# US 2021/0174291 A1 · Computer implemented systems and methods for optimization of a product inventory by intelligent distribution of inbound products > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0174291 A1 - Document ID: US20210174291A1 - Status: Published application - Kind: A1 - Application number: 16/704,293 - Filed: 2019-12-05 - Published: 2021-06-10 - Earliest claimed: 2019-12-05 - Assignees: Coupang Corp. - Applicants: Coupang Corp. - Inventors: Je KIM - Web page: https://patentgrep.com/patent/US20210174291A1 ## CPC - G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders - G06Q 10/08726: {for replenishment processing, procedures, or recommendations using forecasting or optimisation} - G06Q 10/08744: {by distributed inventory management} - G06Q 30/0203: Market surveys; Market polls - G06Q 30/0635: {replenishment orders; recurring orders} - G06Q 30/0641: {utilising user interfaces specially adapted for shopping} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Computer-implemented systems and methods for intelligent generation of purchase orders are disclosed. The systems and methods may be configured to: receive an order quantity for a product from a system determining optimal order quantities for products based on demand forecast data; prioritize the order quantity based on actual constraints; and assign the prioritized order quantity to one or more locations. ## Claims ### Claim 1 A computer-implemented first system for intelligent distribution of products, the first system comprising: a memory storing instructions; and at least one processor configured to execute the instructions for: receiving an order quantity for a product from a second system determining optimal order quantities for products based on demand forecast data, the demand forecast data being generated by a model trained to predict a level of future demand based on at least one of: sales statistics, a number of webpage views, a season, a day of the week, or a holiday; prioritizing the order quantity based on actual constraints; assigning the prioritized order quantity to one or more locations based on current product inventory levels; determining an exceeded amount of quantities over an intake capacity of a first location; and transferring the exceeded amount of quantities to one or more remaining locations in equal amounts across the remaining locations. ### Claim 2 (depends on 1) The computer-implemented system of claim 1, wherein prioritizing the order quantity comprises utilizing a set of rules. ### Claim 3 (depends on 2) The computer-implemented system of claim 2, wherein the set of rules comprises an assignment of a predefined quantity of the product to predetermined locations having the capacity to handle volumes more than a predefined quantity. ### Claim 4 (depends on 1) The computer-implemented system of claim 1, wherein prioritizing the order quantity comprises utilizing a logistical regression model to prioritize the order quantity using actual constraints. ### Claim 5 (depends on 1) The computer-implemented system of claim 1, wherein the constraints comprise a capacity of location to receive the product and a popularity of the product, wherein the popularity of the product is determined by the demand forecast data, the demand forecast data being associated with the product or outbound shipments of the product. ### Claim 6 (depends on 1) The computer-implemented system of claim 1, wherein the locations comprise predetermined fulfillment centers with capabilities to handle large volumes of the product. ### Claim 7 (depends on 1) The computer-implemented system of claim 1, wherein the assigned order quantity is further assigned to different regions associated with the predetermined locations. ### Claim 8 (depends on 1) The computer-implemented system of claim 1, wherein the demand forecast data is generated by the trained model based on a number of webpage views of the product. ### Claim 9 (depends on 1) The computer-implemented system of claim 1, wherein the demand forecast data is generated by the trained model based on a current number of orders of the product, the current number of orders of the product being received at the computer-implemented system from a plurality of user devices using a network interface. ### Claim 10 (depends on 1) The computer-implemented system of claim 1, wherein the instructions further comprise generating purchase orders to a supplier associated with the product based on the assigned order quantity. ### Claim 11 A computer-implemented method for intelligent distribution of products, the system comprising: receiving an order quantity for a product from a system determining optimal order quantities for products based on demand forecast data, the demand forecast data being generated by a model trained to predict a level of future demand based on at least one of: sales statistics, a number of webpage views, a season, a day of the week, or a holiday; prioritizing the order quantity based on actual constraints; and assigning the prioritized order quantity to one or more locations based on current product inventory levels; determining an exceeded amount of quantities over an intake capacity of a first location; and transferring the exceeded amount of quantities to one or more remaining locations in equal amounts across the remaining locations. ### Claim 12 (depends on 11) The method of claim 11, wherein prioritizing the order quantity comprises utilizing a set of rules. ### Claim 13 (depends on 12) The method of claim 12, wherein the set of rules comprises an assignment of predefined quantity of the product to predetermined locations with capabilities to handle volumes more than a predefined amount. ### Claim 14 (depends on 11) The method of claim 11, wherein prioritizing the order quantity comprises utilizing a logistical regression model to prioritize the order quantity using actual constraints. ### Claim 15 (depends on 11) The method of claim 11, wherein the actual constraints comprise a capacity of location to receive the product and a popularity of the product, wherein the popularity of the product is determined by the demand forecast data, the demand forecast data being associated with the product or outbound shipments of the product. ### Claim 16 (depends on 11) The method of claim 11, wherein the locations comprise predetermined fulfillment centers with capabilities to handle large volumes of the product. ### Claim 17 (depends on 11) The method of claim 11, wherein the assigned order quantity is further assigned to different regions associated with the predetermined locations. ### Claim 18 (depends on 11) The method of claim 11, wherein the demand forecast data is generated by the trained model based on a number of webpage views of the product. ### Claim 19 (depends on 11) The method of claim 11, wherein the demand forecast data is generated by the trained model based on a current number of orders of the product, the current number of orders of the product being received at the computer-implemented system from a plurality of user devices using a network interface. ### Claim 20 A computer-implemented system for intelligent distribution of products, the system comprising: a memory storing instructions; and at least one processor configured to execute the instructions for: receiving one or more demand forecast quantities of one or more products, the products corresponding to one or more product identifiers, the demand forecast quantities comprising a demand forecast quantity for each product for each unit of time, and the demand forecast quantities being generated by a model trained to predict a level of future demand based on at least one of: sales statistics, a number of webpage views, a season, a day of the week, or a holiday; receiving supplier statistics data for one or more suppliers, the suppliers being associated with a portion of the products; receiving current product inventory levels and currently ordered quantities of the products; determining order quantities for the products based at least on the demand forecast quantities, the supplier statistics data, and the current product inventory levels; prioritizing the order quantities based on actual constraints, the actual constraints comprising current product inventory levels; assigning the prioritized order quantities to one or more locations; determining an exceeded amount of quantities over an intake capacity of a first location; transferring the exceeded amount of quantities to one or more remaining locations in equal amounts across the remaining locations; and generating purchase orders to the suppliers for the products based on the assigned order quantities.