Apparatus, method and recording medium for determining the stock status of item

Abstract

The present invention provides an apparatus comprising: one or more processors; and one or more memories storing commands to be executed by the one or more processors; the apparatus is configured such that, when executing the commands, the one or more processors as follows: obtain first information relating to the inventory quantity of a product on a first date; generate second information based on a learning model learned in a manner that predicts the delivery rate of the product, the second information relating to the delivery rate of the product during the period from the first date to a second date following the first date; obtain third information relating to the outbound quantity of the product during the period from the first date to the second date; determine the inventory status of the product during the period from the first date to the second date based on the first information, the second information, and the third information; determine whether there is an inventory shortage based on the inventory status; and the learning model is a model learned by using the ordering history and delivery history of the product as learning data.

Description

[Technical Field]

[0001] This invention relates to a technique for determining the inventory status of goods. [Previous Technology]

[0002] Using an e-commerce service that enables the purchase of goods in an online store. The e-commerce service provider may store goods delivered by the goods supplier in a logistics warehouse, receive orders for goods from the goods demander (customer), and release the ordered goods from the logistics warehouse and deliver them to the customer.

[0003] If the inventory of goods is insufficient, it will be unable to process the orders of customers, causing e-commerce service users to abandon their purchases, thereby reducing the sales of e-commerce services. [Summary of the Invention]

[0004] [Problem to be solved by the invention] At least one embodiment of the present invention can predict the inventory status of goods and determine whether there is a shortage of inventory.

[0005] The technical issues addressed by this invention are not limited to those mentioned above. Those skilled in the art can readily understand other unmentioned technical issues based on this description. [Technical Means for Solving the Problem]

[0006] An apparatus according to one embodiment of the present invention may include: one or more processors; and one or more memory, which stores commands for execution by the one or more processors; the apparatus is configured such that, when executing the commands, the one or more processors: obtain first information related to the inventory quantity of goods on a first date; generate second information based on a learning model learned in a manner that predicts the delivery rate of the goods, the second information being related to the delivery rate of the goods during the period from the first date to a second date after the first date; obtain third information related to the outbound quantity of the goods during the period from the first date to the second date; determine the inventory status of the goods during the period from the first date to the second date based on the first information, the second information, and the third information; determine whether there is an out-of-stock situation based on the inventory status; and the learning model is a model learned by using the ordering history and delivery history of the goods as learning data.

[0007] In one embodiment, the above-mentioned apparatus can be configured such that the above-mentioned one or more processors are as follows: when generating the above-mentioned second information related to the delivery rate of the above-mentioned goods, generating one or more order history groups based on one or more variables of the above-mentioned order history; inputting each of the above-mentioned one or more order history groups into the above-mentioned learning model, thereby obtaining information indicating the delivery rate of each of the above-mentioned order history groups as output; determining any one of the delivery rates of the above-mentioned one or more groups as the above-mentioned delivery rate, thereby generating the above-mentioned second information.

[0008] In one embodiment, the above-mentioned one or more variables may include variables related to the demand for the above-mentioned goods.

[0009] In one embodiment, the above-mentioned one or more variables may include variables related to the order type of the above-mentioned goods.

[0010] In one embodiment, the above-mentioned apparatus can be configured such that the one or more processors are as follows: when determining the above-mentioned inventory status, obtaining order information related to the order quantity of the above-mentioned goods before the above-mentioned second date; based on the above-mentioned order information and the above-mentioned second information, determining the delivery quantity during the period from the above-mentioned first date to the above-mentioned second date; and determining the above-mentioned inventory status based on the above-mentioned first information, the above-mentioned delivery quantity, and the above-mentioned third information regarding the outbound quantity.

[0011] In one embodiment, the above-mentioned inventory status may indicate the inventory quantity of the above-mentioned goods during the period between the above-mentioned first date and the above-mentioned second date.

[0012] In one embodiment, the above-mentioned device can be configured such that when one or more processors determine whether the above-mentioned inventory shortage exists, they determine that the inventory quantity of the above-mentioned goods is 0.

[0013] In one embodiment, the above-mentioned device may be configured such that: the one or more processors determine information on the loss of sales of the above-mentioned goods due to the lack of inventory based on the determination that there is a shortage of inventory.

[0014] In one embodiment, the above-mentioned device may be configured such that the one or more processors are configured to: obtain commodity price information of the commodity; and determine the amount of lost sales based on the commodity price information and the information on lost sales volume.

[0015] In one embodiment, the above-mentioned device may be configured such that one or more processors determine the inventory shortage time to maintain the inventory shortage based on the determination that there is an inventory shortage.

[0016] One embodiment of the present invention is implemented in an apparatus, the apparatus including one or more processors and one or more memories storing commands to be executed by the one or more processors. The method may include the following steps: the one or more processors: obtaining first information related to the inventory quantity of goods on a first date; generating second information based on a learning model learned in a manner that predicts the delivery rate of the goods, the second information being related to the delivery rate of the goods during the period from the first date to a second date after the first date; obtaining third information related to the outbound quantity of the goods during the period from the first date to the second date; determining the inventory status of the goods during the period from the first date to the second date based on the first information, the second information and the third information; and determining whether there is an out-of-stock situation based on the inventory status; and the learning model is a model learned by using the order history and delivery history of the goods as learning data.

[0017] In one embodiment, the step of the processor generating the second information related to the delivery rate of the goods may include the following steps: generating one or more order history groups based on one or more variables of the order history; inputting each of the one or more order history groups into the learning model to obtain information indicating the delivery rate of each of the one or more order history groups as output; and determining any one of the delivery rates of the one or more groups as the delivery rate, thereby generating the second information.

[0018] In one embodiment, the above-mentioned one or more variables may include variables related to the demand for the above-mentioned goods.

[0019] In one embodiment, the above-mentioned one or more variables may include variables related to the order type of the above-mentioned goods.

[0020] In one embodiment, the step of determining the inventory status by one or more processors may include the following steps: obtaining order information related to the order quantity of the goods before the second date; determining the delivery quantity during the period from the first date to the second date based on the order information and the second information; and determining the inventory status based on the inventory quantity of the first information, the delivery quantity, and the outbound quantity of the third information.

[0021] In one embodiment, the above-mentioned inventory status may indicate the inventory quantity of the above-mentioned goods during the period between the above-mentioned first date and the above-mentioned second date.

[0022] In one embodiment, the step of determining whether the above-mentioned inventory shortage exists by one or more processors may include the following step: determining that the above-mentioned inventory shortage exists based on determining that the inventory quantity of the above-mentioned goods is 0.

[0023] In one embodiment, the above method may further include the following steps: the one or more processors determine information on the loss of sales volume of the above-mentioned goods due to the lack of inventory based on the determination that there is a shortage of inventory.

[0024] In one embodiment, the above method may further include the following steps, namely, the above one or more processors shall: obtain commodity price information of the commodity; and determine the amount of lost sales based on the commodity price information and the information on lost sales volume.

[0025] In one embodiment of the present invention, the recording medium is a non-transitory computer-readable recording medium that records commands that, when executed by one or more processors, cause the one or more processors to perform actions. The commands can be configured such that the one or more processors as follows: obtain first information related to the inventory of goods in the inventory on a first date; generate second information based on a learning model that learns in a manner that predicts the delivery rate of the goods, the second information being related to the delivery rate of the goods during the period from the first date to a second date after the first date; obtain third information related to the outflow of the goods during the period from the first date to the second date; determine the inventory status of the goods during the period from the first date to the second date based on the first information, the second information, and the third information; determine whether there is an out-of-stock situation based on the inventory status; and the learning model is a model that learns by using the order history and delivery history of the goods as learning data. [Effects of the Invention]

[0026] According to at least one embodiment of the present invention, the inventory status of goods can be predicted and it can be determined whether the inventory is insufficient.

[0027] The effects of the technical concept of the present invention are not limited to the effects mentioned above. Those who are skilled in the art can clearly understand other effects not mentioned in the present invention based on the description.

Implementation Method

[0029] The various embodiments described herein are illustrated for the purpose of clearly explaining the technical concept of the present invention and are not intended to limit them to specific implementation methods. The technical concept of the present invention includes various modifications, equivalents, alternatives, and embodiments obtained by selectively combining all or part of the embodiments described herein. Furthermore, the scope of the technical concept of the present invention is not limited to the various embodiments presented below or their specific descriptions.

[0030] The terms used herein, including technical or scientific terms, shall have the meanings commonly understood by one of common knowledge in the technical field to which this invention pertains, unless otherwise defined.

[0031] The expressions used herein, such as "including," "may include," "possibly possess," "may possess," "have," and "may have," imply the presence of object features (e.g., functions, actions, or constituent elements), and do not exclude the existence of other additional features. That is, the expressions described above should be understood as open-ended terms that have the possibility of including other embodiments.

[0032] The singular expressions used herein may include the meaning of the plural form unless otherwise stated in the context, and this also applies to the singular expressions described in the technical solutions.

[0033] The terms “first,” “second,” “first,” “second,” etc., used in this document are used to distinguish one object from another when referring to multiple objects of the same kind, unless the context otherwise indicates, and are not used to limit the order or importance of such objects.

[0034] As used herein, “A, B and C”, “A, B or C”, “A, B and / or C” or “at least one of A, B and C”, “at least one of A, B or C”, “at least one of A, B and / or C”, etc., may refer to each of the listed items or all combinations of the listed items. For example, “at least one of A or B” may refer to (1) at least one A, (2) at least one B, (3) at least one A and at least one B.

[0035] The term “based on” as used herein is used to describe one or more factors that affect the determination or judgment of an action or behavior described in the statement or article that includes the term. The term does not exclude other factors that affect the determination or judgment of an action or behavior.

[0036] The expression used herein to describe a constituent element (e.g., the first constituent element) as “connected” or “linked” to another constituent element (e.g., the second constituent element) may mean that the aforementioned constituent element is directly connected to or linked to the aforementioned other constituent element, or is connected to or linked to the aforementioned other constituent element through a new other constituent element (e.g., the third constituent element).

[0037] Depending on the context, the expression "configured to" as used herein may mean "configured in a ~ manner", "possessing ~ capabilities", "modified in a ~ manner", "made in a ~ manner", "capable of ~ operations", etc. This expression is not limited to the meaning of "specially designed in hardware". For example, the processor 210 configured to perform a specific action may refer to a generic-purpose processor that can perform that specific action by executing software.

[0038] Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings and descriptions thereof, the same or substantially equivalent constituent elements are given the same reference numerals. Furthermore, in the following descriptions of various embodiments, repeated descriptions of the same or corresponding constituent elements may be omitted, but this does not mean that the constituent element is not included in the embodiment.

[0039] Figure 1 is a diagram illustrating a system 100 according to various embodiments of the present invention. System 100 may include a device 110 and a user terminal 120. Device 110 may be a device for determining the inventory status of goods. The inventory status of goods may refer to the current inventory quantity and / or the expected inventory quantity stored in the logistics warehouse managed by the service provider of the e-commerce service. User terminal 120 may be a device configured to transmit order information for ordered goods to the supplier based on the inventory status of the goods. For example, user terminal 120 may be a portable communication device, a computer device, a portable multimedia device, a home appliance device, or one or more of the above devices.

[0040] User terminal 120 may be a device capable of connecting to the Internet. User terminal 120 may connect to device 110 via the network to send and receive various data. User terminal 120 may receive information from device 110 indicating the inventory status of goods, information indicating insufficient inventory of goods, and / or order information indicating ordered goods. User terminal 120 may display the information obtained from device 110 via a display device. The display device may be a device that provides various data to the user in a visual form. For example, the display device may include a monitor, projector, hologram, etc.

[0041] FIG2 is a diagram illustrating an example of a device 110 according to an embodiment of the present invention. The device 110 according to an embodiment of the present invention may include a processor 210 and / or memory 220. At least one of the components included in the device 110 may be omitted, or other components may be added to the device 110. For example, the device 110 may further include a communication circuit 230 for communicating with an external device. The device 110 may be implemented by additionally or alternatively integrating some of the components, or implemented as a single or multiple entities. At least some of the components within the device 110 may be interconnected via buses, GPIO (General Purpose Input / Output), SPI (Serial Peripheral Interface), or MIPI (Mobile Industry Processor Interface), etc., to transmit and receive data and / or signals.

[0042] According to various embodiments, the processor 210 of the device 110 may be configured to perform control of various components of the device 110 (e.g., memory 220) and / or communication-related calculations or data processing. The processor 210 may drive software to control at least one component of the device 110 connected to the processor 210. Furthermore, the processor 210 may perform various calculations, processing, data generation, and processing operations related to the present invention. Also, the processor 210 may load data from memory 220 or store such data in memory 220. In embodiments of the present invention, the processor 210 may execute commands stored in memory 220 to determine the inventory status of goods, thereby determining whether there is an inventory shortage. The processor 210 disclosed herein may also refer to a collection of more than one processor 210.

[0043] According to various embodiments, the memory 220 of the device 110 may store commands for actions of the processor 210. The data stored in the memory 220 is data obtained, processed, or used by at least one component of the device 110, and may include software (e.g., a program). The memory 220 may include volatile and / or non-volatile memory. The memory 220 may also store information processed during the operation of the processor 210.

[0044] In various embodiments, memory 220 may store commands to be executed in processor 210 and information processed in processor 210, by which processor 210 performs the operations related to inventory status determination technology of the present invention.

[0045] According to various embodiments, the communication circuit 230 of device 110 can establish a wired or wireless communication channel with an external device (e.g., user terminal 120) to send and receive various data. According to one embodiment, the communication circuit 230 may include at least one port for connecting to an external device via a wired cable for wired communication. In the above case, the communication circuit 230 can communicate with a wired external device via at least one port. According to one embodiment, the communication circuit 230 can be configured to include a cellular communication module connected to a cellular network (e.g., 3G, LTE, 5G, Wibro, or WiMAX). According to various embodiments, the communication circuit 230 may include a short-range communication module to send and receive data with an external device using short-range communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), UWB), but is not limited thereto. According to one embodiment, the communication circuit 230 may include a contactless communication module for contactless communication. Contactless communication may include, for example, at least one of the following short-range contactless communication technologies: NFC (Near Field Communication), RFID (Radio Frequency Identification), or MST (Magnetic Secure Transmission).

[0046] Figure 3 is a sequence diagram illustrating the operation method of the apparatus 110 according to various embodiments of the present invention. In step S310, the apparatus 110 can obtain first information related to the inventory quantity of goods. In one embodiment, the first information can indicate the inventory quantity of goods for each date. The inventory quantity of goods for each date can refer to: the quantity of goods remaining after all goods have been shipped out on each date, or the quantity of goods remaining at the beginning of the next day of each date.

[0047] In step S320, device 110 may generate second information related to the delivery rate of goods based on a learning model. In this invention, the delivery rate of goods may refer to the ratio of the quantity delivered to the order quantity. The order quantity may refer to the quantity of goods ordered from the supplier of the goods, and the delivery quantity refers to the quantity of goods delivered from the supplier. For example, in the case where 80 goods are delivered from the supplier in response to a first order of 100 goods requested from the supplier, the delivery rate is 80%.

[0048] The learning model may be a learner that predicts (outputs) the delivery rate of goods. For example, the learning model may be an artificial neural network comprising one or more layers. In one embodiment, the learning model may be a model that learns by using the ordering history and delivery history of goods as learning data. The ordering history may be the history of ordering goods from suppliers that sell the goods. For example, the ordering history may include supplier information, the quantity of goods ordered from suppliers in the past, the category of goods, the order type, etc. The delivery history may be the history of goods delivered from suppliers in the past. For example, the delivery history may include the quantity of goods delivered from suppliers, the time required for delivery, etc.

[0049] Device 110 may input order information of goods into a learning model to obtain second information as output. The order information may include the quantity of goods ordered from the supplier, information about the goods, and / or information about the order type. The second information may include information related to the delivery rate of goods predicted based on the order information. The second information may further include information related to the predicted delivery time of goods based on the order information. In one example, device 110 may set the delivery time to a fixed period after the time the goods were ordered (or, the ordering time). In another example, device 110 may input order information into a learning model that has learned in a manner that predicts delivery times to determine the delivery time.

[0050] In one embodiment, the second information may indicate the product delivery rate during the period from date 1 to date 2. Date 2 may be after date 1. In one example, the period from date 1 to date 2 may include date 1 or exclude date 1. In another example, the period from date 1 to date 2 may include date 2 or exclude date 2. Hereinafter, for ease of explanation, in this invention, the period from date 1 to date 2 is a period that excludes date 1 but includes date 2.

[0051] In step S330, device 110 may obtain third information related to the outbound quantity of goods. The outbound quantity of goods may refer to the number of goods outbound from the logistics warehouse managed by the service provider of the e-commerce service. In one embodiment, the third information may indicate the outbound quantity of goods during the period from date 1 to date 2.

[0052] In one embodiment, the device 110 can determine the quantity of goods shipped out during the period from the first date to the second date based on the quantity of goods shipped out before the first date, thereby obtaining the third information. For example, the quantity of goods shipped out can be determined as the average quantity of goods shipped out during a fixed period before the first date. In another embodiment, the device 110 can also obtain the third information generated by an external device 110.

[0053] In step S340, device 110 may determine the inventory status of goods based on the first information, the second information, and the third information. In one embodiment, device 110 may determine the inventory status during the period from the first date to the second date.

[0054] The device 110 can determine the delivery quantity based on order information and second information related to the order quantity of the goods. For example, the device 110 can determine the delivery quantity by multiplying the order quantity in the order information by the delivery rate in the second information. In one embodiment, the device 110 can determine the delivery quantity during the period from the first date to the second date.

[0055] The device 110 can determine the inventory status based on the inventory quantity, delivery quantity, and outbound quantity of the first information. For example, the device 110 can determine the inventory quantity by subtracting the outbound quantity from the sum of the inventory quantity and the delivery quantity. In one embodiment, the inventory status can indicate the inventory quantity of goods during the period between a first date and a second date.

[0056] In step S350, device 110 may determine whether there is an out-of-stock situation based on the inventory status. In one embodiment, device 110 may determine whether the inventory quantity of the goods is 0, and determine that there is an out-of-stock situation based on the determination that the inventory quantity of the goods is 0. In one embodiment, device 110 may determine whether there are any days during the period between date 1 and date 2 where the inventory quantity of the goods is 0, and if the inventory quantity of the goods on a specific date is 0, then it is determined that there is an out-of-stock situation on that date.

[0057] In one embodiment, based on the determination of insufficient inventory, device 110 can determine information on lost sales volume of goods due to insufficient inventory. Lost sales volume may refer to the quantity of goods that could not be shipped due to insufficient inventory. Lost sales volume may be the value obtained by subtracting the inventory quantity from the quantity shipped.

[0058] In one embodiment, device 110 can obtain product price information. Device 110 can determine the amount of lost sales based on the product price information and information on lost sales volume. The amount of lost sales volume may refer to the total amount of goods that could not be sold due to insufficient inventory. The amount of lost sales volume may be the value obtained by multiplying the lost sales volume by the price of the goods.

[0059] In one embodiment, based on the determination that there is a shortage of inventory, the device 110 can determine the inventory shortage duration. The inventory shortage duration can be determined based on lost sales and outbound volume. For example, the inventory shortage duration can be the value obtained by dividing lost sales by outbound volume and then multiplying by 24 hours.

[0060] Figure 4 is a sequence diagram of an example of generating the second information in step S320 of Figure 3. In step S410, device 110 can generate more than one order history group based on more than one variable of the order history.

[0061] In one embodiment, one or more variables may include a first variable related to the category of the ordered goods. For example, the category of goods may include clothing, cosmetics, home appliances, etc. The device 110 may generate a first order history group, which includes the order history of goods corresponding to the category of the first variable.

[0062] In one embodiment, one or more variables may include a second variable related to the demand for the goods. The demand for the goods may refer to the degree of demand for the goods in e-commerce services. For example, the demand for the goods may be classified into levels A, B, and C according to the degree of demand for the goods from high to low. The device 110 may generate a second order history set, which includes the order history of goods corresponding to the demand for the second variable.

[0063] In one embodiment, one or more variables may include a third variable related to the order type of the goods. For example, the order type may indicate automatic ordering that is automatically ordered at fixed intervals and / or manual ordering that is ordered once. The device 110 may generate a third order history group, which includes the order history of goods corresponding to the order type of the third variable.

[0064] In step S420, device 110 may input one or more order history groups into the learning model to obtain information indicating the delivery rate of one or more order history groups as output. For example, device 110 may output information indicating the first delivery rate by inputting the first order history group into the learning model, output information indicating the second delivery rate by inputting the second order history group into the learning model, and output information indicating the third delivery rate by inputting the third order history group into the learning model.

[0065] In step S430, device 110 may determine the delivery rate based on information indicating the delivery rates of one or more groups, thereby generating the second information.

[0066] In one embodiment, to minimize the risk of insufficient inventory, the device 110 may determine the lowest value among the first to third delivery rates as the delivery rate. In one embodiment, to reduce the risk of excess inventory, the device 110 may determine the highest value among the first to third delivery rates as the delivery rate. In one embodiment, the device 110 may determine the average value of the first to third delivery rates as the delivery rate.

[0067] Figure 5 is a diagram illustrating one example of the operation method of device 110. For example, the first date is July 3rd, and the second date is July 9th. Device 110 can obtain first information indicating the inventory quantity of goods on the first date. The first information can indicate that there are 340 units in stock on July 3rd.

[0068] The order information can indicate that the quantity of goods ordered on the first date is 1,000 units. The device 110 can generate second information indicating the delivery time and delivery rate by inputting the order information into the learning model. For example, the delivery time can be set to 2 days after the order time (July 3), and the delivery rate can be 80%. Therefore, it is expected that 800 units of goods will be delivered on July 5.

[0069] Device 110 can obtain third information, which is related to the outbound volume of goods during the period from date 1 to date 2. For example, the third information may indicate the expected outbound volume for each date between July 3 and July 9.

[0070] Device 110 can determine the inventory status during the period between date 1 and date 2. The inventory status (inventory quantity) for each date can be the value obtained by adding the inventory quantity of the previous day to the delivery quantity of the current day and subtracting the outbound quantity of the current day. For example, the inventory quantity on July 4 can be the value obtained by adding the inventory quantity of July 3 (340 units) to the delivery quantity of July 4 (0 units) and subtracting the outbound quantity of July 4 (330 units), which is 480 units.

[0071] The device 110 can determine whether there is a shortage of inventory during the period from the first date to the second date. If the inventory status is 0, the device 110 can determine that there is a shortage of inventory. For example, the device 110 can determine that there is a shortage of inventory on July 7, July 8 and July 9 when the inventory is 0.

[0072] Based on the determination of insufficient inventory, device 110 can determine the lost sales volume, lost sales amount, and duration of the insufficient inventory. Device 110 can determine the lost sales volume, lost sales amount, and duration of the insufficient inventory for each date on which the insufficient inventory occurs. For example, if the price of the product is 15,000, the lost sales amount is the value obtained by multiplying the lost sales volume by the price of the product. For example, the duration of the insufficient inventory is the value obtained by dividing the lost sales volume for each date by the outgoing volume and then multiplying by 24 hours.

[0073] Figure 6 is a sequence diagram illustrating the operation method of the apparatus 110 according to various embodiments of the present invention. In step S610, the apparatus 110 may determine the delivery quantity and delivery time based on the first order information of the goods. The first order information may include information related to the order quantity of goods ordered periodically. For example, the apparatus 110 may generate the first order information at fixed time intervals. For example, the apparatus 110 may generate the first order information every fixed period based on a first date.

[0074] Device 110 can input first order information into a learning model and determine the delivery quantity of goods based on the delivery rate output to the learning model and the order quantity of the first order information. Device 110 can determine the delivery time of the goods corresponding to the first order information. In one example, device 110 can set the delivery time to a fixed period after the time when the first order information is generated. In another example, device 110 can determine the delivery time by inputting the first order information into a learning model that learns in a way that predicts the delivery time. In one embodiment, device 110 can determine the delivery quantity and delivery time of goods after the first date based on the first order information that is periodically generated from the first date.

[0075] In step S620, device 110 can determine the inventory status of goods based on the inventory quantity, the delivery quantity, and the outbound quantity. The inventory status can indicate the inventory quantity. For example, the inventory status for each date can be the value obtained by adding the previous day's inventory quantity to the current day's delivery quantity and then subtracting the current day's outbound quantity.

[0076] In step S630, the device 110 may determine whether there is a shortage of inventory based on the inventory status of the goods. In one embodiment, the device 110 may determine whether the inventory quantity of the goods is 0, and if the inventory quantity of the goods is determined to be 0, it is determined that there is a shortage of inventory. In one embodiment, based on the determination that there is a shortage of inventory, the device 110 may determine information on the lost sales volume of goods due to the shortage of inventory.

[0077] In step S640, the device 110 can be configured to generate second order information based on the determination of insufficient inventory. The second order information may include information related to orders generated for the product rather than periodically. In one embodiment, the device 110 may determine the order quantity of the product based on information about lost sales, thereby generating the second order information.

[0078] In step S650, device 110 may transmit the second order information to the user's terminal.

[0079] Figure 7 is a diagram illustrating one example of the operation method of device 110. For example, the first date may be July 3, and device 110 receives information indicating that there are 340 units in stock on the first date.

[0080] The first order information can indicate that the quantity of goods ordered on the first date is 1,000 units. The first order information can be generated periodically at fixed intervals. For example, based on July 3, the first order information can be generated every 7 days.

[0081] The device 110 can determine the delivery time and delivery rate by inputting the first order information into the learning model. For example, the delivery time can be set to 2 days after the order time (July 3rd, July 10th), and the delivery rate can be 80%. Therefore, it is expected that 800 items will be delivered on July 5th and July 12th.

[0082] Device 110 can obtain information related to the outbound volume of goods. For example, the information can indicate the estimated outbound volume for each date from July 3 to July 12.

[0083] Device 110 can determine the inventory status from date 1 onwards. The inventory status (inventory quantity) for each date can be the value obtained by adding the inventory quantity of the previous day to the delivery quantity of the current day and subtracting the outbound quantity of the current day. For example, the inventory quantity on July 4 can be the value obtained by adding the inventory quantity of July 3 (340 units) to the delivery quantity of July 4 (0 units) and subtracting the outbound quantity of July 4 (330 units), which is 480 units.

[0084] Device 110 can determine whether there is a shortage of inventory during the period following the first date. If the inventory status is 0, device 110 can determine that there is a shortage of inventory. For example, device 110 can determine that there is a shortage of inventory from July 7th to July 11th when the inventory level is 0. Based on the determination that there is a shortage of inventory, device 110 can determine the lost sales volume for each date.

[0085] Based on the determination of a shortage of inventory, device 110 may generate a second order information (not shown). Device 110 may generate the second order information on the date on which the shortage of inventory is expected. For example, device 110 may generate the second order information on the first day of the period from July 7 to July 11 when the shortage of inventory occurs, i.e., July 7.

[0086] In one embodiment, the device 110 may generate second order information on each date on which a shortage of inventory is determined. The device 110 may determine the order quantity of the goods based on the lost sales volume on each date, thereby generating the second order information. For example, the device 110 may determine that the order quantity for July 7 is 200 units, the order quantity for July 8 is 300 units, the order quantity for July 9 is 310 units, the order quantity for July 10 is 330 units, and the order quantity for July 11 is 310 units, thereby generating the second order information. Based on the generation of the second order information, the device 110 may update the inventory status for each date. For example, the device 110 may determine that the delivery rate of the order quantity corresponding to the second order information is 100% and the delivery time is the same day, thereby updating the inventory status.

[0087] In one embodiment, device 110 may generate second order information on the earliest date within a fixed period during which a shortage of inventory is determined (hereinafter referred to as the shortage period). Device 110 may determine the order quantity of goods based on the sum of lost sales during the shortage period, thereby generating the second order information. For example, device 110 may determine the order quantity of goods for the first day of the shortage period (July 7th to July 11th), i.e., July 7th, as the sum of lost sales during the shortage period, i.e., 1,450 units, thereby generating the second order information.

[0088] Based on the generation of the second order information, the device 110 can update the inventory status for each date. For example, the device 110 can determine that the delivery rate of the order quantity corresponding to the second order information is 100% and the delivery time is the same day, thereby updating the inventory status.

[0089] The steps of the method or algorithm of the present invention are described sequentially in the flowcharts shown herein. However, in addition to performing the steps sequentially, the steps may also be performed in any order that can be combined according to the present invention. The description of this flowchart does not preclude changes or modifications to the method or algorithm, and does not imply that any step is necessary or preferred. In one embodiment, at least some steps may be performed in parallel, repeatedly, or heuristically. In one embodiment, at least some steps may be omitted or other steps may be added.

[0090] Various embodiments of the present invention can be implemented in software form on a machine-readable storage medium. The software can be software used to implement various embodiments of the present invention. Programmers in the art to which this invention pertains can deduce the software based on various embodiments of the present invention. For example, the software can be a program that includes machine-readable commands (e.g., code or code segments). The machine is an apparatus 110 capable of operating according to commands invoked from the storage medium, such as a computer. In one embodiment, the machine can be apparatus 110 of an embodiment of the present invention. In one embodiment, the processor 210 of the machine executes the invoked command, thereby enabling the components of the machine to perform functions corresponding to the command. In one embodiment, the processor 210 can be processor 210 of an embodiment of the present invention. The storage medium can refer to all kinds of recording media capable of being read by a machine and storing data. Storage media may include, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. In one embodiment, the storage medium may be memory 220. In another embodiment, the storage medium may also be implemented in the form of being distributed across computer systems connected to a network. Software may be distributed across computer systems for execution. The storage medium may be a non-transitory storage medium. A non-transitory storage medium refers to a tangible medium that is not related to the semi-permanent or temporary storage of data, excluding transiently transmitted signals.

[0091] The technical concept of the present invention has been described above with reference to various embodiments. However, the technical concept of the present invention includes various substitutions, variations, and modifications that can be implemented within the scope of common knowledge in the technical field to which the present invention pertains. Furthermore, it should be understood that such substitutions, variations, and modifications may be included within the scope of the appended patent applications. [Simplified Explanation of the Diagram]

[0028] Figure 1 is a diagram illustrating a system according to various embodiments of the present invention. Figure 2 is a diagram illustrating an example of a device according to an embodiment of the present invention. Figure 3 is a sequence diagram illustrating the operation method of the device according to various embodiments of the present invention. Figure 4 is a sequence diagram illustrating an example of generating second information in step S320 of Figure 3. Figure 5 is a diagram illustrating an example of a method for explaining the operation of the device. Figure 6 is a sequence diagram illustrating the operation method of the device according to various embodiments of the present invention. Figure 7 is a diagram illustrating an example of a method for explaining the operation of the device.

Claims

1. An apparatus for determining the inventory status of goods, comprising: The device comprises one or more processors and one or more memory units storing commands to be executed by the processors. The apparatus is configured such that, when executing the commands, the processors perform the following: Obtaining first information relating to the inventory level of the goods on date 1; Generating second information based on a learning model that predicts the delivery rate of the goods, the second information relating to the delivery rate of the goods during the period from date 1 to date 2 after date 1; Obtaining third information relating to the outbound volume of the goods during the period from date 1 to date 2; Determining the inventory status of the goods during the period from date 1 to date 2 based on the first, second, and third information; Determining whether there is an inventory shortage based on the inventory status; and the learning model is a model that learns by using the ordering and delivery history of the goods as learning data. The delivery rate is the ratio of the quantity of the goods delivered from the supplier to the quantity of the goods ordered from the supplier.

2. The apparatus of claim 1 is configured such that the processor, when generating the second information related to the delivery rate of the aforementioned goods, generates one or more order history groups based on one or more variables of the order history; inputs each of the one or more order history groups into the learning model, thereby obtaining information indicating the delivery rate of each of the one or more order history groups as output; and determines any one of the delivery rates of the one or more groups as the delivery rate, thereby generating the second information.

3. The apparatus as described in claim 2, wherein one or more of the variables include variables related to the demand for the aforementioned goods.

4. The apparatus as described in claim 2, wherein one or more of the variables mentioned above include variables related to the order type of the aforementioned goods.

5. The apparatus of claim 1 is configured such that the processors described above: when determining the inventory status, obtain order information related to the order quantity of the goods prior to the second date; based on the order information and the second information, determine the delivery quantity during the period from the first date to the second date; and determine the inventory status based on the inventory quantity in the first information, the delivery quantity, and the outbound quantity in the third information.

6. The device as claimed in claim 5, wherein the aforementioned inventory status indicates the inventory quantity of the aforementioned goods during the period between the aforementioned first date and the aforementioned second date.

7. The apparatus of claim 6 is configured such that, when determining whether the aforementioned inventory shortage exists, one or more processors determine the existence of the aforementioned inventory shortage based on determining that the inventory quantity of the aforementioned goods is 0.

8. The apparatus of claim 1 is further configured such that the one or more processors described above are configured to: determine, based on the determination of the existence of the aforementioned inventory shortage, information on the loss of sales volume of the aforementioned goods due to the aforementioned inventory shortage.

9. The apparatus of claim 8 is further configured such that the one or more processors described above: obtain commodity price information of the commodity; determine the amount of lost sales based on the commodity price information and the information on lost sales volume.

10. The apparatus of claim 1 is further configured such that the one or more processors described above are configured to: determine the inventory shortage time for maintaining the inventory shortage based on the determination that the inventory shortage exists.

11. A method for determining the inventory status of goods, performed in an apparatus comprising one or more processors and one or more memories storing commands to be executed by the one or more processors, the method comprising the steps of the one or more processors as follows: obtaining first information relating to the inventory quantity of goods on a first date; generating second information based on a learning model learned in a manner that predicts the delivery rate of the goods, the second information relating to the delivery rate of the goods during the period from the first date to a second date following the first date; obtaining third information relating to the outflow quantity of the goods during the period from the first date to the second date; determining the inventory status of the goods during the period from the first date to the second date based on the first information, the second information, and the third information; and determining, based on the inventory status, whether there is an inventory shortage; wherein the learning model is a model learned by using the ordering history and delivery history of the goods as learning data. The delivery rate is the ratio of the quantity of the goods delivered from the supplier to the quantity of the goods ordered from the supplier.

12. The method of claim 11, wherein the step of the processor generating the second information related to the delivery rate of the commodity comprises the following steps: generating one or more order history groups based on one or more variables of the order history; inputting each of the one or more order history groups into the learning model to obtain information indicating the delivery rate of each of the one or more order history groups as output; and determining any one of the delivery rates of the one or more groups as the delivery rate, thereby generating the second information.

13. The method of request item 12, wherein one or more of the above variables include variables related to the demand for the above-mentioned goods.

14. The method of request item 12, wherein one or more of the above variables include variables related to the order type of the above-mentioned goods.

15. The method of claim 11, wherein the step of determining the inventory status by one or more processors comprises the following steps: obtaining order information relating to the order quantity of the goods prior to the second date; determining the delivery quantity during the period from the first date to the second date based on the order information and the second information; and determining the inventory status based on the inventory quantity of the first information, the delivery quantity, and the outbound quantity of the third information.

16. The method of claim 15, wherein the aforementioned inventory status indicates the quantity of the aforementioned goods in stock during the period between the aforementioned first date and the aforementioned second date.

17. The method of claim 16, wherein the step of determining whether the above-mentioned shortage of inventory exists by one or more processors includes the following steps: determining that the shortage of inventory exists based on determining that the inventory quantity of the above-mentioned goods is 0.

18. The method of claim 11 further includes the following steps: the one or more processors determine information on the lost sales volume of the goods due to the aforementioned inventory shortage based on the determination that the aforementioned inventory shortage exists.

19. The method of claim 18 further includes the steps of: obtaining product price information of the product; and determining the amount of lost sales based on the product price information and the information on lost sales volume.

20. A computer-readable recording medium that records non-transitory computer-readable recording media, when executed by one or more processors, commands that cause the one or more processors to perform actions, the commands being configured such that the one or more processors: obtain first information relating to the inventory quantity of a product on a first date; generate second information based on a learning model learned in a manner that predicts the delivery rate of the product, the second information relating to the delivery rate of the product during the period from the first date to a second date following the first date; obtain third information relating to the outflow quantity of the product during the period from the first date to the second date; determine the inventory status of the product during the period from the first date to the second date based on the first information, the second information, and the third information; determine whether there is an inventory shortage based on the inventory status; and the learning model is a model learned by using the ordering history and delivery history of the product as learning data. The delivery rate is the ratio of the quantity of the goods delivered from the supplier to the quantity of the goods ordered from the supplier.

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