Decision management for items digitally represented in an information processing system

An apparatus comprising:

• at least one computing platform comprising at least one processor device coupled to at least one memory that stores computer program instructions which are executed by the at least one processor device to implement a decision engine that executes on the at least one computing platform to perform a process for providing intelligent recommendations for managing an inventory of items by a given entity, wherein the decision engine operates to:
• obtain, for a set of items, data indicative of a previous percentage division between a first item ownership type and a second item ownership type for each item in the set of items, wherein the first item ownership type for a given item corresponds to items that are owned by the given entity, and the second item ownership type for the given item corresponds to items that are owned by another entity and potentially procurable by the given entity;
• classify each item in the set of items into one of a plurality of clusters, based on the obtained data, wherein each cluster represents a different percentage division range between the first item ownership type and the second item ownership type;
• execute a first machine learning process to identify any items in each cluster that deviate from the percentage division range for the cluster;
• execute a second machine learning process to apply weights to any identified items based on the deviation from the percentage division range;
• re-classify any identified items to another cluster in the plurality of clusters based on the applied weights
• train a third machine learning process based on the re-classified items;
• execute the trained third machine learning process to select and assign a percentage division for a given item from within a percentage division range of a cluster in the plurality of clusters to which the given item corresponds based on one of a set of modes, wherein the set of modes comprises:
• a first mode wherein the assigned percentage division is selected from a first portion of the percentage division range;
• a second mode wherein the assigned percentage division is selected from a second portion of the percentage division range;
• and
• a third mode wherein the assigned percentage division is selected from a third portion of the percentage division range;
• and
• virtualize the selected and assigned percentage division for the given item.

A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to:

• implement a decision engine that executes on a computing platform to perform a process for providing intelligent recommendations for managing an inventory of items by a given entity, wherein the decision engine operates to:
• obtain, for a set of items, data indicative of a previous percentage division between a first item ownership type and a second item ownership type for each item in the set of items, wherein the first item ownership type for a given item corresponds to items that are owned by the given entity, and the second item ownership type for the given item corresponds to items that are owned by another entity and potentially procurable by the given entity;
• classify each item in the set of items into one of a plurality of clusters, based on the obtained data, wherein each cluster represents a different percentage division range between the first item ownership type and the second item ownership type;
• execute a first machine learning process to identify any items in each cluster that deviate from the percentage division range for the cluster;
• execute a second machine learning process to apply weights to any identified items based on the deviation from the percentage division range;
• re-classify any identified items to another cluster in the plurality of clusters based on the applied weights
• train a third machine learning process based on the re-classified items;
• executing the trained third machine learning process to select and assign a percentage division for a given item from within a percentage division range of a cluster in the plurality of clusters to which the given item corresponds based on one of a set of modes, wherein the set of modes comprises:
• a first mode wherein the assigned percentage division is selected from a first portion of the percentage division range;
• a second mode wherein the assigned percentage division is selected from a second portion of the percentage division range;
• and
• a third mode wherein the assigned percentage division is selected from a third portion of the percentage division range;
• and
• virtualize the selected and assigned percentage division for the given item.