hub machine learning component of the composite machine learning model.
14 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises a first item material reconfiguration machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises replacing a first component of the item with a second component, wherein the first component is a resin component of the item.
15 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises a second item material reconfiguration machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises replacing a first component of the item with a second component, wherein the first component is a metal component of the item.
16 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises an item component standardization machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises standardizing one or more components of the item.
17 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises an item matching machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises standardizing one or more dimensions of the item.
18 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises an item formation machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises altering one or more manufacturing operations associated with the item.
19 . The apparatus of claim 12 , wherein the plurality of reconfiguration machine learning components comprises a static item component machine learning component configured to determine a predicted reconfiguration of the item, wherein the predicted reconfiguration of the item comprises removing a static component of the item.
20 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, configures the computer program product for:
receiving item feature data representative of a plurality of item configuration features associated with a plurality of items, wherein a first part of the item feature data is received from an internal item feature database and a second part of the item feature data is received from an external item feature database;
generating a field item feature structure, wherein the field item feature structure comprises at least a portion of the item feature data and one or more field item predictions;
identifying an item of the plurality of items using the field item feature structure and an item reconfiguration candidate machine learning component of a composite machine learning model, wherein the item of the plurality of items is a candidate for reconfiguration;
generating item reconfiguration data for the item of the plurality of items using the field item feature structure and at least one of a plurality of reconfiguration machine learning components of the composite machine learning model; and
initiating performance of one or more item reconfiguration actions based on the item reconfiguration data.