Coupang, Corporation

Other

Je Kim

Rajesh Medidhi

Smita Mohan

Kyu Hoon Lim

Hernan Awad Amar

Hyeong Bae Yang

Kyuhoon Lim

Kwon Sung Son

Dong Hyun Jin

Young Sun Jung

YoungHoon Jeong

Hyo Beom Park

SeungRok Kim

Systems and methods, and computer readable media for target inventory estimation of a geographical region are disclosed. The method receives an item identifier associated with an item for target inventory estimation. The method may access the demand share estimate of the item in the geographical region and the mean and variance estimates of the demand share estimate in the geographical region. The method may then access overall demand forecast data for the item and calculate the mean and variance estimates of the demand forecast. The method may use the generated mean and variance estimates and demand share estimate of the geographical region to determine the target inventory estimation of the region.

Systems and methods for inventory estimation

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.

Computer implemented systems and methods for optimization of a product inventory by intelligent distribution of inbound products

A computer-implemented system and method for automated inventory using data associated with stock keeping unit SKU and fulfillment centers (FC) to initialize a data structure comprising a plurality of nodes, determine a weight associated with each FC based on the capacity of each FC, assignment SKU to one or more FCs, perform a preliminary mapping for the plurality of FCs, determining whether to rebalance the preliminary mapping of the quantity, and rebalancing the preliminary mapping based on the weights associated with each FC, to promote maximum utilization of the network.

Systems and methods for inventory reshuffling and rebalancing

Computer-implemented systems and methods for intelligent distribution of products are disclosed. The systems and methods may be configured to: receive a request to assign a product to a location; retrieve a plurality of attributes associated with the product from a system configured to store attributes of products; retrieve a plurality of rules from a rules system configured to store rules implemented for assigning a product to a location, the retrieved plurality of rules configured by a user using a user interface; determining the location to store the product by applying the retrieved plurality of attributes to the retrieved plurality of rules; and assigning the product to the determined location.

Computer-implemented systems and methods for optimization of a product inventory by intelligent distribution of inbound products using product assignment validation

One aspect of the present disclosure is directed to a computer-implemented system for generating an automated outbound profile. The system may include may perform steps including: receiving data comprising a capacity of a fulfillment center (FC); receiving, a plurality of product identifiers associated with incoming products to the FC; periodically collecting and storing transactional logs for the products at the FC using the product identifier; determining a current inventory for the products stored at the FC using the product identifier; generating an outbound profile for the FC using at least one of the transactional logs and the current inventory using a machine learning algorithm; wherein the outbound profile comprises an expected percentage of outgoing products for a plurality of categories of products; and managing network outbound using the generated outbound profile of the FC by comparing the outbound profile to actual outbound capacity of the FC.

Systems and methods for automated outbound profile generation

Systems and methods are provided for AI-based inbound plan generation. Systems and methods include predicting, using a model, available storage associated with a first fulfillment center; predicting a demand capacity associated with the first fulfillment center; using the predicted available storage and the predicted demand capacity, calculating an excess demand quantity; determining one or more fungible capacity types associated with the excess demand quantity; determining available fungible capacity type quantities in at least one fulfillment center; using the one or more fungible capacity types associated with the excess demand quantity and the available fungible capacity type quantities in at least one fulfillment center, generate an inbound plan for the first fulfillment center; receive a notification of one or more incoming products; and distribute the one or more incoming products according to the generated inbound plan.

Computer-implemented systems and methods for artificial intelligence (AI)-based inbound plan generation using fungibility logic

The present disclosure provides a computerized method for item distribution, including retrieving, from a data structure, an inventory of a SKU at each of a plurality of destinations; determining a buffer of each of the destinations; determining an average buffer across the destinations; identifying at least one high utilization destination and at least one low utilization destination based on differences from the average buffer; and redistributing a network inventory by, iteratively and for each of the high utilization destinations: finding, in the data structure, a low utilization destination having a common attribute with the high utilization destination; and sending, to a user device for display, an instruction to transfer a redistribution quantity of the SKU from the low utilization destination to the high utilization destination.

Systems and computerized methods for balancing inventory

Disclosed are methods and systems for intelligent distribution of products. In one aspect, a system comprises a memory storing instructions and at least one processor configured to execute the instructions. The processor performs operations include receiving forecasting data comprising an expected demand of a stock keeping unit and determining a target inventory of the SKU to satisfy the expected demand. The operations further include determining a regional target inventory of the SKU of a region comprising a plurality of fulfillment centers. The operations further comprise receiving historical data comprising the inbound and outbound shipment history of a fulfillment center in the plurality of fulfillment centers and generating a profile for the fulfillment center. The operations further comprise assigning a portion of the regional target inventory to the fulfillment center and transmitting instructions to a device to stock a number of SKUs in the fulfillment center based on the portion.

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Bellevue, WA, United States of America

Je Kim