System and method for predictive inventory
OwnerMohammad ESMALIFALAK, Akshay IYENGAR +4inventors
InventorsMohammad ESMALIFALAK, Akshay IYENGAR, Seyedmorteza Mirhoseininejad, Francis EMERY, Taylor MATHEWSON, Peter DOULAS
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Abstract
There is provided systems and methods for forecasting usage of one or more parts. Such systems may receive historical usage data, generate machine learning models, generate predictions using the machine learning models, and perform one or more actions based on the generated predictions.
Claims21
A method of forecasting part usage, comprising:
- receiving, by a computing device, historical usage data for a part;
- training, by the computing device, a machine learning model based on the historical usage data, wherein the training comprises:
- discovering correlations between usage of the part and work orders based on analysis of the historical usage data, and
- training the machine learning model based on the correlations;
- determining, by the computing device, a predicted amount of future demand for the part based on the machine learning model;
- and
- performing, by the computing device, an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.
(canceled)
A system for forecasting usage of parts, comprising:
- a memory that stores executable components;
- and
- a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
- a data accumulator module configured to receive historical usage data for a part;
- a model generation module configured to discover, based on analysis of the historical usage data, a correlation between usage of the part and work orders and to train a machine learning model based on the correlation;
- a part forecasting module configured to determine a predicted amount of future demand for the part based on the machine learning model;
- and
- a notification module configured to perform an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.
A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
- receiving historical usage data for a part;
- training a machine learning model based on the historical usage data, wherein the training comprises
- discovering correlations between usage of the part and work orders based on analysis of the historical usage data, and
- training the machine learning model based on the correlations;
- determining a predicted amount of future demand for the part based on the machine learning model;
- and
- performing an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.
Dependencies are inferred from the claim text.