JP2021163000A - Commodity demand prediction program - Google Patents

Abstract

To automatically and accurately predict demands of commodities in a retail store.SOLUTION: A commodity demand prediction program for predicting demands of commodities in a retail store causes a computer to execute: an information acquisition step for acquiring sales history information on sales history of a commodity in a retail store; and a determination step which determines a degree of demand on the commodity, on the basis of the sales history information acquired in the information acquisition step, by referring to three or more stages of associations between reference sales history information on past sales history of the commodity in the retail store acquired in advance and a degree of demand of the commodity in accordance with subsequent order quantity.SELECTED DRAWING: Figure 4

Description

The present invention relates to a product demand forecasting program for forecasting the demand for products in retail stores.

In recent years, retailers that sell products directly to consumers have diversified into stores, supermarkets, shopping centers, and the like. In the retail industry, there is a risk that customers will be separated if they run out of inventory and run out of stock. It becomes a risk. In particular, if a lot of raw foods such as fish and vegetables are left unsold, they may not be sold the next day, which may put pressure on the management of the retail business.

In other words, in order to make more profits in the retail industry, the demand for products is predicted with high accuracy, and products that meet the demand forecast are ordered from wholesalers and purchased, leaving almost no inventory and being out of stock. Ideally, it should be sold out so that it does not become a problem, and it is possible to attract many customers and increase profits.

However, although various techniques for predicting the demand for products sold at such retail stores have been proposed, there is a problem that there is a gap between the demand forecast and the actual sales of the products. there were.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is a product demand forecasting program capable of automatically and accurately forecasting the demand for products in retail stores. Is to provide.

In order to solve the above-mentioned problems, the product demand forecasting program according to the present invention acquires information acquisition of sales history information regarding the sales history of products at retail stores in the product demand forecasting program for forecasting the demand for products at retail stores. Refer to the step, the reference sales history information regarding the past sales history of the product at the retail store acquired in advance, and the degree of association of the product with the demand level according to the number of subsequent orders, and refer to the above. Based on the sales history information acquired in the information acquisition step, the computer is made to execute a determination step of determining the degree of demand for the product.

It is possible to automatically and accurately predict the demand for products at retail stores without any special skill or experience.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the product demand forecast program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a product demand forecasting system 1 in which a product demand forecasting program to which the present invention is applied is implemented. The product demand forecasting system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

The database 3 stores various information necessary for forecasting product demand. Information necessary for forecasting product demand includes sales history information for reference regarding the past sales history of products at the retail store acquired in advance, and visitor for reference regarding the number of past visitors at the retail store acquired in advance. Number information, reference sales promotion event information related to past sales promotion events at the retail store acquired in advance, reference market information regarding market conditions at the time of acquisition of the reference sales history information, reference sales history information at the time of acquisition of the reference sales history information External environment information for reference regarding the external environment, area information for reference regarding the area where the retail store is located, traffic information for reference regarding traffic to the retail store, and purchase behavior information for reference regarding the purchase behavior of customers in the retail store. be.

That is, in the database 3, in addition to such reference sales history information, reference store visitor information, reference sales promotion event information, reference market condition information, reference external environment information, reference area information, reference traffic Any one or more of the information and the purchase behavior information for reference and the degree of demand according to the number of subsequent orders for the product are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the product demand forecasting system 1 having the above-described configuration will be described.

In the product demand forecasting system 1, for example, as shown in FIG. 3, it is premised that the reference sales history information and the degree of association with the degree of demand are set in advance and acquired. The reference sales history information is any information regarding the sales history of a product at a retail store. The term "retail store" as used herein includes any of individual stores, the entire shopping district, supermarkets, department stores, shopping centers, commercial facilities, and outlet parks.

Further, as the reference sales history information, the sales history may be extracted from the quantity data such as the number of sales, the number of stocks, and the number of disposals of each product at the retail store to be predicted. Since the sales history is data on how much the product has been sold, that is, the sales, the number of sales, the ratio of the number of sales to the number of warehousing (orders), and the number of stocks remaining as inventory are usually obtained. Alternatively, this sales may be extracted from the number of discarded products as a result of unsold products. In actually calculating this reference sales history information, quantity data such as the number of sales, the number of stocks, and the number of wastes may be obtained based on any calculation formula.

Further, the reference sales history information may refer to sales data (including accounting data and financial data) obtained by selling the product. Reference sales history information can also be obtained from quotations, invoices, and invoices. This reference sales history information may be extracted from account deposit / withdrawal details, accounting data, WEB sites, financial statements, and book information. The accounting data may be obtained by discriminating the actual inventory turnover status from the sales asset turnover period, inventory turnover period, and the like.

The sales history information for reference may be obtained from the POS data, or whether or not it was actually sold or remains in inventory from the IC tag, barcode, two-dimensional code, etc. attached to the product. You may try to get what you are doing.

Further, the reference sales history information may be composed of a transaction history including time-series changes in past product sales.

The degree of demand may be ranked as "large", "very large", "normal", "small", "very small", etc., or displayed as a score, with 1000 points being the most demanded. High, 0 points may be indicated to mean the lowest demand. The most demanded score may be any score other than 1000 points. Further, if this degree of demand is represented by two values, that is, there is demand and that there is no demand, the two values may be trained as a data set of the output solution. This demand level may be obtained by evaluating the standard set for each retail store, or may be evaluated by the demand level evaluated by a unified standard. In addition to the artificially evaluated demand, the demand may be calculated based on, for example, the number of subsequent orders. In other words, it can be determined that the larger the number of orders placed after the product is sold or after it remains in inventory, the higher the demand, while the smaller the number of orders placed at that timing, the lower the demand. Can be determined. As an alternative to this demand, the number of orders may be output for each product. In such a case, it is premised that the number of orders for each product is learned as output data.

That is, through this reference sales history information and the demand degree data set, the actual demand degree of each product can be known from various data generated in the reference sales history information. That is, the data described in the reference sales history information and the degree of demand are a data set. For this reason, by collecting reference sales history information and a data set of demand, it is possible to know how the demand was judged in the examination after what kind of transaction history was in the past. Become.

In the example of FIG. 3, it is assumed that the input data is, for example, reference sales history information P01 to P03. The reference sales history information as such input data is linked to the output. In this output, the degree of demand as an output solution is displayed.

The reference sales history information is associated with each other through three or more levels of association with the demand level as the output solution. The sales history information for reference is arranged on the left side via this degree of association, and the degree of demand is arranged on the right side via this degree of association. The degree of association indicates the degree of demand and the degree of relevance to the reference sales history information arranged on the left side. In other words, this degree of association is an index showing what kind of demand the sales history information for reference is likely to be associated with, and is used to select the most probable demand from the sales history information for reference. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the degree of demand as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the degree of demand as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference sales history information and the demand degree in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

By the way, these degree of association may be created for each type of product sold at the retail store, or may be created one by one for a plurality of product groups. In the case of a product group, the sales history information for reference shows the sales history of radishes, carrots, peppers, eggplants, etc., and the degree of demand is also radishes, carrots, peppers, eggplants according to the type of the product. Etc. are shown.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference sales history information P01, analysis is performed from the data of the determination result of the past demand degree. In the case of reference sales history information P01, if there are many cases of demand degree A (for example, the demand degree score is 760 points), the degree of association connected to this demand degree A is set higher, and the demand degree C is set. When there are many cases (for example, the score of the demand degree is 433 points), the degree of association leading to the demand degree C is set higher. For example, in the example of the sales history information P01 for reference, the demand degree A and the demand degree C are linked, but from the previous case, the degree of association of w13 connected to the demand degree A is set to 7 points, and the degree of association of w14 connected to the demand degree C is set to 7. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference sales history information is input as input data, each demand degree is output as output data, and at least one or more hidden layers are provided between the input node and the output node. It may be made to be machine-learned. On the contrary, the demand level may be input and the reference sales history information may be output.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the demand level is predicted by using the above-mentioned trained data in actually determining the demand level from now on. In such a case, the latest sales history information of the retail store that actually newly determines the demand level is acquired. This sales history information is composed of the same type of data as the above-mentioned reference sales history information.

The newly acquired sales history information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire such sales history information as electronic data.

Based on the newly acquired sales history information in this way, the demand degree that is likely to be determined is actually searched for the sales history information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired sales history information is the same as or similar to P02, the demand degree B is associated with the association degree w15 and the demand degree C is associated with the association degree w16 through the association degree. In such a case, the demand degree B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree C, which has a low degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search for the degree of demand to be determined from the newly acquired sales history information and display it to the user (the owner of the retail store, the employee, the consultant, the person in charge of distribution). By looking at the search result, the user can grasp the searched demand level. Incidentally, in the process of outputting this demand degree, in addition to simply displaying the searched demand degree, a specific attention may be given or an alarm may be generated based on this demand degree.

In the example of FIG. 5, it is assumed that the input data is, for example, reference sales history information P01 to P03 and reference visitor number information P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference sales history information and the reference visitor number information as such input data. Each intermediate node is further linked to the output. In this output, each demand degree as an output solution is displayed.

In the example of FIG. 5, it is premised that a combination of the reference sales history information and the reference store visitor number information is formed. The reference store visitor number information is information on the number of customers who actually visited the retail store, and may be aggregated on a daily, hourly, weekly, or monthly basis. The reference visitor information may be obtained by counting the number of visitors by using POS data or an image taken by a camera installed at the entrance to count the number of visitors.

In the example of FIG. 5, it is assumed that the input data is, for example, reference sales history information P01 to P03 and reference visitor number information P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference sales history information and the reference visitor number information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference sales history information and the reference visitor number information is associated with each other through three or more levels of association with the demand as the output solution. The reference sales history information and the reference store visitor number information are arranged on the left side through this degree of association, and the demand degree is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to each demand degree with respect to the reference sales history information and the reference store visitor number information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference sales history information and the reference visitor number information are likely to be associated with, and is a reference sales history information and a reference. It shows the accuracy in selecting each most probable demand level from the number of visitors information. In addition to the sales history information, the degree of demand may change according to the actual geographical situation, and it is possible to make a judgment based on this. Therefore, the optimum demand level is searched for by combining the reference sales history information and the reference store visitor number information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference sales history information, the reference store visitor number information, and the demand level in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales history information P01 and the reference visitor number information P16, the demand degree is analyzed from the past data. When there are many cases of demand degree A, the degree of association leading to this degree of demand A is set higher, and when there are many cases of degree of demand B and there are few cases of degree of demand A, the degree of association leading to degree of demand B is set. The degree is set high, and the degree of association that leads to the demand level A is set low. For example, in the example of the intermediate node 61a, the output of the demand degree A and the demand degree B is linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that is a combination of the reference sales history information P01 and the reference visitor number information P14, and the degree of association of the demand degree C is w15 and the degree of demand E. The degree of association is w16. The node 61c is a node that is a combination of the reference sales history information P02 and the reference visitor number information P15 and P17, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually performing a search for determining the demand degree from now on, the above-mentioned learned data will be used. In such a case, in addition to the sales history information, information on the number of visitors is acquired from the retail store that newly determines the degree of demand. This store visitor number information corresponds to the above-mentioned reference store visitor number information, and the acquisition method thereof is also the same.

Based on the newly acquired sales history information and store visitor number information in this way, the optimum demand level is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired sales history information is the same as or similar to P02 and the number of visitor information is the same as or similar to P17, the node 61d is via the degree of association. Is associated with this node 61d, and the demand degree C is associated with w19 and the demand degree D is associated with the association degree w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 6 shows an example in which the combination of the above-mentioned reference sales history information, the reference sales promotion event information, and the degree of demand for the combination are set to three or more levels of association.

The reference sales promotion event information is information on a sales promotion event held at a retail store that has acquired reference sales history information. The sales promotion event here is any event for promoting sales, such as a bargain sale, a bargain, and an inventory disposal sale. In addition to this, sales for various events such as Christmas, the end of the year, entrance celebrations, Tanabata, and Halloween are also included in this promotional event. The reference sales promotion event includes information on the type, scale, and period of the actual sales promotion event, as well as information on the scale of the advertising cost carried out when the event is held.

In the example of FIG. 6, it is assumed that the input data is, for example, reference sales history information P01 to P03 and reference sales promotion event information P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference sales promotion event information and reference sales history information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference sales history information and the reference sales promotion event information is associated with each other through three or more levels of association with the demand level as this output solution. The reference sales history information and the reference sales promotion event information are arranged on the left side through this degree of association, and the demand degree is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the degree of demand with respect to the reference sales history information and the reference sales promotion event information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference sales history information and the reference sales promotion event information are likely to be associated with, and the reference sales history information and the reference sales promotion event. It shows the accuracy in selecting each demand level that is most likely from the information. In addition to sales history information, the degree of demand changes depending on the type and scale of promotional events and the scale of advertising. Therefore, the optimum demand level is searched for by combining the reference sales history information and the reference sales promotion event information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference sales history information, the reference sales promotion event information, and the demand level in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference sales history information P01 is the reference sales promotion event information P20, the demand degree is analyzed from the past data. Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference sales promotion event information P18 is combined with the reference sales history information P01, and the association degree C of the demand degree C is w15 and the demand degree E. The degree of association is w16. The node 61c is a node that is a combination of the reference sales promotion event information P19 and P21 with respect to the reference sales history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned sales history information, the sales promotion event information of the retail store subject to the new demand forecast is acquired. The sales promotion event information corresponds to the reference sales promotion event information.

The demand level is searched based on the newly acquired sales history information and sales promotion event information in this way. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired sales history information is the same as or similar to P02 and the sales promotion event information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 7 shows an example in which the combination of the above-mentioned reference sales history information, the reference area information, and the degree of demand for the combination are set to three or more levels of association.

The reference area information is the information of the area to which the target retail store for which the actual reference sales history information has been acquired belongs, and even if it is designated and classified by country unit, local unit, or city / ward / town / village unit. good. The reference area information may include information on various events, events, and the environment associated with the location of the area, such as closer rivers, stronger winds, more snow in winter, and more yellow sand. .. Further, when there is an event unique to the area such as the Aomori Nebuta Festival, such a regional event may be added to the reference area information.

In the example of FIG. 7, it is assumed that the input data is, for example, reference sales history information P01 to P03 and reference area information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference sales history information and reference area information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference sales history information and the reference area information is associated with each other through three or more levels of association with the demand level as this output solution. The reference sales history information and the reference area information are arranged on the left side through the degree of association, and the demand degree is arranged on the right side through the degree of association. The degree of association indicates the degree of high relevance to the degree of demand with respect to the reference sales history information and the reference area information arranged on the left side. In other words, this degree of association is an index showing what kind of demand the sales history information for reference and the reference area information are likely to be associated with, and from the sales history information for reference and the area information for reference. It shows the accuracy in selecting each of the most probable demand levels. In addition to sales history information, the degree of demand changes depending on the various natural environments in the area and the type and scale of local events. Therefore, the optimum demand level is searched for by combining the reference sales history information and the reference area information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which is more suitable, the reference sales history information, the reference area information, and the degree of demand in that case, in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales history information P01 and the reference area information P20, the degree of demand is analyzed from the past data. Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node that is a combination of the reference sales history information P01 and the reference area information P18, and the degree of association of the demand degree C is w15 and the degree of association of the demand degree E. The degree is w16. The node 61c is a node that is a combination of the reference area information P19 and P21 with respect to the reference sales history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned sales history information, the area information of the retail store subject to the new demand forecast is acquired. The area information corresponds to the reference area information.

The demand level is searched based on the newly acquired sales history information and regional information in this way. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired sales history information is the same as or similar to P02 and the regional information is P21, the node 61d is associated via the degree of association, and this The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In the present invention, as an alternative to the reference area information shown in FIG. 7, the reference traffic information shown below is learned by being associated with the degree of association by a combination of reference sales history information. May be good.

This reference traffic information indicates all information regarding the means of transportation to the retail store, or all information regarding the means of transportation to the area to which the retail store belongs. If there is a train as one of the means of transportation to the retail store, it shows all the information about the train, for example, information about the timetable, information about the number of passengers and the degree of congestion, and depending on the area or the train. If there are more flights in response to events in other areas of the connection, information about them will also be included. In addition to this, the reference traffic information includes information on delays in transportation means, information on accidents, information on traffic congestion, and the like. For shopping malls located in the suburbs, automobiles may be the only means of transportation, but in such cases, demand changes according to traffic information such as traffic congestion leading up to the shopping mall. There is.

In such a case, the combination of the reference sales history information and the reference traffic information and the degree of association with the demand degree in three or more stages are acquired in advance. At the time of actual judgment, traffic information about the retail store targeted for new demand forecast is acquired. This traffic information corresponds to the above-mentioned reference traffic information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired sales history information and the traffic information.

In the present invention, as an alternative to the reference sales promotion event information shown in FIG. 6, the following reference external environment information is learned by being associated with the degree of association by a combination of reference sales history information. There may be.

The reference external environment information referred to here is various information related to the external environment information. The external environmental information here is economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption situation survey, household data, average working hours per week, savings amount). Statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data, etc.) Etc.). The external environmental information includes all information outside the retail store, in addition to the information that reflects some or all of these data. The external environment information for reference may be categorized as the external environment itself. For example, the classification may be performed by separating the data in the employment statistics. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of GDP increases rapidly or gradually) or the like.

In such a case, the combination of the reference sales history information and the reference external environment information and the degree of association with the demand degree are acquired in advance in three or more stages. At the time of actual judgment, external environmental information regarding the outside of the retail store targeted for new demand forecast is acquired. This external environment information corresponds to the above-mentioned reference external environment information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired sales history information and the external environment information.

In the present invention, as an alternative to the reference sales promotion event information shown in FIG. 6, the reference market condition information shown below is learned in association with the degree of association by a combination of reference sales history information. You may.

Reference market information is various information related to market conditions. The market conditions referred to here may target any range including one company, the entire industry including the company, the entire Japan, and the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, MACD, moving average line and the like may be attached. In addition, this market information may include fundamental indicators of each brand's company, and in addition to annual sales, profits, and costs, PER (price-earnings ratio), PBR (price-to-book value ratio), ROE ( Indicators such as return on equity) may be included. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the exchange rate. The reference market information may categorize the market potential itself. The reference market information may be classified by type. For example, the market information may be classified according to whether or not the growth rate of the stock price is 0% or more per year. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of the stock price increases rapidly or gradually) or the like.

In such a case, the combination of the reference sales history information and the reference market condition information and the degree of association with the demand degree in three or more stages are acquired in advance. At the time of actual judgment, the market condition information at the time of acquisition of the sales history information is acquired. This market condition information corresponds to the above-mentioned reference market condition information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired sales history information and the market condition information.

In the present invention, as an alternative to the reference sales promotion event information shown in FIG. 6, the reference purchase behavior information shown below is learned by being associated with the degree of association by combining the reference sales history information. There may be.

The reference purchase behavior information referred to here is all information regarding the purchase behavior of the customer in the retail store. For example, the inside of the store is imaged with a camera, and what kind of product the customer picked up, what kind of product the customer is standing in front of, and the movement line in the customer's store are imaged with the camera. , By performing image analysis, data on purchasing behavior is extracted. Whether or not the product is actually picked up may be obtained via a sensor provided on the product or the display shelf. By collecting and learning such data, the degree of association as shown in FIG. 6 is formed. Incidentally, this purchasing behavior may be categorized by a pattern (for example, a pattern of a flow line, a pattern of stopping, a pair of products to be picked up, etc.).

In such a case, the combination having the reference sales history information and the reference purchase behavior information and the degree of association with the demand degree in three or more stages are acquired in advance. At the time of actual determination, the purchase behavior information at the time of acquisition of the sales history information is acquired. This purchase behavior information corresponds to the above-mentioned reference purchase behavior information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired sales history information and the purchase behavior information.

In addition to sales history information, if any two or more of store visitor information, sales promotion event information, market condition information, external environmental information, regional information, traffic information, purchase behavior information, etc. are acquired, they will be acquired. Two or more reference information (reference store visitor information, reference sales promotion event information, reference market condition information, reference external environment information, reference area information, reference traffic information, reference, according to two or more information By creating learning data consisting of a combination of the reference sales history information and the demand level for the combination, and the degree of association with the demand level for the combination, the solution search for the credit rating is performed in the same manner. be able to.

In addition to sales history information, in addition to any one or more of visitor number information, sales promotion event information, market condition information, external environmental information, regional information, traffic information, purchase behavior information, etc., other information is acquired. Similarly, when doing so, the number of visitors for reference, sales promotion event information for reference, market information for reference, external environment information for reference, area information for reference, traffic information for reference, purchase for reference according to the acquired information. Similarly, by creating learning data consisting of a combination of behavioral information, etc., reference information according to other acquired information, and a degree of association with the degree of demand for the combination, there are three or more levels of association. It can be performed.

Further, as shown in FIG. 8, the present invention determines each demand degree based on the degree of association of two or more types of information, the reference information U and the reference information V. This reference information U is reference sales history information, and reference information V is other reference information (reference store visitor information, reference sales promotion event information, reference market condition information, reference external environment information, reference. Area information for reference, traffic information for reference, purchase behavior information for reference, etc.)

At this time, as shown in FIG. 8, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the demand level of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since the above-mentioned input data and output data often do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

Under the circumstances where there are multiple possible candidates for search solutions by determining the demand level with higher credibility and lower misunderstanding based on the degree of association expressed by such numerical values of three or more levels. In, it is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to market information, when event information, external environmental information, household information, real estate information, expert opinion information, and natural environment information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly. Let me.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Product demand forecast system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (10)

In the product demand forecasting program that forecasts the demand for products in retail stores
Information acquisition step to acquire sales history information about product sales history at retail stores,
The above information acquisition step with reference to the reference sales history information regarding the past sales history of the product at the retail store acquired in advance and the degree of association of three or more stages of the demand degree according to the number of subsequent orders for the product. A product demand forecasting program characterized in that a computer executes a determination step for determining the degree of demand for the product based on the sales history information acquired in. In the information acquisition step, the sales history information is acquired based on the sales data of the product or any one or more of the number of sales, the number of stocks, and the number of discarded products.
The claiming step is characterized in that the sales history information for reference is acquired based on the sales data of the product at the retail store acquired in advance, or any one or more of the number of sales, the number of stocks, and the number of disposals. Item Demand Forecast Program described in Item 1. In the above information acquisition step, further acquisition of the number of visitors information regarding the number of visitors to the above retail store is obtained.
In the determination step, the degree of association of three or more stages of the combination having the reference sales history information and the reference store visitor number information regarding the past visitor number in the retail store acquired in advance is referred to, and the above is described. The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the sales history information acquired in the information acquisition step and the number of visitors. In the above information acquisition step, further acquisition of promotional event information regarding the promotional event at the retail store is performed.
In the above-mentioned determination step, the above-mentioned information acquisition is performed by referring to the above-mentioned degree of association of three or more stages of the combination having the above-mentioned reference sales history information and the above-mentioned reference sales promotion event information regarding the past sales promotion event in the retail store acquired in advance. The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the sales history information and the sales promotion event information acquired in the step. In the above information acquisition step, further market condition information regarding the market condition at the time when the above sales history information is acquired is acquired.
In the above-mentioned determination step, the above-mentioned information acquisition is performed by referring to the above-mentioned degree of association of three or more stages of the combination having the above-mentioned reference sales history information and the reference-referenced market condition information regarding the market condition at the time when the above-mentioned reference sales history information is acquired. The product demand forecasting program according to claim 1 or 2, wherein the demand level for the product is determined based on the sales history information and the market condition information acquired in the step. In the above information acquisition step, the external environment information regarding the external environment at the time when the above sales history information is acquired is further acquired.
In the determination step, the degree of association of three or more stages of the combination having the reference sales history information and the reference external environment information regarding the external environment at the time when the reference sales history information is acquired is referred to, and the above The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the sales history information acquired in the information acquisition step and the external environment information. In the above information acquisition step, further acquisition of regional information regarding the area where the above retail store is located is performed.
In the above-mentioned determination step, the sales history acquired in the above-mentioned information acquisition step is referred to by referring to the above-mentioned degree of association of three or more stages of the combination having the above-mentioned reference sales history information and the reference area information regarding the area where the retail store is located. The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the information and the regional information. In the above information acquisition step, further acquisition of traffic information related to the traffic to the above retail store is performed.
In the determination step, the degree of association of three or more stages of the combination having the reference sales history information and the reference traffic information related to the traffic to the retail store is referred to, and the information is acquired in the information acquisition step. The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the sales history information and the traffic information. In the above information acquisition step, further acquisition of purchase behavior information regarding the customer's purchase behavior in the retail store is performed.
In the determination step, the degree of association of three or more stages of the combination having the reference sales history information and the reference purchase behavior information regarding the customer's purchase behavior in the retail store is referred to, and the information is acquired in the information acquisition step. The product demand forecasting program according to claim 1 or 2, wherein the degree of demand for the product is determined based on the sales history information and the purchase behavior information. The product demand forecasting program according to any one of claims 1 to 9, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used.

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