SYSTEMS AND METHODS FOR OPTIMIZATION OF A PRODUCT INVENTORY BY AN INTELLIGENT ADJUSTMENT OF INBOUND PURCHASE ORDERS

    公开(公告)号:US20210090022A1

    公开(公告)日:2021-03-25

    申请号:US16849143

    申请日:2020-04-15

    Applicant: COUPANG CORP.

    Abstract: A computer-implemented systems and methods for intelligent generation of purchase orders is disclosed. The system may be configured to execute instructions for: receiving one or more demand forecast quantities of one or more products, the products corresponding to one or more product identifiers, and the demand forecast quantities comprising a demand forecast quantity for each product for each unit of time; 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 preliminary order quantities for the products; constraining the preliminary order quantities to obtain recommended order quantities based at least on the supplier statistics data, the current product inventory levels, and the currently ordered quantities; and generating purchase orders to the suppliers for the products based on the recommended order quantities.

    COMPUTERIZED SYSTEMS AND METHODS FOR PREDICTING AND MANAGING SCRAP

    公开(公告)号:US20220188846A1

    公开(公告)日:2022-06-16

    申请号:US17546250

    申请日:2021-12-09

    Applicant: COUPANG CORP.

    Abstract: Systems and methods for predicting that a stock keeping unit (SKU) is likely to be scrapped and performing preventative actions accordingly. The system receives a request to initiate a process related to predicting scrap likelihood associated with a plurality of SKUs, inputs data associated with each SKU into a trained prediction model, outputs, from the trained prediction model, a prediction value for each SKU, measures the trained prediction model by calculating a set of values, and generates a matrix comprising the calculated set of values. Using the matrix, a threshold prediction value is determined, and each SKU prediction value is compared to the threshold prediction value. Based on the comparison, a SKU of the plurality of SKUs is predicted as likely to be scrapped and at least one of a plurality of preventative actions is performed.

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