Replacement component management using machine learning

An apparatus comprising:

• a processing device operatively coupled to a memory and configured:
• to predict one or more device types for which one or more components thereof will be replaced, wherein the predicting is performed using at least a first machine learning algorithm;
• to identify locations of respective devices of a plurality of devices corresponding to the one or more device types;
• to determine one or more component distribution sources that are in proximity to the locations of the respective devices, wherein the determining is performed using at least a second machine learning algorithm;
• to identify at least one device of the respective devices qualifying for at least one replacement component;
• to cause dispatching of the at least one replacement component to a location of the at least one device from a component distribution source of the one or more component distribution sources in proximity to the location of the at least one device;
• and
• to further cause dispatching of respective ones of a plurality of replacement components to respective ones of a plurality of devices from the one or more component distribution sources based on at least one of component degradation rates of the respective ones of the plurality of devices, durations for the respective ones of the plurality of replacement components to reach the respective ones of the plurality of devices from the one or more component distribution sources, durations to replace degraded components in the respective ones of the plurality of devices with the respective ones of the plurality of replacement components and durations to test the respective ones of the plurality of replacement components in the respective ones of the plurality of devices.

An article of manufacture 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 said at least one processing device to perform the steps of:

• predicting one or more device types for which one or more components thereof will be replaced, wherein the predicting is performed using at least a first machine learning algorithm;
• identifying locations of respective devices of a plurality of devices corresponding to the one or more device types;
• determining one or more component distribution sources that are in proximity to the locations of the respective devices, wherein the determining is performed using at least a second machine learning algorithm;
• identifying at least one device of the respective devices qualifying for at least one replacement component;
• causing dispatching of the at least one replacement component to a location of the at least one device from a component distribution source of the one or more component distribution sources in proximity to the location of the at least one device;
• and
• further causing dispatching of respective ones of a plurality of replacement components to respective ones of a plurality of devices from the one or more component distribution sources based on at least one of component degradation rates of the respective ones of the plurality of devices, durations for the respective ones of the plurality of replacement components to reach the respective ones of the plurality of devices from the one or more component distribution sources, durations to replace degraded components in the respective ones of the plurality of devices with the respective ones of the plurality of replacement components and durations to test the respective ones of the plurality of replacement components in the respective ones of the plurality of devices.