JP2025165439A - Store management device and method - Google Patents

Abstract

[Problem] The problem is to understand the impact of the opening of a temporary store on a facility, particularly on its permanent stores. [Solution] In a store management device 1 that predicts the impact on other stores when a store opens, the store is a temporary store and the other stores are permanent stores, and the store management device 1 has a store predicted usage information creation unit 15 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 candidate store and information on the permanent stores that occupy the permanent store. [Selected Figure] Figure 1

Description

The present invention relates to technology for managing store openings. In particular, it relates to technology for managing the opening of temporary stores in areas of influence, such as facilities where permanent stores are located or their surrounding areas.

Currently, information processing technology is being used to formulate and evaluate store opening plans for franchise stores and other stores, as well as to make decisions about opening new stores. This requires considering various factors related to store openings and understanding their impact before making decisions. For example, Patent Document 1 addresses the issue of "enhancing objective understanding of the contents of sales forecast models so that store opening personnel can appropriately evaluate new store opening plans," 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, data search and aggregation means 3 that searches and aggregates data from statistical database 10, store database 11, and performance database 12 using search criteria, classification means 4 that inputs competing store data and store attributes for classification and hierarchically classifies competing stores, model estimation and evaluation means 5 that estimates an optimal sales forecast model for the classified competing store group and calculates its evaluation value, classification control means 6 that controls the promotion and suspension of competing store classification, and output means 7 that outputs the hierarchical classification of competing stores and the optimal sales forecast model."

Japanese Patent Application Publication No. 10-240799

At this point, facilities that have permanent stores may be tempted to open temporary stores. By opening such temporary stores within the facility's premises or other areas of influence, they can improve convenience for facility users and attract new users. For example, if a building already has permanent restaurants, the opening of temporary restaurants such as food trucks or stalls will increase the options available to employees in the building.

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

In response to 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. As a result, Patent Document 1 is unable to grasp the impact on permanent stores in a facility of the opening of temporary stores in the affected area of the facility. Therefore, the objective of this invention is to grasp the impact on permanent stores in a facility of the opening of temporary stores in the affected area (area) of the facility, etc.

To solve the above problem, the present invention predicts the usage of permanent stores based on usage information from permanent stores currently located 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.

More specifically, in a store management device that predicts the impact on other stores when a new 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 indicating 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 about the permanent store that is located there.

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

This invention makes it possible to understand the impact of opening a temporary store on a facility, particularly the impact on permanent stores.

1 is a system configuration diagram of a store management system according to an embodiment of the present invention. 1 is a hardware configuration diagram of a store management device 1 according to an embodiment of the present invention. FIG. 10 is a diagram showing a correlation table 181 used in one embodiment of the present invention. FIG. 10 is a diagram showing a potential definition table 182 used in one embodiment of the present invention. FIG. 10 is a diagram showing an adjusted utilization ratio management table 183 used in one embodiment of the present invention. FIG. 10 is a diagram illustrating prediction information 184 used in one embodiment of the present invention. FIG. 3 is a diagram showing permanent store information 31 used in one embodiment of the present invention. FIG. 3 is a diagram showing permanent store occupancy building information 32 used in one embodiment of the present invention. 10 is information showing temporary store information 41 used in one embodiment of the present invention. 1 is a flowchart showing a processing flow according to an embodiment of the present invention. 1 is a flowchart showing a processing flow in the first embodiment. 10 is a flowchart illustrating an example of a method for creating predicted store usage information in the first and second embodiments. 10 is a flowchart showing a processing flow in a second embodiment. 11 is a flowchart showing the processing flow of step S403 in the third embodiment. FIG. 10 is a diagram showing permanent store occupancy building information (history) 321 in specific example 2. FIG. 10 is a diagram showing an extracted adjusted utilization ratio management table 183 in specific example 2. FIG. 18 is a diagram showing an adjusted utilization ratio management table (transfer) 1831 used for transfer to other fields in specific example 2. FIG. 18 is a diagram showing an adjusted utilization ratio management table (transfer) 1832 used for transfer to other fields in specific example 3 of embodiment 3.

Embodiments 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 when considering opening a temporary store within a specified distance from a building, including the building's grounds. Here, "within a specified distance" can be expressed in units such as the usual kilometers, or in terms of the number of minutes it takes to walk, or in units of addresses on the same or neighboring street, such as the block. Note that this example includes temporary stores opened by tenants in a building occupied by multiple tenants, operated by the same business. Tenants include various retail businesses such as restaurants, grocery stores, apparel stores, and general stores. Temporary stores include food trucks and food stalls, which are examples of restaurants. Note that the equipment for temporary stores may be provided by the building or the temporary store itself.

Note that "same industry" includes businesses of the same type, such as restaurants, as well as related businesses, such as restaurants and food 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.

<Configuration>
Next, the configuration of this embodiment will be described. Fig. 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.

The store management device 1 also has 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 way, the communication unit 11 is an example of an input unit or 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 conditions for the potential store.

In addition, in response to a prediction request, the candidate information identification unit 12 identifies candidate temporary store information that indicates characteristics related to the use of the candidate temporary store (temporary store). Here, the candidate temporary store information can use, as characteristics, usage quantity (e.g., number of customers and sales), products and services offered (e.g., menu items offered), genre (e.g., representative menu items), attributes, and store opening information (period and area). These will be explained later. Note that identifying candidate temporary store information includes calculating, acquiring, and estimating candidate temporary store information.

The temporary store information acquisition unit 13 also acquires temporary store information corresponding to the temporary store candidate information. That is, it acquires temporary store information for temporary stores that meet the conditions included in the prediction request. To do this, the temporary store information acquisition unit 13 identifies a temporary store that meets the temporary store candidate information. This temporary store is an existing store that can be opened. The temporary store information acquisition unit 13 then acquires temporary store candidate information that indicates the characteristics of the identified temporary store.

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.

Here, predicted store usage information refers to information that indicates 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 that indicates the usage of the permanent store and the temporary store as a whole, created from the predicted permanent store usage information and the predicted temporary store usage information. The number of customers and sales refer to the number of customers (number of people) and amount of money spent per unit period (e.g., one day) or during the opening period of the temporary store. Alternatively, the average customer spending (amount) can also be used.

For example, usage quantities such as the number of customers and sales can be used as predicted usage information. As mentioned above, usage quantities indicate 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 can be used as predicted usage information. The predicted usage quantity may also be a quantity that represents a change in the predicted usage quantity. Furthermore, recommended information is calculated from the store predicted usage information and the predicted usage information and indicates suggested store openings. For example, it may suggest at least one of the recommended temporary store itself, its type, and opening date.

The recommendation information is then 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 meets the threshold value, recommendation information suggesting the opening of a temporary store is created. Also, if the increase, maintenance, or decline of sales at permanent stores due to the opening of a new store is below a threshold value (sales are equal to or greater than the sales threshold value), recommendation information suggesting the opening of a temporary store may be created.

The memory 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.

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, where it is used to manage stores in multiple buildings, and therefore 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.

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

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.

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 also 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 also be stored in the temporary store database 4.

The permanent store terminal 5 is a device used by those involved and managers of the permanent store, and can be implemented by a computer. The permanent store terminal 5 accepts forecast information about the permanent store itself and information about planned openings of 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 implemented by a computer. The temporary store terminal 6 accepts requests to open a temporary store and forecast information about the temporary store itself.

Furthermore, the network 7 can be realized as a wide area network such as the Internet, and connects the above-mentioned devices together. The above-mentioned building management terminal 21a, building management terminal 21b, permanent store terminal 5, and temporary store terminal 6 can be realized as PCs, smartphones, tablets, etc. 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.

Figure 2 is a hardware configuration diagram of the store management device 1 in 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. As shown in Figure 2, the store management device 1 has a processing device 101, a communication device 102, a memory 103, and a secondary storage device 104, which are connected to each other via a communication path.

First, the processing device 101 can be realized by a processor such as a CPU, and performs calculations in accordance with a store management program 105 stored in a secondary storage device 104, which will be described later. The store management program 105 will be described later. Furthermore, the communication device 102 corresponds to the communication unit 11 in Figure 1, connects to the network 7, and communicates with other devices.

The memory 103 and 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, correlation table 181, potential definition table 182, adjusted utilization ratio management table 183, and forecast information 184. The secondary storage device 104 may be implemented as various storage media such as an external HDD (Hard Disk Drive), SSD (Solid State Drive), 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 building information 32 for permanent stores, and the non-permanent store information 41.

Here, the store management program 105 is composed 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 implemented as an individual program or a partial combination.

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
Breakout store information acquisition module 107: Breakout 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.

For this reason, the processing device 101 executes the processing 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 a separate program.

<Information>
Next, each piece of information used in this embodiment will be described. 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 potential temporary stores. Here, FIG. 3 lists the "permanent store" usage ratio, but by subtracting this value from 1, the temporary store usage ratio (proportion of temporary store users, proportion of temporary store sales) of the temporary store can be determined. Therefore, 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.

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 permanent and temporary stores. The correlation table 181 is a table for allocating the expected usage volume from the opening of a temporary store to the permanent and temporary stores. While this embodiment uses the number of customers and sales as the usage volume, the present invention is not limited to these.

For this reason, correlation table 181 has the following fields: permanent store genre/non-permanent store genre, permanent store attribute flag/non-permanent store attribute flag, and permanent store usage ratio. First, the permanent store genre/non-permanent store genre indicates the relationship between the genres of permanent stores and non-permanent 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 representative menu can be used as the genre. Other categories include Japanese cuisine and Western cuisine, and characteristics such as kaiseki, fast food, sashimi, and light flavors can also be used. Furthermore, if an apparel store is used, and clothing is analyzed, categories such as casual and formal can be used. In this embodiment, this relationship is expressed as "same" and "different." In other words, the permanent store genre/non-permanent store genre indicates whether the permanent store and the non-permanent store are the same or different. Note that the permanent store genre/non-permanent store genre may also use "similar," i.e., a relationship in which the permanent store and the non-permanent store are similar.

The permanent store attribute flag/temporary store attribute flag indicates the relationship between the attributes of permanent stores and temporary stores. Here, attributes refer to attributes related to the use of each store. For example, popularity among men and popularity among women can be used as attributes. Attributes can also include the age, occupation, family, and address (store area) of frequent customers. Attributes can also include the season or time of year when the number of users is high. Attribute flags are used to identify attributes, but it is not necessary to use "flags" as long as the attributes or their relationships can be identified.

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.

Next, Figure 4 is a diagram showing the potential definition table 182 used in this embodiment. The potential definition table 182 shows the potential capacity in terms of usage volume at a facility represented by the relevant building. In this embodiment, the potential definition table 182 defines the potential number of people, which indicates the number of potential users, which is an example of usage volume, 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 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.

Next, Figure 5 shows the 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 Figure 3, it is created for each of four combinations (same/different). Figure 5 shows one of these.

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

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 adjusted permanent store sales ratio are merely examples, and other ratios may be used, or some or a combination of these may be used.

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, which will be described later. In this case, it is possible to set usage ratios regardless of genre or attribute, and to omit adjusting usage ratios based on the number of stores.

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

Furthermore, the predicted usage information is information that indicates 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 target temporary store if it 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.

Furthermore, the permanent store predicted usage information indicates the predicted usage volume of the temporary store if the target temporary store opens. This usage volume is calculated using sales and the number of customers, i.e., the number of users. More preferably, in addition to this usage volume, the usage volume before the temporary store opens is also recorded. Note that these are 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 from before the temporary store opens may also be used as the permanent store predicted usage information.

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 store opening, similar to the permanent store predicted usage information. Furthermore, the combined value is an example of information that combines 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 combined information such as this combined value, it is possible to understand the impact of opening a temporary store on the entire building.

Furthermore, the recommended information is information that indicates suggestions and advice regarding store openings derived from predicted usage information. Note that this recommended information may be omitted, or may be created or edited by the user.

Next, Figure 7 is a diagram showing the permanent store information 31 used in this embodiment. The permanent store information 31 in Figure 7 represents the permanent store information 31-1 in Building A and the permanent store information 31-2 in Building B shown in Figures 1 and 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.

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 fields: 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 target building. The genre indicates the genre of the permanent store. The sales indicate the sales of the permanent store. A profit margin field may also be added to the sales field.

The number of customers indicates the number of customers using the relevant permanent store. For users, a field for user information (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 relevant permanent store. More preferably, the number of customers that can be accommodated per unit period (e.g., unit time) is used. It is desirable to use data for the same period for sales, number of customers, and number of customers that can be accommodated. 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 relevant permanent store. In this embodiment, the location of the permanent store can be obtained from the permanent store's occupied building information 32, but the permanent store information 31 may also include a field for the location of each permanent store.

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

Next, Figure 8 is a diagram showing the 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 Figure 8, the permanent store occupancy building information 32 has the following fields: building name, location, size, tenant 1, tenant 2, and tenant 3.

First, the building name identifies the building in question. Furthermore, the location indicates the location (area) of the building in question. The number of employees in the building indicates the number of employees, which is an example of the size of the building in question. Furthermore, tenants 1 to 3 each indicate permanent stores located in the building in question.

Next, Figure 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 may be candidates for opening. In this embodiment, as shown in Figure 9, the information includes the following items: store name, genre, sales, number of customers, number of people that can be accommodated, menu items 1 and 2, attribute flag, available opening date, and available opening area.

First, the store name identifies a temporary store that could be a candidate for opening. Furthermore, the genre and attribute flags are the same items as those in the permanent store information 31 for the relevant temporary store. Furthermore, the available opening date indicates the date and time when the relevant temporary store can open. Furthermore, the available opening area indicates the area (location) in which the relevant temporary store 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.

<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 below. In addition, the following explanation will be given using an example of opening a temporary store in Building A.

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 potential 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).

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 its user. In this way, 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.

Furthermore, in step S2, the candidate information identification unit 12 identifies candidate temporary store information that indicates the characteristics related to the usage of the candidate temporary store in response to the received prediction request. To do this, the candidate information identification unit 12 extracts the conditions included in the prediction request and sets these as characteristics. For example, the combination of usage quantity, genre, attributes, opening date and time, and opening area included in the conditions is set as a characteristic. Note that items not included in the conditions (for example, genre) can all be accepted as characteristics. As a result, the candidate information identification unit 12 identifies candidate temporary store information that includes these characteristics.

Furthermore, 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 also search for information that satisfies the required items and is similar in other items. Note that "all acceptable" can be represented by using NULL as 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, it is possible to acquire temporary store candidate information that identifies existing temporary stores that are candidates for opening from temporary store candidate information that indicates virtual temporary stores.

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. This predicted usage information for permanent stores and predicted usage information for temporary stores can be referred to as predicted store usage information. Furthermore, the prediction information may include predicted usage information represented by the combined value of predicted store usage information and recommendation information indicating proposed store opening details.

First, the creation of predicted store usage information will be explained. The predicted store usage information creation unit 15 of the prediction unit 14 uses the occupied permanent store information 31 indicating usage information for permanent stores in building A and the acquired temporary store information to create predicted permanent store usage information for a candidate temporary store in the event that the candidate temporary store opens. Furthermore, the predicted store usage information creation unit 15 can also use the occupied permanent store information 31 indicating usage information for permanent stores in building A and the acquired temporary store information to create predicted temporary store usage information for the candidate temporary store in the event that the candidate temporary store opens.

Here, we will explain how to create permanent store predicted usage information and temporary store predicted usage information. These each contain predicted usage quantities. To do this, the store predicted usage information creation unit 15 identifies the genre, attribute flag, and capacity of the permanent stores in building A and the temporary stores that are candidates for opening from the occupied 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 stores in building A and the temporary stores that are candidates for opening if they are 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.

In this embodiment, the permanent store information 31 is used, but there may be cases where no permanent store information exists for some permanent stores. In this case, the predicted store usage information creation unit 15 estimates the permanent store information for the relevant permanent store from the permanent store information 31 for other permanent stores. For example, the predicted store usage information creation unit 15 extracts the permanent store information for other permanent stores based on at least one of the location, size, and tenants (permanent stores) of the relevant building. When extracting, buildings similar to the relevant permanent store are extracted from buildings that have similar items to the relevant building.

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.

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 summed value as the predicted usage information. Note that the summed value 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, and the amount of change in usage quantity, etc. Finally, the creation of recommended information will be described. The recommendation information creation unit 17 of the prediction unit 14 creates recommended 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 occupied permanent store information 31 are used to create the recommended information. These can be managed as information for each period. In this case, the recommended information can also be created as recommendation information that indicates suggestions for each period for which each of these pieces of information is managed.

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 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. Note that in this embodiment, the output unit of the store management device 1 may display the forecast information.

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 plans to open a store can be reported to permanent stores. For example, the building management terminal 21a notifies the temporary store terminal 6 of the relevant temporary store of a store opening request. Furthermore, if the response to this store opening request is to accept the main store, the building management terminal 21a may report the store opening to the permanent store terminal 5 of the permanent store located in building A.

As described above, this embodiment makes it possible to grasp the impact of opening a temporary store, allowing for an appropriate judgment on whether or not to open a store. Below, we will explain each example, which is a specific example of this embodiment.

In Example 1, an example will be described in which the target store is a restaurant. In other words, 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 (Figures 1 and 2) and information (Figures 3 to 9) of Example 1 are nearly identical to those of the above-mentioned embodiments, so their explanation will be omitted for the time being and the processing flow will be explained. However, there is information specific to Examples 2 and 3, which will be explained in the relevant sections.

<Processing flow>
11 is a flowchart showing the processing flow in Example 1. 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 prediction request conditions. In this way, Example 1 predicts the impact of a store opening through operations by a user such as the building manager.

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

Furthermore, in step S31, which corresponds to step S3, the temporary store information acquisition unit 13 acquires temporary store restaurant information corresponding to the temporary restaurant candidate information. In other words, to do this, 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.

Furthermore, 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 is described using Figure 12. Note that this creation method can also be applied to Example 2, which will be described later.

Figure 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 building A permanent store information 31-1. Here, it is desirable for the predicted store usage information creation unit 15 to extract information on permanent restaurants located in the building A permanent store information 31-1 according to the conditions included in the store opening request information. For example, the building A permanent store information 31-1 includes information on permanent stores other than restaurants, such as apparel stores, from which restaurant information is extracted.

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 non-permanent restaurants. To do this, 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 non-permanent restaurants from the information on the non-permanent 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 non-permanent restaurants that are candidates for opening are identified.

In addition, in step S403, the store predicted usage information creation unit 15 determines the usage ratio between permanent restaurants in building A and the candidate non-permanent restaurant using the genre and attribute flags of each permanent restaurant and non-permanent restaurant. This usage ratio indicates the ratio of usage volume between the permanent restaurant and the non-permanent restaurant if the candidate non-permanent restaurant opens.

More preferably, the store predicted usage information creation unit 15 uses the correlation table 181 to identify usage ratios 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.

Furthermore, in step S404, the store predicted usage information creation unit 15 calculates the number of diners that can be accommodated by each of the permanent restaurants in building A and the non-permanent restaurants that are candidates for opening. To do this, the store predicted usage information creation unit 15 extracts the number of diners that can be accommodated by the permanent restaurants from the extracted information on the occupying permanent restaurants. Furthermore, the store predicted usage information creation unit 15 extracts the number of diners that can be accommodated by the non-permanent restaurants from the information on the non-permanent restaurants acquired in step S31.

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 Figure 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 Figure 4, the potential number of people corresponding to the identified number of building employees of 1,200 is identified as 500.

Furthermore, 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.

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 customer count to each of the permanent restaurants. This reallocation may be done equally among the permanent restaurants, or it may be done by allocating a skewed number of customers according to the number of customers or sales of the permanent restaurant information 31-1 located in Building A. The number of customers will also be allocated to non-permanent restaurants that are candidates for opening. In the example above, 300 customers would be allocated to the non-permanent restaurants.

Furthermore, in step S407, the store predicted usage information creation unit 15 compares the number of customers allocated to the permanent restaurant with the number of people it can handle. As a result, if the allocated number of customers is large (N), the process proceeds to step S408. On the other hand, if the allocated number of customers is small (Y), the process proceeds to step S409.

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

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

This concludes the explanation of Figure 12, and we will return to Figure 11 and explain step S51 and beyond. 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.

Furthermore, 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. In other words, it determines whether there is any further prediction request information. If there are 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.

Furthermore, in step S43, which corresponds to step S4, the predicted usage information creation unit 16 uses the created permanent store predicted usage information and temporary store predicted usage information to calculate predicted usage information. For example, it calculates the sum of the usage quantities of the permanent store predicted usage information and temporary store predicted usage information. Then, the predicted usage information creation unit 16 records the calculated sum in the prediction information 184.

Furthermore, in step S44, which corresponds to step S4, the recommendation information creation 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. The recommendation information creation unit 17 then records the created recommendation information in the prediction information 184.

The communication unit 11 then notifies the building management terminal 21a of building A of each piece of forecast information that has been created. 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 also 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 the building administrator.

Next, Example 2 will be described using an example where the target store is a restaurant. That is, an example of a facility is a building where a permanent restaurant is located, 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). As with Example 1, the processing flow will be described below.

<Processing flow>
FIG. 13 is a flowchart showing the processing flow in Example 2. 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 request for prediction of temporary restaurants in building A. This is configured to detect activation of the automatic mode. Note that this activation may be based on a time condition. For example, it may be executed periodically.

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

Then, in step S23, which corresponds to step S2, the candidate information identification unit 12 creates candidate non-permanent restaurant information indicating the usage characteristics of the candidate non-permanent restaurant based on the received desired restaurant opening conditions. This can be achieved by processing similar to step S21 in Example 1. Furthermore, in step S24, which corresponds to step S2, the candidate information identification unit 12 creates candidate non-permanent restaurant information indicating the usage characteristics 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.

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

Furthermore, in step S521, which corresponds to step S5, the communication unit 11 notifies the building management terminal 21a of the created forecast information, which is then displayed by the building management terminal 21a. Note that this step may also be executed by displaying it on the display unit of the store management device 1. Then, in step S522, which corresponds to step S5, the communication unit 11 sends a store opening request notification to the temporary store terminal 6. Here, step S522 may be conditional on the recommendation information being positive about opening a store, or on consent to the transmission from the building management terminal 21a.

As described above, according to Example 2, predictions can be made without the hassle of users such as building managers.

Example 3 is an example showing details of an example of step S403 in Examples 1 and 2. In other words, Example 3 performs a usage ratio adjustment process. The processing flow of Example 3 is explained below using Figure 14.

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

Furthermore, in step S4032, the store predicted 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 store predicted 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 Figure 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 in building A and the non-permanent restaurants that are candidates for opening, for each combination of genre/attribute flag. Here, in Example 3, the adjusted usage ratio management table 183 was used, but the predicted store usage information may also be calculated using (Equation 1).

Alternatively, recording in the adjusted utilization ratio management table 183 may be performed using (Equation 1). This concludes the explanation of Example 3. Next, Example 3 will be explained in more detail using specific examples.

<Specific examples>
Here, specific examples 1 to 3 of creating predicted store usage information for Example 3, i.e., predicting customer numbers and sales, are described. First, specific examples 1 to 3 illustrate 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 are 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 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.

<Specific Example 1>
First, the preconditions for the first specific example are as follows.
Permanent restaurants: 3 restaurants with curry as the genre. Temporary restaurants that are candidates for opening: 1 restaurant with curry as the genre. Total number of restaurants: 3 + 1 = 4 restaurants. Capacity per restaurant: 60 people. Capacity (total) for all 4 restaurants: 60 x 4 = 240 people. Potential capacity: 250 people (more than the 240 capacity of all 4 restaurants).
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.

The processing details of Specific Example 1 are 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 genre relationship between the permanent restaurant and the temporary restaurant is the same (step S402). The predicted store usage information creation unit 15 then calculates a usage ratio based on the number of restaurants and the genre relationship (step S403). Here, in Specific Example 1, the adjusted permanent store usage ratio is calculated by adjusting the permanent store usage ratio as this usage ratio.

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

First, we will explain how to calculate the adjusted permanent store usage ratio. From the adjusted usage ratio management table 183, the store predicted usage information creation unit 15 identifies an adjusted permanent store customer ratio of 0.80, which is calculated when the number of permanent stores is 3 and the number of non-permanent stores is 1.

The predicted store usage information creation unit 15 also calculates the number of customers at permanent restaurants by multiplying the potential number of customers by the adjusted ratio of customers at permanent restaurants. In other words, by multiplying 250 x 0.8, 200 customers are calculated as the number of customers at permanent restaurants.

Next, the number of customers that can be accommodated by the three permanent restaurants is calculated as 60 x 3, resulting in 180 customers. 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, calculated as 250 x (1 - 0.8). Therefore, the capacity of the temporary restaurants (60 customers) has a surplus of 10 customers. Therefore, it is estimated that these 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).

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

The above explains the case where the potential number of customers is greater than the number of customers that four stores can handle, but 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 assumptions are as described above. Therefore, the processing up to the calculation of the number of customers at permanent restaurants (step S407) is the same, so we will only explain what happens from here on.

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

Furthermore, in Specific Example 1, the adjusted utilization ratio management table 183 was used, but the store predicted utilization information may also be calculated using the above-mentioned (Equation 1). Furthermore, recording in the adjusted utilization ratio management table 183 may also be performed using (Equation 1). This concludes the explanation of Specific Example 1.

<Specific Example 2>
In Example 1, the genre of each store is the same, but in Example 2, a plurality of genres are mixed. The prerequisites including genres in Example 2 are as follows.
Permanent restaurants: 2 curry restaurants and 1 udon restaurant, for a total of 3 restaurants. Temporary restaurants that are candidates for opening: 1 udon restaurant. Total number of restaurants: 3 + 1 = 4 restaurants. Capacity per restaurant: 60 people. Capacity (total) for all 4 restaurants: 60 x 4 = 240 people. Potential capacity: 250 people (more than the 240 capacity of all 4 restaurants).
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.

The processing details of specific example 2 are 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. The predicted store usage information creation unit 15 also identifies the genre of the non-permanent restaurant that is a candidate for opening as udon (1 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). It is also determined that there is a mixture of genres among the permanent restaurants.

The store predicted usage information creation unit 15 also 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 genres (steps S403 to S405). In this case, the permanent store occupancy building information (history) 321 shown in Figure 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. Calculation of the usage ratio is explained below.

First, the predicted store usage information creation unit 15 determines that, among the permanent 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, and one temporary udon restaurant, for a total of two udon restaurants. In other words, the ratio of curry to udon restaurants is 1:1.

Next, the predicted store 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 predicted store usage information creation unit 15 multiplies the potential number of customers (250) by 0.6, which is the customer ratio for genre 1 (curry), to calculate 150 customers. As a result, the potential number of customers at the udon restaurant is calculated as 100 customers by subtracting 150 customers from 250 customers.

The store predicted usage information creation unit 15 also calculates that the number of customers that can be accommodated by the curry store and the udon store is 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 in question, 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 memory unit 18, and implemented as part of the permanent store occupancy building information 32.

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 and temporary restaurants can be expressed as a combination of stores in the same genre. That is, for the curry genre, there are two permanent restaurants and zero temporary restaurants. Also, for the udon genre, there is one permanent restaurant and one temporary restaurant. Therefore, the predicted store utilization information creation unit 15 extracts records that correspond to these from the adjusted utilization ratio management table 183. That is, a record with two permanent restaurants and zero temporary restaurants is extracted as the curry genre, and a record with one permanent restaurant and one temporary restaurant is extracted as the udon genre. Examples of these are shown in Figure 16.

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 people, 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 people. And the number of customers at non-permanent curry restaurants is 0 people.

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, 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.

As a result of the above, the store predicted usage information creation unit 15 can determine that the number of customers at the permanent curry restaurants and the permanent udon restaurants exceeds their respective capacity. In other words, the number of customers at the two permanent curry restaurants is 150, exceeding the capacity of 120. Furthermore, the number of customers at 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).

Since there are no temporary curry restaurants, the predicted store usage information creation unit 15 first transfers the excess udon customers (step S408). In other words, the predicted store usage information creation unit 15 transfers 20 customers, calculated by subtracting the 60 customers that can be accommodated from the 80 customers that are customers of the permanent udon restaurant, to the temporary restaurant.

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

Specifically, the store predicted usage information creation unit 15 allocates 24 people, or 60% (0.6), of the 40 excess curry customers, to permanent restaurants of a different genre. Furthermore, the store predicted usage information creation unit 15 transfers 8 people, 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 is the sum of the initially allocated number of customers (20) and the number of customers turned back from the permanent udon restaurants (20), plus the 8 people turned back now. Furthermore, the store predicted usage information creation unit 15 determines that 8 people, or 20% (0.2) of the 40 excess curry customers, will not make a purchase in the building. However, since the number of customers using the temporary udon restaurant is 48, which is a surplus of 12 people compared to the capacity of 60 people, the predicted store usage information creation unit 15 may further transfer these 8 people to the temporary udon restaurant.

As a result of the above, the following (A) to (C) are calculated as predicted store usage information.
(A) Forecasted usage information for permanent udon restaurants (number of customers): 60 people (60 people per restaurant)
(B) Predicted usage information for temporary udon restaurants (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 a case where the potential number of customers is greater than the number of customers that four stores can handle. Below, we will explain a 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. Note that 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 permanent restaurants and non-permanent restaurants when the relationship between the genres is the same or different (step S402). Furthermore, it is determined that there is a mixture of multiple genres among permanent restaurants.

Furthermore, as in the above example, 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 genres (steps S403 to S405). In other words, the ratio of the number of curry and udon stores is determined to be 1:1.

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

Next, based on the predicted potential number of customers, the adjusted utilization ratio management table 183 is used to calculate the number of customers for each store. In other words, records with 2 permanent stores and 0 temporary stores are extracted as the curry genre, and records with 1 permanent store and 1 temporary store are extracted as the udon genre. Examples of these are shown in Figure 16, as described above.

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, 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.

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 there is only one permanent udon restaurant, so 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.

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 (number of customers) for permanent udon restaurants: 32 people (32 people per restaurant) (B) Forecasted usage information (number of customers) for temporary udon restaurants: 8 people (8 people 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 second specific example, but in this second example as well, the predicted store usage information may be calculated using (Equation 1).

<Specific 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: Two curry restaurants (one for men, one for women), one udon restaurant (for women), for a total of three restaurants. Potential temporary restaurants: One udon restaurant (for men).
Total number of stores: 3 + 1 = 4 stores. Capacity per store: 60 people. Capacity for all 4 stores (total): 60 x 4 = 240 people. Potential capacity: 250 people (more than the 240 people that can be accommodated by all 4 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 restaurant and the udon restaurant to be 150 and 100, respectively.

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

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

The store predicted usage information creation unit 15 therefore performs the transfer using the adjusted usage ratio management table (transfer) 1832. The adjusted usage ratio management table (transfer) 1832 is shown in Figure 18. As shown in Figure 18, the adjusted usage ratio management table (transfer) 1832 divides the different genre items in the adjusted usage ratio management table (transfer) 1831 of specific example 2 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 as a separate table. Furthermore, it may be configured as an integrated unit with the adjusted usage ratio management table (transfer) 1831.

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

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 flag 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)
Since the remaining capacity of the women's udon store is 20 people, it is possible to transfer all 17 people (5 people + 12 people). However, if it is not possible to transfer all of them, the transfer may be split equally (for example, half and half) between the transfer stores.

The above describes a case where the potential number of customers is greater than the number of customers that can be handled by four stores. Below, we will explain a 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: Two curry restaurants (one for men, one for women), one udon restaurant (for women), for a total of three restaurants. Potential temporary restaurants: One udon restaurant (for men).
Total number of stores: 3 + 1 = 4 stores. Capacity per store: 50 people. Capacity for all 4 stores (total): 50 x 4 = 200 people. Potential capacity: 150 people (less than the 200 people that can be accommodated by all 4 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).

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.

Then, the store predictive usage information creation unit 15 calculates the breakdown of the potential number of customers for curry as follows: 63 customers for men, calculated as 90 x 0.7, and 27 customers for women, calculated as 90 x 0.3. Furthermore, the breakdown of the potential number of customers for udon is calculated as follows: 42 customers for men, calculated as 60 x 0.7, and 18 customers for women, calculated as 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 is not exceeded at other curry restaurants for women, udon restaurants for men, and udon restaurants for women (step S407).

Therefore, the store predicted usage information creation unit 15 uses the adjusted usage ratio management table (transfer) 1832 to perform a transfer for the curry store that caters to men (more than 13 people).
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 in Adjusted Usage Ratio Management Table (Transfer) 1832, rounding up is also possible)
Abandoned purchase within building: 13-2-7=4 people. In this way, the predicted store usage information creation unit 15 counts the abandonment of purchase within building A if the transfer destination store cannot accommodate the request.

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

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, 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 areas other than buildings, and their impact areas include plazas and the like outside of building structures. Furthermore, the target stores can be in various industries other than restaurants, such as apparel.

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...Recommendation information creation unit, 18...Storage unit, 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 (15)

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