KR102352329B1 - Method, device and system for providing service of goods ordering, logistics, distribution based on artificial intelligence - Google Patents

Abstract

According to one embodiment, a method for providing an artificial intelligence-based product order, logistics, and distribution service is provided, which includes: a step of receiving from an administrator terminal warehousing product information corresponding to a product to be placed in a logistics warehouse and the quantity; a step of receiving from a user terminal shipping product information corresponding to a product to be shipped from the logistics warehouse and the quantity; a step of generating and correcting product inventory information based on the warehousing product information and the shipping product information; a step of generating safety inventory information based on the warehousing product information, the shipping product information, and the product inventory information based on an artificial intelligence module; a step of generating recommended additional order amount information based on the safety inventory information; and a step of transmitting the recommended additional order amount information and the shipping product information to the administrator terminal.

Description

A method, device and system for ordering goods, logistics, and distribution services based on artificial intelligence

The following embodiments relate to artificial intelligence-based product ordering, logistics, and distribution service providing technology.

As the market of companies is globalized, global marketing, sourcing, manufacturing, regional specialization, and global product strategy establishment are required. In the era of unlimited competition, the importance of high quality, low price, and on-time delivery increases, and 60~70% of the supply chain Since added value occurs in areas other than manufacturing, the entire line needs to be managed.

In particular, along with the growth of the domestic restaurant franchise industry in recent years, major food material distribution and logistics companies are each growing large and small, and efforts are being made to establish SCM, that is, a supply chain management system.

Supply chain management (供給網管理, supply chain management, SCM) refers to a system that grasps the flow of logistics from parts suppliers to producers, distributors, and customers from a single value chain point of view and supports the smooth flow of necessary information. It is a management innovation technique that aims to achieve overall process optimization among the elements of the supply chain by breaking away from optimization by sector or individual company unit within a company.

Accordingly, as an SCM platform, we would like to present a technology that can manage inventory in a distribution warehouse and review orders from an orderer.

Korea Patent No. 10-1586408 (2016.01.12) Korean Patent Registration No. 10-1987625 (2019.06.04) Korean Patent Registration No. 10-2080439 (2020.02.17) Korean Patent Registration No. 10-2251607 (2021.05.07)

Embodiments are to provide an SCM platform that allows a manager to place an order, manage the inventory of goods stored in a warehouse, and place an order for goods according to a user's order.

In addition, embodiments are to recommend the quantity of goods to the manager.

In addition, the embodiments provide feedback to the user by reviewing the order placed by the user to determine the misplaced order and the like.

According to one embodiment, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by a device includes the steps of: receiving, from a manager terminal, information on goods in stock corresponding to goods and quantities to be stored in a warehouse; a user; Receiving outgoing product information corresponding to the product and quantity to be shipped from the warehouse from the terminal, generating and modifying product inventory information based on the stocking product information and the outgoing product information, based on an artificial intelligence module Creating safety stock amount information based on the stocked article information, the outgoing article information and the article stock information, generating recommended additional order amount information based on the safety stock amount information, and the recommended additional order amount information and the outgoing goods It may include transmitting the information to the manager terminal.

At this time, the step of generating the safety stock information is machine learning for outputting demand forecast information by inputting demand environment information that affects demand, such as the stocked article information, the outgoing article information, the article stock information, and the weather. It may include generating the forecasted demand information through a model and generating the safety stock information including a preset error range in the forecasted demand information.

At this time, the artificial intelligence-based product ordering, logistics and distribution service providing method according to an embodiment of the present invention is based on the existing shipment information and the demand forecast information previously received from the user terminal. Determining whether there is an ordering error, according to the determination result of the ordering error, generating corrected shipment information based on the existing shipment product information and the demand forecast information, and transferring the corrected shipment product information to the user terminal It may further include the step of transmitting.

At this time, the step of determining whether or not the ordering error is generated by generating a maximum ordering standard through the following equation,

Create a minimum order standard through the formula below,

S_MAX (Standard MAX) means the maximum ordering standard, S_MIN (Standard MIN) means the minimum ordering standard, and NPS (Number of Previous Shipment) means the number of existing shipments included in the existing shipment information and NDF (Number of Demand Forecast) means the demand forecast quantity included in the demand forecast information, and EW (Environment Weight) is an environment weight generated based on the demand environment information through the artificial intelligence module, and the If the quantity of goods shipped out included in the information on outgoing goods is smaller than the maximum order standard and greater than the minimum order criterion, it is determined that the goods shipped out information is normal, and the quantity of goods shipped out in the information on the goods shipped out is the maximum order standard In this case, it is determined that the outgoing product information is an over-order, and when the outgoing product quantity included in the outgoing product information is less than the minimum order standard, the outgoing product information is determined as an under-order,

In the generating of the corrected shipment information, if the determination result of the ordering error is over-ordering, the corrected shipment information is generated based on the maximum order standard, and the determination result of whether the ordering error is under-ordered In the case of , the modified shipment information may be generated based on the minimum order standard.

At this time, the artificial intelligence-based product ordering, logistics and distribution service providing method according to an embodiment of the present invention includes the steps of transmitting a satisfaction survey request for the modified shipment product information to a user terminal, and the satisfaction survey from the user terminal Receiving a satisfaction survey response as a reply to a request, comparing the satisfaction survey response and analyzing user satisfaction improvement data, when the improvement satisfaction confirmed through the user satisfaction improvement data rises by more than a first threshold assigning a first weight to the modified shipment information; if the improvement satisfaction confirmed through the user satisfaction improvement data falls by more than a second threshold value, assigning a second weight to the modified shipment information; information, generating second modified shipment information in consideration of the first weight and the second weight, transmitting the second modified shipment product information to the user terminal, and the second modified shipment information from the user terminal The method may further include obtaining approval of product information and correcting the corrected shipment information by reflecting the second corrected shipment information.

An apparatus according to an embodiment may be controlled by a computer program stored in a medium to execute the method of any one of the above-described methods in combination with hardware.

Through embodiments, it is possible to provide an SCM platform that allows an administrator to place an order and manage the inventory of goods stored in a warehouse, and to place an order for goods according to a user's order.

In addition, through the embodiments, it is possible to recommend the quantity of goods to the manager.

In addition, through the embodiments, it is possible to provide feedback to the user by reviewing the order placed by the user and determining the wrong order.

1 is a configuration diagram illustrating an artificial intelligence-based product ordering, logistics, and distribution service providing system according to an embodiment.
2 is a flowchart illustrating a method of providing an artificial intelligence-based product order, logistics, and distribution service according to an exemplary embodiment.
3 is a flowchart for explaining a process of generating and transmitting modified shipment information to a user terminal according to an embodiment.
4 is a flowchart for explaining a process of examining satisfaction with corrected shipment information according to an embodiment.
5 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various elements, these terms should be interpreted only for the purpose of distinguishing one element from another. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

When a component is referred to as being “connected to” another component, it may be directly connected or connected to the other component, but it should be understood that another component may exist in between.

The terms used in the examples are used for the purpose of description only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that a feature, number, step, operation, component, part, or a combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are given the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In describing the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments may be implemented in various types of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, wearable devices, and the like.

Hereinafter, with reference to the drawings, an artificial intelligence-based product ordering, logistics and distribution service providing method, apparatus and system will be described.

1 is a configuration diagram illustrating an artificial intelligence-based product ordering, logistics, and distribution service providing system according to an embodiment.

Referring to FIG. 1 , the system for providing goods ordering, logistics and distribution service based on artificial intelligence according to an embodiment of the present invention can order goods to be stored in a logistics warehouse by a manager and analyze the amount of inventory stored in the warehouse. A device that allows a user to order goods stored in a logistics warehouse, a manager terminal that allows the manager to input information on goods to be stocked, check inventory information, and It may include a user terminal through which the user can input product information.

In this case, the manager terminal and the user terminal are a computer, a smart phone, a mobile phone, a tablet PC, a digital camera, a camcorder, an e-book terminal, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a navigation system, an MP3 player , a wearable device, an audio device, a DVD player, and the like may include all devices capable of communication.

In this case, the device may be a server owned by a person or organization that provides services using the device, a cloud server, or a peer-to-peer (p2p) set of distributed nodes. The device may be configured to perform all or part of an arithmetic function, a storage/referencing function, an input/output function, and a control function of a typical computer. The device may be configured to communicate with the manager terminal and the user terminal by wire or wireless.

In this case, the device can achieve the object of the present invention by performing an artificial intelligence-based product ordering, logistics and distribution service providing method according to an embodiment to be described later, and detailed operations are artificial intelligence-based product ordering, logistics and The method of providing distribution services will be explained in detail.

2 is a flowchart illustrating an artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment.

Referring to Figure 2, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention is first, the warehouse corresponding to the goods and quantity to be stored in the warehouse from the manager terminal It is possible to receive product information (S201).

In this case, the logistics warehouse may be a space in which the manager stores the items purchased and the user can release the items to be shipped out.

At this time, the goods in stock information may include information about the goods to be received, that is, information about the name of the goods, attributes of raw materials or finished products, expiration dates, storage methods, etc., and information about the quantity of goods currently in stock. .

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method performed by the device according to an embodiment of the present invention receives information on the products to be shipped from the warehouse and the quantity to be shipped from the user terminal. It can be done (S203).

In this case, the user using the user terminal may be a seller who releases and sells goods stored in the distribution warehouse, and the shipped product information includes information about the name and quantity of goods to be ordered and shipped from the distribution warehouse. can

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention can create and modify product inventory information based on the stocked product information and the outgoing product information There is (S205).

Based on the quantity of goods included in the stocked goods information and the quantity of goods included in the outgoing goods information, it is possible to determine the quantity of the corresponding goods in the current warehouse, and through this, the goods inventory information can be generated. In addition, when new information on goods in stock and information on goods out is received based on the previously created goods inventory information, the existing goods inventory information may be modified to suit the current situation.

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention is based on an artificial intelligence module, the warehoused goods information, the outgoing goods information, and the goods inventory information It is possible to generate safety inventory information based on (S207).

The safety stock amount information means information on the appropriate storage amount of the product based on the space of the logistics warehouse, the stocking speed and the outgoing speed, and the like. For example, livestock products that require refrigeration should be stored in a refrigerator, but cannot be stored in excess of a certain amount, and if the delivery speed is slower than the stocking speed due to the short shelf life, it may be disposed of. It is necessary to create safety inventory information taking all of these into consideration.

At this time, in step S207, the demand forecast amount through a machine learning model that outputs demand forecast information by inputting demand environment information that affects demand, such as the stocked article information, the outgoing article information, the article stock information, and weather, etc. information can be created.

In this case, the demand environment information refers to environmental factors that affect demand, such as weather, as described above. In contrast, when the weather gets colder, it refers to factors that affect demand, such as an increase in the demand for tea types.

In addition, the demand environment information may include specific social issues such as bird flu and swine cholera as well as weather.

At this time, as described above, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention as described above includes the goods in stock information, the outgoing goods information, It is possible to generate a machine learning model that outputs demand forecast information by inputting demand environment information that affects demand, such as the goods inventory information and weather, and can generate the demand forecast information through the machine learning model.

Through this, the manager can additionally adjust the quantity of goods to be stocked, and can create safety stock information through the present invention as will be described later. In addition, based on the demand forecast amount information, as will be described later, it is also possible to determine whether the user's ordering error of the goods shipped out.

At this time, the artificial intelligence module uses a deep learning technique, which is a field of machine learning, and a demand environment that affects demand, such as the stocked product information, the outgoing product information, the inventory information, and the weather. Learning may be performed to derive a correlation between the information and the forecasted demand information, and to output the forecasted demand information based on the correlation.

In addition, the artificial intelligence module may calculate the weight of a plurality of inputs in the function through deep learning through learning. In addition, various models such as RNN (Recurrent Neural Network), DNN (Deep Neural Network), and DRNN (Dynamic Recurrent Neural Network) may be used as an AI network model used for such learning.

Here, RNN is a deep learning technique that considers current data and past data simultaneously. Recurrent neural network (RNN) refers to a neural network in which connections between units constituting an artificial neural network constitute a directed cycle. Furthermore, various methods may be used for a structure capable of constructing a recurrent neural network (RNN), for example, a fully recurrent network, a hopfield network, an Elman network, an ESN (Echo). state network), long short term memory network (LSTM), bi-directional RNN, continuous-time RNN (CTRNN), hierarchical RNN, and secondary RNN are representative examples. In addition, as a method for learning a recurrent neural network (RNN), methods such as gradient descent, Hessian Free Optimization, and Global Optimization Method may be used.

In addition, the step S207 may include a preset error range in the demand forecast information to generate the safety stock information. Since the demand forecast information may not be complete, the safety inventory information is generated to include an error range, and the user can select it.

For example, when 100 pieces of predicted demand information and the error range is 10%, 90 to 110 pieces of safety stock information may be generated.

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention may generate recommended additional order amount information based on the safety stock amount information (S209).

In this case, the recommended additional order amount information may be generated based on the safety stock amount information, and may be reflected as it is with the safety stock amount information.

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention may transmit the recommended additional order amount information and the shipped product information to the manager terminal ( S211).

Through this, the manager can make a plan for the type and quantity of goods to be stored in the warehouse, review the information requested by the user, and deliver the corresponding contents to the distribution department to release the goods.

3 is a flowchart for explaining a process of generating and transmitting modified shipment information to a user terminal according to an embodiment.

According to an embodiment of the present invention, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention is the shipment based on the existing shipment information and the demand forecast information previously received from the user terminal. It is possible to determine whether there is an ordering error in the product information.

This is to prevent a user who orders the goods to be shipped from accidentally ordering too much or too little of the quantity of the goods to be shipped out.

At this time, the artificial intelligence-based product ordering, logistics, and distribution service providing method performed by the device according to an embodiment of the present invention is based on whether the quantity of goods shipped out included in the goods shipped out information is included in the safe order range. It can be determined whether there is an ordering error (S301).

In this case, the safe order range may be set based on the maximum order standard and the minimum order standard suitable for placing an order.

In this case, the maximum ordering criterion may be generated by Equation 1 below.

In addition, the minimum order standard may be generated by Equation 2 below.

In this case, S_MAX (Standard MAX) means the maximum ordering standard, S_MIN (Standard MIN) means the minimum ordering standard, and NPS (Number of Previous Shipment) is the existing shipment information included in the existing shipment information. means quantity, NDF (Number of Demand Forecast) means the demand forecast quantity included in the demand forecast information, and EW (Environment Weight) is an environmental weight generated based on the demand environment information through the artificial intelligence module can be

Through this, it is possible to set the safe order range by reflecting the demand forecast amount based on the quantity previously ordered by the user.

In this case, the environmental weight may be calculated through the artificial intelligence module according to the importance of the demand environment information by adjusting the ratio of the difference between the predicted demand quantity and the existing shipment quantity. For example, when the demand environment information when generating the predicted demand quantity is an important factor directly affecting life, such as bird flu, the environmental weight may be set low by considering its importance to be high. That is, the importance of the demand environment information and the environmental weight may be set to be inversely proportional to each other.

For example, when the quantity of the existing shipments is 100, the predicted demand is 150, and the environmental weight is 2, the maximum order standard may be 125, and the minimum order standard may be 75.

In addition, in the method of providing an artificial intelligence-based product order, logistics and distribution service, performed by the device according to an embodiment of the present invention, the quantity of shipments included in the shipment information is smaller than the maximum order standard, and the minimum If it is greater than the order standard, it is determined that the shipped article information is normal (S303), and the outgoing article information can be transmitted to the manager terminal for the release of the corresponding article.

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention is returned to the maximum order standard and the minimum order standard when the outgoing product information is out of the safe range. It can be compared once (S305).

At this time, in the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention, when the quantity of goods shipped out included in the goods information is greater than or equal to the maximum order standard, the It is determined that the outgoing product information is an over-order (S307), and when the quantity of outgoing products included in the outgoing product information is less than or equal to the minimum order standard, it can be determined that the outgoing product information is an under-order (S311).

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention is based on the determination result of the ordering error, the existing shipment information and the demand forecast information It is possible to create modified shipment information based on

This may induce the user to adjust the order quantity when it is determined that there is an error as a result of determining whether there is an order error, and may recommend the quantity to be ordered.

In this case, the modified shipment product information may include information on changes in the shipment time of the corresponding product, changes in the shipment quantity, and the like.

In this case, the step of generating the corrected shipment information includes generating the corrected shipment information based on the maximum ordering standard when the determination result of the ordering error is over-ordering (S309), and whether the ordering error When the determination result is under-order, the corrected shipment information may be generated based on the minimum order standard (S313).

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method performed by the device according to an embodiment of the present invention may transmit the corrected shipment product information to the user terminal (S315). Through this, it is possible to induce the user to release the goods within the safe ordering range.

4 is a flowchart for explaining a process of examining satisfaction with corrected shipment information according to an embodiment.

Referring to FIG. 4 , in the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention, a satisfaction survey request for the modified shipment product information may be transmitted to a user terminal (S401).

In addition, the artificial intelligence-based product ordering, logistics and distribution service providing method according to an embodiment of the present invention may receive a satisfaction survey response as a reply to the satisfaction survey request from the user terminal (S403).

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention may analyze the user satisfaction improvement data by comparing the satisfaction survey responses (S405).

In addition, in the artificial intelligence-based product ordering, logistics and distribution service providing method according to an embodiment of the present invention, when the improvement satisfaction confirmed through the user satisfaction improvement data rises by more than a first threshold value, the modified shipment information is added to the information. 1 weight may be assigned, and when the improvement satisfaction level confirmed through the user satisfaction improvement data falls by more than a second threshold value, a second weight may be assigned to the corrected shipment product information (S407).

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention forms second corrected shipment information in consideration of the modified shipment product information, the first weight, and the second weight It can be done (S409).

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention may transmit the second modified shipment product information to the user terminal (S411).

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention may obtain approval of the second modified shipment product information from the user terminal (S413).

In addition, the artificial intelligence-based product ordering, logistics, and distribution service providing method according to an embodiment of the present invention may modify the corrected shipment information by reflecting the second corrected shipment information (S415).

5 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Referring to FIG. 5 , the device 100 according to an embodiment includes a processor 510 and a memory 520 . The apparatus 100 according to an embodiment may be the above-described server or terminal. The processor may include at least one of the devices described above with reference to FIGS. 1 to 4 , or may perform at least one method described above with reference to FIGS. 1 to 4 . The memory 520 may store information related to the above-described method or a program in which the above-described method is implemented. The memory 520 may be a volatile memory or a non-volatile memory.

The processor 510 may execute a program and control the apparatus 100 . The code of the program executed by the processor 510 may be stored in the memory 520 . The device 100 may be connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and exchange data.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, the apparatus, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA) array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (3)

In the method of providing an artificial intelligence-based product order, logistics and distribution service performed by a device,
Receiving the goods in stock information corresponding to the goods and quantity to be stored in the warehouse from the manager terminal;
receiving product information to be shipped from the warehouse from the user terminal and product information corresponding to the quantity;
creating and modifying product inventory information based on the stocked product information and the outgoing product information;
On the basis of the artificial intelligence module, safety stock information is generated based on the stocked article information, the outgoing article information, and the article stock information, but the stocked article information, the outgoing article information, the goods stock information and the weather are on demand. generating the demand forecast information through a machine learning model that outputs demand forecast information by inputting demand environment information that affects it, and generating the safety stock information including a preset error range in the demand forecast information;
generating recommended additional order quantity information based on the safety stock quantity information;
transmitting the recommended additional order quantity information and the shipped product information to the manager terminal;
determining whether there is an order error of the outgoing product information based on the previously delivered product information and the demand forecast information received from the user terminal;
generating corrected shipment information based on the existing shipment product information and the demand forecast quantity information according to a determination result of the ordering error; and
transmitting the corrected shipment information to the user terminal; including,
The step of determining whether the ordering error is,
Create the maximum order standard through the formula below,

Create the minimum order standard through the formula below,

S_MAX (Standard MAX) means the maximum ordering standard, S_MIN (Standard MIN) means the minimum ordering standard, and NPS (Number of Previous Shipment) means the number of existing shipments included in the existing shipment information and NDF (Number of Demand Forecast) means the demand forecast quantity included in the demand forecast information, and EW (Environment Weight) is an environmental weight generated based on the demand environment information through the artificial intelligence module,
When the quantity of goods shipped out in the shipment information is smaller than the maximum order standard and is greater than the minimum order criterion, it is determined that the goods shipped out information is normal, and the quantity of goods shipped out in the shipment information information is the maximum order If it is more than the standard, it is determined that the outgoing product information is an over-order, and when the outgoing product quantity included in the outgoing product information is less than the minimum order standard, it is determined that the outgoing product information is an under-order,
The step of generating the corrected shipment information is,
If the determination result of the ordering error is over-ordering, the corrected shipment product information is generated based on the maximum ordering standard, and when the determination result of the ordering error is under-ordering, based on the minimum ordering standard, the An artificial intelligence-based product ordering, logistics and distribution service providing method, characterized in that it generates modified shipment information.

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