KR102614789B1 - Vending machine operation assistance method, device and system using vending machine sales data analysis - Google Patents

Abstract

The present invention is a vending machine operation assistance method, device, and system that can achieve optimal sales through data analysis of vending machine sales records and data analysis using artificial intelligence, and can also automatically determine efficient beverage restocking cycles and order quantities. Regarding this, a registration step of registering vending machine information of a vending machine installed at a predetermined location and operating to sell beverages to a server, data acquisition of transmitting raw data about beverages sold by the vending machine to the server and storing the raw data on the server. Step, sales data generation step to generate sales data by processing raw data stored in the server, regional data group classification step to classify sales data into the same regional data group with data on vending machines installed in the same region, each regional data It relates to a vending machine operation assistance method, device, and system that includes an optimization step to analyze the group and optimize the beverage sales strategy through vending machines in the corresponding area.

Description

Vending machine operation assistance method, device and system using vending machine sales data analysis}

The present invention relates to a method, device, and system for assisting vending machine operation through analysis of vending machine sales data. Specifically, the item, batch quantity, sales price, and restocking cycle of beverages are analyzed to generate optimal profits by analyzing vending machine sales data. It concerns a method of assisting the operation of a vending machine that automatically calculates the bill.

In operating a vending machine that sells beverages, selecting the beverage items to be sold in the vending machine and determining the batch quantity and selling price for each beverage item are important matters that have a very significant impact on sales. For this reason, vending machine operators are putting a lot of effort into optimizing the beverage items, batch quantities, and selling prices to be sold in vending machines.

In the past, vending machine operators either walked around the area where the vending machine was installed and acquired the information necessary to operate the vending machine, or acquired the know-how or knowledge and sense necessary for operating the vending machine through many years of operating experience. For this reason, the importance of the operator's individual analytical ability or experience was great, and if the operator's analytical ability or experience was lacking, it was very difficult to make decisions to increase the necessary sales. There is also a method of seeking advice from a third party with excellent analytical skills or extensive experience, but this method has the problem of incurring additional costs.

In addition, the conventional method has the problem of not only being highly dependent on the capabilities of individual operators, but also requiring a lot of labor and time to determine beverage items, batch quantities, and sales prices that can achieve optimal sales. The time and labor required to acquire a sense of the operational elements that can achieve optimal sales act as a barrier to entry when starting a vending machine operation business, or, although not to that extent, require additional cost-consuming work. There is.

Recently, automated analysis methods using artificial intelligence have been used in various fields. Analysis using artificial intelligence is showing excellent effectiveness in generating accurate and fast analysis information based on massive data while minimizing the human labor directly involved in the analysis work.

In terms of how to operate vending machines, the need for a method of utilizing analysis using artificial intelligence has emerged as an efficient means to maximize sales.

Korean Patent Registration No. 2040574 Korean Patent Application Publication No. 2020-0101527 Korean Patent Application Publication No. 2017-0092845 Korean Patent Application Publication No. 2015-0124015

The purpose of the present invention is to provide a vending machine operation assistance method, device, and system that can convert sales records of vending machines into data and achieve optimal sales through data analysis using artificial intelligence.

The purpose of the present invention is to provide a vending machine operation assistance method, device, and system that can automatically determine an efficient beverage restocking cycle and order quantity using artificial intelligence.

A method of assisting vending machine operation according to an embodiment of the present invention includes a registration step of registering vending machine information of a vending machine installed at a predetermined location and operating to sell beverages to a server, sending raw data about beverages sold by the vending machine to the server, and A data acquisition stage in which raw data is stored on a server, a sales data generation stage in which sales data is generated by processing raw data stored in the server, and a region in which sales data is classified into the same regional data group with data on vending machines installed in the same region. It includes a data group classification step and an optimization step to analyze each regional data group and optimize the beverage sales strategy through vending machines in the region. The region consists of a plurality of zones, and the vending machine information is the identification ID of the vending machine. , may include the installation area and zone of the vending machine.

In the method of assisting vending machine operation according to an embodiment of the present invention, the sales data includes vending machine information, beverage items being sold by the vending machine, batch quantity for each beverage item, daily sales volume for each beverage item, and sales price information for each beverage item, In the optimization step, sales data can be analyzed to optimize the beverage items assigned to each vending machine, the batch quantity of each beverage item, and the sales price.

In the assisting method for operating a vending machine according to an embodiment of the present invention, the optimization step analyzes sales data belonging to each regional data group, generates a sales ranking for each beverage item within each region, and drinks falling within a predetermined ranking. A sales item selection step in which items are selected as sales items, a sales graph generation step in which a sales graph of the vending machine is created based on sales data for each beverage item selected as a sales item, and the expected maximum sales of the vending machine based on the sales graph. Calculate the maximum sales calculation step to determine the batch quantity and sales price for each beverage item corresponding to the expected maximum sales, and reflect the sales data newly obtained from the vending machine to which the batch quantity and sales price for each beverage product determined through the maximum sales calculation step are applied. Thus, it includes an adjustment step of adjusting the batch quantity and sales price of each beverage item, and in the sales graph, the sales amount is a function of the batch quantity and sales price of the beverage, and the sales graph generation step is based on point data formed by sales data. It includes calculating a trend line, and the maximum sales calculation step calculates the expected maximum sales based on the trend line, and the adjustment step is, after applying the batch quantity and sales price for each beverage item determined through the maximum sales calculation step, the sales amount is The batch quantity and sales price of beverages are adjusted downward for items that have decreased, and the batch quantity and sales price of beverages are adjusted upward for items with increased sales. In the adjustment stage, when the batch quantity and sales price of beverages are adjusted upward or downward, sales The larger the slope of the trend line of the graph, the smaller the adjustment range, and the smaller the slope of the trend line of the sales graph, the larger the adjustment range.

In the assisting method for operating a vending machine according to an embodiment of the present invention, the optimization step further includes a restocking cycle determination step of determining the beverage restocking cycle of the vending machine, and the selling item selection step includes each item being selected based on statistics of sales data. It further includes calculating the exhaustion speed for each beverage item and assigning weight to beverage items with a fast exhaustion speed. The maximum sales calculation step calculates the expected maximum sales based on the sales graph during the beverage restocking cycle, and the restocking cycle is , can be equally applied to all vending machines installed in the same area.

In the vending machine operation assistance method according to an embodiment of the present invention, based on the batch quantity of beverages for each item of each vending machine determined in the maximum sales calculation step, it further includes an order quantity calculation step of calculating the order quantity for each item of beverages to be stored in the warehouse, , In the order quantity calculation step, the order quantity is calculated so that the beverage order cycle is n (n is a natural number of 3 or more) times the beverage restocking cycle, and one or more warehouses can be provided for each region.

A method of assisting the operation of a vending machine according to an embodiment of the present invention further includes a transport step of transporting beverages stored in a warehouse to a plurality of vending machines installed in an area where the warehouse is provided, and the transport step includes receiving real-time traffic information and receiving real-time traffic information. , It may include calculating the optimal route to return to the warehouse through a plurality of vending machines based on real-time traffic information.

In the assisting method for operating a vending machine according to an embodiment of the present invention, the transport step further includes an optimal arrangement calculation step of calculating the optimal arrangement for loading beverages into a transport vehicle based on the optimal route and the batch quantity of beverages for each item of each vending machine. In the optimal arrangement calculation step, the beverages to be placed in each vending machine are placed in a bundle, and the optimal arrangement is made to load the drinks to be placed in each vending machine onto the transport vehicle in the opposite order of the visit order of each vending machine. The step of calculating and calculating the optimal arrangement may include automatically generating visual data according to the optimal arrangement and transmitting it to the worker's portable terminal.

According to an embodiment of the present invention, it is possible to provide a vending machine operation assistance method, device, and system that can achieve optimal sales through data analysis of vending machine sales records and data analysis using artificial intelligence.

The purpose of the present invention is to provide a vending machine operation assistance method, device, and system that can automatically determine an efficient beverage restocking cycle and order quantity using artificial intelligence.

1 is a flowchart of a method for assisting the operation of a vending machine according to an embodiment of the present invention.
Figure 2 is a schematic diagram of sales data generated by a vending machine operation assistance method according to an embodiment of the present invention.
Figure 3 is a schematic diagram of a sales graph generated by the vending machine operation assistance method according to an embodiment of the present invention.

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

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

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

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

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, 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 generally understood by a person of ordinary skill in the technical field to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike existing rule-based smart systems, it is a system in which machines learn and make decisions on their own. As artificial intelligence systems are used, their recognition rates improve and they can more accurately understand user preferences, and existing rule-based smart systems are gradually being replaced by deep learning-based artificial intelligence systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithmic technology that classifies/learns the characteristics of input data on its own, and elemental technology is a technology that mimics the functions of the human brain such as cognition and judgment by utilizing machine learning algorithms such as deep learning, including linguistic understanding and visual It consists of technical areas such as understanding, reasoning/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology that recognizes and applies/processes human language/characters and includes natural language processing, machine translation, conversation systems, question and answer, and voice recognition/synthesis. Visual understanding is a technology that recognizes and processes objects like human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, spatial understanding, and image improvement. Inferential prediction is a technology that judges information to make logical inferences and predictions, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendations. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data creation/classification) and knowledge management (data utilization). Motion control is a technology that controls the autonomous driving of vehicles and the movement of robots, and includes motion control (navigation, collision, driving), operation control (behavior control), etc.

Generally, in order to apply machine learning algorithms to real life, learning is performed using a trial and error method due to the nature of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in an actual physical external environment, so instead, the actual physical external environment is virtually implemented on a computer and learning is performed through simulation.

1 is a flowchart of a method for assisting the operation of a vending machine according to an embodiment of the present invention. Figure 2 is a schematic diagram of sales data generated by a vending machine operation assistance method according to an embodiment of the present invention.

A method of assisting vending machine operation according to an embodiment of the present invention includes a registration step (S1) of registering vending machine information of a vending machine installed at a predetermined location and operating to sell beverages on a server, and raw information about beverages sold by the vending machine. A data acquisition step (S2) in which data is sent to the server and the raw data is stored in the server, a sales data generation step (S3) in which the raw data stored in the server is processed to generate sales data, and the sales data is sent to vending machines installed in the same area. It can include a regional data group classification step (S4) that classifies data into the same regional data group, and an optimization step (S5) that analyzes each regional data group to optimize the beverage sales strategy through vending machines in the region. there is.

Locations where vending machines are installed can be divided into regions and districts. Hereinafter, region is defined to refer to an administrative district of a certain size or larger. For example, a region may be an administrative district with a resident population or area above a certain level. A region consists of multiple districts. For example, if the region is designated as Gangnam-gu, Seoul, the district could be an administrative district such as Daechi-dong or Samseong-dong belonging to Gangnam-gu, Seoul.

The registration step (S1) is a step of registering vending machine information including the identification ID of the vending machine and the installation area and zone of the vending machine on the central server. At this time, the identification ID of the vending machine is a unique number assigned to each vending machine and is a number assigned to distinguish a plurality of vending machines from each other. Through the registration step (S1), it is possible to easily distinguish which vending machine the data stored in the server is from, and accurate data analysis is possible. In the registration step (S1), the vending machine information may be entered directly into the central server by the operator, or may be automatically transmitted to the central server through a wireless communication device mounted on the vending machine.

The data acquisition step (S2) is the step of transmitting raw data about beverages sold by the vending machine to the central server through a wireless communication device mounted on the vending machine and storing the raw data on the server. At this time, the raw data is unprocessed. Data may include, for example, beverage items sold by the vending machine, date and time of sale, sales price, etc. In addition to the above, raw data may include any data about beverages sold.

Raw data stored in the server can be processed into a form that is easy to analyze through the sales data creation step (S3). Sales data may include, for example, vending machine information, beverage items being sold by the vending machine, batch quantity for each beverage item, daily sales volume for each beverage item, and sales price information for each beverage item. In addition to the above-described information, sales data may further include any information included in raw data if necessary for sales optimization analysis. In one embodiment, the items of information included in the sales data may be set directly by the operator. For example, the operator can access the central server through a mobile terminal such as a mobile phone and directly set the information items to be included in the sales data.

The regional data group classification step (S4) is a step in which sales data for vending machines installed in the same region are classified to facilitate analysis of sales data for each region separately. By classifying regional data groups in this way, sales data for each region can be analyzed separately, and sales strategies can be separately designed for each region. In other words, by classifying sales data groups, the characteristics of each region can be distinguished, and it is possible to devise differentiated strategies tailored to the characteristics of each region.

The optimization step (S5) is a step to optimize the beverage sales strategy by analyzing each regional data group. In other words, the optimization step (S5) is a step that analyzes sales data and optimizes the beverage items assigned to each vending machine, the batch quantity of each beverage item, and the sales price.

Hereinafter, the optimization step (S5) will be described in detail with reference to FIG. 3.

Figure 3 is a schematic diagram of a sales graph generated by the vending machine operation assistance method according to an embodiment of the present invention. Figure 3 shows a two-dimensional graph of sales data of vending machines according to the sales price of beverages for convenience, but the sales graph according to the present invention is a three-dimensional graph of sales data of vending machines according to the batch quantity and sales price of each beverage item. Includes.

In one embodiment, the optimization step (S5) analyzes sales data belonging to each regional data group, generates a sales ranking for each beverage item in each region, and selects beverage items that fall within a predetermined ranking as sales items. Sales item selection step (S5-1), sales graph generation step (S5-2) in which a sales graph of the vending machine is generated based on sales data for each beverage item selected as a sales item, and prediction of the vending machine based on the sales graph. The maximum sales calculation step (S5-3), which calculates the maximum sales and determines the batch quantity and sales price for each beverage item corresponding to the expected maximum sales, and the batch quantity for each beverage item determined through the maximum sales calculation step (S5-3) And it may include an adjustment step (S5-4) in which the batch quantity and sales price of each beverage item are adjusted by reflecting the sales data newly obtained from the vending machine to which the sales price is applied.

Once the installation location for the vending machine is decided, the next thing to decide is what products will be sold. The sale item selection step (S5-1) is the step of selecting beverage items to achieve optimal sales.

In one embodiment, the sales item selection step (S5-1) may be a step of selecting items with excellent sales as sales items by referring to sales data from other vending machines in the same area. Specifically, by analyzing sales data belonging to the regional data group to which the vending machine belongs, a sales ranking for each beverage item within the region can be created, and beverage items falling within a certain rank can be selected as sales items. In addition, when a certain period of time passes after the vending machine is installed and operated, and the sales data of the vending machine itself is accumulated, the exhaustion speed of each beverage item is calculated based on the statistics of the sales data, and weight is given to beverage items with a faster exhaustion speed. By assigning it, sales items can be selected in the order of beverages with the highest weight. In other words, after the vending machine's own sales data has accumulated to a certain level, previously selected sales items can be adjusted.

The sales graph generation step (S5-2) is a step of generating a sales graph of the vending machine based on sales data for each beverage item selected as a sales item, and the sales graph may be a three-dimensional graph. Since a sales graph is generated for each beverage item selected as a sales item, if the number of beverage items sold in a vending machine is 10, 10 sales graphs can also be generated. At this time, the z-axis of the sales graph may be the total sales of the vending machine, and the x-axis and y-axis may be the batch quantity and sales price of a specific beverage item, respectively. In other words, sales may be a function of the batch quantity and selling price of the beverage. At this time, the sales graph can be expressed as the following equation (1).

Equation (1)... Sales = f(price, quantity)

In this way, the sales graph is a graph showing the effect or correlation of the price and batch quantity of a specific beverage item on the overall sales of the vending machine.

Each beverage item may have a different impact on the overall sales of the vending machine. With this in mind, a sales graph is created as a means of analyzing the impact of each beverage item on the overall sales of the vending machine according to the batch quantity and sales price. The sales graph is created after the items sold in the vending machine are confirmed.

In one embodiment, the sales graph for each beverage item can be compared and analyzed to determine the batch quantity and sales price to maximize sales, starting with the beverage item that has a significant impact on changes in the sales graph. As a result, the optimization step (S5) can be performed efficiently.

In one embodiment, a sales graph may include calculating a trend line based on point data formed by sales data. By calculating the trend line, it is possible to estimate the approximate sales amount of the vending machine corresponding to the batch quantity and sales price of beverages that are not in the sales data.

The maximum sales calculation step (S5-3) is a step of calculating the expected maximum sales of the vending machine based on the sales graph for each beverage item selected as a sales item and determining the batch quantity and sales price for each beverage item corresponding to the expected maximum sales. am. At this time, the expected maximum sales amount can be calculated based on the trend line.

According to the above-described embodiment, even if sales data for the batch quantity and sales price for each item of a specific value are not included in the sales data, by creating a trend line, how does the sales increase depending on the batch quantity and sales price for each item of the beverage based on the trend line? You can predict whether things will change.

In the adjustment step (S5-4), after the batch quantity and sales price for each beverage item determined through the maximum sales calculation step (S5-3) are applied to the vending machine, when the vending machine's own sales data is sufficiently accumulated, the newly accumulated sales data This is the step to adjust the expected maximum sales and the corresponding batch quantity and sales price for each beverage item based on this.

In one embodiment, in the adjustment step (S5-4), after applying the batch quantity and sales price for each beverage item determined through the maximum sales calculation step, the batch quantity and sales price of beverages are adjusted downward for items whose sales have decreased, and the sales price is adjusted downward. This increase may include upwardly adjusting the batch quantity and sales price of beverages.

According to the above-mentioned adjustment step (S5-4), the change in expected maximum sales is predicted in real time based on data accumulated as the vending machine is actually operated, and the batch quantity and sales price of each beverage item corresponding to the changed expected maximum sales are determined. By adjusting, the expected maximum sales can be predicted more accurately, and adjustments to achieve the expected maximum sales can be easily performed.

In addition, in the adjustment step (S5-4), when adjusting the batch quantity and sales price of beverages upward or downward, the larger the slope of the trend line of the sales graph, the smaller the adjustment range, and the smaller the slope of the trend line of the sales graph, the smaller the adjustment range. You can make it big.

According to the above-mentioned adjustment step (S5-4), detailed adjustment work is possible by analyzing the accumulated sales data and varying the degree of adjustment according to the characteristics of the sold items, thereby predicting the expected maximum sales and achieving the expected maximum sales. Adjustments can be performed more precisely.

In one embodiment, the optimization step (S5) may further include a restocking cycle determination step (S5-5) of determining the beverage restocking cycle of the vending machine.

In one embodiment, the maximum sales calculation step (S5-3) may calculate the expected maximum sales based on a sales graph during the beverage restocking cycle determined through the restocking cycle determining step (S5-5).

In one embodiment, the restocking cycle may be applied equally to all vending machines installed in the same area.

According to the above-described embodiment, the beverage restocking cycle is first set as a constant and then the expected maximum sales during the cycle is calculated. Therefore, the sales speed of each beverage is not considered and only the batch quantity and sales price of the beverage are determined, so the expected maximum sales calculation process This is simplified. In addition, since the beverage restocking cycle is first set as a constant and the remaining variables are processed accordingly, there is no need to worry or calculate separately how to determine the beverage restocking cycle to be efficient.

The vending machine operation assistance method according to an embodiment of the present invention includes an order quantity calculation step of calculating the order quantity for each item of beverage to be stored in the warehouse based on the batch quantity for each beverage item of each vending machine determined in the maximum sales calculation step (S5-3). (S6) may further be included.

In one embodiment, the order quantity calculation step (S6) may calculate the order quantity so that the beverage order cycle is n (n is a natural number of 3 or more) times the beverage restocking cycle. In other words, the order quantity calculation step (S6) can appropriately set not only the order quantity of beverage inventory to be stored in the warehouse but also the beverage order cycle.

In one embodiment, one or more warehouses may be provided for each region.

According to the above-described embodiment, the appropriate order quantity can be automatically calculated by artificial intelligence without the operator having to individually check the inventory of beverages stored in the warehouse.

In addition, by calculating the order quantity so that the beverage order cycle is n times the beverage restocking cycle (n is a natural number of 3 or more), the order cycle can be limited to not be too frequent, thereby minimizing unnecessary costs incurred in logistics work. . In addition, since the restocking cycle is applied equally to all vending machines installed in the same area, the restocking process for vending machines installed in the same area can be processed at the same time, thereby streamlining the logistics transportation process.

The method of assisting the operation of a vending machine according to an embodiment of the present invention may further include a transport step (S7) of transporting beverages stored in a warehouse to a plurality of vending machines installed in an area where the warehouse is provided.

In one embodiment, the transport step (S7) may receive real-time traffic information and calculate an optimal route to return to the warehouse through a plurality of vending machines based on the real-time traffic information. Thereby, the transportation cost of transporting beverages can be optimized or minimized.

Figure 4 is visual data according to the optimal arrangement generated by the vending machine operation assistance method according to an embodiment of the present invention.

In one embodiment, the transport step (S7) may further include an optimal arrangement calculation step (S7-1) of calculating the optimal arrangement for loading beverages into a transport vehicle based on the optimal route and the batch quantity of beverages for each item of each vending machine. there is.

In one embodiment, the optimal arrangement calculation step (S7-1) arranges the drinks to be placed in each vending machine as a bundle, and also places the drinks to be placed in each vending machine on the transport vehicle in the opposite order of the visit order of each vending machine. It may include calculating the optimal arrangement for loading.

Additionally, the optimal arrangement calculation step (S7-1) may include automatically generating visual data according to the optimal arrangement and transmitting it to the worker's portable terminal.

This has the effect of reducing transportation costs by simplifying the work of workers loading beverages on transport vehicles and enabling quick and efficient work.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, 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, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

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 on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. 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 computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. 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 include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

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

S1: Registration phase
S2: Data acquisition phase
S3: Sales Data Generation Step
S4: Regional data group classification step
S5: Optimization step
S5-1: Sales item selection stage
S5-2: Sales graph creation step
S5-3: Maximum revenue generation stage
S5-4: Adjustment phase
S5-5: Restocking cycle decision step
S6: Order quantity calculation step
S7: Transport phase
S7-1: Optimal batch calculation step

Claims (5)

It is a vending machine operation assistance method executed by artificial intelligence software stored in a computer or computer peripheral device.
A registration step (S1) of registering vending machine information of a vending machine installed at a predetermined location and operating to sell beverages on a server;
A data acquisition step (S2) of transmitting raw data about beverages sold by the vending machine to the server and storing the raw data in the server;
A sales data generation step (S3) of generating sales data by processing the raw data stored in the server;
A regional data group classification step (S4) of classifying the sales data into the same regional data group with data on vending machines installed in the same region;
An optimization step (S5) that analyzes each of the regional data groups and assists in establishing a beverage sales strategy through vending machines in the region;
Including,
The region consists of a plurality of districts,
The vending machine information includes the identification ID of the vending machine, the installation area and zone of the vending machine,
The sales data includes the vending machine information, beverage items being sold by the vending machine, batch quantity for each beverage item, daily sales volume for each beverage item, and sales price information for each beverage item,
The optimization step (S5) analyzes the sales data to assist in setting the beverage items assigned to each vending machine, the batch quantity and sales price for each beverage item,
The optimization step (S5) is,
A sales item selection step (S5-1) of analyzing sales data belonging to each regional data group, generating a sales ranking for each beverage item within each region, and selecting beverage items falling within a predetermined ranking as sales items;
A sales graph generation step (S5-2) of generating a sales graph of a vending machine based on the sales data for each beverage item selected as the sales item;
A maximum sales calculation step (S5-3) of calculating the expected maximum sales of the vending machine based on the sales graph and determining the batch quantity and sales price for each beverage item corresponding to the expected maximum sales;
An adjustment step (S5-4) in which the batch quantity and sales price for each beverage item are adjusted by reflecting the sales data newly obtained from the vending machine to which the batch quantity and sales price for each beverage item determined through the maximum sales calculation step (S5-3) were applied. ;
Including,
In the sales graph, sales are a function of the batch quantity and sales price of the beverage,
The sales graph generation step (S5-2) includes calculating a trend line based on point data formed by the sales data,
In the maximum sales calculation step (S5-3), the expected maximum sales is calculated based on the trend line,
In the adjustment step (S5-4), after applying the batch quantity and sales price for each beverage item determined through the maximum sales calculation step (S5-3), the batch quantity and sales price of beverages are adjusted downward for items whose sales have decreased. , for items with increased sales, the batch quantity and sales price of beverages are adjusted upward;
In the adjustment step (S5-4), when adjusting the batch quantity and sales price of beverages upward or downward, the larger the slope of the trend line in the sales graph, the smaller the adjustment range, and the smaller the slope of the trend line in the sales graph. To increase the adjustment range,
How to assist with vending machine operation. delete delete According to paragraph 1,
The optimization step (S5) is,
It further includes a restocking cycle determination step (S5-5) for determining the beverage restocking cycle of the vending machine,
The sale item selection step (S5-1) further includes calculating the exhaustion speed for each beverage item based on the statistics of the sales data and assigning weight to beverage items with a rapid exhaustion speed,
The maximum sales calculation step (S5-3) calculates the expected maximum sales based on the sales graph during the beverage restocking cycle,
The restocking cycle applies equally to all vending machines installed in the same area.
How to assist with vending machine operation. According to paragraph 4,
Based on the batch quantity of beverages for each item of each vending machine determined in the maximum sales calculation step (S5-3), it further includes an order quantity calculation step (S6) of calculating the order quantity for each item of beverages to be stored in the warehouse,
In the order quantity calculation step (S6), the order quantity is calculated so that the beverage order cycle is n (n is a natural number of 3 or more) times the beverage restocking cycle,
The warehouse is provided at least one in each region,
How to assist with vending machine operation.