Systems and methods for inventory control and optimization
AssigneeThrive Technologies, Inc.
InventorLouis Rick Morris
Life of this patent
Estimating the term…
Abstract
The present systems and methods generally relate to optimizing inventory levels using machine learning methodologies. Using novel techniques, the present systems and methods can process and analyze inventory data to determine inventory items that are stocked out or overstocked and provide recommendations for mitigating these events, such that users can optimize their inventory levels with minimal manual intervention. For example, in various embodiments, the present systems and methods ingest inventory data, normalize and extract relevant portions of the inventory data, and sort the inventory data into one or more categories based on the root cause of any identified stockout or overstock events.
Claims20
A method of managing product inventory levels comprising:
- defining a plurality of categories of stock events;
- receiving a training dataset of inventory data, wherein the training dataset comprises a plurality of items stocked or to be stocked with a particular entity;
- associating each of the plurality of items in the training dataset with a respective category of the plurality of categories of stock events;
- generating and training a machine learning model based on the training dataset of inventory data;
- receiving an evaluation dataset of inventory data, wherein the evaluation dataset comprises a pre-associated category of stock events for each of the plurality of items;
- applying the machine learning model to the evaluation dataset of inventory data to generate an estimated category of stock events for each of the plurality of items;
- and
- comparing, for each of the plurality of items, the pre-associated category of stock events to the estimated category of stock events to determine an efficacy of the machine learning model.
A system for managing product inventory levels comprising:
- a database configured to store inventory data;
- a processor;
- and
- a non-transitory computer-readable medium having instructions stored thereon, the instructions executable by the processor for performing operations comprising:
- defining a plurality of categories of stock events;
- receiving a training dataset of inventory data from the database, wherein the training dataset comprises a plurality of items stocked or to be stocked with a particular entity;
- associating each of the plurality of items in the training dataset with a respective category of the plurality of categories of stock events;
- generating and training a machine learning model based on the training dataset of inventory data;
- receiving an evaluation dataset of inventory data from the database, wherein the evaluation dataset comprises a pre-associated category of stock events for each of the plurality of items;
- applying the machine learning model to the evaluation dataset of inventory data to generate an estimated category of stock events for each of the plurality of items;
- and
- comparing, for each of the plurality of items, the pre-associated category of stock events to the estimated category of stock events to determine an efficacy of the machine learning model.
A non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising:
- defining a plurality of categories of stock events;
- receiving a training dataset of inventory data, wherein the training dataset comprises a plurality of items stocked or to be stocked with a particular entity;
- associating each of the plurality of items in the training dataset with a respective category of the plurality of categories of stock events;
- generating and training a machine learning model based on the training dataset of inventory data;
- receiving an evaluation dataset of inventory data, wherein the evaluation dataset comprises a pre-associated category of stock events for each of the plurality of items;
- applying the machine learning model to the evaluation dataset of inventory data to generate an estimated category of stock events for each of the plurality of items;
- and
- comparing, for each of the plurality of items, the pre-associated category of stock events to the estimated category of stock events to determine an efficacy of the machine learning model.
Dependencies are inferred from the claim text.