Store data processing device, store data processing method, and program

Abstract

A shop data processing device (10) is provided with a sales data acquisition unit (110), a customer data acquisition unit (120), and a relevance data generation unit (130). The sales data acquisition unit (110) acquires sales data and disposal data. The sales data indicate, for each unit period, the sold number and / or the prepared number of commodities at a first shop. The disposal data indicate, for each unit period, the discarded number of commodities. The customer data acquisition unit (120) acquires customer data. The customer data indicate, for each unit period, the number of customers at the first shop and / or the traffic volume in a first area. The first area is a region that includes the first shop. The relevance data generation unit (130) generates relevance data indicating relevance among the sales data, the disposal data, and the customer data, and displays the relevance data on a display (150).

Description

[Technical field]

[0001] The present invention relates to a store data processing device, a store data processing method, and a program. [Background technology]

[0002] Many products sold in stores have a best-before date or expiration date. If the number of such products purchased or the number of products cooked in the store is too high, the amount of products that are wasted will increase. On the other hand, if these numbers are too low, sales opportunities will be lost. For this reason, in order to improve the management of a store, it is desirable to optimize the number of products purchased and the number of products cooked in the store.

[0003] In response to such demands, Patent Document 1 discloses a prediction model that reduces opportunity loss. This prediction model calculates a prediction value for product demand using demand information indicating the past sales volume of the product in the store and external information related to the sales volume. The external information includes, for example, information on the time or time period, day of the week or holiday, temperature, weather, and number of customers.

[0004] In addition, the system described in Patent Document 2 calculates the expected profit for each unit of each product up to a specific number of units based on sales performance data for the most recent past period, sorts the calculation results for all products in order of highest expected profit, and calculates the optimal ordering pattern based on the sorting results. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2019 / 159772 [Patent Document 2] JP 2002-297958 A Summary of the Invention [Problem to be solved by the invention]

[0006] In many cases, the store clerk decides the number of products to be purchased and the number to be cooked in the store. Therefore, it is important to make the store clerk aware of the appropriateness of the number of products to be purchased and the number to be cooked in the store. One example of the objective of the present invention is to make it easier for the store clerk to recognize the appropriateness of the number of products to be purchased and the number to be cooked in the store. [Means for solving the problem]

[0007] According to one aspect of the present invention, a sales data acquisition means acquires sales data indicating at least one of the number of products sold and the number of products prepared in a first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition means for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generating means for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; A store data processing device is provided.

[0008] According to one aspect of the present invention, there is provided an estimated data acquisition means for acquiring estimated data including at least one of an estimated number of customers of a first store and an estimated traffic volume of a first area including the first store in a first period in the future; a performance data acquisition means for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity cooked at the second store, during a second period in the past having the same attributes as the first period; a recommended number specifying means for specifying a recommended number indicating the number of the products to be prepared in the first store in the first period, using the expected data and the actual data; A store data processing device is provided.

[0009] According to one aspect of the present invention, a computer includes: a sales data acquisition process for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition process for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generation process for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; A store data processing method is provided that performs the above.

[0010] According to one aspect of the present invention, a computer includes: an expected data acquisition process for acquiring expected data including at least one of an expected number of customers at a first store and an expected traffic volume in a first area including the first store, in a first period in the future; a performance data acquisition process for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity cooked at the second store, during a second period in the past having the same attributes as the first period; a recommended number determination process for determining a recommended number indicating the number of the products to be prepared in the first store in the first period by using the expected data and the actual data; A store data processing method is provided that performs the above.

[0011] According to one aspect of the present invention, a computer includes: a sales data acquisition function for acquiring sales data indicating at least one of the number of products sold and the number of products in stock at the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition function that acquires customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating function for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; A program will be provided to help students develop these skills.

[0012] According to one aspect of the present invention, a computer includes: an expected data acquisition function for acquiring expected data including at least one of an expected number of customers at a first store and an expected traffic volume in a first area including the first store in a first period in the future; a performance data acquisition function for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity to be cooked at the second store, during a second period that is in the past and has the same attributes as the first period; a recommended number determination function that determines a recommended number indicating the number of the products to be prepared in the first store in the first period by using the expected data and the actual data; A program will be provided to help students develop these skills. Effect of the Invention

[0013] According to one aspect of the present invention, store staff can easily recognize the appropriateness of the number of items purchased and the number of items to be cooked in the store. [Brief description of the drawings]

[0014] The above objects, as well as other objects, features and advantages, will become more apparent from the following preferred embodiments and the accompanying drawings.

[0015] [Figure 1] 1 is a diagram illustrating an example of a functional configuration of a store data processing device according to a first embodiment. [Diagram 2] FIG. 11 is a diagram showing an example of a screen that a relationship data generating unit causes to be displayed on a display; [Diagram 3] FIG. 13 is a diagram for explaining a method for selecting a plurality of similar stores. [Figure 4]FIG. 13 is a diagram for explaining an example of location conditions. [Diagram 5] 3 is a diagram showing a modified example of the data display field shown in FIG. 2. [Figure 6] FIG. 11 is a diagram showing an example of a method for selecting a second store. [Figure 7] FIG. 11 is a diagram showing an example of a method for selecting a second store. [Figure 8] FIG. 2 is a diagram illustrating an example of a hardware configuration of a store data processing device. [Figure 9] 10 is a flowchart showing an example of processing performed by a store data processing device. [Figure 10] FIG. 11 is a diagram illustrating an example of a functional configuration of a store data processing device according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, the same components are given the same reference numerals and the description will be omitted as appropriate.

[0017] (First embodiment) 1 is a diagram showing an example of the functional configuration of a store data processing device 10 according to this embodiment. The store data processing device 10 causes a display 140 to display at least one of the number of products sold and the number of products prepared in a target store (hereinafter referred to as a first store), such as food products, and the number of products to be discarded. At this time, the store data processing device 10 also displays the number of customers at the first store or the traffic volume around the first store.

[0018] Here, the sales number may be any information that can identify the number of products sold. For example, the sales number may be the number of products sold itself, or the value of the products sold (i.e., the sales amount). Another example of the prepared number is the purchase quantity. However, if the target product is food that is prepared in the store, the prepared number is the number of dishes prepared of that product. The prepared number may be any information that can identify the number of products prepared. For example, the prepared number may be the number of products prepared itself, or the value of the products prepared. The discarded number may be any information that can identify the number of products discarded. For example, the discarded number may be the number of products discarded itself, or the discarded value of the products discarded.

[0019] The store data processing device 10 includes a sales data acquisition unit 110, a customer data acquisition unit 120, and an association data generation unit 130. The store data processing device 10 can also use information stored in a storage unit 150. The storage unit 150 may be a part of the store data processing device 10, or may be located outside the store data processing device 10.

[0020] The sales data acquisition unit 110 acquires sales data and disposal data. The sales data indicates at least one of the number of products sold and the number of products prepared in the first store by unit period. The disposal data indicates the number of products to be disposed of by unit period. The unit period may be, for example, quarterly, monthly, weekly, or daily. When the unit period is daily, the sales data and disposal data may further indicate the respective numbers by time period. The sales data and disposal data include the respective numbers of a predetermined period (hereinafter, referred to as a target period) that is longer than the above-mentioned unit period. This target period includes a plurality of the above-mentioned unit periods. When the unit period is quarterly or monthly, an example of the target period is one year. When the unit period is weekly, an example of the target period is a month or a quarter. When the unit period is daily, an example of the target period is a week or a month. The sales data and disposal data are stored in the storage unit 150.

[0021] The sales data acquisition unit 110 also acquires information designating the products that are the subject of the sales data and disposal data (hereinafter, product designation information) from the store clerk. In other words, the sales data acquisition unit 110 also serves as a product designation acquisition unit that acquires the product designation information. The sales data acquisition unit 110 further acquires information designating the above-mentioned predetermined period (hereinafter, described as period designation information) from the store clerk.

[0022] The customer data acquisition unit 120 acquires customer data. The customer data indicates at least one of the number of customers at the first store and the traffic volume in the first area for each unit period. The first area is an area including the first store, and may be, for example, No. 1 This is an area where the required travel time from the store is within a predetermined time (for example, within 10 minutes). The customer data is stored in the storage unit 150. The unit period used here is the same as the unit period used in the sales data and disposal data. The customer data includes the numbers for each of the target periods described above.

[0023] The association data generating unit 130 generates association data. The association data is data for allowing a store clerk to recognize the associations between sales data, disposal data, and customer data during a target period, and indicates, for example, the associations between these data. The association data generating unit 130 then causes the display 140 to display the association data.

[0024] 2 is a diagram showing an example of a screen that the association data generating unit 130 causes the display 140 to display. This screen has a period selection field 202, a product selection field 204, and a data display field 210.

[0025] The period selection field 202 is a field for the store clerk to input period designation information. In the example shown in the figure, the period selection field 202 allows the selection of the start date and end date of the target period.

[0026] The product selection field 204 is a field for the store clerk to select a product for which sales data and disposal data are to be checked. Multiple products can be selected in the product selection field 204. The sales data acquisition unit 110 then acquires information indicating the product selected in the product selection field 204 as product designation information.

[0027] The data display field 210 is a field where the association data is displayed. In the example shown in the figure, the association data shows trends in at least one of the number of customers and traffic volume, at least one of the number of sales and the number of preparations, and the number of discards. More specifically, the association data includes a graph that simultaneously shows the number of sales (sales amount in the example of the figure), the number of discards (discard amount in the example of the figure), and the number of customers. In this graph, the horizontal axis is the unit period. In other words, this graph shows each number by unit period. The number of sales and the number of discards are shown as bar graphs, and the number of customers is shown as a line graph. However, the style of the graph is not limited to this example.

[0028] In the example shown in this figure, the unit period is days. The horizontal axis of the graph represents days of the week. In other words, this graph allows us to recognize the relationship between at least one of the number of customers and traffic volume, at least one of the number of sales and number of preparations, and the number of discards by day of the week.

[0029] By looking at the data display column 210, the salesperson can recognize the attributes (e.g., day of the week or time period) of a unit period in which the number of customers is high but sales are low. In such a unit period, if the number of products prepared is increased, the sales amount of the product may also increase. For this reason, the salesperson considers increasing the number of products prepared for a unit period having the same attributes as the unit period.

[0030] Furthermore, by looking at the data display column 210, the store clerk can recognize the attributes (e.g., day of the week or time period) of a unit period with a low number of customers and a high number of discards. In such a unit period, even if the number of products prepared is reduced, the sales amount of the product may not decrease. For this reason, the store clerk considers reducing the number of products prepared for a unit period that has the same attributes as the unit period.

[0031] In the data display field 210, the association data generating unit 130 displays a unit period (hereinafter, referred to as a specific period) that satisfies a predetermined condition in a manner similar to that of the other data. Single It is preferable to display the specific period in a different manner from the other periods. unit The specific period may be indicated with a different pattern or color from the period, or may be given a specific mark, frame, or lettering, which makes it easier for store clerks to recognize the specific period.

[0032] An example of a predetermined condition is "a change in the number of preparations is necessary." The change here may be an increase or a decrease.

[0033] For example, the predetermined condition is at least one of the following: 1) The ratio of at least one of the number of customers or traffic volume to at least one of the number of sales or number of preparations must meet the criteria. 2) Disposal data meets standards

[0034] The above 1) means, for example, that this ratio falls outside a reference range. This reference range is determined, for example, using the performance values ​​of multiple stores (hereinafter, referred to as similar stores) whose conditions are similar to those of the first store. Specific examples of "similar conditions" will be described later.

[0035] The above-mentioned 2) means, for example, that the discarded data is equal to or greater than a reference value. This reference value is also determined, for example, using the performance values ​​of a number of similar stores.

[0036] The store data processing device 10 may be installed in the first store, or in a management center that manages multiple stores. In the latter case, the display 140 is connected to a terminal installed in the first store. Then, the association data generation unit 130 transmits the association data to the store terminal.

[0037] FIG. 3 is a diagram for explaining a method for selecting similar stores from a plurality of stores. The plurality of stores are clustered in advance using a plurality of conditions. The population for this clustering is, for example, stores with similar location conditions within the local government (prefecture or city, ward, town, or village) to which the first store belongs. Then, each of the plurality of stores that belong to the same cluster as the first store is selected as a similar store. Information for identifying this population is stored in the storage unit 150.

[0038] In the example shown in the figure, the conditions used for clustering are related to sales trends. One example of the conditions related to sales trends is the number of customers and the sales amount. The sales amount may be the sales amount of the store or the sales amount of the product indicated by the product designation information.

[0039] FIG. 4 is a diagram for explaining an example of location conditions. Location conditions are first classified according to whether or not the store is located on the roadside, for example, whether or not it faces a main road. Stores that are not located on the roadside are further classified using the working population ratio. An example of the working population ratio is "working population around the store" / "(number of households around the store) + (working population around the store)". Note that "around the store" means being within a certain range from the store, for example, within a certain hour on foot from the store. In the example shown in this figure, stores that are not located on the roadside are classified into one of three groups: residential locations, mixed locations, and business locations.

[0040] Fig. 5 is a diagram showing a modified example of the data display field 210 shown in Fig. 2. In the example shown in this figure, the association data generating unit 130 causes the data display field 210 to further display information capable of identifying the recommended number of preparations in a specific period.

[0041] In the example shown in the figure, the association data generating unit 130 calculates the number of recommendations for each of the multiple unit periods included in the graph. The method of calculating this number of recommendations will be described later. The association data generating unit 130 then displays this number of recommendations on the graph. In the example shown in the figure, the number of recommendations is shown in the same manner as the number of sales and the number of disposals, for example, as a bar graph.

[0042] Furthermore, the association data generating unit 130 displays information indicating the difference between the recommended number and the prepared number in the graph for a unit period in which the difference is equal to or greater than a reference value (hereinafter, referred to as difference information). In the example shown in the figure, the difference information is displayed as a bar that fills the difference on top of the bar graph of the smaller of the prepared number and the recommended number. Furthermore, the association data generating unit 130 displays the difference in different ways depending on whether the prepared number is smaller than the recommended number or the prepared number is larger than the recommended number. The way in which this is displayed includes at least one of a color, a pattern, and an outline.

[0043] Here, the association data generating unit 130 sets the recommended number using the performance values ​​of a plurality of stores (hereinafter, referred to as the second stores) that have high sales amounts and low waste amounts among stores that belong to the same cluster as the first store (i.e., the similar stores described above). Specifically, the association data generating unit 130 calculates the recommended number by multiple regression analysis using the performance values ​​of the second store. vinegar In this multiple regression analysis, the objective variable is the number of sales (for example, the number of sales by time period). Examples of explanatory variables are attributes of a unit period (for example, the day of the week), the number of customers, and the traffic volume in the area to which the store belongs. The data used in this multiple regression analysis is stored in the storage unit 150.

[0044] When the multiple regression analysis described above is used, expected data including at least one of the number of customers at the first store during the unit period and the traffic volume in the first area is required. The association data generating unit 130 calculates this expected data using the past records of the number of customers and the traffic volume in the first area. The records used here are records for a period having the same attributes as the unit period (for example, the same day of the week). The second store may be one or more.

[0045] 6 and 7 are diagrams showing an example of a method for selecting a second store. FIG. 6 shows the result of plotting a number of stores belonging to the same cluster as the first store (i.e., a number of similar stores) on a graph with two axes of sales amount and disposal amount. The sales amount and disposal amount relate to the product indicated by the product designation information. The association data generation unit 130 selects, from among the number of similar stores, a number of stores having a sales amount higher than that of the first store and a disposal amount lower than that of the first store as candidates for the second store.

[0046] The association data generating unit 130 then selects, from the selected candidates, a store whose sales trends by time period are similar to that of the first store as the second store, as shown in Fig. 7. As an example, the association data generating unit 130 determines that the similarity is high when the time periods with the highest sales volume during the day and at night are the same. The association data generating unit 130 then selects the store with the highest similarity as the second store. The number of stores selected here may be one or more.

[0047] In the example shown in FIG. 2 or FIG. 5, the association data generation unit 130 may display the same association data (e.g., a graph) for the second store as for the first store in the data display field 210 at the same time as the association data for the first store. In this case, the sales data acquisition unit 110 and the customer data acquisition unit 120 perform the same processing for the second store as for the first store. In the example shown in FIG. 5, the graph for the second store may not include information that can identify the recommended number. When the data display field 210 displays in this manner, the sales staff of the first store can easily recognize the difference between the first store and the second store.

[0048] 8 is a diagram showing an example of the hardware configuration of the store data processing device 10. The store data processing device 10 includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, an input / output interface 1050, and a network interface 1060.

[0049] The bus 1010 is a data transmission path for transmitting and receiving data among the processor 1020, memory 1030, storage device 1040, input / output interface 1050, and network interface 1060. However, the method of connecting the processor 1020 and the like to each other is not limited to bus connection.

[0050] The processor 1020 is implemented by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like.

[0051] The memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.

[0052] The storage device 1040 is an auxiliary storage device realized by a hard disk drive (HDD), a solid state drive (SSD), a memory card, a read only memory (ROM), or the like. The storage device 1040 stores program modules that realize each function of the store data processing device 10 (for example, the sales data acquisition unit 110, the customer data acquisition unit 120, and the association data generation unit 130). The processor 1020 loads each of these program modules into the memory 1030 and executes them, thereby realizing each function corresponding to the program module. The storage device 1040 also functions as a memory unit 150.

[0053] The input / output interface 1050 is an interface for connecting the store data processing device 10 to various input / output devices. For example, the store data processing device 10 communicates with the display 140 via the input / output interface 1050.

[0054] The network interface 1060 is an interface for connecting the store data processing device 10 to a network. This network is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network). The method for connecting the network interface 1060 to the network may be a wireless connection or a wired connection.

[0055] 9 is a flowchart showing an example of processing performed by the store data processing device 10. First, the store data processing device 10 displays an initial screen. This screen includes, for example, the period selection field 202 and the product selection field 204 shown in FIG. 2. A salesperson at the first store inputs product designation information and period designation information to the store data processing device 10 via the period selection field 202 and the product selection field 204 (step S10).

[0056] The sales data acquisition unit 110 acquires sales data and disposal data of the product corresponding to the product designation information from the storage unit 150. The acquired sales data and disposal data are data for the period indicated by the period designation information. The customer data acquisition unit 120 acquires customer data for the period indicated by the period designation information from the storage unit 150 (step S20).

[0057] Next, the association data generation unit 130 generates association data using the sales data, disposal data, and customer data acquired in step S20 (step S30), and displays this association data on the display 140 (step S40). The salesperson of the first store recognizes the association data by looking at the display 140.

[0058] As described above, according to this embodiment, the store data processing device 10 generates association data indicating the association between the sales data, waste data, and customer data of the first store, and displays the association data on the display 140. Therefore, the staff of the first store can easily recognize the appropriateness of the number of products prepared, for example, the number of items purchased or the number of items to be cooked.

[0059] 5, the association data generating unit 130 also displays the recommended number of products to be prepared on the display 140. This makes it easier for the salesperson at the first store to recognize the appropriateness of the number of products to be prepared. Moreover, by looking at the display 140, the salesperson can easily recognize whether or not there is a need to change the number of products to be prepared.

[0060] Second embodiment Fig. 10 is a diagram showing an example of the functional configuration of the store data processing device 30 according to this embodiment. The store data processing device 30 shown in this figure calculates the recommendation number described with reference to Fig. 5 of the first embodiment, but does not generate association data.

[0061] Specifically, the store data processing device 30 includes an expected data acquisition unit 310, a performance data acquisition unit 320, and a recommended quantity specification unit 330.

[0062] The assumed data acquisition unit 310 acquires data including at least one of the assumed number of customers at the first store and the assumed traffic volume in the first area in a unit period (hereinafter referred to as the first period) for which the number of recommendations is to be calculated. This data is the same as the assumed data described with reference to FIG. 5 in the first embodiment. Since the first period is in the future, there are no actual values ​​for the number of customers and the traffic volume. Thus, the assumed data acquisition unit 310 calculates the assumed data using at least one actual value of the number of customers and the traffic volume in the first area in the same way as the association data generation unit 130 in the first embodiment. The actual values ​​used here are those for a period having the same attributes as the first period.

[0063] The performance data acquisition unit 320 acquires performance data of the second store for a second period that is in the past and has the same attributes (for example, at least one of the days of the week and the time period) as the first period.

[0064] The second store is as described in the first embodiment with reference to Fig. 5. That is, the performance data acquisition unit 320 can use the results of clustering a plurality of stores. An example of this clustering is as described with reference to Fig. 3. Then, the performance data acquisition unit 320 selects at least a part of the stores that belong to the same cluster as the first store as the second store.

[0065] The performance data includes at least one of the performance number of customers and the performance traffic volume in the second area including the second store, and at least one of the sales number and the preparation number of the product cooked in the second store. Note that a specific example of the performance data acquired by the performance data acquisition unit 320 is the same as the data used for the multiple regression analysis shown in Fig. 5, and is stored in the storage unit 350. Note that the storage unit 350 also stores the same data as the storage unit 150 shown in the first embodiment.

[0066] The recommended number identification unit 330 identifies a recommended number indicating the number of products to be prepared in the first store in the first time period, using the expected data and the actual data. The process performed by the recommended number identification unit 330 is similar to the process performed by the association data generation unit 130 described with reference to FIG. 5. The recommended number identification unit 330 then displays the identified recommended number on the display 340. The display performed here is, for example, the data display field 210 shown in FIG. 5, excluding at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards.

[0067] The store data processing device 30 further includes a product designation acquisition unit 360 that acquires product designation information. The product designation acquisition unit 360 acquires the product designation information, for example, via the product selection field 204 shown in Fig. 2. The expected data acquisition unit 310, the performance data acquisition unit 320, and the recommended number determination unit 330 then perform the above-mentioned processes on the products indicated by the product designation information acquired by the product designation acquisition unit 360.

[0068] According to this embodiment, the recommended number specification unit 330 also displays the recommended number of products to be prepared. 3Therefore, the salesperson at the first store can easily recognize the validity of the actual value of the number of products prepared.

[0069] Although the embodiments of the present invention have been described above with reference to the drawings, these are merely examples of the present invention, and various configurations other than those described above can also be adopted.

[0070] In addition, in the flowcharts used in the above description, multiple steps (processes) are described in order, but the order of execution of the steps performed in each embodiment is not limited to the order described. In each embodiment, the order of the steps shown in the figures can be changed to the extent that the content is not affected. In addition, the above-mentioned embodiments can be combined to the extent that the content is not contradictory.

[0071] A part or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes. 1. A sales data acquisition means for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition means for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generating means for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; A store data processing device comprising: 2. In the store data processing device according to the above item 1, A store data processing device, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded. 3. In the store data processing device according to 1 or 2 above, The unit period is in days, A store data processing device, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded. 4. In the store data processing device according to any one of 1 to 3 above, The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The store data processing device, wherein the association data generating means displays a specific period, which is the unit period that satisfies a predetermined condition, in a manner different from other unit periods. 5. In the store data processing device according to 4 above, The predetermined conditions include at least one of the following: 1) The ratio of at least one of the number of customers and the traffic volume to at least one of the number of sales and the number of preparations meets the criteria. 2) The discarded data meets the criteria. 3) It is determined that it would be better to change the number of preparations. 6. In the store data processing device according to the above item 5, The store data processing device, wherein the association data generation means includes, in the association data, information capable of identifying the recommended number of preparations for the specific period. 7. In the store data processing device according to any one of 1 to 5 above, the sales data acquisition means acquires the sales data and the disposal data of a second store different from the first store, The customer data acquisition means acquires the customer data of the second store, The store data processing device, wherein the association data generation means generates the association data for the second store and causes the association data to be displayed on the display simultaneously with the association data for the first store. 8. An estimated data acquisition means for acquiring estimated data including at least one of an estimated number of customers at a first store and an estimated traffic volume in a first area including the first store in a first period in the future; a performance data acquisition means for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity cooked at the second store, during a second period in the past having the same attributes as the first period; a recommended number specifying means for specifying a recommended number indicating the number of the products to be prepared in the first store in the first period, using the expected data and the actual data; A store data processing device comprising: 9. In the store data processing device according to the above item 8, The store data processing device, wherein the attributes include at least one of a day of the week and a time period. 10. In the store data processing device according to the above 8 or 9, The assumed data acquisition means generates the assumed data using at least one of an actual value of the number of customers at the first store and an actual value of the traffic volume in the first area. 11. In the store data processing device according to any one of claims 8 to 10, The performance data acquisition means includes: The results of clustering multiple stores are available. a second store selection means for selecting, as the second store, at least a part of the stores that belong to the same cluster as the first store; A store data processing device comprising: 12. In the store data processing device according to any one of claims 1 to 11, The store data processing device further comprises a product designation acquisition means for acquiring product designation information for designating the product. 13. The computer: a sales data acquisition process for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition process for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating process for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; A store data processing method. 14. In the store data processing method according to 13 above, A store data processing method, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded. 15. In the store data processing method according to 13 or 14 above, The unit period is in days, A store data processing method, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded. 16. In the store data processing method according to any one of claims 13 to 15, The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The store data processing method, wherein the computer, in the association data generation process, displays a specific period, which is the unit period that satisfies a predetermined condition, in a form different from other unit periods. 17. In the store data processing method according to claim 16, A store data processing method, wherein the predetermined conditions include at least one of the following: 1) The ratio of at least one of the number of customers and the traffic volume to at least one of the number of sales and the number of preparations meets the criteria. 2) The discarded data meets the criteria. 3) It is determined that it would be better to change the number of preparations. 18. In the store data processing method according to claim 17, The store data processing method, wherein the computer, in the association data generation process, includes information capable of identifying the recommended number of preparation quantities for the specific period in the association data. 19. In the store data processing method according to any one of claims 14 to 17, The computer includes: In the sales data acquisition process, the sales data and the disposal data of a second store different from the first store are acquired; In the customer data acquisition process, the customer data of the second store is acquired, A store data processing method, wherein in the association data generation process, the association data of the second store is generated, and the association data is displayed on the display simultaneously with the association data of the first store. 20. The computer an expected data acquisition process for acquiring expected data including at least one of an expected number of customers at a first store and an expected traffic volume in a first area including the first store, in a first period in the future; a performance data acquisition process for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity cooked at the second store, during a second period in the past having the same attributes as the first period; a recommended number determination process for determining a recommended number indicating the number of the products to be prepared in the first store in the first period by using the expected data and the actual data; A store data processing method. 21. In the store data processing method according to claim 20, The store data processing method, wherein the attribute includes at least one of a day of the week and a time period. 22. In the store data processing method according to 20 or 21 above, A store data processing method, wherein the computer generates the assumed data in the assumed data acquisition process using at least one of an actual value of the number of customers at the first store and an actual value of the traffic volume in the first area. 23. In the store data processing method according to any one of claims 20 to 22, In the performance data acquisition process, the computer The results of clustering multiple stores are available. a second store selection means for selecting, as the second store, at least a part of the stores that belong to the same cluster as the first store; A store data processing method comprising: 24. In the store data processing method according to any one of claims 13 to 23, The store data processing method further comprises a product designation acquisition process in which the computer acquires product designation information for designating the product. 25. To the computer, a sales data acquisition function for acquiring sales data indicating at least one of the number of products sold and the number of products in stock at the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition function that acquires customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating function for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; A program that allows you to have 26. In the program according to 25 above, The program, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded. 27. In the program according to 25 or 26 above, The unit period is in days, The program, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded. 28. In the program according to any one of the above 25 to 27, The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The association data generation function is a program for displaying a specific period, which is the unit period that satisfies a predetermined condition, in a manner different from other unit periods. 29. In the program according to 28 above, The predetermined condition includes at least one of the following. 1) The ratio of at least one of the number of customers and the traffic volume to at least one of the number of sales and the number of preparations meets the criteria. 2) The discarded data meets the criteria. 3) It is determined that it would be better to change the number of preparations. 30. In the program according to claim 29, The association data generation function is a program that includes, in the association data, information that can identify the recommended number of preparations for the specific period. 31. In the program according to any one of the above 25 to 29, The sales data acquisition function acquires the sales data and the disposal data of a second store different from the first store, The customer data acquisition function acquires the customer data of the second store, The association data generation function is a program that generates the association data for the second store and displays the association data on the display simultaneously with the association data for the first store. 32. To the computer, an expected data acquisition function for acquiring expected data including at least one of an expected number of customers at a first store and an expected traffic volume in a first area including the first store in a first period in the future; a performance data acquisition function for acquiring performance data including at least one of a performance number of customers at a second store and a performance traffic volume in a second area including the second store, and at least one of a sales number and a preparation number of a commodity to be cooked at the second store, during a second period that is in the past and has the same attributes as the first period; a recommended number determination function that determines a recommended number indicating the number of the products to be prepared in the first store in the first period by using the expected data and the actual data; A program that allows you to have 33. In the program according to 32 above, The program, wherein the attributes include at least one of a day of the week and a time period. 34. In the program according to 32 or 33 above, The estimated data acquisition function is a program that generates the estimated data using at least one of the actual values ​​of the number of customers at the first store and the traffic volume in the first area. 35. In the program according to any one of the above 32 to 34, The performance data acquisition function is The results of clustering multiple stores are available. a second store selection means for selecting, as the second store, at least a part of the stores that belong to the same cluster as the first store; A program that includes: 36. In the program according to any one of the above 25 to 35, The program further provides the computer with a product designation acquisition function for acquiring product designation information for designating the product. [Explanation of symbols]

[0072] 10 Store data processing device 30 Store data processing device 110 Sales Data Acquisition Department 120 Customer Data Acquisition Department 130 Relevance data generation unit 140 Display 150 Storage section 310 Expected Data Acquisition Department 320 Performance Data Acquisition Department 330 Recommended Number Specification Section 340 Display 350 Storage section 360 Product Designation Acquisition Department

Claims

1. a sales data acquisition means for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; a customer data acquisition means for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generating means for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Equipped with A store data processing device, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded.

2. a sales data acquisition means for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; a customer data acquisition means for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generating means for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Equipped with The unit period is in days, A store data processing device, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded.

3. a sales data acquisition means for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; a customer data acquisition means for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generating means for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Equipped with The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The store data processing device, wherein the association data generating means displays a specific period, which is the unit period that satisfies a predetermined condition, in a manner different from other unit periods.

4. The computer a sales data acquisition process for acquiring sales data indicating at least one of a sales quantity and a stock quantity of the products in the first store for each unit period, and disposal data indicating a disposal quantity of the products for each unit period; A customer data acquisition process for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generation process for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Do the following: A store data processing method, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded.

5. The computer a sales data acquisition process for acquiring sales data indicating at least one of a sales quantity and a stock quantity of the products in the first store for each unit period, and disposal data indicating a disposal quantity of the products for each unit period; A customer data acquisition process for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generation process for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Do the following: The unit period is in days, A store data processing method, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded.

6. The computer a sales data acquisition process for acquiring sales data indicating at least one of a sales quantity and a stock quantity of the products in the first store for each unit period, and disposal data indicating a disposal quantity of the products for each unit period; A customer data acquisition process for acquiring customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; an association data generation process for generating association data indicating associations between the sales data, the disposal data, and the customer data, and displaying the association data on a display; Do the following: The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The store data processing method, in which the association data generation process displays a specific period, which is the unit period that satisfies a predetermined condition, in a manner different from other unit periods.

7. On the computer, a sales data acquisition function for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition function that acquires customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating function for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; Let them have The program, wherein the association data indicates trends in at least one of the number of customers and the traffic volume, at least one of the number of sold items and the number of items prepared, and the number of items discarded.

8. On the computer, a sales data acquisition function for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition function that acquires customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating function for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; Let them have The unit period is in days, The program, wherein the association data indicates, by day of the week, at least one of the number of customers and the traffic volume, at least one of the number of items sold and the number of items prepared, and the number of items discarded.

9. On the computer, a sales data acquisition function for acquiring sales data indicating at least one of the number of products sold and the number of products prepared in the first store for each unit period, and disposal data indicating the number of products disposed of for each unit period; A customer data acquisition function that acquires customer data indicating at least one of the number of customers at the first store and the traffic volume in a first area including the first store for each unit period; a correlation data generating function for generating correlation data indicating correlations between the sales data, the disposal data, and the customer data, and displaying the correlation data on a display; Let them have The association data includes data indicating, for each unit period, at least one of the number of customers and the traffic volume, at least one of the number of sales and the number of preparations, and the number of discards; The association data generation function is a program for displaying a specific period, which is the unit period that satisfies a predetermined condition, in a manner different from other unit periods.

Images