Work support device, work support method, and work support program

Abstract

To enable a wholesaler side to predict future sales of a commodity sold to a retailer side.SOLUTION: A current stock quantity calculation part acquires sales data including the sales quantity of merchandise from a customer, and calculates the current stock quantity of the customer by subtracting the sales quantity from the sales quantity of the merchandise sold to the customer. The predicted order quantity calculating section subtracts the current stock quantity of the customer calculated by the current stock quantity calculating section from a reference quantity which is a stock quantity serving as a reference of the commodity in the customer stored in the reference quantity master table, thereby calculating a predicted order quantity which enables prediction of future order reception of the commodity based on an increase or decrease of the current stock quantity with respect to the reference quantity. The output control unit outputs the calculated predicted order quantity to the output target device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a business support device, a business support method, and a business support program. [Background technology]

[0002] In the inventory management device disclosed in Patent Document 1 (JP 2023-168864 A), an information acquisition means acquires performance information including information regarding the number of parts received, the number of parts sent, and the number of parts in stock. A required parts quantity calculation means calculates the number of parts to be sent for multiple periods prior to a predetermined future point in time based on the number of parts sent based on the sales plan for products including the parts and the number of parts sent identified by the performance information. A parts order quantity calculation means sets the average number of parts sent for multiple periods calculated by the required parts quantity calculation means as the order quantity of parts, which is the number of parts received for multiple periods. This makes it possible to easily determine an appropriate order quantity and perform appropriate inventory management. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-168864 Summary of the Invention [Problem to be solved by the invention]

[0004] A typical retailer's business model involves purchasing products from wholesalers and selling them at their retail stores. In this business model, the wholesaler can recognize the number of products it has sold to the retailer and the number of products it has in stock, but it cannot recognize the number of sales or the number of products in stock at the retailer, which makes it difficult to predict future sales of the products it sells to the retailer.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a business support device, a business support method, and a business support program that enable wholesalers to predict future sales of products they sell to retailers. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the business support device of the present invention has: a current inventory calculation unit that acquires sales data including the number of sales of products from a customer and calculates the current inventory of the customer by subtracting the sales number from the sales number of products sold to the customer; an order forecast quantity calculation unit that calculates an order forecast quantity that enables prediction of future orders for the product based on the increase or decrease in the current inventory quantity against the reference quantity by subtracting the current inventory quantity of the customer calculated by the current inventory calculation unit from a reference quantity that is the reference inventory quantity of the product at the customer stored in the reference quantity master table; and an output control unit that outputs the calculated order forecast quantity to an output target device.

[0007] In addition, in order to solve the above-mentioned problems and achieve the object, the business support method of the present invention includes a current stock quantity calculation step in which a current stock quantity calculation unit acquires sales data including sales quantities of products from the customer and calculates the current stock quantity of the customer by subtracting the sales quantity from the sales quantity of products sold to the customer; an order forecast quantity calculation step in which an order forecast quantity calculation unit calculates an order forecast quantity that enables prediction of future orders for the product based on the increase or decrease in the current stock quantity against the reference quantity by subtracting the current stock quantity of the customer calculated in the current stock quantity calculation step from a reference quantity that is a reference stock quantity of the product at the customer stored in the reference quantity master table; and an output control step in which an output control unit outputs the calculated order forecast quantity to an output target device.

[0008] In addition, in order to solve the above-mentioned problems and achieve the object, the business support program of the present invention causes a computer to function as: a current inventory calculation unit that acquires sales data including the number of sales of products from a customer and calculates the current inventory of a customer by subtracting the sales number from the sales number of products sold to the customer; an order forecast quantity calculation unit that calculates an order forecast quantity that enables prediction of future orders for a product based on the increase or decrease in the current inventory quantity relative to the reference quantity by subtracting the current inventory quantity of the customer calculated by the current inventory calculation unit from the reference quantity, which is the reference inventory quantity of the product at the customer stored in the reference quantity master table; and an output control unit that outputs the calculated order forecast quantity to an output target device. [Effects of the Invention]

[0009] The present invention enables wholesalers to predict future sales of products they sell to retailers. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram illustrating a hardware configuration of a task assistance device according to an embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the in-store reference quantity master table. [Figure 3] FIG. 3 is a diagram illustrating an example of the sales data storage unit. [Figure 4] FIG. 4 is a diagram illustrating an example of the store sales data storage unit. [Figure 5] FIG. 5 is a diagram showing an example of the order forecast quantity inquiry screen. [Figure 6] FIG. 6 is a diagram showing an example of in-store sales data for a specified period. [Figure 7] Figure 7 shows how the actual customer sales quantity, which is the actual number of products sold by a wholesaler to a customer, is calculated by subtracting the number of returned products from the number of products sold by the wholesaler to a retailer. [Figure 8] FIG. 8 is a diagram showing how in-store sales figures, which are the sales figures for each product sold by a retail store, are calculated. [Figure 9] FIG. 9 is a diagram showing how the in-store inventory quantity, which is the current inventory quantity on the retail store side, is calculated by subtracting the in-store sales quantity from the actual customer sales quantity. [Figure 10] FIG. 10 is a diagram showing how to calculate the forecast order quantity, which is the difference between the reference quantity serving as the reference for each product stored in the store reference quantity master table and the current in-store inventory quantity. [Figure 11] FIG. 11 is a diagram showing an example of sales data for February and March for calculating an average of the total sales numbers for February and March, which is compared with the current total sales number for April. [Figure 12] FIG. 12 is a diagram showing how the average number of total sales for February and March is calculated. [Figure 13] FIG. 13 is a diagram showing an example of sales data for April, which is referenced to calculate the total sales number for April. [Figure 14] FIG. 14 is a diagram showing how the comparative sales number is calculated by subtracting the average of the total sales numbers for February and March from the total sales number for April. [Figure 15] FIG. 15 is a diagram showing an average ratio, which is the ratio of the total sales volume in April to the average number of the total sales volume in February and March. [Figure 16] FIG. 16 is a diagram showing an example of sales data acquired when determining short-term popularity. [Figure 17] FIG. 17 is a diagram showing the sales figures for the second and third weeks of April obtained when determining short-term popularity. [Figure 18] FIG. 18 is a diagram showing the sales volume for the third week of April relative to the sales volume for the second week of April. [Figure 19] FIG. 19 is a diagram showing an example in which the sales volume for the third week of April is -50% of the sales volume for the second week of April. [Figure 20] FIG. 20 is a diagram showing an example of order forecast quantity data generated based on the popularity determination result. DETAILED DESCRIPTION OF THE INVENTION

[0011] The following describes in detail, with reference to the drawings, an embodiment of a business support system that is an example of application of the present invention to a wholesaler's terminal device in a sales model in which retailers sell products sold by wholesalers to general consumers. Note that the present invention is not limited to the following embodiment.

[0012] (overview) Nowadays, after a product is sold to a retailer, it is difficult to confirm the sales status (sales information to general consumers) of the retailer. It is difficult to grasp the retailer's sales record and inventory, making it difficult for the product wholesaler to forecast future orders from the retailer.

[0013] Furthermore, for example, if a celebrity posts on a social networking service (SNS), sales of a product may suddenly increase, leading to an increase in orders from retailers to wholesalers, resulting in inventory shortages at the wholesaler. In this case, the wholesaler may lose sales opportunities. If it is difficult for the wholesaler to forecast orders, it may be difficult for them to respond to such sudden increases in sales.

[0014] In the business support system of the embodiment, the wholesaler side grasps the sales performance and inventory quantity of the retail store, thereby analyzing and predicting the future order quantity from the retail store side, and maintaining the appropriate amount of inventory in the warehouse without excess or shortage, thereby preventing inconveniences such as loss of sales opportunities.

[0015] Furthermore, the business support device according to the embodiment grasps the sales performance of the retail store and determines the popularity / unpopularity of the product among general consumers, thereby enabling more accurate prediction of order quantities.

[0016] (Hardware configuration) As shown in Fig. 1, the business support device 1 of the embodiment includes a storage unit 2, a control unit 3, a communication interface unit 4, and an input / output interface unit 5. An input device 6 and an output device 7 are connected to the input / output interface unit 5. The output device 7 corresponds to a display unit such as a monitor device (including a home television), a printing device, or a speaker device. The input device 6 may be a keyboard device, a mouse device, a microphone device, or a monitor device that cooperates with a mouse device to realize a pointing device function.

[0017] The communication interface unit 4 is connected to a network 50, which may be a wide area network such as the Internet or a private network such as a local area network (LAN). A customer terminal device 51, which is a terminal device on the retail store side, is connected to this network 50. The customer terminal device 51 transmits sales data (sales data for general consumers) of products purchased from wholesalers for each predetermined accounting period, such as monthly or weekly, to the business support device 1. The business support device 1 acquires the sales data transmitted from the customer terminal device 51 via the communication interface unit 4.

[0018] A storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), or an SSD (Solid State Drive) can be used as the storage unit 2. The storage unit 2 stores a business support program that enables the wholesaler to predict future sales of products that it sells to retailers.

[0019] The storage unit 2 also includes a store reference quantity master table 11, a sales data storage unit 12, a store sales data storage unit 13, and an order forecast data storage unit 14, each of which is a storage area.

[0020] The in-store reference quantity master table 11 stores in-store reference quantity data, including in-store reference quantities that indicate the retail store's standard inventory quantity, for each product sold to the retail store, as shown in Fig. 2. This in-store reference quantity data includes a customer code, which is unique identification information assigned to each retail store, and a product code and product name, which are unique identification information assigned to the products sold by the retail store (products sold wholesale). The in-store reference quantity data also includes the in-store reference quantity and comparison threshold value described above.

[0021] The example in Figure 2 shows that the standard in-store quantity for "shoulder bag (black)" with product code "S001-10" sold to a customer with customer code "T001" is "10 units," the standard in-store quantity for "shoulder bag (gray)" with product code "S001-20" is "5 units," and the standard in-store quantity for "tote bag (black)" with product code "S002-10" is "8 units."

[0022] The comparison threshold is the percentage increase or decrease in the number of sales of a product that is used as the basis for determining whether the product is popular or unpopular. In other words, the comparison threshold for the "shoulder bag (black)" with the product code "S001-10" is set to "20%." Therefore, if the percentage increase or decrease in the number of sales of a product at a retail store significantly exceeds this "20%," the product is determined to be a "popular product," and if it is significantly below this "20%," the product is determined to be an "unpopular product."

[0023] 3, the sales data storage unit 12 stores sales data of products sold (wholesaled products) to retail stores by the wholesaler that owns the business support device 1. This sales data includes a sales number, a detail line number, a sales date, a sales / return category, a customer code, a product code, a product name, and a number of products sold / returned.

[0024] The example in Figure 3 shows that 20 units of "shoulder bags (black)" with product code "S001-10" were sold to a customer with customer code "T001" on February 1, 2024, and that 6 units of "shoulder bags (black)" with product code "S001-10" were sold to a customer with customer code "T001" on February 8, 2024. The example in Figure 3 also shows that 2 units of "shoulder bags (black)" with product code "S001-10" sold to a customer with customer code "T001" were returned on February 8, 2024.

[0025] As shown in FIG. 4, the in-store sales data storage unit 13 stores in-store sales data received from a customer terminal device 51, which is a terminal device on the retail store side. This in-store sales data includes the in-store sales date, customer code, line number, product code, and in-store sales quantity. The communication control unit 24 communicates with the customer terminal device 51 via the network 50 and the communication interface unit 4 to acquire the in-store sales data. The acquired in-store sales data is stored in the in-store sales data storage unit 13 by the control unit 3.

[0026] The store sales data may be transmitted from the customer terminal device 51 to the business support device 1 for each predetermined accounting period, such as each month or each week, or may be acquired by making a transmission request from the business support device 1 to the customer terminal device 51 for each predetermined accounting period, such as each month or each week. Alternatively, when making an inquiry about the forecast order quantity, the business support device 1 may request transmission of store sales data for a desired accounting period and acquire the data.

[0027] (Functional configuration of business support device) Next, the control unit 3 executes the business support program stored in the storage unit 2, thereby functioning as a calculation unit 21, a display control unit 22, an output control unit 23, a communication control unit 24, and a data generation unit 25, as shown in Fig. 1. The calculation unit 21 includes the functions of a current inventory quantity calculation unit 26, a forecast order quantity calculation unit 27, an average sales quantity calculation unit 28, a current sales quantity calculation unit 29, and an average ratio calculation unit 30.

[0028] In this example, the calculation unit 21 to the data generation unit 25 are described as being realized by software using a business support program, but all or part of the calculation unit 21 to the data generation unit 25 may be realized by hardware. In either case, the same effects as those described below can be obtained.

[0029] The current stock quantity calculation unit 26 acquires sales data including the number of products sold from the customer, and calculates the current stock quantity of the customer by subtracting the number of products sold from the number of products sold to the customer.

[0030] The order forecast quantity calculation unit 27 calculates an order forecast quantity that enables prediction of future orders for the product based on the increase or decrease in the current inventory quantity relative to the reference quantity, by subtracting the current inventory quantity of the customer calculated by the current inventory quantity calculation unit 26 from the reference quantity, which is the standard inventory quantity of the product at the customer stored in the reference quantity master table.

[0031] The output control unit 23 outputs the calculated forecast order quantity to an output target device. The output target device may be the output device 7, the memory unit 2, an external memory device, a server device on the network 50, or the like. If the output device 7 is a monitor device, the output control unit 23 obtains a video output by supplying a video signal corresponding to the forecast order quantity to the monitor device. If the output device 7 is a printer device, the output control unit 23 obtains a print output by supplying a print signal corresponding to the forecast order quantity to the printer device. If the output device 7 is a speaker device, the output control unit 23 obtains an audio output by supplying an audio signal corresponding to the forecast order quantity to the speaker device.

[0032] The current stock quantity calculation unit 26 subtracts the number of returned products from customers from the total number of products sold to customers, and uses this value as the sales quantity to perform subtraction processing on the sales quantity.

[0033] The average sales number calculation unit 28 calculates the average sales number, which is the average of the sales numbers for a predetermined period of the past accounting period.

[0034] The current sales quantity calculation unit 29 calculates the sales quantity for the current accounting period.

[0035] The average ratio calculation unit 30 calculates an average ratio, which is the ratio of the sales volume of the current accounting period to the average sales volume.

[0036] The output control unit 23 outputs the calculated average ratio to the output target device.

[0037] Furthermore, the average ratio calculation unit 30 calculates the ratio between the number of sales in the past accounting period and the number of sales in the current accounting period (for example, the ratio to the previous week in FIG. 19). The output control unit 23 outputs the calculated ratio to the output target device.

[0038] The data generation unit 25 generates forecast order quantity data including the number of products sold, the number of sales, the forecast order quantity, the popular / unpopular classification indicating the popularity or unpopularity of the product determined based on the average ratio, and the increase or decrease in the number of sales for the current accounting period compared to the average sales quantity.

[0039] The output control unit 23 outputs the generated order forecast quantity data to the output target device.

[0040] (Order quantity forecast behavior) Next, an operation of predicting order quantities from retail stores in the business support device 1 of the embodiment will be described. The person in charge at the wholesaler specifies the display of a forecast order quantity inquiry screen by operating the input device 6 of the business support device 1. When this specification is made, the display control unit 22 displays the forecast order quantity inquiry screen shown in FIG. 5 via the output device 7.

[0041] This order forecast quantity inquiry screen has an input field for the product code, an input field for the customer code, a field for selecting the comparison period, an input field for the period, and an input field for the retroactive multiplier. The input field for the product code is a field for entering the product code of the product for which an order forecast is being made. In the example of Figure 5, the product code "S001-10" has been entered to make an order forecast for the product "shoulder bag (black)."

[0042] The customer code input field is a field for inputting the customer code of the customer who will be the wholesaler of the product. In the example of Figure 5, a customer with customer code "T001" is specified.

[0043] The comparison period selection field is a field for selecting the period used to predict the order quantity. For example, when predicting the order quantity for a long-term accounting period such as monthly, "long term" is selected, and when predicting the order quantity for a short-term accounting period such as weekly, "short term" is selected.

[0044] In the period input field, the desired period is entered, taking into consideration the selected comparison period. In the example of Figure 5, "April 15th to April 21st" has been entered as the "short-term" comparison period.

[0045] The retroactive multiplier is a multiplier that is multiplied by the number of days in the input period, and is a multiplier that specifies the period of past retail store sales to be compared with the retail store sales for the input period. In other words, if the input period is "7 days" from "April 15th to April 21st" and the retroactive multiplier is "1", then "7 days x 1 = 7 days" is specified, specifying the period one week prior to April 15th, and the retail store sales for this period one week prior are compared with the retail store sales for the week of "April 15th to April 21st".

[0046] Similarly, if the entered period is "7 days" from "April 15th to April 21st" and the retroactive multiplier is "2", then "7 days x 2 = 14 days" is used, specifying the period two weeks prior to April 15th, and the average weekly retail store sales for the two weeks prior will be compared with the retail store sales for the week from "April 15th to April 21st".

[0047] Also, if the entered period is "30 days" from "April 1st to April 30th" and the retroactive multiplier is "2", then "30 days x 2 = 60 days" is used, specifying the period two months prior to April 1st, and the average retail store sales for each month from these two months prior will be compared with the retail store sales for the month of "April 1st to April 30th".

[0048] When input and selection are made on the order forecast quantity inquiry screen in this manner, the current inventory calculation unit 26 acquires the in-store sales data for the period specified in this example, from April 15, 2024 to April 20, 2024, from the in-store sales data stored in the in-store sales data storage unit 13, as shown in Figure 6. Note that this in-store sales data may be acquired from in-store sales data transmitted in advance from the customer terminal device 51 and stored in the in-store sales data storage unit 13, or the communication control unit 24 may request the customer terminal device 51 to transmit the in-store sales data for April 15, 2024 to April 20, 2024, and acquire the data.

[0049] Next, as shown in FIG. 7(a), the current inventory calculation unit 26 acquires sales data corresponding to the period "April 15th to April 21st" specified on the order forecast quantity inquiry screen from among the sales data of products sold to retailers stored in the sales data storage unit 12. This sales data includes the number of products sold as well as the number of products returned by retailers. That is, in FIG. 7(a), the number of sales / returns for which the sales / return category is set to "1: Sold" is the number of products sold by wholesalers to retailers. In contrast, the number of sales / returns for which the sales / return category is set to "2: Return" is the number of products returned by retailers to wholesalers.

[0050] Based on such sales data, the current stock quantity calculation unit 26 calculates the actual customer sales quantity by subtracting the number of returns from the number of sales for each product, as shown in Figure 7(b). That is, in the example of Figure 7(b), the number of sales of the product "shoulder bag (black)" is "20 units + 5 units = 25 units," and the number of returns of the product "shoulder bag (black)" is "5 units + 1 unit = 6 units." Therefore, the current stock quantity calculation unit 26 calculates "19 units" as the actual customer sales quantity of the product "shoulder bag (black)" by calculating "25 units of sales - 6 units of returns."

[0051] Similarly, in the example of Figure 7(b), the number of sales of the "gray shoulder bag" product is "10," and the number of returns of the "gray shoulder bag" product is "2." Therefore, the current stock quantity calculation unit 26 calculates "8 units" as the actual sales number of the "gray shoulder bag" product to the customer by calculating "10 units sold - 2 units returned."

[0052] Next, current stock quantity calculation unit 26 calculates the total number of in-store sales data (in-store sales quantity) shown in Figure 8(a) for the period from April 15th to April 21st for each product as shown in Figure 8(b). Figure 8(b) is an example in which the in-store sales quantity of the product "shoulder bag (black)" of "S001-10" is "15 units," the in-store sales quantity of the product "shoulder bag (gray)" of "S001-20" is "2 units," and the in-store sales quantity of the product "tote bag (black)" of "S002-10" is "17 units."

[0053] Next, the current stock quantity calculation unit 26 performs a calculation for each product to subtract the retailer's in-store sales quantity from the actual customer sales quantity, which is obtained by subtracting the number of returned products from the number of products sold by the wholesaler to the retailer, as shown in Figure 9(a).This makes it possible to calculate the in-store stock quantity, which indicates the current stock quantity of each product at the retail store, as shown in Figure 9(b).

[0054] Figure 9(b) is an example showing that there are currently four "shoulder bag (black)" items "S001-10" left at the retail store, six "shoulder bag (gray)" items "S001-20" left at the retail store, and one "tote bag (black)" item "S002-10" left at the retail store.

[0055] Next, as shown in Fig. 10(a), the order forecast quantity calculation unit 27 calculates the order forecast quantity for each product by subtracting the above-mentioned in-store inventory quantity from the in-store reference quantity set in the in-store reference quantity master table 11. As a result, the order forecast quantity is calculated for each product, as shown in Fig. 10(b). The output control unit 23 displays the calculated order forecast quantity for each product via the output device 7.

[0056] Figure 10(b) shows an example in which the retailer's inventory (standard in-store quantity) for the "shoulder bag (black)" product "S001-10" is based on "10 units," but the current inventory (in-store inventory quantity) is "4 units," so the predicted order quantity is calculated as "standard in-store quantity 10 units - in-store inventory quantity 4 units = 6 units."

[0057] This indicates that the retailer's current inventory, which is based on a standard of 10 units, is 6 units, falling below the standard inventory level. This allows the person in charge to predict that there will be orders for the product in the near future. Alternatively, the person in charge can contact the retailer to urge them to place an order for the product that is in short supply. Furthermore, since there is an expected order, the person in charge can check whether the wholesaler's warehouse has enough stock to match the predicted order quantity, and if there is a shortage, they can purchase the product in advance or produce the product immediately to increase the inventory and prepare for the order from the retailer.

[0058] Also, Figure 10(b) shows an example in which the retailer's inventory quantity (in-store standard quantity) for the product "S001-20" "Shoulder bag (gray)" is based on "5 units," but the current inventory quantity (in-store inventory quantity) is "6 units," so the predicted order quantity is calculated as "in-store standard quantity 5 units - in-store inventory quantity 6 units = -1 unit."

[0059] This indicates that the retailer's current inventory, which is based on a standard of 5 units, is 6 units, exceeding the standard inventory level. Therefore, the person in charge can predict that there is no prospect of receiving an order for the product. If there is no prospect of an order, the person in charge can determine whether the inventory level in the wholesaler's warehouse is appropriate, and if there is excess inventory, they can take appropriate action, such as halting production.

[0060] Also, Figure 10(b) shows an example in which the retailer's inventory quantity (standard in-store quantity) for the "Tote Bag (Black)" product "S002-10" is based on "8 units," but the current inventory quantity (in-store inventory quantity) is "1 unit," so the predicted order quantity is calculated as "standard in-store quantity 8 units - in-store inventory quantity 1 unit = 7 units."

[0061] This indicates that the retailer's inventory, which has a standard of eight units, is currently one unit, falling below the standard inventory level. This allows the person in charge to predict that an order for the product will be placed in the near future. Alternatively, the person in charge can contact the retailer to urge them to place an order for the product that is in short supply. Furthermore, since an order is expected, the person in charge can check whether the wholesaler's warehouse has enough stock to match the predicted order quantity, and if there is a shortage, they can purchase the product in advance or produce the product immediately to increase the inventory and prepare for the order from the retailer.

[0062] (Popularity determination action) Next, the business support device 1 of the embodiment calculates and displays an average ratio corresponding to the current popularity of a product based on the sales of the product over a long accounting period such as a month or a short accounting period such as a week, thereby enabling more accurate order forecasting.

[0063] (Long-term popularity determination) Specifically, when determining long-term popularity, the person in charge enters the product code and customer code of the desired product on the order forecast quantity inquiry screen described using Figure 5, selects "Long Term" as the comparison period, and enters, for example, "2" as the "retroactive multiplier." This "retroactive multiplier" indicates the number of months (or weeks) from which sales data will be obtained going back from the current month (or current week), as described above.

[0064] Therefore, when the current month is "April," the "long-term" comparison period is selected, and a "retroactive multiplier" of "2" is input, the average sales number calculation unit 28 acquires sales data for February and March, which are sales data for two months prior to the current April, from the sales data storage unit 12, as shown in FIG. 11. Furthermore, the average sales number calculation unit 28 calculates the total sales numbers for February and March by subtracting the number of returned items from the number of sales based on the sales data for February and March, as shown in FIG. 12(a). Then, the average sales number calculation unit 28 calculates the average sales number, which is the average of the total sales numbers for February and March.

[0065] In this example, the total number of sales in February is "41 units" and the total number of sales in March is "27 units", so the average sales number calculation unit 28 calculates the average sales number to be "34 units".

[0066] Next, the current sales quantity calculation unit 29 acquires the sales data for April, which is the current month in this example, from the sales data storage unit 12, and calculates the total sales quantity for April, as shown in Figure 13. In this example, the total sales quantity for April is "57 units."

[0067] Next, the average ratio calculation unit 30 calculates the comparative sales number by subtracting the average sales number of February and March from the total sales number for April, as shown in Figure 14. In this example, the comparative sales number of "+23 units" is calculated by subtracting the average sales number of "34 units", which is the average sales number of February and March, from the total sales number of "57 units" for April.

[0068] The average ratio calculation unit 30 calculates an average ratio, which is the ratio of the total sales volume for April to the average sales volume for February and March, based on the following calculation formula.

[0069] Average ratio = (April sales total 57 units x 100%) ÷ February and March average sales 34 units ≒ 168%

[0070] The output control unit displays this average ratio via the output device 7. In this example, the average ratio of "168%" indicates that sales in April were significantly higher than in February and March. Therefore, it can be seen that the "shoulder bag (black)" is a "popular item" as of April. This allows the person in charge to predict that orders for the product will be received in the near future. Alternatively, the person in charge can contact the retailer to encourage them to place an order for the product. Furthermore, since there is an expected order, the person in charge can purchase the product in advance or produce the product quickly to increase inventory and prepare for orders from the retailer. (Short-term popularity determination) Next, when determining short-term popularity, the person in charge enters the product code and customer code of the desired product on the order forecast quantity inquiry screen described using Figure 5, selects "short term" as the comparison period, and enters, for example, "1" as the "lookback multiplier." When "short term" is selected as the comparison period, the "lookback multiplier" indicates the number of weeks from the current week for which sales data will be obtained.

[0071] Therefore, if the current week is the "third week of April," the average sales quantity calculation unit 28 acquires sales data for the "second week of April," which is one week prior to the current third week of April, from the sales data storage unit 12, as shown in Fig. 16. Also, the average sales quantity calculation unit 28 acquires sales data for the current week, the "third week of April," from the sales data storage unit 12, as shown in Fig. 16.

[0072] Next, average sales quantity calculation unit 28 calculates the sales quantities for "the second week of April" and "the third week of April" by subtracting the number of returned items from the number of sales, as shown in Fig. 17. In this example, the sales quantity for "the second week of April" is "38 units," and the sales quantity for "the third week of April" is "19 units."

[0073] Next, the current sales quantity calculation unit 29 subtracts the sales quantity for the "second week of April", which is "38 units", from the sales quantity for the current week, which is "third week of April", which is "19 units", as shown in Figure 18, to calculate the comparative sales quantity of "-19 units", as shown in Figure 19.

[0074] The average ratio calculation unit 30 calculates the ratio of the sales volume in the "third week of April" to the sales volume in the "second week of April", that is, the ratio of the sales volume in the "third week of April" to the sales volume in the "second week of April", based on the following calculation formula.

[0075] Compared to the previous week = (sales in the third week of April compared to sales in the second week of April - 19 units x 100%) ÷ sales in the second week of April 38 units ≒ -50%

[0076] The output control unit displays this comparison with the previous week via the output device 7. In this example, the comparison with the previous week of "-50%" indicates that sales in the third week of April were significantly lower than in the second week. Therefore, as of the third week of April, it is clear that the "shoulder bag (black)" is an "unpopular item." Therefore, the person in charge can predict that there is no prospect of receiving orders for the product. If there is no prospect of an order, the person in charge can determine whether the inventory level in the wholesaler's warehouse is appropriate, and if there is excess inventory, can take appropriate action, such as halting production.

[0077] (Order forecast quantity data generation behavior) Once the average ratio or the comparison with the previous week is calculated in this manner, the data generation unit 25 generates order forecast quantity data including a "popular / unpopular category" automatically determined based on the average ratio or the comparison with the previous week, and stores the data in the order forecast data storage unit 14.

[0078] Specifically, when an average ratio of, for example, "168%" is calculated as the average ratio of the product "shoulder bag (black)" as described above, the data generation unit 25 compares the calculated average ratio with the comparison threshold value of "20%" for the product "shoulder bag (black)" stored in the store standard quantity master table shown in Fig. 2. In this example, since the average ratio significantly exceeds the comparison threshold value of "20%," the data generation unit 25 determines that the product "shoulder bag (black)" is "popular."

[0079] Then, the data generation unit 25 generates order forecast quantity data including the product code, customer code, product name, sales quantity, in-store sales quantity, next order forecast quantity, in-house warehouse inventory quantity, popular / unpopular classification, and increase / decrease number, and stores this in the order forecast data storage unit 14.

[0080] In this example, the product code is "S001-10" and the customer code is "T001." The product name is "shoulder bag (black)," the number of units sold is "19 units (see Figure 7(b))," and the in-store sales volume is "15 units (see Figure 8(b))." The next forecast order volume is "6 units (see Figure 10(b))," the in-house warehouse inventory volume is, for example, "30 units," the popular / unpopular category is "popular," and the increase / decrease is "+23 units."

[0081] As another example, when a week-over-week comparison of, for example, "-50%" is calculated as the week-over-week comparison for the product "shoulder bag (black)" as described above, the data generation unit 25 compares the calculated week-over-week comparison with the comparison threshold of "20%" for the product "shoulder bag (black)" stored in the store standard quantity master table shown in Fig. 2. In this example, since the week-over-week comparison is significantly lower than the comparison threshold of "20%," the data generation unit 25 determines that the product "shoulder bag (black)" is "unpopular."

[0082] Then, the data generation unit 25 generates order forecast quantity data including the product code, customer code, product name, sales quantity, in-store sales quantity, next order forecast quantity, in-house warehouse inventory quantity, popular / unpopular classification, and increase / decrease number, as shown in Figure 20, and stores this in the order forecast data storage unit 14.

[0083] In this example, the product code is "S001-10" and the customer code is "T001." The product name is "Shoulder bag (black)," the number sold is "19 units," and the in-store sales volume is "15 units." The next order forecast volume is "6 units," the in-house warehouse inventory volume is, for example, "30 units," the popular / unpopular category is "unpopular," and the increase / decrease volume is "-19 units."

[0084] By referring to this order forecast quantity data at the desired time, the person in charge can take measures in advance, such as keeping more warehouse inventory than usual, since if the product is determined to be popular (the popular / unpopular category is "popular"), an increase in the number of orders is expected.

[0085] Furthermore, if the product is determined to be unpopular (the popular / unpopular category is "unpopular"), the person in charge can place an order or produce the product while taking into consideration the decrease in the number of orders.

[0086] Furthermore, the characteristics of popular and unpopular products can be analyzed to quickly grasp the needs of the distribution retail industry, where trends change rapidly, and the analysis results can be reflected when planning new products, for example.

[0087] (Effects of the embodiment) As is clear from the above explanation, the business support device 1 of the embodiment can grasp the sales performance of a retail store as well as the inventory quantity of the retail store. Therefore, it is possible to analyze and predict future order quantities, and to prevent inconveniences such as lost sales opportunities. In addition, it is possible to maintain the inventory in the warehouse at an appropriate level, without excess or shortage.

[0088] It is also possible to grasp the sales performance of retail stores and determine the popularity or unpopularity of products among the general public, which allows for more accurate analysis and prediction of future order quantities.

[0089] [Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This invention can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of goals "8" and "9" of the SDGs.

[0090] Furthermore, this invention can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs goals 12, 13, and 15.

[0091] Furthermore, the present invention can contribute to strengthening control and governance, thereby contributing to the achievement of Goal 16 of the SDGs.

[0092] [Other embodiments] The present invention can be implemented in various different forms other than the above-described embodiments within the scope of the technical concept described in the claims.

[0093] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically may be performed manually, or all or part of the processes described as being performed manually may be performed automatically using a known method or the like.

[0094] Furthermore, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in the specification or drawings may be changed as desired unless otherwise specified.

[0095] Furthermore, the components of the business support device 1 shown in the figure are conceptual functional components and do not necessarily have to have the physical configuration shown in the figure. For example, all or any part of the processing functions of the business support device 1, particularly the processing functions performed by the control unit 3, may be realized by a program interpreted and executed by the control unit 3 (CPU: Central Processing Unit), or may be realized by hardware using wired logic.

[0096] The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processes described in the embodiments, and is mechanically read by the business support device 1 as needed. That is, a computer program is recorded in the storage unit 2, such as a ROM or HDD, for working with an OS (Operating System) to give instructions to a control unit 3 (CPU) and perform various processes. The computer program is loaded into RAM, expanded, and executed by the control unit 3 as appropriate.

[0097] In addition, the business support program of this business support device 1 may be stored in another server device connected to the business support device 1 via any network, and all or part of it may be downloaded and executed as needed.

[0098] Furthermore, the business support program for executing the processes described in the embodiments may be stored in a non-transitory computer-readable recording medium, or may be configured as a program product.

[0099] Here, the "recording medium" can be any "portable physical medium" such as a memory card, a USB (Universal Serial Bus) memory, an SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (registered trademark) (Electrically Erasable and Programmable Read Only Memory), a CD-ROM (Compact Disk Read Only Memory), an MO (Magneto-Optical Disk), a DVD (Digital Versatile Disk), and a Blu-ray (registered trademark) Disc.

[0100] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code.

[0101] It should be noted that a "program" is not necessarily limited to a single structure, but includes a structure that is distributed as multiple modules or libraries, and a structure that achieves its function by working together with other programs, such as an OS.

[0102] Furthermore, the specific configuration for reading the recording medium in the task support device 1 of the embodiment, the reading procedure, and the installation procedure after reading can be any known configuration or procedure.

[0103] The memory unit 2 is a storage means such as a memory device such as RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, or an optical disk, and stores various programs, tables, databases, web page files, etc. used for various processes or to provide websites.

[0104] The business support device 1 may be configured as an information processing device such as a known personal computer or a workstation, or may be configured as an information processing device connected to any peripheral device. The information processing device may be implemented with software (including programs or data) that realizes the processes described in the embodiments.

[0105] Furthermore, the specific forms of distribution and integration of the devices are not limited to those shown in the drawings, and all or part of them can be functionally or physically distributed or integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments can be selectively implemented by combining them in any way. [Industrial Applicability]

[0106] The present invention is suitable for application to a sales business in which a retail store purchases products from a wholesaler and sells them, such as in the food distribution retail industry, for example. [Explanation of symbols]

[0107] 1 Business support equipment 2 Storage section 3. Control Unit 4. Communication interface section 5 Input / output interface section 6 Input Devices 7 Output Devices 11 Store standard quantity master table 12 Sales data storage unit 13 Store sales data storage section 14 Order forecast data storage section 21 Calculation section 22 Display control unit 23 Output control section 24 Communication control section 25 Data Generation Unit 26 Current Stock Calculation Section 27 Order Forecast Quantity Calculation Department 28 Average Sales Calculation Section 29 Current Sales Calculation Department 30 Average Ratio Calculation Part

Claims

1. a current inventory calculation unit that acquires sales data including the number of products sold from a customer and calculates the current inventory number of the customer by subtracting the number of products sold to the customer from the number of products sold to the customer; an order forecast quantity calculation unit that calculates an order forecast quantity that enables prediction of future orders for the product based on an increase or decrease in the current inventory quantity relative to the reference quantity by subtracting the current inventory quantity of the customer calculated by the current inventory quantity calculation unit from a reference quantity that is a reference inventory quantity of the product at the customer stored in a reference quantity master table; an output control unit that outputs the calculated order forecast quantity to an output target device; A business support device having the above.

2. the current inventory calculation unit subtracts the number of returned products from the customer from the total number of products sold to the customer, and uses the result as the sales number to perform subtraction processing of the sales number; 2. The business support device according to claim 1, wherein:

3. an average sales quantity calculation unit that calculates an average sales quantity that is an average of the sales quantities for a predetermined period of past accounting periods; a current sales quantity calculation unit that calculates the sales quantity for the current accounting period; an average ratio calculation unit that calculates an average ratio, which is a ratio of the sales volume of the current accounting period to the average sales volume; the output control unit outputs the calculated average ratio to the output target device; 3. The business support device according to claim 2, wherein:

4. the average ratio calculation unit calculates a ratio between the sales volume in a past accounting period and the sales volume in a current accounting period; the output control unit outputs the calculated ratio to the output target device; 4. The business support device according to claim 3, wherein:

5. a data generation unit that generates order forecast quantity data including the number of sales of the product, the sales number, the order forecast quantity, a popularity / unpopularity classification indicating the popularity or unpopularity of the product determined based on the average ratio or the ratio, and an increase or decrease in the sales number for the current accounting period relative to the average sales number; the output control unit outputs the generated order forecast quantity data to the output target device; 5. The business support device according to claim 3 or 4, wherein:

6. a current stock quantity calculation step in which a current stock quantity calculation unit acquires sales data including the number of products sold from the customer and calculates the current stock quantity of the customer by subtracting the number of products sold to the customer from the number of products sold to the customer; an order forecast quantity calculation step in which an order forecast quantity calculation unit calculates an order forecast quantity that enables prediction of future orders for the product based on an increase or decrease in the current inventory quantity relative to the reference quantity by subtracting the current inventory quantity of the customer calculated in the current inventory quantity calculation step from a reference quantity that is a reference inventory quantity of the product at the customer stored in a reference quantity master table; an output control step in which an output control unit outputs the calculated order forecast quantity to an output target device; A business support method having the above.

7. Computer, a current inventory calculation unit that acquires sales data including the number of products sold from a customer and calculates the current inventory number of the customer by subtracting the number of products sold to the customer from the number of products sold to the customer; an order forecast quantity calculation unit that calculates an order forecast quantity that enables prediction of future orders for the product based on an increase or decrease in the current inventory quantity relative to the reference quantity by subtracting the current inventory quantity of the customer calculated by the current inventory quantity calculation unit from a reference quantity that is a reference inventory quantity of the product at the customer stored in a reference quantity master table; An output control unit that outputs the calculated order forecast quantity to an output target device. A business support program that functions as a

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