Variety optimization support system, method, and program

Abstract

To provide a variety optimization support system, method and program which support optimization of variety.SOLUTION: A variety optimization support system includes a server and terminals used by a plurality of users, the system including: a receiving unit which receives a designation of a commodity which is to be included in the variety of commodities in a target store determined by a user and a designation of a commodity which is not to be included in the variety of commodities in the target store; a simulation unit which simulates the variety depending on the received designations of the commodities; and an evaluation unit which displays a result by evaluating the simulated variation.SELECTED DRAWING: Figure 10

Description

[Technical field]

[0001] The present invention relates to assisting in assortment optimization. [Background technology]

[0002] Traditionally, in retailers that operate multiple stores, the product lineup for each store is decided by the store manager or supervisor of each store, or by buyers and merchandisers at headquarters. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-154510 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional methods, the person in charge of deciding the product lineup for each store is unable to obtain information that is useful for deciding the product lineup, making it difficult to create an optimal product lineup.

[0005] Therefore, an object of the present invention is to support the optimization of product lineups. [Means for solving the problem]

[0006] A product assortment optimization support system according to one embodiment of the present invention comprises a reception unit configured to receive designations of products to be included in the product assortment at a target store and products not to be included in the product assortment at the target store, as determined by a user, a simulation unit configured to simulate the product assortment in accordance with the received product designations, and an evaluation display unit configured to evaluate the simulated product assortment and display the results. Effect of the Invention

[0007] According to the present invention, it is possible to support the optimization of a store's product lineup. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram showing an overall configuration according to an embodiment of the present invention; [Diagram 2] 11 is a flowchart of a process for determining an assortment according to an embodiment of the present invention. [Diagram 3] 1 is a flowchart of a process for simulating and evaluating an assortment according to an embodiment of the present invention. [Figure 4] 1 is an example of a dashboard (displaying information on products to be included in an assortment) according to an embodiment of the present invention. [Diagram 5] 1 is an example of a dashboard (displaying information on products not included in the product lineup) according to an embodiment of the present invention. [Figure 6] 13 is a diagram showing an example of a dashboard (displaying the results of simulating and evaluating a product lineup) according to an embodiment of the present invention. [Figure 7] 13 is an example of a dashboard according to an embodiment of the present invention (displaying the degree of match between the product lineup and the purchasing tendencies of customers who visit a target store). [Figure 8] 1 is an example of a dashboard (displaying the match degree for each customer) according to an embodiment of the present invention. [Figure 9] 13 is an example of a dashboard according to an embodiment of the present invention (displaying the degree of match between the product lineup and the purchasing tendencies of customers who visit a target store). [Figure 10] 1 is a functional block diagram of an inventory optimization support system according to an embodiment of the present invention. [Figure 11] FIG. 2 is a diagram illustrating a hardware configuration of a server and a terminal according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] <Terminology explanation> The terms used in this specification will be explained below. In this specification, the term "dashboard (also called a digital dashboard)" refers to a display on a terminal of information useful for deciding on product lineup. In this specification, "SKU (Stock Keeping Unit)" is the minimum management unit when ordering and managing inventory. In other words, the number of SKUs is the number of types of products handled in a store (note that SKUs may be managed by product size (e.g., xx ml, xx ml), packaging form (e.g., cans, plastic bottles), sales unit (e.g., sold individually, sold in cases), etc.). In this specification, a "category" is a classification of a product. A category is divided into multiple hierarchical levels. For example, a product has two attributes: category and subcategory (e.g., "beverage" and "coffee"). Note that a product may have three or more attributes (e.g., "beverage", "coffee", and "unsweetened coffee").

[0011] <Overall composition> FIG. 1 is a diagram showing an overall configuration according to an embodiment of the present invention. The product lineup optimization support system 1 can include a server 10 and terminals 20 used by users 21 (for example, a terminal 20A used by a user 21A who decides on the product lineup for store A, a terminal 20B used by a user 21B who decides on the product lineup for store B, and a terminal 20C used by a user 21C who decides on the product lineup for store C) (Note that the terminals 20A, 20B, and 20C are collectively referred to as terminals 20, and the users 21A, 21B, and 21C are collectively referred to as users 21. The number of terminals 20 and users 21 is not limited to three, and may be any number of one or more). The server 10 and the terminals 20 can transmit and receive data via any network. Each will be described below.

[0012] The server 10 causes a dashboard including information useful for determining product lineups to be displayed on the terminal 20. The server 10 is composed of one or more computers.

[0013] Terminal 20 is a terminal operated by a user 21 (for example, a manager or person in charge of each store, a buyer or merchandiser at headquarters, or another person who decides the product lineup for each store). Terminal 20 can receive and display dashboard data generated by server 10. User 21 can view the dashboard displayed on terminal 20 when ordering (purchasing) a product. For example, terminal 20 is a personal computer, a tablet, a smartphone, etc.

[0014] Although FIG. 1 shows a case where the product lineup for each store is determined at each store (that is, at each terminal 20), the product lineups for a plurality of stores may be determined at a headquarters or the like (that is, at one terminal 20).

[0015] <Processing method> The processing method will be described below with reference to Figures 2 and 3. First, the overall flow of determining the product lineup will be described with reference to Figure 2, and then the process of simulating and evaluating the product lineup will be described with reference to Figure 3.

[0016] 2 is a flowchart of a process for determining an assortment (i.e., determining products to be ordered by a store) according to an embodiment of the present invention. Note that, although a case where categories are divided into two hierarchical levels (categories and subcategories) will be described, categories may be divided into three or more hierarchical levels.

[0017] In step 1 (S1), the number of SKUs for each product category (eg, the number of SKUs for "beverages") is determined.

[0018] In detail, the user 21 looks at the dashboard displayed on the terminal 20, decides the number of SKUs for each product category (for example, the number of SKUs for "beverages"), and inputs the decided number of SKUs to the terminal 20. The terminal 20 then notifies the server 10 of the number of SKUs for each category input to the terminal 20.

[0019] Note that instead of the user 21 deciding the number of SKUs, the server 10 may decide the number of SKUs (for example, the number of SKUs that the server 10 judges to be optimal based on past performance).

[0020] In step 2 (S2), the number of SKUs for each product subcategory (the number of SKUs for the subcategories of the category in S1, e.g., the number of SKUs for the subcategory "coffee" of the category "beverages") is determined.

[0021] In detail, the user 21 looks at the dashboard displayed on the terminal 20, decides the number of SKUs for each subcategory of the product (the number of SKUs for the subcategories of the category S1; for example, the number of SKUs for the subcategory "coffee" of the category "beverages"), and inputs the decided number of SKUs to the terminal 20. The terminal 20 then notifies the server 10 of the number of SKUs for each subcategory input to the terminal 20.

[0022] Note that instead of the user 21 deciding the number of SKUs, the server 10 may decide the number of SKUs (for example, the number of SKUs that the server 10 judges to be optimal based on past performance).

[0023] In step 3 (S3), the products to be ordered by the store within the subcategories of S2 are determined.

[0024] In detail, the user 21 looks at the dashboard displayed on the terminal 20, decides which products to stock in the store within the subcategory of S2, and inputs the decided products to the terminal 20. Then, the terminal 20 notifies the server 10 of the information on the products input to the terminal 20.

[0025] Note that the user 21 may not decide on a product, but the server 10 may decide on a product (for example, a product that the server 10 has determined to be optimal based on past performance).

[0026] In this way, the user 21 can make decisions using the dashboard in line with the flow of actual purchasing operations.

[0027] Fig. 3 is a flowchart of a process for simulating and evaluating an assortment according to an embodiment of the present invention. In detail, Fig. 3 is a flowchart of a process for simulating an assortment and displaying the results of the evaluation on a dashboard when determining products in S3 of Fig. 2.

[0028] In step 11 (S11), the server 10 accepts the designation of products to be included in the product lineup at the target store (i.e., the store for which the product lineup is being determined) determined by the user, and products to be excluded from the product lineup at the target store (i.e., the store for which the product lineup is being determined). Specifically, the terminal 20 transmits to the server 10 the designation information of products to be included in the product lineup at the target store and products to be excluded from the product lineup at the target store, which the user 21 has input to the terminal 20.

[0029] In step 12 (S12), the server 10 simulates the product lineup in accordance with the product designation received in S11. Specifically, the server 10 calculates the degree of match between the product lineup and the purchasing tendency of customers who visit the target store.

[0030] In step 13 (S13), the server 10 evaluates the product lineup simulated in S12 and displays the results. Specifically, the server 10 causes the terminal 20 to display a dashboard (screens described in Figs. 4 to 9) showing the results of simulating and evaluating the product lineup.

[0031] <Screen example> Hereinafter, the dashboard (that is, the screen displayed on the terminal 20) will be described with reference to Figures 4 to 9. Note that Figures 4 to 9 may be displayed on one screen.

[0032] FIG. 4 is an example of a dashboard (displaying information on products to be included in an assortment) according to an embodiment of the present invention. As shown in FIG. 4, the dashboard displays information on products to be included in an assortment when simulating the assortment. For example, the product information is at least one of the following: the recommendation level of the product, the past performance value of the product in the target store (e.g., the performance value of sales of the product in the target store (e.g., sales volume, sales rate (specifically, the number of products sold / the number of products purchased in the past target period)), the performance value of one of the number of products discarded, the inventory turnover rate, the return rate, and the gross profit or gross profit rate in the target store, and the performance value of the number of products sold to specific customers (e.g., heavy users, regular customers, etc.) in the target store), etc.

[0033] The "recommendation level" is an index that indicates the degree to which a customer who visits the target store is predicted to purchase a product. Specifically, the recommendation level is a score based on a prediction of the degree to which a customer who recently visited the target store is likely to purchase a product (for example, a score predicted by a trained model trained by machine learning using data on customers' past purchase records).

[0034] The "number of discarded items" is the number of items that were purchased at the target store and discarded during the past target period (for example, the last 7 days, etc.).

[0035] "Inventory turnover rate" is the number of times products were replaced at a target store during the past target period (e.g., the last 7 days, etc.).

[0036] "Return rate" is the return rate of products purchased at a target store during a past target period (e.g., the last 7 days, etc.).

[0037] "Gross profit or gross profit margin" is the gross profit or gross profit margin of the products sold at the target store during the past target period (e.g., the last 7 days, etc.).

[0038] FIG. 5 is an example of a dashboard according to an embodiment of the present invention (displaying information on products not included in the product lineup). As shown in FIG. 5, the dashboard displays information on products not included in the product lineup when simulating the product lineup. For example, the product information includes the recommendation level of the product, past performance values ​​of the product at other stores (specifically, similar stores similar to the target store (described later)) (e.g., at least one of the following: the handling rate of the product at other stores, the performance value of the sales volume of the product at other stores, the number of products purchased at other stores by customers of the target store (e.g., heavy users, regular customers, etc.), the co-purchase rate of the product with other products at the target store, the number of products discarded at other stores, the inventory turnover rate, the return rate, and the gross profit or gross profit margin).

[0039] The "recommendation level" is an index that indicates the degree to which a customer who visits the target store is predicted to purchase a product. Specifically, the recommendation level is a score based on a prediction of the degree to which a customer who recently visited the target store is likely to purchase a product (for example, a score predicted by a trained model trained by machine learning using data on customers' past purchase records).

[0040] The "rate at which products not included in the product lineup are handled at other stores" is the rate at which products not included in the product lineup are handled at similar stores.

[0041] The "actual sales volume of products not included in the product lineup at other stores" is the average number of units sold per day at similar stores that sell products not included in the product lineup, normalized by the daily sales of that store.

[0042] The "number of items not included in the product lineup purchased by customers of the target store at other stores" is the number of items (i.e., items not included in the product lineup) purchased by customers (e.g., heavy users, regular customers, etc.) of the target store (own store) when visiting other stores.

[0043] The "co-purchase rate of products not included in the product assortment with other products in the target store" indicates the degree to which a product (i.e., a product not included in the product assortment) is likely to be purchased simultaneously (co-purchased) with other products (e.g., products in other categories) in the target store (own store).

[0044] The "number of discarded items" is the number of items that were purchased at similar stores and discarded during a past target period (for example, the last 7 days).

[0045] "Inventory turnover" is the number of times products were replaced at similar stores during the past target period (e.g., the last 7 days, etc.).

[0046] "Return rate" is the return rate of products purchased at similar stores during a past target period (e.g., the last 7 days, etc.).

[0047] "Gross profit or gross profit margin" is the gross profit or gross profit margin of products sold at similar stores during the past target period (e.g., the last 7 days, etc.).

[0048] The products displayed in Figures 4 and 5 (i.e., products to be included in the assortment and products not to be included in the assortment) can be selected by clicking etc., and each time a product is selected by clicking etc., it moves back and forth between the areas in Figure 4 and Figure 5. This makes it possible to specify whether or not to include a product in the assortment.

[0049] [Similar stores] Here, a similar store will be described. A similar store is a store similar to the target store. The similarity between the target store and a similar store is expressed by the similarity between the store vector of the target store and the store vector of another store.

[0050] An example of a method for generating a store vector (a vector synthesized from a product vector and a store's past sales performance values) will be described. First, generate a product vector for each product. A product vector is a vector (e.g., a low-dimensional, dense vector) that is assigned to an individual product. For example, the product vector is trained based on ID-POS (Point of Sales) data. The training method can be to extract a product ID column from the product IDs of the products displayed on the receipt, and then apply word2vec to represent the product ID column as a vector. Next, the product vectors weighted by the number of times a customer has purchased a product are combined to generate a customer vector. Next, the customer vectors of customers who visited the store during a certain period are synthesized to generate a store vector.

[0051] The similarity between the target store and other stores may be any of the following: "similarity in sales volume," "similarity in customer attributes," "similarity in number of visitors by day of the week or time of day," "similarity calculated using a store vector synthesized from a product vector and the store's past sales performance values," or it may be an overall similarity such as two or more representative values ​​(e.g., average value).

[0052] Fig. 6 is an example of a dashboard (displaying the results of simulating and evaluating a product lineup) according to one embodiment of the present invention. As shown in Fig. 4, the dashboard displays the changes in the scores of various indices for the product lineup specified in Figs. 4 and 5 (i.e., the evaluation scores for a virtual product lineup). Note that Fig. 6 displays three indices, namely, "product lineup match degree," "total branch sales volume efficiency," and "total purchase volume by heavy users," but the indices are not limited to these.

[0053] [Product assortment match] The product lineup match degree is the sum of the similarities between the product vector and the customer vectors of all customers who have recently visited the target store (your store). It has been confirmed that the product lineup match degree correlates with the purchase rate of a category (the proportion of people who purchased the target category out of the total number of purchasers). The product lineup match degree will be explained in detail later.

[0054] [Branch sales volume efficiency total] The total branch sales efficiency is the number of units that would be expected to be sold per day if a store with the average daily sales volume and hypothetical product lineup were located in the same area.

[0055] [Total purchases by heavy users] The total number of purchases by heavy users is the actual value of the most recent number of purchases by a particular customer (e.g., heavy user, regular customer, etc.) of the target store (own store).

[0056] Here, we will explain the product match degree in detail. For all products j, the product vector of product j is v j Let us assume that.

[0057]

number

[0058] The number of purchases of product j by customer u during the target period is w j Then, customer vector (v u ) is defined as follows:

[0059]

number

[0060] The set of products that a store h has in its inventory is

[0061]

number

[0062] Let U be a customer who purchased at store h. h Then, the degree of match, which indicates the degree to which the product lineup of store h is similar to the past purchasing trends of customer u, is defined as follows using the cosine similarity between product j included in the lineup of store h and the customer:

[0063]

number

[0064]

number

[0065]

number

[0066] Regarding the use of max, other possible methods include using the average of the top N cosine similarities, or using the average for all products included in the lineup. When max is used, it can be interpreted as a method that evaluates the degree of match based on the most similar product in the store, and when the average is used, it can be interpreted as a method that evaluates the degree of match taking into account all products in the store.

[0067] In this way, the customer vector can represent the preferences of each individual customer, making it possible to optimize the store's product lineup by taking into account all customers who visit the store.

[0068] FIG. 7 is an example of a dashboard according to an embodiment of the present invention (displaying the degree of match between the product lineup and the purchasing tendencies of customers who visited a target store). FIG. 7 is an example of visualizing the degree of match of all customers who visited a store during a target period (most recently) when analyzing a certain store. Each point on the scatter plot corresponds to one customer, the horizontal axis indicates the degree of match, and the vertical axis indicates the number of visits during the target period. When one point (i.e., one customer) is selected, information about that customer (customer ID, gender, age, etc.) is displayed (for example, FIG. 8), and the top products that the customer is likely to purchase (specifically, products with a high "recommended level" as described above) are displayed.

[0069] FIG. 9 is an example of a dashboard according to an embodiment of the present invention (displaying the degree of match between the product lineup and the purchasing tendency of customers who visit a target store). FIG. 9 is an example of visualization of how a change in the product lineup affects each customer when the product lineup to be simulated is changed. In FIG. 9, each point in the scatter plot of FIG. 7 is colored, and the color corresponds to the degree of change in the degree of match. By displaying an increase in the degree of match in a first color and a decrease in the degree of match in a second color, the user 21 can visually grasp and dig deeper into what kind of change in the degree of match appears for which customer by adding or deleting a specific product.

[0070] In addition, a scatter diagram such as that shown in Figure 9 may be displayed on a screen that displays the results of simulating and evaluating the product lineup (for example, a screen that displays Figures 4 to 6), allowing user 21 to simultaneously see the impact on each customer.

[0071] [Other indicators (modeling alternative probabilities)] Opportunity loss and substitute products may be used as indicators of the results of simulating and evaluating product lineups. Specifically, it is possible to model which other products will be substituted when a product is unavailable (when not available, out of stock, etc.), and to predict and display "how much opportunity loss will occur if this product is deleted."

[0072] In modeling the substitution probability, we first assume that product substitution only occurs within a subcategory. We calculate the similarity of product vectors for all products in the same subcategory, and apply a softmax function or similarity between the product vector of a product and the product vector of other products in the same subcategory to obtain a value that represents the probability of substitution for each product. By adopting a simple model in which substitution will not occur if the substitute product is also unavailable (not available, out of stock, etc.), leading to opportunity loss, we can predict the impact on sales revenue when adding or removing products from the lineup. In this way, we can expect to model behavior in which a product is substituted not simply because it is a popular product, but because it resembles a product that is not available or out of stock.

[0073] <Function block> 10 is a functional block diagram of a product assortment optimization support system 1 according to one embodiment of the present invention. The product assortment optimization support system 1 includes a data collection unit 101, a reception unit 102, a simulation unit 103, an evaluation display unit 104, a product information display unit 105, and a data storage unit 106. The product assortment optimization support system 1 functions as the data collection unit 101, the reception unit 102, the simulation unit 103, the evaluation display unit 104, and the product information display unit 105 by executing a program.

[0074] In addition, the server 10 may include all of the data collection unit 101, the reception unit 102, the simulation unit 103, the evaluation display unit 104, the product information display unit 105, and the data storage unit 106, or the server 10 and the terminal 20 may include the data collection unit 101, the reception unit 102, the simulation unit 103, the evaluation display unit 104, the product information display unit 105, and the data storage unit 106 in a distributed manner, or the terminal 20 may include all of the data collection unit 101, the reception unit 102, the simulation unit 103, the evaluation display unit 104, the product information display unit 105, and the data storage unit 106.

[0075] The data collection unit 101 collects data for each store. For example, the data collection unit 101 collects ID-POS (Point of Sales) data (more specifically, purchase data linked to a customer ID (i.e., various customer information) (e.g., data indicating when, at which store, and what products were sold)).

[0076] For example, the data collection unit 101 acquires information necessary for simulating and evaluating the product lineup at a predetermined timing (for example, on a daily basis, an hourly basis, or in real time).

[0077] The reception unit 102 receives designation of products to be included in the product lineup at the target store and products not to be included in the product lineup at the target store, determined by the user 21.

[0078] The simulation unit 103 simulates an assortment of products according to the product specifications received by the reception unit 102.

[0079] The evaluation display unit 104 evaluates the product lineup simulated by the simulation unit 103, and displays a dashboard including the evaluation results on the terminal 20. For example, the evaluation is performed based on the degree of match between the product lineup and the purchasing tendency of customers who visit the target store.

[0080] For example, the evaluation display unit 104 can display the degree of match between the product lineup and the purchasing tendencies of a particular customer.

[0081] For example, the evaluation display unit 104 can display the opportunity loss and the substitute product. Note that the evaluation display unit 104 may display the opportunity loss and the substitute product when the product to be included in the product lineup at the target store is unavailable, or may display the opportunity loss and the substitute product when the product to be included in the product lineup at the target store is available (that is, information such as the extent to which the product is taking away sales of other products (cannibalization) can be displayed).

[0082] The product information display unit 105 displays information on products to be included in the product lineup at the target store, as determined by the user 21 (i.e., as received by the reception unit 102), and information on products to be excluded from the product lineup at the target store. The product information is the degree to which a customer visiting the target store is predicted to purchase the product.

[0083] The data storage unit 106 stores data collected by the data collection unit 101, the results of the simulation by the simulation unit 103, and dashboard data generated by the evaluation display unit 104 and the product information display unit 105.

[0084] <Effects> In one embodiment of the present invention, when considering whether or not to handle an individual product, the product lineup can be virtually changed and sales, etc. of the changed product lineup can be simulated.

[0085] <Hardware configuration> 11 is a diagram showing the hardware configuration of the server 10 and the terminal 20 according to an embodiment of the present invention. The server 10 and the terminal 20 can include a control unit 1001, a main memory unit 1002, an auxiliary memory unit 1003, an input unit 1004, an output unit 1005, and an interface unit 1006. Each of these will be described below.

[0086] The control unit 1001 is a processor (for example, a central processing unit (CPU), a graphics processing unit (GPU), etc.) that executes various programs installed in the auxiliary storage unit 1003.

[0087] The main memory unit 1002 includes a non-volatile memory (Read Only Memory (ROM)) and a volatile memory (Random Access Memory (RAM)). The ROM stores various programs, data, etc. required for the control unit 1001 to execute various programs installed in the auxiliary memory unit 1003. The RAM provides a working area into which the various programs installed in the auxiliary memory unit 1003 are expanded when the control unit 1001 executes them.

[0088] The auxiliary storage unit 1003 is an auxiliary storage device that stores various programs and information used when the various programs are executed.

[0089] The input unit 1004 is an input device through which the operator of the server 10 and the terminal 20 inputs various instructions to the server 10 and the terminal 20 .

[0090] The output unit 1005 is an output device that outputs the internal states of the server 10 and the terminal 20, and the like.

[0091] The interface unit 1006 is a communication device for connecting to a network and communicating with other devices.

[0092] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to the specific embodiments described above, and various modifications and variations are possible within the scope of the gist of the present invention as defined in the claims. [Explanation of symbols]

[0093] 1 Product lineup optimization support system 10 Server 20 Terminals 21 users 101 Data Collection Department 102 Reception 103 Simulation Department 104 Evaluation display section 105 Product information display section 106 Data storage unit 1001 Control section 1002 Main memory 1003 Auxiliary storage unit 1004 Input section 1005 Output section 1006 Interface section

Claims

1. A reception unit configured to receive designation of products to be included in a product lineup at a target store and products to be excluded from the product lineup at the target store, the designation being determined by a user; a simulation unit configured to simulate the product lineup in response to the received product designation; an evaluation display unit configured to evaluate the simulated product lineup and display the results; A product lineup optimization support system equipped with the above.

2. 2. The product lineup optimization support system according to claim 1, wherein the evaluation is based on a degree of match between the product lineup and purchasing tendencies of customers who visit the target store.

3. 3. The product lineup optimization support system according to claim 2, wherein the evaluation display unit displays a degree of match between the product lineup and a purchasing tendency of a specific customer.

4. The product lineup optimization support system according to claim 1 , wherein the evaluation display unit displays opportunity losses and alternative products.

5. The product assortment optimization support system of claim 1, further comprising a product information display unit configured to display information about products to be included in the product assortment at the target store, as determined by the user, and information about products not to be included in the product assortment at the target store.

6. 6. The product lineup optimization support system according to claim 5, wherein the product information is a predicted degree to which a customer visiting the target store is likely to purchase the product.

7. 1. A computer-implemented method comprising: Accepting designation of products to be included in the product lineup at the target store and products not to be included in the product lineup at the target store, determined by the user; simulating the product lineup in response to the received product designation; evaluating the simulated assortment and displaying the results; The method includes:

8. Computer a reception unit that receives designation of products to be included in the product lineup at the target store and products not to be included in the product lineup at the target store, the designation being determined by the user; a simulation unit that simulates the product lineup in response to the received product designation; A program for functioning as an evaluation display unit that evaluates the simulated product lineup and displays the results.

Images