Store management device and method

Abstract

To grasp an influence on a facility due to opening of a temporary store, particularly an influence on a permanent store of the facility.SOLUTION: In a store management device 1 for predicting an influence on other stores in the case of opening a store, the store is a temporary store, and the other stores are permanent stores. The store management device 1 has a store prediction use information creation unit 15 for creating permanent store prediction use information showing the use number of permanent stores when the temporary store is opened on the basis of temporary store information about use of the temporary store according to a condition to store opening and occupied permanent store information about the permanent stores.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technology for managing store openings, and particularly to a technology for managing the opening of temporary stores in an area of ​​influence such as a facility where permanent stores are located or the surrounding area. [Background technology]

[0002] Currently, information processing technology is being used to formulate and evaluate store opening plans and make decisions about store openings, such as for franchise stores. This requires that various factors related to store openings be taken into consideration, and their impacts be understood before making decisions. For example, Patent Document 1 addresses the issue of "enabling store opening managers to appropriately evaluate new store opening plans by making the contents of the sales forecast model easy to understand objectively," and describes a new store opening evaluation support device that "includes a statistical database 10 storing statistical data, a store database 11 storing store data of competing stores, a performance database 12 storing sales performance data of competing stores, input means 2, a data search and aggregation means 3 that searches and aggregates data from the statistical database 10, the store database 11, and the performance database 12 using search conditions, a classification means 4 that inputs competing store data and store attributes to be classified and hierarchically classifies the competing stores, a model estimation and evaluation means 5 that estimates an optimal sales forecast model for the classified competing store group and calculates its evaluation value, a classification control means 6 that controls the promotion and suspension of the classification of competing stores, and an output means 7 that outputs the hierarchical classification of competing stores and the optimal sales forecast model." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 10-240799 Summary of the Invention [Problem to be solved by the invention]

[0004] In such cases, facilities with permanent stores may invite temporary stores to open. By opening such temporary stores within the facility's premises or other areas of influence, the convenience of facility users may be improved and new users may be attracted. For example, if temporary restaurants such as food trucks or stalls open in a building with permanent restaurants, the options for employees in the building will increase.

[0005] However, the opening of temporary stores such as food trucks can have a negative impact on permanent stores. For example, sales at permanent stores may decrease. This can lead to dissatisfaction among the owners and other stakeholders, and continued decline in sales can lead to permanent stores moving out. As a result, building operators are faced with the problem of an increasing number of vacant tenants.

[0006] To address these issues, Patent Document 1 predicts sales for a new store based on store data and sales performance data for competing stores of the new store, but does not take into account the opening of temporary stores. In particular, the number and duration of temporary stores change constantly, but Patent Document 1 does not take this into account. For this reason, Patent Document 1 was unable to grasp the impact on permanent stores in a facility of the opening of temporary stores in an affected area of ​​the facility. Therefore, the present invention aims to grasp the impact on permanent stores in the facility of the opening of temporary stores in an affected area (area) of a facility, etc. [Means for solving the problem]

[0007] In order to solve the above problems, the present invention predicts the usage of permanent stores based on usage information of permanent stores in the facility and temporary stores that are candidates for opening in the affected area. Note that the candidate stores may be requests from the temporary stores, or proposals or ideas from the facility.

[0008] More specifically, in a store management device that predicts the impact on other stores when a store opens, the store is a temporary store, and the store management device has a store predicted usage information creation unit that creates permanent store predicted usage information that indicates the usage volume of the permanent store when the temporary store opens, based on temporary store information regarding the use of the temporary store in accordance with the conditions for the potential store and information on the permanent stores that are located there.

[0009] The present invention also includes a store management method executed by the store management device, a program for causing the store management device to function as a computer, and a storage medium for storing this program. [Effects of the Invention]

[0010] According to the present invention, it is possible to grasp the impact on a facility, particularly the impact on permanent stores, of the opening of a temporary store. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a system configuration diagram of a store management system according to an embodiment of the present invention. [Figure 2] 1 is a diagram illustrating the hardware configuration of a store management device 1 according to an embodiment of the present invention. [Figure 3] FIG. 10 is a diagram showing a correlation table 181 used in one embodiment of the present invention. [Figure 4] FIG. 10 is a diagram showing a potential definition table 182 used in one embodiment of the present invention. [Figure 5] FIG. 10 is a diagram showing an adjusted utilization ratio management table 183 used in one embodiment of the present invention. [Figure 6] FIG. 10 is a diagram showing prediction information 184 used in one embodiment of the present invention. [Figure 7] FIG. 3 is a diagram showing permanent store information 31 used in one embodiment of the present invention. [Figure 8] FIG. 3 is a diagram showing permanent store occupancy building information 32 used in one embodiment of the present invention. [Figure 9]10 is information showing temporary store information 41 used in one embodiment of the present invention. [Figure 10] 1 is a flowchart showing a processing flow according to an embodiment of the present invention. [Figure 11] 1 is a flowchart showing a processing flow in the first embodiment. [Figure 12] 10 is a flowchart showing an example of a method for creating predicted store usage information in the first and second embodiments. [Figure 13] 10 is a flowchart showing a processing flow in the second embodiment. [Figure 14] 11 is a flowchart showing the process flow of step S403 in the third embodiment. [Figure 15] FIG. 10 is a diagram showing information (history) 321 of buildings in which permanent stores are located in specific example 2. [Figure 16] FIG. 10 is a diagram showing an adjusted utilization ratio management table 183 extracted in the specific example 2. [Figure 17] 18 is a diagram showing an adjusted utilization ratio management table (transfer) 1831 used for transferring to other fields in specific example 2. FIG. [Figure 18] FIG. 18 is a diagram showing an adjusted utilization ratio management table (transfer) 1832 used for transfer to other fields in a specific example 3 of the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] An embodiment of the present invention will be described below. First, the subject of this embodiment will be described. In this embodiment, as an example of the impact area of ​​a facility, the impact prediction will be described using an example of the impact of considering opening a temporary store in an area within a predetermined distance from a building, including the building's premises. Here, "within a predetermined distance" can be expressed in units such as kilometers, as well as in terms of walking time (minutes), or in units of addresses on the same or neighboring street (block number). Note that this example includes temporary stores opened by tenants in a building occupied by multiple tenants, with the same tenants opening temporary stores. Tenants include various retail businesses such as restaurants, grocery stores, apparel stores, and general stores. Furthermore, temporary stores include food trucks and food stalls, which are examples of restaurants. Note that the equipment for the temporary store may be provided by the building itself or by the temporary store itself.

[0013] Note that the term "same industry" includes businesses of the same type, such as restaurants, as well as related businesses, such as restaurants and grocery stores, but is not limited to the same industry in this invention. Furthermore, buildings can be of any type, such as office buildings, residential buildings, or commercial buildings, as long as they are occupied by tenants. Commercial buildings also include station buildings, department stores, and shopping malls. Furthermore, facilities also include service areas, roadside stations, amusement parks, and amusement parks such as zoos.

[0014] <Configuration> Next, the configuration of this embodiment will be described. Figure 1 is a system configuration diagram of a store management system in this embodiment. The store management system includes a store management device 1, building management systems 2a and 2b, a building management database 3, a temporary store database 4, a permanent store terminal 5, and a temporary store terminal 6, all of which are connected to one another via a network 7. First, the store management device 1 is a device that executes the main processing of this embodiment. The store management device 1 can be realized by a computer, or more preferably, a server.

[0015] The store management device 1 also includes a communication unit 11, a candidate information identification unit 12, a temporary store information acquisition unit 13, a prediction unit 14, and a memory unit 18. The communication unit 11 communicates with other devices via the network 7. To this end, the communication unit 11 receives various information, requests, and demands from other devices and outputs various information, such as calculation results, to other devices. In this manner, the communication unit 11 is an example of an input unit or an output unit. For this reason, in addition to or instead of the communication unit 11, the store management device 1 may be provided with an input device such as a keyboard or mouse that a user operates to input information, or a display device such as a display monitor. The communication unit 11 then receives a request to predict the impact of a potential temporary store, including, for example, conditions for the potential store.

[0016] In addition, the candidate information identification unit 12 identifies temporary store candidate information indicating characteristics related to the use of the temporary store candidate (temporary store) in response to the prediction request. Here, the temporary store candidate information can use, as characteristics, usage quantity (e.g., number of customers and sales), offered products and services (e.g., offered menus), genre (e.g., representative menus), attributes, and store opening availability information (period and area). These will be explained later. Note that the identification of temporary store candidate information includes calculation, acquisition, and estimation of temporary store candidate information.

[0017] Furthermore, the temporary store information acquisition unit 13 acquires temporary store information corresponding to the temporary store candidate information. That is, it acquires temporary store information of temporary stores according to the conditions included in the prediction request. To this end, the temporary store information acquisition unit 13 identifies a temporary store according to the temporary store candidate information. This temporary store is an existing store that can be opened. Then, the temporary store information acquisition unit 13 acquires temporary store candidate information that indicates the characteristics of the identified temporary store.

[0018] The prediction unit 14 also predicts the impact of opening a temporary store identified by the temporary store information acquisition unit 13. This prediction includes creating prediction information indicating the impact of opening a permanent store or a temporary store on at least one of the permanent stores and the temporary store. The prediction information includes store usage information, predicted usage information, and recommendation information. For this reason, the prediction unit 14 has a store predicted usage information creation unit 15 that creates predicted store usage information, a predicted usage information creation unit 16 that creates predicted usage information, and a recommendation information creation unit 17 that creates recommendation information.

[0019] Here, predicted store usage information is information indicating the usage volume of a store after it opens, and can be, for example, the number of customers or sales. The predicted store usage information can be at least one of the predicted permanent store usage information for permanent stores and the predicted temporary store usage information for temporary stores. The predicted usage information is information indicating the overall usage of permanent stores and temporary stores, created from the predicted permanent store usage information and the predicted temporary store usage information. The number of customers and sales indicate the number of customers (number of people) and amount of money during a unit period (e.g., one day) or the opening period of a temporary store. Alternatively, the average customer spending (amount) can also be used.

[0020] For example, the predicted usage information may be a usage quantity such as the number of customers or sales. As described above, the usage quantity indicates the number of customers (number of people) and amount during a unit period (e.g., one day) or the opening period of a temporary store. Alternatively, the average customer spending (amount) may be used. The combined value of the permanent store predicted usage information and the temporary store predicted usage information may be used as the predicted usage information. The predicted usage quantity may also be a quantity that indicates a change in the predicted usage quantity. Furthermore, the recommendation information is calculated from the store predicted usage information and the predicted usage information and indicates a proposal regarding the store opening. For example, the recommendation may suggest at least one of the recommended temporary store itself, its type, and the opening date.

[0021] The recommendation information is created based on whether the usage quantity satisfies a predetermined condition. The usage quantity satisfies a predetermined condition when the total value is equal to or greater than a threshold value. For example, if the total value satisfies a threshold value, recommendation information proposing the opening of a temporary store is created. Alternatively, the recommendation information may be configured to create recommendation information proposing the opening of a temporary store when the sales of a permanent store resulting from the opening increase, remain constant, or decrease below a threshold value (sales equal to or greater than the sales threshold value).

[0022] The storage unit 18 also stores a correlation table 181, a potential definition table 182, an adjusted utilization ratio management table 183, and prediction information 184. Details of each of these pieces of information will be explained later.

[0023] Next, the building management systems 2a and 2b are used to manage buildings and are utilized by the owners and managers of the buildings. In this embodiment, the store management device 1 is implemented in a so-called cloud environment used to manage stores in multiple buildings, so multiple building management systems are illustrated. However, this may be a single system, or the functions of the store management device 1 may be provided in the building management system.

[0024] First, building management system 2a can be realized by a computer system used to manage building A. Building management system 2a is composed of building management terminal 21a and building management server 22a. Building management terminal 21a notifies store management device 1 of prediction requests and displays prediction information created by store management device 1. Building management system 2a may also be used to manage multiple buildings. Building management system 2b is used to manage building B, and an example is shown in which it is composed of building management terminal 21b. Building management terminal 21b is also used to manage buildings, notifying of prediction requests and displaying prediction information. Building management system 2b can also be used to manage multiple buildings.

[0025] The building management database 3 is a database that stores information 31-1 about permanent stores located in building A, information 31-2 about permanent stores located in building B, and information 32 about buildings where permanent stores are located. At least some of this information may be stored in the store management device 1 or the building management systems 2a and 2b. Conversely, at least some of the correlation table 181 to the prediction information 184 may be stored in the building management database 3. This information is about permanent stores and buildings. This information will be explained later.

[0026] The temporary store database 4 is a database that stores temporary store information 41 related to existing temporary stores. Details of the temporary store information 41 will be explained later. The temporary store information 41 may be stored in the store management device 1, the building management systems 2a and 2b, or the building management database 3. Conversely, at least a portion of the correlation table 181 to the prediction information 184 may be stored in the temporary store database 4.

[0027] The permanent store terminal 5 is a device used by those involved and managers of the permanent store, and can be realized by a computer. The permanent store terminal 5 accepts forecast information about the permanent store itself and information about plans to open temporary stores from the building management systems 2a and 2b. The temporary store terminal 6 is a device used by those involved and managers of the temporary store, and can be realized by a computer. The temporary store terminal 6 accepts requests to open a temporary store and forecast information about the temporary store itself.

[0028] The network 7 can be realized by a wide area network such as the Internet, and connects the above-mentioned devices together. The building management terminal 21a, building management terminal 21b, permanent store terminal 5, and temporary store terminal 6 can be realized by a PC, smartphone, tablet, or the like. This concludes the explanation of Figure 1. Next, we will explain an example implementation of the store management device 1 that performs the main processing of this embodiment.

[0029] Figure 2 is a hardware configuration diagram of a store management device 1 according to this embodiment. In this embodiment, the store management device 1 can be implemented as a server, which is an example of a computer, and in particular, as a cloud service. As shown in Figure 2, the store management device 1 includes a processing device 101, a communication device 102, a memory 103, and a secondary storage device 104, all of which are connected to each other via a communication path.

[0030] First, the processing device 101 can be realized by a processor such as a CPU, and executes calculations in accordance with a store management program 105 stored in a secondary storage device 104 (described later). The store management program 105 will be described later. The communication device 102 corresponds to the communication unit 11 in Fig. 1, and is connected to the network 7 to communicate with other devices.

[0031] The memory 103 and the secondary storage device 104 correspond to the storage unit 18 in FIG. 1 . The memory 103 expands the store management program 105 stored in the secondary storage device 104 and information used for processing by the processing device 101. The secondary storage device 104 can be implemented as a storage device, and stores the store management program 105, a correlation table 181, a potential definition table 182, an adjusted utilization ratio management table 183, and prediction information 184. The secondary storage device 104 may be implemented as various storage media such as an external hard disk drive (HDD), solid state drive (SSD), or memory card, or may be implemented as a device separate from the store management device 1, such as a file server. As described above, the secondary storage device 104 may store at least a portion of the permanent store information 31-1 in building A, the permanent store information 31-2 in building B, the permanent store occupancy building information 32, and the temporary store information 41.

[0032] Here, the store management program 105 is made up of a candidate information module 106, a temporary store information acquisition module 107, and a prediction module 108. Note that each of these modules may be realized as an individual program or a partial combination.

[0033] Here, the configuration shown in FIG. 1, which performs the same functions as each module, is as follows. Candidate information module 106: Candidate information identification unit 12 Non-permanent store information acquisition module 107: Non-permanent store information acquisition unit 13 Prediction module 108: Prediction unit 14 In addition, the prediction module 108 may be composed of a store predicted usage information creation module, a predicted usage information creation module, and a recommendation information creation module, which correspond to the store predicted usage information creation unit 15, the predicted usage information creation unit 16, and the recommendation information creation unit 17, respectively.

[0034] Therefore, the processing device 101 executes the processes of the candidate information identification unit 12, temporary store information acquisition unit 13, and prediction unit 14 in accordance with the store management program 105. Note that each of these modules may be configured as an individual program.

[0035] <Information> Next, each piece of information used in this embodiment will be explained. FIG. 3 is a diagram showing a correlation table 181 used in this embodiment. The correlation table 181 is a table that defines the user ratios of permanent stores and temporary stores that are candidates for opening. Here, FIG. 3 lists the "permanent store" usage ratio, but by subtracting this value from 1, the temporary store usage ratio of the temporary store (proportion of the number of customers using the temporary store, proportion of sales at the temporary store) can be determined. For this reason, it is sufficient to record at least one of the permanent store usage ratio and the temporary store usage ratio in the correlation table 181.

[0036] Furthermore, the term "candidate store" refers to a temporary store that is the subject of prediction. The correlation table 181 of this embodiment defines the usage ratio for each relationship between the genre and attributes of the permanent store and the temporary store. The correlation table 181 is a table for allocating the usage quantity expected from the opening of a temporary store to the permanent store and the temporary store. In this embodiment, the number of customers and sales are used as the usage quantity, but the present invention is not limited to these.

[0037] For this reason, the correlation table 181 includes the following fields: permanent store genre / temporary store genre, permanent store attribute flag / temporary store attribute flag, and permanent store usage ratio. The permanent store genre / temporary store genre indicates the relationship between the genres of permanent stores and temporary stores. Here, genre refers to the type of store, such as a representative product or service. For example, in the case of a restaurant, the genre can be a representative menu item. Other categories include Japanese cuisine and Western cuisine, and characteristics such as kaiseki, fast food, lightly seasoned, and lightly seasoned. Furthermore, when analyzing apparel stores, categories such as casual and formal can be used. In this embodiment, the relationship is expressed as "same" and "different." In other words, the permanent store genre / temporary store genre indicates whether the permanent store and the temporary store are the same or different. The permanent store genre / temporary store genre can also be expressed as "similar," i.e., whether the permanent store and the temporary store are similar.

[0038] Furthermore, the permanent store attribute flag / temporary store attribute flag indicates the relationship between the attributes of permanent stores and temporary stores. Here, the attribute indicates an attribute related to the use of each store. For example, popularity among men and popularity among women can be used as attributes. Furthermore, the age, occupation, family, address (store area) of frequent customers can be used as attributes. Furthermore, the season or time of year when the number of users is high can also be used as an attribute. Note that the attribute flag is a flag for identifying attributes, but it is not necessary to use a "flag" as long as the attribute or its relationship can be identified.

[0039] The permanent store usage ratio indicates the proportion of usage to be allocated to permanent stores for each permanent store genre / temporary store genre and permanent store attribute flag / temporary store attribute flag. Here, allocation means estimating the usage quantity for permanent stores out of the predicted usage quantity for permanent stores and temporary stores as a whole due to the opening of the target temporary store. In this embodiment, the permanent store utilization ratio, which indicates the allocation ratio to permanent stores, is used, but of course, the non-permanent store utilization ratio may also be used. Also, in this embodiment, the permanent store utilization ratio is the ratio of the number of customers using the permanent store and the ratio of sales at the permanent store. These respectively use the number of customers and sales as the utilization quantity. Furthermore, in the present invention, at least one of these may be used, or other utilization ratios may be used. Furthermore, a utilization index that combines the ratio of the number of customers using the permanent store and the ratio of sales at the permanent store may also be used.

[0040] Next, FIG. 4 is a diagram showing a potential definition table 182 used in this embodiment. The potential definition table 182 shows the potential capacity in terms of usage quantity at a facility represented by a corresponding building. In this embodiment, the potential definition table 182 defines a potential number of people, which indicates the number of potential users, which is an example of usage quantity, for each number of employees, which is an example of the size of the building. Note that the potential definition table 182 does not have to be defined by the number of employees in the building. For example, the potential number of people may be defined for each building, or may be defined by time of day, day of the week, or other date and time. Furthermore, the potential definition table 182 may be defined by the total floor area of ​​the building.

[0041] Next, FIG. 5 is a diagram showing an adjusted utilization ratio management table 183 used in this embodiment. The adjusted utilization ratio management table 183 is a table for adjusting the above-mentioned permanent store utilization ratio for each target number of permanent stores and temporary stores. Note that the adjusted utilization ratio management table 183 is created for each combination of the relationship indicated by the permanent store genre / temporary store genre and the permanent store attribute flag / temporary store attribute flag. For example, as shown in FIG. 3, it is created for each of four combinations (same / different). FIG. 5 shows one of these.

[0042] Here, it is considered that the usage quantity to be allocated will change depending on the number or ratio of the target permanent stores and temporary stores. Therefore, in this embodiment, the above-mentioned permanent store usage ratio is adjusted depending on the number or ratio of the permanent stores and temporary stores. For this purpose, in this embodiment, this adjusted usage ratio management table 183 is used.

[0043] In this embodiment, the adjusted utilization ratio management table 183 defines an adjusted permanent store utilization ratio for each target number of permanent stores and number of non-permanent stores. Similarly to the permanent store utilization ratio, the adjusted permanent store utilization ratio includes an adjusted permanent store customer count ratio and an adjusted permanent store sales ratio. However, the adjusted permanent store customer count ratio and the adjusted permanent store sales ratio are merely examples, and other ratios may be used, or some or a combination of these may be used.

[0044] The correlation table 181 and adjusted usage ratio management table 183 described above may be omitted. In particular, the adjusted usage ratio management table 183 is used in Example 3 described later. In this case, it is possible to determine the usage ratio regardless of the genre or attribute, and to omit adjusting the usage ratio based on the number of stores.

[0045] Next, Fig. 6 is a diagram showing the prediction information 184 used in this embodiment. The prediction information 184 is created by the prediction unit 14 and indicates the impact of opening a temporary store. In this embodiment, the prediction information 184 has items of predicted usage information and recommendation information for each building name. The building name is an item that indicates the target building.

[0046] Furthermore, the predicted usage information is information indicating the usage volume if the target temporary store opens. In this embodiment, it has the following items: temporary store predicted usage information, permanent store predicted usage information, and combined value. The temporary store predicted usage information indicates the predicted usage volume of the temporary store if the target temporary store opens. Sales and number of customers, i.e., the number of users, are used as this usage volume. Note that these are examples, and other information may be used as the temporary store predicted usage information, or at least one or a combination of these may be used.

[0047] Furthermore, the permanent store predicted usage information indicates the predicted usage volume of the temporary store if the target temporary store opens. Sales and the number of customers, i.e., the number of users, are used as this usage volume. More preferably, in addition to this usage volume, the usage volume before the temporary store opens is also recorded. Note that these are merely examples, and other information may be used as the permanent store predicted usage information, or at least one or a combination of these may be used. Furthermore, the increase or decrease in usage from before the temporary store opens may also be used as the permanent store predicted usage information.

[0048] The combined value indicates the combined value of the permanent store predicted usage information and the temporary store predicted usage information. This combined value may include increases or decreases before and after the opening of the store, as with the permanent store predicted usage information. Furthermore, the combined value is an example of information that integrates the permanent store predicted usage information and the temporary store predicted usage information, and other information may be used or may be omitted. Note that by using integrated information such as this combined value, the impact of opening a temporary store on the entire building can be understood.

[0049] Furthermore, the recommendation information is information indicating suggestions and advice regarding store openings derived from the predicted usage information. Note that this recommendation information may be omitted, or may be created or edited by the user.

[0050] Next, Fig. 7 is a diagram showing the permanent store information 31 used in this embodiment. The permanent store information 31 in Fig. 7 is representative of the permanent store information 31-1 in Building A and the permanent store information 31-2 in Building B shown in Fig. 1 and Fig. 2. Therefore, the permanent store information 31-1 in Building A and the permanent store information 31-2 in Building B have the same items as the permanent store information 31.

[0051] The permanent store information 31 is information about permanent stores located in the target building. In this embodiment, the permanent store information 31 has the following items: store name, genre, sales, number of customers, number of people that can be accommodated, menu items 1 and 2, and attribute flag. The store name identifies the permanent store located in the building. The genre indicates the genre of the permanent store. The sales indicates the sales of the permanent store. A profit margin item may be added to the sales information.

[0052] The number of customers indicates the number of customers using the corresponding permanent store. For users, a user information field (e.g., membership information) for each user may be provided. The number of customers that can be accommodated indicates the number of customers that can be accommodated at the corresponding permanent store. More preferably, the number of customers that can be accommodated per unit period (e.g., unit time) is used. For sales, number of customers, and number of customers that can be accommodated, it is desirable to use data from the same period. Representative values ​​such as the average or median for each unit period can be used for sales and number of customers. Furthermore, the number of customers that can be accommodated may be determined based on the number of seats at the corresponding permanent store. In this embodiment, the location of the permanent store can be obtained from the permanent store's building information 32, but the permanent store information 31 may also include a field for the location of each permanent store.

[0053] Furthermore, the offered menu 1 and offered menu 2 indicate menus that are examples of products and services provided at the permanent store. In addition to these, a representative menu may be set in the permanent store information 31. Furthermore, the attribute flag indicates the attribute of the permanent store.

[0054] Next, Fig. 8 is a diagram showing permanent store occupancy building information 32 used in this embodiment. The permanent store occupancy building information 32 is information about the building in which the permanent store is located. Therefore, as shown in Fig. 8, the permanent store occupancy building information 32 has the following items: building name, location, size, tenant 1, tenant 2, and tenant 3.

[0055] First, the building name identifies the building. The location indicates the location (area) of the building. The number of employees in the building indicates the number of employees, which is an example of the size of the building. Tenants 1 to 3 indicate the permanent stores located in the building.

[0056] Next, Fig. 9 shows information showing temporary store information 41 used in this embodiment. The temporary store information 41 is information about the use of temporary stores that can be candidates for opening. In this embodiment, as shown in Fig. 9, the information includes the following items: store name, genre, sales, number of customers, number of people that can be accommodated, offered menu 1, offered menu 2, attribute flag, available opening date, and available opening area.

[0057] First, the store name identifies a temporary store that may be a candidate for opening. Furthermore, the genre to attribute flag are the same items as those in the permanent store information 31 for the temporary store in question. Furthermore, the available opening date indicates the date and time when the temporary store in question is available to open. Furthermore, the available opening area indicates the area (location) in which the temporary store in question can open. This concludes the explanation of the information used in this embodiment, but some of this information is illustrated as being specialized for buildings as facilities and restaurants as stores. However, this embodiment is not limited to buildings and restaurants.

[0058] <Processing flow> Next, the processing flow of this embodiment will be explained. Figure 10 is a flowchart showing the processing flow of this embodiment. The following explanation will be given along this flowchart, but the processing will mainly use the configuration of Figure 1. This also applies to each example described later. In addition, the following explanation will be given using an example of opening a temporary store in Building A.

[0059] First, in step S1, the store management device 1 receives a request to predict the impact of opening a temporary store in building A. In this way, the store management device 1 functions as an input unit. This prediction request includes conditions for the candidate temporary store. These conditions can be at least one of the following: genre, attributes, opening date and time, and opening area (location of building A).

[0060] Furthermore, the prediction request may be received by the communication unit 11 from the building management terminal 21a, or may be received automatically by the communication unit 11 or the candidate information identification unit 12. In the former case, the conditions for the prediction request are received by the building management terminal 21a from the user. In this manner, in this embodiment, the impact of opening a temporary store may be predicted by user operation, or may be predicted automatically (automatic mode). Furthermore, the conditions may include the designation of the temporary store itself.

[0061] In step S2, the candidate information identification unit 12 identifies candidate temporary store information indicating characteristics related to the usage of the candidate temporary store in response to the received prediction request. To this end, the candidate information identification unit 12 extracts the conditions included in the prediction request and defines these as characteristics. For example, the combination of usage quantity, genre, attribute, opening date and time, and opening area included in the conditions is defined as a characteristic. Note that items not included in the conditions (for example, genre) can all be considered acceptable as characteristics. As a result, the candidate information identification unit 12 identifies candidate temporary store information including these characteristics.

[0062] In step S3, the temporary store information acquisition unit 13 acquires temporary store information corresponding to the temporary store candidate information. To this end, the temporary store information acquisition unit 13 searches for temporary store information 41 using the characteristics of the temporary store candidate information as a key. If the characteristics include multiple items other than "all acceptable," the temporary store information acquisition unit 13 may search for temporary store information 41 that satisfies each of these items, or may search for information that satisfies the required items but has similar other items. Note that "all acceptable" can be set to NULL data, indicating no conditions. Furthermore, it is desirable to search for temporary store information 41 that indicates a quantity greater than or equal to the quantity of the conditions or characteristics. As a result, temporary store candidate information that identifies existing temporary stores that are candidates for opening can be acquired from the temporary store candidate information that indicates virtual temporary stores.

[0063] Furthermore, in step S4, the prediction unit 14 predicts the impact of opening a temporary store that is a candidate for a main store based on the acquired temporary store candidate information. For this purpose, the prediction unit 14 creates prediction information. This prediction information includes at least predicted usage information for permanent stores and may also include predicted usage information for temporary stores. The predicted usage information for permanent stores and the predicted usage information for temporary stores can be referred to as predicted store usage information. The prediction information may also include predicted usage information represented by the sum of predicted store usage information and recommendation information indicating proposals regarding store openings.

[0064] First, the creation of predicted store usage information will be described. The predicted store usage information creation unit 15 of the prediction unit 14 creates predicted permanent store usage information for a candidate temporary store when it opens, using the occupied permanent store information 31 indicating usage information for permanent stores in building A and the acquired temporary store information. Furthermore, the predicted store usage information creation unit 15 can also create predicted temporary store usage information for a candidate temporary store when it opens, using the occupied permanent store information 31 indicating usage information for permanent stores in building A and the acquired temporary store information.

[0065] Here, the creation of permanent store predicted usage information and temporary store predicted usage information will be explained. These each include predicted usage quantities. To do this, the store predicted usage information creation unit 15 identifies the genre, attribute flag, and capacity of the permanent store in building A and the temporary store that is a candidate for opening from the occupant permanent store information 31 and the temporary store information 41, respectively. The store predicted usage information creation unit 15 also identifies the usage ratio of the permanent store in building A and the temporary store that is a candidate for opening if the store is opened. At this time, the store predicted usage information creation unit 15 uses the correlation table 181 to calculate the permanent store usage ratio corresponding to the identified genre and attribute flag.

[0066] In this embodiment, the permanent store information 31 is used, but there may be cases where the permanent store information does not exist for some permanent stores. In this case, the store predicted usage information creation unit 15 estimates the permanent store information of the relevant permanent store from the permanent store information 31 of other permanent stores. For example, the store predicted usage information creation unit 15 extracts the permanent store information of other permanent stores based on at least one of the location, size, and tenants (permanent stores) of the relevant building. In this extraction, buildings similar to the relevant permanent store are extracted from buildings that have similar items to the relevant building.

[0067] The store predicted usage information creation unit 15 also identifies the potential number of customers in building A from the permanent store occupancy building information 32. Then, the store predicted usage information creation unit 15 calculates the usage volume of permanent stores in building A and potential temporary stores using the permanent store usage ratio, the number of customers that can be accommodated, and the potential. In response to this, the store predicted usage information creation unit 15 creates at least one of permanent store predicted usage information and temporary store predicted usage information that include these, more preferably permanent store predicted usage information. In this way, the store predicted usage information creation unit 15 creates at least one of permanent store predicted usage information and temporary store predicted usage information based on the occupancy permanent store information 31 and temporary store information 41.

[0068] Next, the creation of predicted usage information will be described. The predicted usage information creation unit 16 of the prediction unit 14 adds up the usage quantities included in the permanent store predicted usage information and the temporary store predicted usage information, and calculates the sum as the predicted usage information. Note that the sum is an example of predicted usage information, and any information indicating the impact of the store opening on Building A as a whole may be used, such as the amount of change in usage quantity. Finally, the creation of recommendation information will be described. The recommendation information creation unit 17 of the prediction unit 14 creates recommendation information based on at least one of the store predicted usage information and the predicted usage information. Here, the store predicted usage information (permanent store predicted usage information and temporary store predicted usage information), the predicted usage information, and the information on occupied permanent stores 31 are used to create the recommendation information. These can be managed as information for each period. In this case, the recommendation information can also be created as recommendation information indicating suggestions for each period for which each of these pieces of information is managed.

[0069] Then, in step S5, the communication unit 11 notifies the building management terminal 21a of the created forecast information. The building management terminal 21a then accepts this and displays it on its own display device. In this way, the communication unit 11 functions as an output unit. After receiving these processes, users such as the manager of building A can check the forecast information. As a result, the manager of building A can determine whether or not to open a temporary store. In this embodiment, the output unit of the store management device 1 may display the forecast information.

[0070] In addition, in this embodiment, a request to open a temporary store that has been determined by a manager or the like to open a store can be notified, and a plan to open a store can be reported to a permanent store. For example, the building management terminal 21a notifies the temporary store terminal 6 of the temporary store in question of a store opening request. Furthermore, if there is a response to this store opening request that the main store will be accepted, the building management terminal 21a may report the store opening to the permanent store terminal 5 of a permanent store located in building A.

[0071] As described above, according to this embodiment, it is possible to grasp the impact of opening a temporary store, and to appropriately determine whether or not to open a store. Note that, below, examples that are specific examples of this embodiment will be described. [Example]

[0072] In Example 1, an example will be described in which the target store is a restaurant. That is, an example will be described in which a permanent restaurant is located in a building, which is an example of a facility, and the opening of a temporary restaurant on the premises of this building is being considered. Note that the configuration (FIGS. 1 and 2) and information (FIGS. 3 to 9) of Example 1 are almost the same as those of the above-mentioned embodiment, so their explanation will be omitted for the time being and the processing flow will be explained. However, with regard to the information, there is information specific to Examples 2 and 3, which will be explained later in the relevant sections.

[0073] <Processing flow> FIG. 11 is a flowchart showing the processing flow in the first embodiment. In step S11, which corresponds to step S1, the communication unit 11 of the store management device 1 receives store opening request information from the building management terminal 21a as a prediction request for a temporary restaurant in building A. The store opening request information indicates a request from the manager of building A or the like to open a temporary restaurant, and is input to the building management terminal 21a. This store opening request information includes the above-mentioned conditions for the prediction request. In this way, the first embodiment predicts the impact of a store opening through operations by a user such as the building manager.

[0074] In step S21, which corresponds to step S2, the candidate information identification unit 12 creates candidate non-permanent restaurant information indicating characteristics related to the use of the candidate non-permanent restaurant in accordance with the received restaurant opening request information. That is, the candidate information identification unit 12 extracts conditions included in the restaurant opening request information, defines these as characteristics, and creates candidate non-permanent restaurant information including these characteristics.

[0075] In step S31, which corresponds to step S3, the temporary store information acquisition unit 13 acquires the temporary store restaurant information corresponding to the temporary restaurant candidate information. That is, for this purpose, the temporary store information acquisition unit 13 searches the temporary store information 41 using the characteristics of the temporary restaurant candidate information as a key, and acquires the temporary store restaurant information.

[0076] In step S41, which corresponds to step S4, the predicted store usage information creation unit 15 of the prediction unit 14 creates predicted store usage information for temporary restaurants and permanent restaurants based on the acquired temporary restaurant candidate information. An example of a method for creating this predicted store usage information will be described with reference to FIG. 12. This creation method can also be applied to Example 2, which will be described later.

[0077] 12 is a flowchart showing an example of a method for creating predicted store usage information in Examples 1 and 2. First, in step S401, the predicted store usage information creation unit 15 extracts information on permanent restaurants located in the target building A from the permanent store information 31-1 located in building A. Here, it is desirable that the predicted store usage information creation unit 15 extracts information on permanent restaurants located in the target building A from the permanent store information 31-1 located in building A according to the conditions included in the store opening request information. For example, the permanent store information 31-1 located in building A includes information on permanent stores other than restaurants, such as apparel stores, from which information on restaurants is extracted.

[0078] Furthermore, in step S402, the store predicted usage information creation unit 15 identifies the genre and attribute flag of each of the permanent restaurants and the temporary restaurants. To this end, the store predicted usage information creation unit 15 extracts the genre and attribute flag of the permanent restaurants from the extracted information on the permanent restaurants that will be located. Furthermore, the store predicted usage information creation unit 15 extracts the genre and attribute flag of the temporary restaurants from the information on the temporary restaurants that will be located in step S31. In this step, the genre and attribute flag of each of the permanent restaurants in building A and the temporary restaurants that will be candidates for opening are identified.

[0079] In step S403, the store predicted usage information creation unit 15 identifies the usage ratio between the permanent restaurants in building A and the candidate non-permanent restaurant using the genres and attribute flags of the permanent restaurants and the non-permanent restaurants. This usage ratio indicates the ratio of the usage volume between the permanent restaurants and the non-permanent restaurants when the candidate non-permanent restaurant opens.

[0080] More preferably, the store predicted usage information creation unit 15 uses the correlation table 181 to identify a usage ratio according to the relationship between the genre and the attribute flag. For example, if the genres and attribute flags of permanent restaurants and non-permanent restaurants are the same, the permanent store usage ratio can be identified as 0.4 for the number of customers and 0.6 for the sales. Furthermore, the non-permanent store usage ratio can be identified as 0.6 for the number of customers and 0.4 for the sales. Another example of this step will be described in Example 3 below. In this step, the permanent store usage ratio may be calculated by calculating at least one of the permanent store customer ratio and the permanent store sales ratio, or may be calculated using an algorithm that uses the permanent store customer ratio and the permanent store sales ratio as parameters. In this example, a combination of genre and attribute flag is used, but either one may be used.

[0081] In step S404, the store predicted usage information creation unit 15 calculates the number of customers that can be accommodated by each of the permanent restaurants in building A and the non-permanent restaurants that are candidates for opening. To this end, the store predicted usage information creation unit 15 extracts the number of customers that can be accommodated by each permanent restaurant from the extracted information on the permanent restaurants that are located in building A. In addition, the store predicted usage information creation unit 15 extracts the number of customers that can be accommodated by each non-permanent restaurant from the information on the non-permanent restaurants acquired in step S31.

[0082] Furthermore, in step S405, the store predicted usage information creation unit 15 calculates the potential number of people in building A using the permanent store occupancy building information 32 and the potential definition table 182. To do this, the store predicted usage information creation unit 15 identifies the number of building employees in building A from the permanent store occupancy building information 32. In the example of FIG. 8, the number of building employees in building A can be identified as 1,200. Next, the store predicted usage information creation unit 15 identifies the potential number of people corresponding to the identified number of building employees from the potential definition table 182. In the example of FIG. 4, the potential number of people corresponding to the identified number of building employees of 1,200 is identified as 500.

[0083] In step S406, the store predicted usage information creation unit 15 allocates the number of customers to the permanent restaurants in building A according to the usage ratio identified in step S403. In this embodiment, the potential number of customers calculated in step S405 is used as the number of customers, but other numerical values ​​may be used. Furthermore, a permanent restaurant customer ratio of 0.4 is used as the usage ratio. As a result, of the potential number of customers of 500, 200 customers are allocated to the permanent restaurants.

[0084] Here, if multiple permanent restaurants are located in Building A, it is desirable for the store predicted usage information creation unit 15 to reallocate this number of customers to each of the permanent restaurants. This reallocation may be done equally among the permanent restaurants, or may be done by allocating the number of customers according to the number of customers or sales of the permanent restaurant information 31-1 located in Building A. Note that the number of customers will also be allocated to non-permanent restaurants that are candidates for opening. In the above example, 300 customers will be allocated to the non-permanent restaurants.

[0085] In step S407, the store predicted usage information creation unit 15 compares the number of customers allocated to the permanent eating and drinking establishment with the number of customers that the permanent eating and drinking establishment can handle. If the result shows that the allocated number of customers is large (N), the process proceeds to step S408. If the allocated number of customers is small (Y), the process proceeds to step S409.

[0086] Then, in step S408, the store predicted usage information creation unit 15 transfers the allocated number of customers that exceeds the capacity to the non-permanent restaurant that is a candidate for opening. Furthermore, in step S409, the store predicted usage information creation unit 15 creates store predicted usage information based on the allocated number of customers. For example, the store predicted usage information creation unit 15 predicts the allocated number of customers as the number of customers if the restaurant is opened, and predicts sales by multiplying this number of customers by the unit price of the corresponding restaurant. Furthermore, the store predicted usage information creation unit 15 may predict sales based on past sales data that includes the number of customers. As a result, the store predicted usage information creation unit 15 creates store predicted usage information that includes the number of customers and sales.

[0087] Furthermore, in this step, as the predicted store usage information, at least the permanent restaurant predicted usage information for the permanent restaurants in building A is created, and the creation of the non-permanent restaurant predicted usage information for the non-permanent restaurants may be omitted or may be performed at a different time. Furthermore, the permanent restaurant predicted usage information may include the results of a comparison with the latest actual data on sales and number of customers. Then, the predicted store usage information creation unit 15 records this permanent restaurant predicted usage information and non-permanent restaurant predicted usage information as predicted usage information (permanent store predicted usage information and non-permanent store predicted usage information) in the prediction information 184.

[0088] This concludes the explanation of Fig. 12, and we will return to Fig. 11 to explain step S51 and subsequent steps. In step S51, which corresponds to step S5, the communication unit 11 notifies the building management terminal 21a of the created predicted store usage information. As a result, the predicted store usage information is displayed on the display device of the building management terminal 21a. In this way, the communication unit 11 and the display device of the building management terminal 21a function as output units.

[0089] In step S42, which corresponds to step S4, the predicted store usage information creation unit 15 determines whether there are any other non-permanent restaurants that are candidates for opening. That is, it determines whether there is any more prediction request information. If there are any other non-permanent restaurants that are candidates for opening (Y), the process proceeds to step S21, where processing is performed for the other non-permanent restaurants that are candidates for opening. If there are no other non-permanent restaurants that are candidates for opening (N), the process proceeds to step S43.

[0090] In step S43, which corresponds to step S4, the predicted usage information creation unit 16 calculates predicted usage information using the created permanent store predicted usage information and temporary store predicted usage information. For example, the predicted usage information creation unit 16 calculates the sum of the usage quantities of the permanent store predicted usage information and temporary store predicted usage information. The predicted usage information creation unit 16 then records the calculated sum in the prediction information 184.

[0091] In step S44, which corresponds to step S4, the recommendation information creating unit 17 creates recommendation information for opening a temporary restaurant using at least one of the generated permanent store predicted usage information, temporary store predicted usage information, and the combined value. Then, the recommendation information creating unit 17 records the created recommendation information in the prediction information 184.

[0092] The communication unit 11 then notifies the building management terminal 21a of building A of each piece of created forecast information. As a result, the building management terminal 21a displays the notified forecast information. In this way, the display device of the communication unit 11 or the building management terminal 21a functions as an output unit. Note that a display unit may be provided in the store management device 1 to display the forecast information. According to the above-described first embodiment, it is possible to proceed with predictions while presenting store forecast usage information to a manager such as a building administrator. [Example]

[0093] Next, Example 2 will also be described using an example where a restaurant is the target store. That is, an example will be described where a permanent restaurant is located in a building, which is an example of a facility, and the opening of a temporary restaurant on the premises of this building is being considered. However, while Example 1 predicts the impact of the opening of a temporary restaurant through user operation, Example 2 predicts it automatically (automatic mode). Below, as with Example 1, the processing flow will be described.

[0094] <Processing flow> FIG. 13 is a flowchart showing the processing flow in the second embodiment. In step S12, which corresponds to step S1, the candidate information identification unit 12 of the store management device 1 receives store opening request information, which is a prediction request for a non-permanent restaurant in building A. This is configured to detect the activation of the automatic mode. Note that this activation may be based on a time condition. For example, it may be executed periodically.

[0095] In step S22, which corresponds to step S2, the candidate information identification unit 12 determines whether the received store opening request information includes the store opening request conditions for a non-permanent restaurant. If the result indicates that the store opening request conditions are included (Y), the process proceeds to step S23. If the result indicates that the store opening request conditions are not included (N), the process proceeds to step S24.

[0096] Then, in step S23, which corresponds to step S2, the candidate information identification unit 12 creates candidate non-permanent restaurant information indicating characteristics related to the use of the candidate non-permanent restaurant based on the received desired restaurant opening conditions. This can be achieved by the same process as step S21 in the first embodiment. Furthermore, in step S24, which corresponds to step S2, the candidate information identification unit 12 creates candidate non-permanent restaurant information indicating characteristics related to the use of the candidate non-permanent restaurant based on default conditions. Here, the default conditions are conditions set in advance in the storage unit 18, and more preferably, are set for each building. In this case, the default conditions are recorded in the permanent restaurant occupancy building information 32. Furthermore, the default conditions may be recorded by the building management terminal 21a or the building management terminal 21b in response to input from the owner or manager of each building.

[0097] Furthermore, step S31 is executed in the same manner as in Example 1. Furthermore, in step S45, which corresponds to step S4, step S41 (steps S401 to S408), step S43, and step S44 in Example 1 are executed.

[0098] In step S521, which corresponds to step S5, the communication unit 11 notifies the building management terminal 21a of the created prediction information, which is then displayed by the building management terminal 21a. This step may also be executed by displaying the information on the display unit of the store management device 1. In step S522, which corresponds to step S5, the communication unit 11 transmits a store opening request notification to the temporary store terminal 6. Here, step S522 may be performed on the condition that the recommendation information is positive for opening a store, or on the condition that the building management terminal 21a agrees to the transmission.

[0099] As described above, according to the second embodiment, prediction can be carried out without the need for the building manager or other users. [Example]

[0100] The third embodiment is an example showing details of an example of step S403 in the first and second embodiments. That is, the third embodiment performs a process of adjusting the utilization ratio. The process flow of the third embodiment will be described below with reference to FIG.

[0101] <Processing flow> FIG. 14 is a flowchart showing the processing flow of step S403 in Example 3. After step S402, in step S4031, the store predicted usage information creation unit 15 classifies, for each permanent restaurant in building A, a combination of genre / attribute flag in terms of its relationship with a non-permanent restaurant that is a candidate for opening. In other words, the relationship between the permanent restaurant and the non-permanent restaurant that is a candidate for opening is identified. In the example of FIG. 3, it is identified whether the relationship between the permanent restaurant genre / non-permanent restaurant genre and the permanent restaurant attribute flag / non-permanent restaurant attribute flag is "same" (same) or "different" (different).

[0102] Furthermore, in step S4032, the predicted store usage information creation unit 15 counts the number of stores for each combination of genre / attribute flags. That is, the number of permanent stores and the number of temporary stores are counted. Then, in step S4033, the predicted store usage information creation unit 15 calculates an adjusted usage ratio from the adjusted usage ratio management table 183 for each classified combination and the number of permanent stores and temporary stores. For example, if the combinations classified in the adjusted usage ratio management table 183 are both "same," and the number of permanent stores is 1 and the number of temporary stores is 3, the adjusted permanent store customer ratio is calculated to be 0.70 and the adjusted temporary store customer ratio is calculated to be 0.72. This is an adjustment from the permanent store customer ratio of 0.40 and the temporary store customer ratio of 0.60 (see FIG. 3) in the permanent store ratio that does not take the number of stores into account. Then, in step S4041, which corresponds to step S404, the predicted store usage information creation unit 15 calculates the number of customers that can be accommodated by each of the permanent restaurants and non-permanent restaurants that are candidates for opening in building A for each combination of genre / attribute flag. Here, in the third embodiment, the adjusted usage ratio management table 183 is used, but the predicted store usage information may also be calculated using (Equation 1).

[0103]

number

[0104] Furthermore, the recording in the adjusted utilization ratio management table 183 may be performed using (Equation 1). This concludes the explanation of the third embodiment, and next, the third embodiment will be explained in more detail using a specific example.

[0105] <Example> Here, specific examples 1 to 3 of creating predicted store usage information for Example 3, i.e., predicting the number of customers and sales, will be described. First, specific examples 1 to 3 show examples of adjusting the usage ratio depending on the number of permanent restaurants and candidate temporary restaurants. However, this adjustment is not necessary in this example. Next, the underlying concepts of these specific examples 1 to 3 will be described. First, temporary restaurants are novel and are expected to attract a certain number of customers. Furthermore, (1) when the number of customers that a candidate temporary restaurant can accommodate per unit period is less than the potential number of customers, it can be estimated that increasing the number of temporary restaurants will not reduce the number of customers at the permanent restaurants. (2) When the number of customers that a candidate temporary restaurant can accommodate per unit period is equal to or greater than the potential number of customers, increasing the number of temporary restaurants will reduce the number of customers at one of the permanent restaurants. Note that these specific examples can also be applied to Examples 1 and 2 described above.

[0106] <Example 1> First, the preconditions for Example 1 are as follows: Permanent restaurants: 3 curry restaurants Potential temporary restaurant: One curry restaurant All stores: 3+1=4 stores Each store can accommodate up to 60 people. Capacity of all 4 stores (total): 60 x 4 = 240 people Potential number of people: 250 (more than the 240 people that can be accommodated by the four stores) In Example 1, the attribute flag is not used, but the genre is used for processing. Also, the ratio of the number of customers using the permanent store is used as the permanent store usage ratio.

[0107] The processing details of Specific Example 1 will be explained below. The predicted store usage information creation unit 15 identifies the genre of both the permanent restaurant and the candidate temporary restaurant as curry. As a result, the predicted store usage information creation unit 15 determines that the relationship between the genres of the permanent restaurant and the temporary restaurant is the same (step S402). Then, the predicted store usage information creation unit 15 calculates a usage ratio based on the number of restaurants and the relationship between the genres (step S403). Here, in Specific Example 1, the permanent restaurant usage ratio is adjusted to calculate an adjusted permanent restaurant usage ratio as this usage ratio.

[0108] As described above, the available number of customers and the potential number of customers are specified as 240 and 250, respectively (steps S404 and S405). Then, the store predicted usage information creation unit 15 allocates the potential number of customers to a total of four stores, including permanent restaurants and non-permanent restaurants (step S406). Here, details of the allocation will be explained below, focusing on the calculation of the adjusted permanent store usage ratio.

[0109] First, the calculation of the adjusted permanent store usage ratio will be described. The predicted store usage information creation unit 15 identifies 0.80, which is the adjusted permanent store customer ratio when the number of permanent stores is 3 and the number of non-permanent stores is 1, from the adjusted usage ratio management table 183.

[0110] Furthermore, the store predicted usage information creation unit 15 calculates the number of customers of the permanent restaurant by multiplying the potential number of customers by the adjusted ratio of the number of customers of the permanent restaurant. In other words, by multiplying 250 x 0.8, 200 people is calculated as the number of customers of the permanent restaurant.

[0111] Next, the number of customers that the three permanent restaurants can accommodate is calculated as 180 by multiplying 60 by 3. Therefore, the predicted restaurant usage information creation unit 15 determines that the number of customers at the permanent restaurants, 200, exceeds the capacity of 180 (step S407). Furthermore, the number of customers at the temporary restaurants is calculated as 50 by multiplying 250 by (1-0.8). Therefore, the capacity of the temporary restaurants (60 customers) has a surplus of 10 customers. Therefore, it is estimated that the 20 excess customers will flow to the temporary restaurants up to their capacity. Furthermore, it is estimated that the temporary restaurants can accommodate up to 10 of the excess customers (20 customers).

[0112] Therefore, the predicted store usage information creation unit 15 transfers the excess number of customers (20 people) or 10 people to the temporary stores (step S408). As a result, the following predicted store usage information is calculated: (A) predicted permanent restaurant usage information (number of customers): 180 people (60 people per store), (B) predicted temporary restaurant usage information (number of customers): 60 people.

[0113] The above describes the case where the potential number of customers is greater than the number of customers that four stores can handle. Below, we will explain the case where the potential number of customers is less than the number of customers that four stores can handle. Here, we will assume that the potential number of customers is 100, which is less than the 240 customers that four stores can handle. Note that other prerequisites are as described above. Therefore, the processing up to the calculation of the number of customers of permanent restaurants (step S407) is the same, so only the processing from that point onwards will be explained.

[0114] The predicted store usage information creation unit 15 calculates the number of customers at the permanent restaurants by multiplying the potential number of customers by the adjusted ratio of the number of customers at the permanent restaurants. That is, 100 × 0.8 is calculated to calculate 80 customers as the number of customers at the permanent restaurants. Then, 60 × 3 is calculated to calculate 180 customers as the number of customers that can be accommodated by three permanent restaurants. Therefore, the predicted store usage information creation unit 15 determines that the number of customers at the permanent restaurants, 80, does not exceed the capacity of 180 customers (step S407). Furthermore, the number of customers at the temporary restaurants is calculated to be 20 by calculating 100 × (1 - 0.8). As a result, the following predicted store usage information is calculated: (A) predicted usage information (number of customers) for permanent restaurants: 80 customers (approximately 27 customers per restaurant), and (B) predicted usage information (number of customers) for temporary restaurants: 20 customers.

[0115] Furthermore, in specific example 1, adjusted utilization ratio management table 183 is used, but the store predicted utilization information may be calculated using the above-mentioned (Equation 1). Furthermore, recording in adjusted utilization ratio management table 183 may be performed using (Equation 1). This concludes the explanation of specific example 1.

[0116] <Example 2> In Example 1, the genre of each store is the same, but in Example 2, multiple genres are mixed. The prerequisites for Example 2, including genres, are as follows: Permanent restaurants: 2 curry restaurants and 1 udon restaurant, for a total of 3 restaurants Potential temporary restaurant: One udon restaurant All stores: 3+1=4 stores Each store can accommodate up to 60 people. Capacity of all 4 stores (total): 60 x 4 = 240 people Potential number of people: 250 (more than the 240 people that can be accommodated by the four stores) In Example 2, the attribute flag is not used, but the genre is used for processing. Also, the ratio of the number of customers using the permanent store is used as the permanent store usage ratio.

[0117] The processing details of specific example 2 will be explained below. The predicted store usage information creation unit 15 identifies the genres of the permanent restaurants as two curry restaurants and one udon restaurant. Furthermore, the predicted store usage information creation unit 15 identifies the genre of the non-permanent restaurant that is a candidate for opening as udon (one tempo). As a result, the predicted store usage information creation unit 15 determines that there is a mixture of genres when the relationship between the genres of the permanent restaurants and the non-permanent restaurants is the same or different (step S402). Furthermore, it is determined that there is a mixture of genres among the permanent restaurants.

[0118] Furthermore, the store predicted usage information creation unit 15 calculates the usage ratio, the number of customers that can be accommodated for each genre, and the potential number of customers based on the number of stores, genres, and the relationships between the genres (steps S403 to S405). At this time, the permanent store occupancy building information (history) 321 shown in FIG. 15 is used. The permanent store occupancy building information (history) 321 is information that indicates the usage quantity ratio for each relationship between different genres. The calculation of the usage ratio will be explained below.

[0119] First, the predicted store usage information creation unit 15 identifies that, among the permanent restaurants and temporary restaurants, there are two curry restaurants and two udon restaurants. This means that there are two permanent curry restaurants, one permanent udon restaurant, one temporary udon restaurant, and a total of two udon restaurants. In other words, the ratio of curry restaurants to udon restaurants is 1:1.

[0120] Next, the store predicted usage information creation unit 15 calculates the potential number of customers at the curry restaurant using the permanent restaurant occupancy building information (history) 321. Specifically, the store predicted usage information creation unit 15 multiplies the potential number of customers (250) by 0.6, which is the ratio of customers to genre 1 (curry), to calculate 150. As a result, the potential number of customers at the udon restaurant is calculated as 100 by subtracting 150 from 250.

[0121] Furthermore, the store predicted usage information creation unit 15 calculates the number of customers that can be accommodated by the curry store and the udon store, respectively, as 60 people x 2 stores, or 120 people. The permanent store occupancy building information (history) 321 can be created based on the past history of the building or by using other data analysis techniques. Furthermore, the permanent store occupancy building information (history) 321 may be stored in the building management database 3 or the storage unit 18 and realized as part of the permanent store occupancy building information 32.

[0122] Next, the number of customers for each store is calculated based on the predicted potential number of customers. Here, the adjusted utilization ratio management table 183 is used. First, the target permanent restaurants and non-permanent restaurants can be expressed as a combination of stores of the same genre. That is, for the curry genre, there are two permanent restaurants and zero non-permanent restaurants. Also, for the udon genre, there is one permanent restaurant and one non-permanent restaurant. Therefore, the predicted store utilization information creation unit 15 extracts records corresponding to these from the adjusted utilization ratio management table 183. That is, a record with two permanent restaurants and zero non-permanent restaurants is extracted as the curry genre, and a record with one permanent restaurant and one non-permanent restaurant is extracted as the udon genre. Examples of these are shown in FIG. 16.

[0123] Then, the predicted store usage information creation unit 15 calculates the number of customers at permanent curry restaurants by multiplying the potential number of customers by the adjusted permanent restaurant customer ratio. In other words, 150 customers, calculated as 150 x 1, is calculated as the number of customers at permanent curry restaurants. Furthermore, the number of customers per permanent curry restaurant is 75 customers. And the number of customers at non-permanent curry restaurants is 0 customers.

[0124] The store predicted usage information creation unit 15 also calculates the number of customers at the permanent udon restaurant by multiplying the potential number of customers by the adjusted permanent restaurant customer ratio. In other words, 80 customers, calculated by multiplying 100 x 0.8, is calculated as the number of customers at the permanent udon restaurant. Note that since there is only one permanent udon restaurant, 80 customers is the number of customers per restaurant. The number of customers at the temporary udon restaurant is 20, which is also the number of customers per temporary restaurant.

[0125] As a result of the above, the predicted store usage information creating unit 15 can determine that the number of customers at the permanent curry restaurants and the permanent udon restaurants exceeds their respective capacity. That is, the number of customers at the two permanent curry restaurants is 150, exceeding the capacity of 120. Also, the number of customers at the one permanent curry restaurant is 80, exceeding the capacity of 60. And the number of customers at the temporary udon restaurant is 20, which is more than the capacity of 60 (step S407).

[0126] Therefore, since there are no temporary curry restaurants, the predicted store usage information creation unit 15 first transfers the excess udon customers (step S408). That is, the predicted store usage information creation unit 15 transfers 20 customers, which is the number of customers of the permanent udon restaurant (80 customers) minus the capacity of the restaurant (60 customers), to the temporary restaurant.

[0127] Furthermore, excess curry sales may be transferred to stores in other categories (step S408). Other categories include stores in different genres and stores in the same genre but with different attribute flags. For this transfer, specific example 2 uses an adjusted utilization ratio management table (transfer) 1831. This adjusted utilization ratio management table (transfer) 1831 is shown in FIG. 17. The adjusted utilization ratio management table (transfer) 1831 indicates, as transfer destinations, different genres, the same genre but with different attribute flags, and the rate of abandoning purchases within the building. The adjusted utilization ratio management table (transfer) 1831 may be configured as part of the adjusted utilization ratio management table 183, or may be configured as a separate table. Note that these items are merely examples and are not limiting. Furthermore, each rate may be set arbitrarily, or may be automatically determined from historical data, etc.

[0128] Specifically, the store predictive usage information creation unit 15 allocates 24 customers, or 60% (0.6), of the 40 excess curry customers, to permanent restaurants of a different genre. Furthermore, the store predictive usage information creation unit 15 transfers 8 customers, or 20% (0.2) of the 40 excess curry customers, to temporary udon restaurants, which have the same genre but a different attribute flag. As a result, the number of customers at the temporary udon restaurants becomes 48. This value is calculated by adding the 20 customers initially allocated and the 20 customers turned back from the permanent udon restaurants, to the 8 customers who are turned back now. Furthermore, the store predictive usage information creation unit 15 determines that the 8 customers, or 20% (0.2) of the 40 excess curry customers, will not make a purchase in the building. However, since the number of customers at the temporary udon restaurant is 48, which is 12 people more than the capacity of 60 people, the store predicted usage information creation unit 15 may further transfer these 8 people to the temporary udon restaurant.

[0129] As a result of the above, the following (A) to (C) are calculated as predicted store usage information. (A) Udon restaurant forecast (number of customers): 60 people (60 people per restaurant) (B) Udon restaurant temporary use information (number of customers): 48 people (48 people per restaurant) (C) Forecasted usage information for permanent curry restaurants (number of customers): 120 people (60 people per restaurant) The above describes the case where the potential number of customers is greater than the number of customers that four stores can handle. Below, we will explain the case where the potential number of customers is less than the number of customers that four stores can handle. Here, we assume that the potential number of customers is 100, which is less than the 240 customers that four stores can handle. Other prerequisites are as described above. First, as in the above example, the store predicted usage information creation unit 15 determines that there is a mixture of genres when the relationship between the genres of permanent restaurants and non-permanent restaurants is the same or different (step S402). It is also determined that there is a mixture of genres among permanent restaurants.

[0130] Furthermore, the store predicted usage information creation unit 15 calculates the usage ratio, the number of customers that can be accommodated for each category, and the potential number of customers based on the number of stores, the categories, and the relationship between the categories, as in the above example (steps S403 to S405). In other words, the ratio of the number of stores selling curry and udon is specified to be 1:1.

[0131] Next, the store predicted usage information creation unit 15 calculates the potential number of customers for the curry restaurant using the information (history) 321 on buildings where permanent restaurants are located. Specifically, the store predicted usage information creation unit 15 multiplies the potential number of customers (100) by the ratio of customers to genre 1 (curry) (0.6) to calculate 60 customers. As a result, the potential number of customers for the udon restaurant is calculated as 40 customers by subtracting 60 customers from 100 customers. Furthermore, as in the above example, the store predicted usage information creation unit 15 calculates the capacity of the curry restaurant and the udon restaurant as 60 customers x 2 restaurants, or 120 customers each.

[0132] Next, based on the predicted potential number of customers, the number of customers for each store is calculated using the adjusted utilization ratio management table 183. That is, a record with 2 permanent stores and 0 temporary stores is extracted as the curry genre, and a record with 1 permanent store and 1 temporary store is extracted as the udon genre. These examples are shown in FIG. 16 as described above.

[0133] Then, the store predicted usage information creation unit 15 calculates the number of customers at permanent curry restaurants by multiplying the potential number of customers by the adjusted permanent restaurant customer ratio. In other words, 60 x 1 = 60 customers is calculated as the number of customers at permanent curry restaurants. Furthermore, the number of customers per permanent curry restaurant is 30 customers. Furthermore, the number of customers at non-permanent curry restaurants is 0 customers.

[0134] The predicted store usage information creation unit 15 also calculates the number of customers at the permanent udon restaurant by multiplying the potential number of customers by the adjusted permanent restaurant customer ratio. In other words, 40 x 0.8 = 32 customers is calculated as the number of customers at the permanent udon restaurant. Note that since there is only one permanent udon restaurant, 32 customers is the number of customers per restaurant. The number of customers at the temporary udon restaurant is 8, which is also the number of customers per temporary restaurant.

[0135] As a result of the above, the predicted store usage information creation unit 15 determines that the number of customers at the permanent curry restaurant and the permanent udon restaurant does not exceed their respective capacity, and therefore determines that no transfer is necessary (step S407). As a result, the following (A) to (C) are calculated as predicted store usage information. (A) Forecasted usage information for permanent udon restaurants (number of customers): 32 (32 per restaurant) (B) Forecasted usage information for temporary udon restaurants (number of customers): 8 (8 per restaurant) (C) Forecasted usage information for permanent curry restaurants (number of customers): 60 people (30 people per restaurant) This concludes the explanation of the specific example 2, but in this example 2 as well, the predicted store usage information may be calculated using (Equation 1).

[0136] <Example 3> In Specific Examples 1 and 2, predicted store usage information is calculated using genre, but in Specific Example 3, attribute flags are also used. The prerequisites including genre in Specific Example 3 are as follows, and compared to Specific Example 2, gender-specific characteristics are added. Permanent restaurants: 2 curry restaurants (1 for men, 1 for women) and 1 udon restaurant (for women) for a total of 3 restaurants Potential temporary restaurant: One udon restaurant (for men) All stores: 3+1=4 stores Each store can accommodate up to 60 people. Capacity of all 4 stores (total): 60 x 4 = 240 people Potential number of people: 250 (more than the 240 people that can be accommodated by the four stores) Male to female potential candidates: 7 to 3 First, as in specific example 2, the store predicted usage information creation unit 15 uses the permanent store occupancy building information (history) 321 to calculate the potential number of customers for the curry store and the udon store to be 150 and 100, respectively.

[0137] The predicted store usage information creation unit 15 then calculates the breakdown of the potential number of customers for curry as follows: 150×0.7=105 customers for men, 150×0.3=45 customers for women. The unit 15 also calculates the breakdown of the potential number of customers for udon as follows: 100×0.7=70 customers for men, 100×0.3=30 customers for women.

[0138] As a result, the store predicted usage information creation unit 15 can determine that the number of customers that a curry restaurant that caters to men can handle is 105 - 60, which is more than 55. Also, the store predicted usage information creation unit 15 can determine that the number of customers that a udon restaurant that caters to men can handle is 70 - 60, which is more than 10 (step S407).

[0139] Therefore, the store predicted usage information creation unit 15 performs the transfer using an adjusted usage ratio management table (transfer) 1832. The adjusted usage ratio management table (transfer) 1832 is shown in FIG. 18. As shown in FIG. 18, in the adjusted usage ratio management table (transfer) 1832, the items for different genres in the adjusted usage ratio management table (transfer) 1831 of specific example 2 are divided into different genres / same attribute flags and different genres / different attribute flags. The adjusted usage ratio management table (transfer) 1832 may also be configured as part of the adjusted usage ratio management table 183, or may be configured as a separate table. Furthermore, it may be configured as an integrated unit with the adjusted usage ratio management table (transfer) 1831.

[0140] The specific details of the transfer process (calculation) are explained below.

[0141] Transfer from a curry restaurant for men to a curry restaurant for women: 55 x 0.6 = 33 people (0.6: same genre / same attribute flag in Adjusted Usage Ratio Management Table (Transfer) 1832) The curry shop for women has a remaining capacity of 5 people, so there are 28 people in excess. Transfer from a curry restaurant for men to an udon restaurant for women: 55 x 0.1 = 5.5 ≒ 5 people (0.1: Different genre / same attribute flags in Adjusted Usage Ratio Management Table (Transfer) 1832) Transfer from a udon restaurant for men to a udon restaurant for women: 20 x 0.6 = 12 people (0.6: same genre / same attribute flag in Adjusted Usage Ratio Management Table (Transfer) 1832) The remaining capacity of the women's udon store is 20 people, so it is possible to transfer all 17 people (5 people + 12 people). However, if it is not possible to move all of them, the transfer can be split equally (for example, half and half) between the stores to which they are transferred.

[0142] The above explains the case where the potential number of customers is greater than the number of customers that can be handled by four stores, but below we will explain the case where the potential number of customers is less than the number of customers that can be handled by four stores. Here, the number of customers that can be handled and the potential number of customers are changed from the above example. Permanent restaurants: 2 curry restaurants (1 for men, 1 for women) and 1 udon restaurant (for women) for a total of 3 restaurants Potential temporary restaurant: One udon restaurant (for men) All stores: 3+1=4 stores Each store can accommodate up to 50 people. Capacity of all 4 stores (total): 50 x 4 = 200 people Potential number of people: 150 (less than the 200 people that can be accommodated by the four stores) Male to female potential candidates: 7 to 3 First, as in the above example, the store predicted usage information creation unit 15 determines that there is a mixture of genres when the genre relationships between permanent restaurants and non-permanent restaurants are the same or different (step S402). Also, it is determined that there is a mixture of genres among permanent restaurants.

[0143] First, as in specific example 2, the store predicted usage information creation unit 15 uses the permanent store occupancy building information (history) 321 to calculate the potential number of customers for the curry store and the udon store to be 90 and 60, respectively.

[0144] The store predictive usage information creation unit 15 then calculates the breakdown of the potential number of customers for curry as follows: 63 customers for men, calculated by 90 x 0.7, and 27 customers for women, calculated by 90 x 0.3. Furthermore, the store predictive usage information creation unit 15 calculates the breakdown of the potential number of customers for udon as follows: 42 customers for men, calculated by 60 x 0.7, and 18 customers for women, calculated by 60 x 0.3. As a result, the store predictive usage information creation unit 15 can determine that the number of customers that can be served at curry restaurants for men exceeds the capacity by 13, calculated as 63 customers - 50 customers. Furthermore, the store predictive usage information creation unit 15 determines that the capacity of other curry restaurants for women, udon restaurants for men, and udon restaurants for women is not exceeded (step S407).

[0145] Therefore, the predicted store usage information creating unit 15 uses the adjusted usage ratio management table (transfer) 1832 to perform transfer for the curry store that caters to men (more than 13 customers). Transfer to a udon restaurant for men: 13 x 0.2 = 2.6 ≒ 2 people (0.2: Different genre / same attribute flag in Adjusted Usage Ratio Management Table (Transfer) 1832, also rounding up is possible) Transfer to a curry restaurant for women: 13 x 0.6 = 7.8 ≒ 7 people 0.6: Same genre / same attribute flag of Adjusted Usage Ratio Management Table (Transfer) 1832, rounding up is also possible) Abandoned purchases in building: 13-2-7=4 people. In this way, the store usage forecast information creating unit 15 counts purchases in building A as abandoned if the transfer destination store cannot accommodate the request.

[0146] Furthermore, in these specific examples, the prediction unit 14 may dynamically create and modify store predicted usage information, predicted usage information, and recommended information depending on the popularity of permanent or temporary restaurants, i.e., the usage volume.

[0147] According to Example 3 and each specific example, it is possible to calculate a more appropriate usage fee rate according to the expected number of temporary and permanent restaurants, thereby improving the accuracy of predictions regarding the impact of opening temporary restaurants. This concludes the explanation of the embodiments, examples, and specific examples of the present invention, but the present invention is not limited to these. For example, facilities include things other than buildings, and their impact areas include plazas and the like outside of building structures. Furthermore, target stores can be in various industries other than restaurants, such as apparel. [Explanation of symbols]

[0148] 1...store management device, 11...communication unit, 12...candidate information identification unit, 13...temporary store information acquisition unit, 14...prediction unit, 15...store predicted usage information creation unit, 16...predicted usage information creation unit, 17...recommended information creation unit, 18...storage unit 18, 181...correlation table, 182...potential definition table, 183...adjusted usage ratio management table, 184...prediction information, 2a, 2b...building management system, 21a, 21b...building management terminal, 22a...building management server, 3...building management database, 31-1...information on permanent stores located in building A, 31-2...information on permanent stores located in building B, 32...information on buildings where permanent stores are located, 4...temporary store database, 41...temporary store information, 5...permanent store terminal, 6...temporary store terminal, 7...network

Claims

1. A store management device that predicts the impact of a new store opening on other stores, The store is a temporary store and the other store is a permanent store, A store management device having a store predicted usage information creation unit that creates permanent store predicted usage information indicating the usage volume of the permanent store if the temporary store opens, based on temporary store information regarding the use of the temporary store in accordance with the conditions for the candidate store and information on the permanent store that will be occupied by the permanent store.

2. 2. The store management device according to claim 1, further comprising an output unit that outputs forecast information including the permanent store forecast usage information.

3. The store management device according to claim 2, The store forecast usage information creation unit is a store management device that allocates the usage quantity of the permanent store based on a correlation table that defines the user ratio between permanent stores and temporary stores, and based on potential, to create the permanent store forecast usage information.

4. The store management device according to claim 3, The correlation table is a store management device that defines the user ratio for each relationship between genres and attributes of permanent stores and temporary stores.

5. The store management device according to claim 4, The store predicted usage information creation unit adjusts the user ratio in the correlation table according to the number of permanent stores and temporary stores, allocates the number of customers using the adjusted user ratio, and creates the permanent store predicted usage information based on the allocated number of customers.

6. The store management device according to claim 1, The store predicted usage information creation unit is a store management device that uses the temporary store information and the permanent store information to create temporary store predicted usage information for the temporary store when the temporary store opens.

7. The store management device according to claim 1, moreover, an input unit that receives a request for prediction of the impact of the store candidate, including the conditions; The store management device includes a candidate information identification unit that identifies candidate temporary store information indicating usage characteristics of the candidate store in response to the prediction request.

8. The store management device according to claim 1, A store management device in which the permanent store is located in a specified facility, and the temporary store is set up within an influence range, which is an area within a specified distance from the facility.

9. The store management device according to claim 6, The store management device further has a recommendation information creation unit that displays proposals regarding store openings based on the permanent store predicted usage information and the temporary store predicted usage information, and creates recommendation information including at least one of the store name of the temporary store proposed to be opened, the type of temporary store proposed to be opened, and the opening date.

10. The store management device according to claim 9, The permanent store forecast usage information includes sales of the corresponding permanent store, and the temporary store forecast usage information includes sales of the corresponding temporary store, The recommendation information creation unit creates the recommendation information based on the total sales of the permanent stores and the temporary stores.

11. The store management device according to claim 10, The recommendation information creation unit creates the recommendation information that suggests opening the temporary store if the permanent store's sales are equal to or greater than a sales threshold as a result of the store opening.

12. The store management device according to claim 1, The store predicted usage information creation unit selects information about permanent stores other than the permanent store based on at least one of the location, size, and tenants of the facility in which the permanent store is located, and creates the permanent store predicted usage information based on the selected information about the permanent stores.

13. The store management device according to claim 9, the information on the permanently-occupied store, the information on the temporary store, the information on the predicted permanent store usage, and the information on the predicted temporary store usage are information for each time period; The recommendation information creation unit is a store management device that creates the recommendation information for each time period.

14. The store management device according to claim 7, The store management device further includes a temporary store information acquisition unit that uses the temporary store candidate information to identify a temporary store that is a candidate for opening, and acquires temporary store information about the temporary store.

15. A store management method in which a store management device predicts the impact of a new store opening on other stores, The store is a temporary store and the other store is a permanent store, A store management method in which the store forecast usage information creation unit of the store management device creates permanent store forecast usage information indicating the usage volume of the permanent store if the temporary store opens, based on temporary store information regarding the use of the temporary store in accordance with the conditions for the candidate store and information on the permanent store that is located in the permanent store.

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