System and method for machine learning based inventory management

A method comprising

• receiving device status data from each of a plurality of multifunction peripherals into a memory via a network interface,
• storing service history data for each of the multifunction peripherals;
• storing service call history data for the multifunction peripherals;
• storing replacement parts data corresponding to replacement parts used in prior repairs of the multifunction peripherals;
• detecting, via a processor, patterns in the service history data, the service call history data and the replacement parts data;
• generating, via the processor, predictive replacement part data for future replacement parts that will be needed by the multifunction peripherals based at least in part on the device status data and the detected patterns;
• and
• outputting the predictive replacement part data.

A method comprising:

• periodically communicating current device status data from each of a plurality of multifunction peripherals to a server;
• periodically communicating service call log data relative to servicing of the multifunction peripherals to the server from an associated device service center;
• periodically communicating replacement parts data corresponding to multifunction peripheral replacement parts used to service the multifunction peripherals responsive to service calls reflected in the service call log data;
• performing ongoing machine learning predictive of replacement parts needed for future service calls for the multifunction peripherals in accordance with device status data, service call log data and replacements parts data;
• and
• generating a list of predicted replacement parts needed from the machine learning.