Variety optimization support system, method, and program

Abstract

To provide a variety optimization support system, method and program for supporting optimization of variety.SOLUTION: A variety optimization support system includes a server and terminals used by each of users, the system being configured to display data obtained by visualizing a relationship between a past result value of the total number of SKUs (stock keeping units) for all commodities belonging to a target category dealt in a target store in a past target period, and a past result value of the total sales of all the commodities belonging to the target category for the SKUs, and the number of values of the SKUs in the past target period.SELECTED DRAWING: Figure 3

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 includes a display unit configured to display data visualizing the relationship between the actual total number of SKUs (stock keeping units) of all products belonging to a target category handled at a target store during a past target period and the actual total sales volume of all products belonging to the target category when the SKU number was used, and the number of each value of the SKU number during the past target period. 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 displaying a dashboard according to an embodiment of the present invention. [Figure 4] 1 is an example of a dashboard according to an embodiment of the present invention. [Diagram 5] 13 is an example of a display of the relationship between the number of SKUs and the number of sales included in a dashboard according to an embodiment of the present invention. [Figure 6] 13 is an example of a display of the relationship between the number of SKUs and the number of discards included in a dashboard according to an embodiment of the present invention. [Figure 7] 13 is an example of a display of a frequency distribution of SKU counts included in a dashboard according to an embodiment of the present invention. [Figure 8] 13 is another example of a display of the relationship between the number of SKUs and the number of sales included in a dashboard according to an embodiment of the present invention. [Figure 9] 1 is a functional block diagram of an inventory optimization support system according to an embodiment of the present invention. [Figure 10] 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 product lineup determination will be described with reference to Figure 2, and then the process of displaying a dashboard on terminal 20 when determining the number of SKUs for each category 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 in 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] 3 is a flowchart of a process for displaying a dashboard according to an embodiment of the present invention. In particular, FIG. 3 is a flowchart of a process for displaying a dashboard in at least one of S1 and S2 in FIG.

[0028] In step 11 (S11), based on the data collected from the stores, the server 10 tallyes up the actual value of the total SKU count of all products belonging to a target category (e.g., a category such as "beverages" or a subcategory such as "coffee") handled at a target store (e.g., store A) during a past target period (e.g., the past year) and the actual value of the total sales volume of all products belonging to the target category when that SKU count was used, and generates a graph or the like that visualizes the relationship between the two.

[0029] In step 12 (S12), based on the data collected from the stores, the server 10 compiles the actual value of the total number of SKUs of all products belonging to a target category (e.g., a category such as "beverages" or a subcategory such as "coffee") handled at a target store (e.g., store A) during a past target period (e.g., the past year), and the actual value of one of the total number of discards of all products belonging to the target category when that SKU number was present, the inventory turnover rate, the return rate, and the gross profit or gross profit rate, and generates a graph or the like that visualizes the relationship between the two.

[0030] In step 13 (S13), the server 10 counts the number of each value of the number of SKUs of all products belonging to a target category (e.g., a category such as "beverages" or a subcategory such as "coffee") handled at a target store (e.g., store A) during a past target period (e.g., the past year) based on the data collected from the store, and generates a frequency distribution or the like that visualizes the number of each value.

[0031] In step 14 (S14), the server 10 generates a dashboard including the results (eg, graphs, frequency distributions, etc.) tabulated and generated in S11, S12, and S13, and causes the terminal 20 to display the dashboard.

[0032] <Screen example> The dashboard (that is, the screen displayed on the terminal 20) will be described below with reference to FIGS.

[0033] Fig. 4 is an example of a dashboard according to an embodiment of the present invention. As shown in Fig. 4, the dashboard 110 visualizes the past performance of products belonging to a target category in a target store (e.g., store A). In detail, the relationship between the number of SKUs of all products in the target category and the "sales volume" and "other information such as the number of discards, inventory turnover, return rate, gross profit or gross profit rate" at that SKU number is displayed. In addition, the distribution (frequency distribution) of the number of each value of the SKU number is displayed.

[0034] Dashboard 110 may include data 111 (described in detail with reference to FIG. 5) that visualizes the relationship between the actual value of the total SKU number of all products belonging to the target category and the actual value of the total sales volume of all products belonging to the target category when that SKU number is used, data 112 (described in detail with reference to FIG. 6) that visualizes the relationship between the actual value of the total SKU number of all products belonging to the target category and the actual value of one of the total discard volume, inventory turnover rate, return rate, and gross profit or gross profit rate of all products belonging to the target category when that SKU number is used, and a frequency distribution 113 (described in detail with reference to FIG. 7) that shows the number of each value of SKU number.

[0035] FIG. 5 is an example of a display of the relationship between the number of SKUs and the number of sales included in a dashboard according to an embodiment of the present invention.

[0036] In detail, the horizontal axis of Fig. 5 indicates the total number of SKUs (actual value in the target period) of all products belonging to the target category of the target store, and the vertical axis of Fig. 5 indicates the total sales volume (actual value in the target period) of all products belonging to the target category when the number of SKUs is in question. Note that the actual value of the sales volume may be at least one of a statistical amount of the sales volume of all products belonging to the target category (e.g., the average value or standard deviation of the sales volume of all products) and raw data.

[0037] The server 10 tally up on a daily basis the number of SKUs and the number of sales of all products belonging to a target category handled at a target store during a target period (for example, the past year). The server 10 can visualize the tally up results with a scatter plot in which the horizontal axis is the number of SKUs and the vertical axis is the number of sales (each point represents one day). When there are points where multiple points overlap, the server 10 can express the degree of overlap by the darkness of the color.

[0038] Since there is variation in sales volume even with the same number of SKUs, server 10 can visualize the average sales volume and the range of variation expected for a specific number of SKUs by connecting the average sales volume for the same number of SKUs and the average value ±2 standard deviations with a line graph.

[0039] By utilizing such a graph, user 21 can find the number of SKUs that reduces opportunity loss. For example, if the number of SKUs increases as the number of sales increases, it is assumed that increasing the number of SKUs will reduce opportunity loss. On the other hand, if the number of sales remains flat even when the number of SKUs is increased, it is assumed that increasing the number of SKUs further (i.e., increasing the number of SKUs beyond the number at which sales began to level off) will not contribute to an increase in sales.

[0040] In addition, since customer purchasing behavior changes depending on the day of the week, month, season, etc., the server 10 may display on the dashboard the relationship between the number of SKUs and sales volume narrowed down to the day of the week, month, season, etc. specified by the user 21 (for example, only Sundays for the past year).

[0041] FIG. 6 is an example of a display of the relationship between the number of SKUs and the number of discards included in a dashboard according to an embodiment of the present invention.

[0042] In detail, the horizontal axis of Fig. 6 indicates the total number of SKUs of all products belonging to the target category of the target store (actual value in the target period), and the vertical axis of Fig. 6 indicates the total number of discards of all products belonging to the target category when the SKU was in question (actual value in the target period). Note that the actual value of the number of discards may be at least one of a statistic of the number of discards of all products belonging to the target category (for example, the average value or standard deviation of the number of discards of all products) and raw data.

[0043] The discard number may be the number of discards N (N>=0) days after the date on which the SKU number was obtained. In other words, the discard number may be the number of discards on the same day as the date on which the SKU number was obtained (when N=0), or may be the number of discards a predetermined number of days after the date on which the SKU number was obtained (when N>1) (that is, the date on which the SKU number was obtained and the date on which the discard number was obtained are not the same day). In the example of FIG. 6, since many of the products had a deadline of about 2 to 3 days before they were discarded, the relationship between the SKU number and the number of discards two days later is visualized.

[0044] The server 10 daily tally up the number of SKUs of all products belonging to a target category handled at a target store during a target period (for example, the past year) and the number of products to be discarded N days later. The server 10 can visualize the tally up results with a scatter plot in which the horizontal axis is the number of SKUs and the vertical axis is the number of products to be discarded (each point represents one day). When there are points where multiple points overlap, the server 10 can express the degree of overlap by the darkness of the color.

[0045] Since there is variation in the number of discards even with the same number of SKUs, server 10 can visualize the average number of discards expected for a specific number of SKUs and the range of variation by connecting the average value of the number of discards for the same number of SKUs and the average value ±2 standard deviations with a line.

[0046] By utilizing such a graph, if there is a situation where there is excess inventory due to having too many SKUs, the number of discards N days later (two days later in the example of Figure 6) will reflect this figure, so user 21 can determine the number of SKUs that will reduce waste by selecting a number of SKUs that will not result in a large increase in the number of discards N days later (two days later in the example of Figure 6).

[0047] The value of N, which is the number of items to be discarded N days later, is determined for each category, taking into consideration the expiration date and other circumstances. For example, for items such as prepared foods and bento lunches, whose expiration dates are set to the same day or the next day, the number of items to be discarded on that day should be looked at (i.e., N=0). The server 10 may determine the value of N based on information such as the expiration date of the item, or may determine the value of N according to a specification by the user 21.

[0048] [Total waste, inventory turnover, return rate, gross profit or gross profit margin] The server 10 can determine which of the total number of discards, the inventory turnover, the return rate, and the gross profit or gross margin for all products belonging to the target category should be displayed for each target category. For example, in a category such as soft drinks or non-food products where discards due to expiration dates rarely occur, the server 10 should quantify and visualize the opportunity loss (opportunity loss) caused by new products and best-selling products not being able to be displayed due to low turnover and occupying shelf space (i.e., the "inventory turnover" should be used). The server 10 may determine which of the total number of discards, the inventory turnover, the return rate, and the gross profit or gross margin should be used based on the characteristics of the target category, or may determine which of the total number of discards, the inventory turnover, the return rate, and the gross profit or gross margin should be used according to a designation by the user 21.

[0049] FIG. 7 is an example of a frequency distribution display of the number of SKUs included in a dashboard according to one embodiment of the present invention.

[0050] In detail, the horizontal axis of FIG. 7 indicates the total number of SKUs of all products belonging to a target category in a target store (actual value in a target period), and the vertical axis of FIG. 7 indicates the number of each value of the number of SKUs.

[0051] By utilizing such frequencies (the number of each value of the SKU number), user 21 can interpret the trends in sales numbers, discard numbers, etc., while confirming that for SKU numbers with low frequencies (the number of each value of the SKU number), the actual sales numbers, discard numbers, etc. are from a small number of days in the past and are therefore less reliable.

[0052] The visualization method is not limited to frequency distribution, and may be a method of expressing the frequency (the number of each value of the SKU number) by the average value in Figure 5 or Figure 6, or by expressing the density of the line of ±2 standard deviations, or by excluding SKU numbers with low frequencies (the number of each value of the SKU number) and low reliability from the graphs in Figure 5 or Figure 6 and not displaying them on the dashboard, etc.

[0053] FIG. 8 is another example of a display of the relationship between the number of SKUs and the number of sales included in a dashboard according to an embodiment of the present invention.

[0054] In Figures 5 and 6 above, the density at each point is expressed by the intensity of the color (i.e., it is expressed that it is for multiple days rather than one day), but the intensity of the color at each point can also be used to express other indicators such as the number of items in stock or the day of the week. Figure 8 is an example where the number of items in stock is expressed by the intensity of the color.

[0055] By utilizing a different index in this manner, the user 21 can visually grasp whether the cause of the variation in sales volume, waste volume, etc. when the same number of SKUs is due to another index such as inventory volume.

[0056] <Function block> 9 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 tallying and visualization unit 102, a dashboard generation unit 103, a display unit 104, and a data storage unit 105. The product assortment optimization support system 1 functions as the data collection unit 101, the tallying and visualization unit 102, the dashboard generation unit 103, and the display unit 104 by executing programs.

[0057] In addition, the server 10 may include all of the data collection unit 101, the aggregation and visualization unit 102, the dashboard generation unit 103, the display unit 104, and the data storage unit 105, or the server 10 and the terminal 20 may include the data collection unit 101, the aggregation and visualization unit 102, the dashboard generation unit 103, the display unit 104, and the data storage unit 105 in a distributed manner, or the terminal 20 may include all of the data collection unit 101, the aggregation and visualization unit 102, the dashboard generation unit 103, the display unit 104, and the data storage unit 105.

[0058] 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)).

[0059] For example, the data collection unit 101 acquires, at a predetermined timing (for example, on a daily basis, an hourly basis, or in real time), data on the total number of SKUs of all products belonging to each category (or subcategory, etc.) of each store, data on the total number of sales of all products belonging to that category when that SKU number is reached, and data on the total number of discards, inventory turnover, return rate, and gross profit or gross profit rate of all products belonging to that category when that SKU number is reached.

[0060] Based on the data collected by the data collection unit 101, the aggregation and visualization unit 102 aggregates the actual values ​​of the total SKU count of all products belonging to a target category (e.g., a category such as "beverages" or a subcategory such as "coffee") handled in a target store (e.g., store A) during a past target period (e.g., the past year) and the actual values ​​of the total sales volume of all products belonging to the target category when that SKU count was present, and generates a graph (e.g., the graph described in Figure 5) that visualizes the relationship between the two.

[0061] Based on the data collected by the data collection unit 101, the aggregation and visualization unit 102 aggregates the actual value of the total number of SKUs of all products belonging to a target category (for example, a category such as "beverages" or a subcategory such as "coffee") handled at a target store (for example, store A) during a past target period (for example, the past one year), and the actual value of one of the total number of discards of all products belonging to the target category when that SKU number was present, the inventory turnover rate, the return rate, and the gross profit or gross profit rate, and generates a graph (for example, the graph described in Figure 6) that visualizes the relationship between the two.

[0062] Based on the data collected by the data collection unit 101, the aggregation / visualization unit 102 aggregates the number of each value of the number of SKUs of all products belonging to a target category (e.g., a category such as "beverages" or a subcategory such as "coffee") handled at a target store (e.g., store A) during a past target period (e.g., the past year), and generates a frequency distribution (e.g., the frequency distribution described in Figure 7) that visualizes the number of each value.

[0063] The dashboard generating unit 103 generates a dashboard including the results (for example, graphs, frequency distribution, etc.) tabulated and generated by the tabulation / visualization unit 102 .

[0064] The display unit 104 displays the dashboard generated by the dashboard generating unit 103 on the terminal 20 .

[0065] The data storage unit 105 stores data collected by the data collection unit 101, data collected and generated by the collection and visualization unit 102, and dashboard data generated by the dashboard generation unit 103.

[0066] <Effects> In one embodiment of the present invention, a user 21 who decides the product lineup for a store (i.e., decides which products the store will order) can decide the number of SKUs to order (purchase) after considering information on past performance (more specifically, the relationship between the number of SKUs of all products in the target category and the "sales volume" and "other information such as the number of discards, inventory turnover, return rate, gross profit or gross profit rate" when that number of SKUs is used), thereby realizing an optimal product lineup. Furthermore, the user 21 can decide the number of SKUs to order (purchase) after considering the reliability of the relationship between the number of SKUs of all products in the target category and the "sales volume" when that number of SKUs is used, etc., based on the frequency distribution of the number of SKUs.

[0067] <Hardware configuration> 10 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.

[0068] 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.

[0069] 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.

[0070] The auxiliary storage unit 1003 is an auxiliary storage device that stores various programs and information used when the various programs are executed.

[0071] 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 .

[0072] The output unit 1005 is an output device that outputs the internal states of the server 10 and the terminal 20, and the like.

[0073] The interface unit 1006 is a communication device for connecting to a network and communicating with other devices.

[0074] 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 described in the claims. [Explanation of symbols]

[0075] 1 Product lineup optimization support system 10 Server 20 Terminals 21 users 101 Data Collection Department 102 Tabulation and Visualization Department 103 Dashboard Generation Unit 104 Display section 105 Data storage unit 1001 Control section 1002 Main memory 1003 Auxiliary storage unit 1004 Input section 1005 Output section 1006 Interface section

Claims

1. Data visualizing the relationship between the actual value of the total number of SKUs (stock keeping units) of all products belonging to a target category handled at a target store during a past target period and the actual value of the total sales volume of all products belonging to the target category when the number of SKUs was the number of SKUs; The number of each value of the number of SKUs in the past target period; and A product lineup optimization support system having a display unit configured to display the above.

2. The display unit is 2. The product assortment optimization support system of claim 1, further configured to display data visualizing the relationship between the actual value of the total number of SKUs of all products belonging to the target category handled at the target store during the past target period and the actual value of one of the total number of discards of all products belonging to the target category when that SKU number was, inventory turnover rate, return rate, and gross profit or gross profit rate.

3. The product assortment optimization support system according to claim 2 , wherein for each target category, it is determined which of the total number of discarded items, inventory turnover rate, return rate, gross profit, or gross profit rate to display.

4. 2. The product lineup optimization support system according to claim 1, wherein the actual value of the total sales volume is at least one of a statistical amount and raw data of the sales volume of all products belonging to the target category when the SKU number is the number.

5. The product lineup optimization support system according to claim 1 , wherein the target categories are divided into a plurality of hierarchical levels.

6. 1. A computer-implemented method comprising: Data visualizing the relationship between the actual value of the total number of SKUs (stock keeping units) of all products belonging to a target category handled at a target store during a past target period and the actual value of the total sales volume of all products belonging to the target category when the number of SKUs was the number of SKUs; The number of each value of the number of SKUs in the past target period; and Displaying the

7. Computer Data visualizing the relationship between the actual value of the total number of SKUs (stock keeping units) of all products belonging to a target category handled at a target store during a past target period and the actual value of the total sales volume of all products belonging to the target category when the number of SKUs was the number of SKUs; The number of each value of the number of SKUs in the past target period; and A program for functioning as a display unit that displays the above.

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