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KR-102963446-B1 - APPARATUS AND METHOD FOR PREDICTING WAREHOUSING AND LABOR THROUGH DEMAND PREDICTION OF GOODS

KR102963446B1KR 102963446 B1KR102963446 B1KR 102963446B1KR-102963446-B1

Abstract

An apparatus and method for predicting inventory and labor force through product demand forecasting are provided. The method comprises the steps of: collecting order information for a customer's product during a predetermined unit period; analyzing the collected order information by order type; generating data on the order quantity by order type as time-series data for the unit period; analyzing the time-series data accumulated by the unit period using a predetermined forecasting model to forecast product demand by order type; and forecasting labor force demand using the predicted product demand information (hereinafter referred to as demand forecasting information).

Inventors

  • 장보영

Assignees

  • 위킵(주)

Dates

Publication Date
20260513
Application Date
20230906

Claims (10)

  1. In a method performed by a device, A step of collecting order information for a client's products during a pre-set unit period; A step of analyzing the above-mentioned collected order information by order type; A step of generating data on order volumes by order type as time series data for the unit period; A step of predicting product demand by order type by analyzing time series data accumulated for each unit period using a previously established prediction model; and The method includes the step of predicting labor demand using the above-mentioned predicted product demand information (hereinafter, demand forecast information); The step of predicting the above labor demand is, Predict the labor demand using the quantity of prepacked goods determined based on the pre-set work hours for the above client and the above demand forecast information, and The above order types include a single order for one SKU and one quantity, a single order for one SKU and two or more quantities, and a combined order for two or more SKUs and two or more quantities. The above working time includes a first working time for the single order to which a first weight is applied, a second working time for the single item order to which a second weight smaller than the first weight is applied, and a third working time for the combined order to which a third weight larger than the first weight is applied. A method for predicting receiving and labor through demand forecasting of goods, wherein the first working time, the second working time, and the third working time are subject to an additional fourth weighting when processing work for goods is required.
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  4. In paragraph 1, A step of confirming the predicted quantity by skew based on the above demand forecast information; A step of verifying the inventory quantity by skew for the above-mentioned customer's products received in the logistics center; and A method for predicting stocking and labor through product demand forecasting, further comprising the step of requesting automatic stocking for a skew where the inventory quantity is less than the predicted quantity.
  5. In a method performed by a device, A step of collecting order information for a client's products during a pre-set unit period; A step of analyzing the above-mentioned collected order information by order type; A step of generating data on order volumes by order type as time series data for the unit period; A step of predicting product demand by order type by analyzing time series data accumulated for each unit period using a previously established prediction model; and The method includes the step of predicting labor demand using the above-mentioned predicted product demand information (hereinafter, demand forecast information); The step of predicting the above labor demand is, Predict the labor demand using the quantity of prepacked goods determined based on the pre-set work hours for the above client and the above demand forecast information, and A step of generating a prepicking list of prepackaged products determined based on the above demand forecast information; and The method further includes the step of performing pre-packaging based on the above pre-picking list, and The step of performing the above pre-packaging is, A step of creating a pre-packing box when the pre-picking operation corresponding to each pre-picking list generated for each of the above order types is completed; A step of generating a prepacking label containing information on prepacked products included in the above pre-picking list and information on the above prepacking box; and The method includes the step of completing the prepacking through mapping the prepacking box and the prepacking label; A method for predicting inbound and labor force through product demand forecasting, further comprising the step of: when order information for a product identical to the information of a specific pre-packaged product is collected, extracting a pre-packing box of the specific pre-packaged product, and performing the shipment of the specific pre-packaged product based on a pre-packing label mapped to the pre-packing box and an invoice number corresponding to the order information.
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  8. In a method performed by a device, A step of collecting order information for a client's products during a pre-set unit period; A step of analyzing the above-mentioned collected order information by order type; A step of generating data on order volumes by order type as time series data for the unit period; A step of predicting product demand by order type by analyzing time series data accumulated for each unit period using a previously established prediction model; and The method includes the step of predicting labor demand using the above-mentioned predicted product demand information (hereinafter, demand forecast information); The step of predicting the above labor demand is, Predict the labor demand using the quantity of prepacked goods determined based on the pre-set work hours for the above client and the above demand forecast information, and A step of checking the inventory of the above-mentioned customer's product by sales channel; A step of determining available inventory for each product at each sales outlet based on the above demand forecast information, the above inventory by sales outlet, and the importance by sales outlet; and A method for predicting stocking and labor through product demand forecasting, further comprising the step of adjusting available inventory by sales outlet based on the above available inventory.
  9. A computer-readable recording medium that is combined with a computer, which is hardware, and stores a computer program that executes the method of any one of claims 1, 4, 5 and 8.
  10. Communications Department; A memory storing at least one process for predicting product stocking and labor force through the forecasting of demand for a customer's product; and A processor comprising: collecting order information for the products of the above-mentioned client company during a pre-set unit period, analyzing the collected order information by order type, generating data on the order quantity by order type as time-series data for the unit period, analyzing the time-series data accumulated by the unit period using a pre-established prediction model to predict product demand by order type, and predicting labor demand using the predicted product demand information (hereinafter, demand prediction information); The above processor, when forecasting the labor demand, Predict the labor demand using the quantity of prepacked goods determined based on the pre-set work hours for the above client and the above demand forecast information, and The above order types include a single order for one SKU and one quantity, a single order for one SKU and two or more quantities, and a combined order for two or more SKUs and two or more quantities. The above working time includes a first working time for the single order to which a first weight is applied, a second working time for the single item order to which a second weight smaller than the first weight is applied, and a third working time for the combined order to which a third weight larger than the first weight is applied. A device for predicting receiving and labor through product demand forecasting, wherein the first working time, the second working time, and the third working time are additionally subjected to a fourth weighting when contract processing work is required for the product.

Description

Apparatus and Method for Predicting Warehousing and Labor Force Through Demand Forecasting of Goods The present disclosure relates to an apparatus and method for predicting inventory and labor force through product demand forecasting. As the online purchasing market, which is more convenient than offline shopping, grows, purchasing patterns are shifting toward the easy purchase of a wide variety of products via mobile devices. In line with this change, the demand from consumers for quick delivery of ordered goods is steadily increasing. Accordingly, a system capable of rapid and accurate inventory and packing management for products for sale is required. FIG. 1 is a drawing for explaining a system for predicting inventory and labor through demand forecasting of goods according to one embodiment of the present disclosure. FIG. 2 is a block diagram of a device for predicting stocking and labor force through demand forecasting of goods according to one embodiment of the present disclosure. FIG. 3 is a flowchart of a method for predicting labor force through demand forecasting of a product according to one embodiment of the present disclosure. FIG. 4 is a flowchart of a method for requesting stock through demand forecasting of goods according to one embodiment of the present disclosure. FIGS. 5 and 6 are flowcharts of a pre-packaging method through demand forecasting of a product according to one embodiment of the present disclosure. FIG. 7 is a flowchart of a method for adjusting inventory by sales outlet through demand forecasting of a product according to one embodiment of the present disclosure. Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms ‘part, module, component, block’ as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of ‘parts, modules, components, blocks’ may be implemented as a single component, or a single ‘part, module, component, block’ may include a plurality of components. Throughout the specification, when a part is described as being “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network. Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components. The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms. Singular expressions include plural expressions unless there is an obvious exception in the context. In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context. The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings. Prior to the explanation, the meanings of the terms used in this specification are briefly explained. However, since the explanation of terms is intended to aid in understanding this specification, it should be noted that unless explicitly stated as a limiting factor, they are not used to limit the technical scope of this disclosure. In this specification, the term "device" includes all various devices capable of performing computational processing and providing results to a user. For example, a device may include a computer, a server device, and a portable terminal, or be any one of these forms. Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser. The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server. The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro