JP2001338189A - Automatic product ordering system, automatic product ordering method, and recording medium therefor - Google Patents

Abstract

(57) [Summary] [PROBLEMS] To provide an automatic product ordering system and an automatic product ordering method in a retail store, which can reduce in-store work related to ordering. An automatic product ordering system according to the present invention includes a result DB storing information required for ordering a product, and information including a reference value / days master including information on a maximum stock and information serving as a reference for ordering. Storage unit, actual sales data input unit, bargain purchase data input unit, actual shelf data input unit, and actual sales data and bargain purchase data, the actual sales of basic products by store and product An actual sales data extracting unit for extracting data, an actual DB updating unit for updating the contents of the actual DB based on the extracted actual sales data and actual shelf data of the standard product, and contents and reference of the updated actual DB An arithmetic unit for calculating information including the order quantity of the product based on the information of the value / days master is provided.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an automatic product ordering system and an automatic product ordering method for automatically ordering a wholesaler according to the stock status of products at a retail store.

[0002]

2. Description of the Related Art At present, exhibit competition in the retail industry is intensifying. This has led to a rapid increase in the number of stores due to the stagnant population in recent years, and has led to a decrease in the supporting population per store. That is, the trade area is shrinking and becoming a small trade area, and the sales per store is decreasing. With such changes in the environment surrounding retail stores, retail stores are required to further reduce costs. If this low cost is implemented, for example, by simply reducing personnel costs, the quality and quantity of staff at the store will be reduced, and there is a concern that the sales floor management capability will be reduced.

FIG. 41 shows an example of a store floor layout. As shown in the figure, inside the retail store, there are shelves called a standard gondola (standard sales section) where standard items are placed, and shelves called an end-out space (special sales section) where special sale items are placed. The standard sales area is a space where consumers can buy for the purpose, and the special sales area is a space where businesses can stimulate consumer demand.
The layout is as shown in the figure.

[0004] Such over-the-counter operations actually performed in a retail store include order placement, receiving, inspection, replenishment, shelf management, inventory management, sales floor inspection, sales floor staging, cleaning, customer service, part-time job part work instruction management, and the like. is there. FIG. 42 is a graph showing the time composition ratio of work types of general storefront work. From this graph, it can be seen that, for example, 13% of the time is spent only on basic product ordering work. In this standard product ordering work, a clerk generally looks at the display shelf and places an order so as to replenish the shortage so as to fill the display shelf.

[0005]

If an order is placed so as to fill all the display shelves in this way, the cost for purchasing the goods increases, and the cash flow of the retailer deteriorates.
Therefore, it is ideal for a retail store to keep the stock of goods to a minimum. That is, it is preferable to place on the display shelf a product that does not degrade the appearance at the store (the impression given to the consumer) due to the inventory amount being too small and does not run out of stock. In addition, from the viewpoint of cost reduction, it is desired to automate a troublesome ordering operation.

[0006] On the other hand, there is known a technology of automatic ordering and inventory management for a manufacturer or a trading company at a wholesale wholesaler. In general, the way of selling differs depending on the type of product. However, since these wholesale dealers are classified by business type, these technologies are based on the characteristics (such as how to sell) of the products handled by the wholesale dealer. And depending on the characteristics of the product to be handled. However, in the case of retail stores, unlike wholesale wholesalers by business category, there are many cases where a variety of products are handled, and each product having various product characteristics is managed. Also, unlike a warehouse of a wholesale wholesaler, the actual inventory (the actual inventory) fluctuates due to illegal acts such as shoplifting, and thus differs from the calculated inventory (theoretical inventory).

[0007] The present invention has been made in view of such circumstances, and in a retail store that handles a wide variety of products and has inventory at the store, it is possible to reduce the number of store operations related to ordering (particularly, the standard type). An object of the present invention is to provide an automatic product ordering system and an automatic product ordering method for automating product ordering work.

[0008]

In order to achieve the above object, according to the first aspect of the present invention, an achievement DB storing information required for ordering a product, a sales DB of the product, Storage means for storing information including information relating to the maximum stock set according to the number of days of the sale date of the product and a reference value / days master including information serving as a reference for ordering; and products collected for each store. Receiving actual sales data input, receiving actual sales data input means for storing in the storage means, receiving special purchase data input, receiving special sales data input means for storing in the storage means, and receiving input of actual shelf data. A real shelf data inputting means to be stored in a storage means; an actual sales data extracting means for extracting actual sales data of a standard product for each store and each product from the actual sales data and the special purchase data; Sales data extraction Means for updating the contents of the results DB based on the actual sales data of the standard product extracted by the means and the actual shelf data, and information of the updated results DB and the reference value / days master information. And calculating means for calculating information including the order quantity of the product based on the information.

According to a second aspect of the present invention, in a method for automatically ordering goods using a computer device, a performance database storing information required for ordering goods.
A procedure for storing information including information relating to the maximum inventory set according to the sales amount of the product and the number of days of the product sales day, and a reference value / days master including information serving as a reference for ordering; Receiving the input of the actual sales data of the goods collected in the storage means, receiving the input of the special purchase data, storing the data in the storage means, receiving the input of the actual shelf data, A procedure of storing, a procedure of extracting actual sales data of a staple product for each store and each product from the actual sales data and the bargain purchase data, and an actual sales data of the staple product extracted in the procedure and the actual sales data. A procedure for updating the contents of the achievement DB based on the shelf data, and a procedure for calculating information including the order quantity of the product based on the updated contents of the achievement DB and the information of the reference value / days master;
It is characterized by including.

According to a third aspect of the present invention, when the computer is installed in a computer device, the device is connected to the second device.
And a computer-readable recording medium on which software serving as an apparatus for executing the method described in (1) is recorded.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an automatic product ordering system according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a diagram illustrating an example of a configuration of an automatic product ordering system according to the present embodiment.

First, an outline of the automatic product ordering system 1 of the present embodiment will be described (see FIG. 2). The automatic product ordering system 1 is installed in one retail store or a facility that performs order processing for a business having a plurality of retail stores.
Each retail store has a POS (Point of Point).
Sales) terminal (not shown) is installed. The automatic merchandise ordering system 1 collects the POS data from these POS terminals or from a host computer or the like that stores the POS data of these POS terminals. The POS data (actual sales data) is the sum of the sales volume and the sales amount for each retail store, each product, and each sales day (the date on which the cash register is passed). From the POS data, data such as sales date and sales quantity for each store and each product is obtained. Then, using the actual shelves data (data of actual inventory at the store at a certain point in time),
Correct the theoretical stock for each product. At the retail store, the inventory amount fluctuates due to illegal activities as described above, so it is necessary to appropriately acquire the actual shelf data. Furthermore, using the special purchase data on the purchase of special products ordered and purchased separately from retail stores, calculate the stock amount and order amount of standard products,
Send order data (defined separately) to a given wholesaler.

An example of the above-described process from collection of POS data to calculation of order quantity, transmission of order data (EOS data) to a wholesaler (wholesale store), and receipt and delivery of goods from the wholesale store to the store. Is shown in FIG. As described above, since the automatic product ordering system 1 automates the ordering of the standard product, there is an effect that the standard ordering operation, which accounts for 13% of the storefront operation shown in FIG. 42, can be eliminated.

Further, the automatic product ordering system 1 focuses on the point that the selling method differs for each category (line) of the product, and furthermore, the selling method differs for each product even in the same category. The price rank and the number of days rank are set.

The price rank is an index for ranking each product according to the sales. As shown in FIG.
In the example of ranking in the stages (S, A, B, C, D), a product rank of “S” is assigned to products up to the top 20% of the sales amount, and products with a sales amount of 21% to 40%. Is assigned a price rank “A”, and so on.

The number of days rank is an index for ranking according to the number of days (= number of appearances) in which sales of the product have occurred within a predetermined period. As shown in FIG. 5, in the example of ranking in five stages (1 to 5) and the predetermined period as eight weeks (business days are 7 days × 8 weeks = 56 days), the face appearance ratio (= Number of appearances @ 56 days x 10
0) is 100% to 80% of the product for the number of days rank "1"
, And the number of days rank “2” is assigned to the product having a face-up ratio of 79% to 50%.

FIG. 6 shows the amount rank and the number of days rank.
Example of product classification (both price rank and days rank are 5
). As can be seen from this figure, products that sell well are ranked higher in both the price rank and the number of days rank. In the example of FIG. 6, there is no product ranked in the amount rank “S” and the number of days rank “1”.
80%) is preferably set appropriately so as to be distributed on a diagonal line from the money rank “S” and the day rank “1” to the money rank “D” and the day rank “5”.

In addition, since the way of selling differs depending on the category (line) of a product, there are cases where the above-described five-stage ranking cannot be applied to some lines. Therefore, in the automatic product ordering system 1, as shown in FIGS. 7 and 8, 5 × 5 (5 rows and 5 columns)
6 × 5, 5 × 4, 4 × 5, 4 ×
4, 4 × 3, 3 × 4, 3 × 3, a plurality of table layouts (line group TB layout: A1 to A
8, see FIG. 7). Further, a table layout (B1 to B8, see FIG. 8) for stocking exceeding the maximum stock amount of a standard gondola is also set separately. These are appropriately selected and set according to the characteristics of the sales of the product and the line.

Next, the configuration of the automatic ordering system 1 according to the present embodiment will be described. The automatic ordering system 1 includes a processing unit 2 that performs each processing described below, and a storage unit 3 that stores various data that is a processing target or a processing result.

First, each data stored in the storage unit 3 will be described. Store master: a store code for identifying each retail store, a store name, a code for identifying a store group,
It consists of data having items of the number of deliveries per week (the number of deliveries per week) and the code of the delivery day (delivery day code) (see FIG. 9). In this store master, a store code is used as a search key. Note that a store group is a group of stores that are similar in size and sales trend. For stores in the same group, a common reference value and days master are used when using the following reference values and days masters. . This eliminates the need to prepare a reference value / days master for each store, and facilitates maintenance of the reference values / days master.

Line name table: It is composed of line codes and data having line name items (see FIG. 10). In this table, the line code is used as a search key. Line group master table: Stores a store group, a line code (line code), a line name, and data having items of a later-described reference value TB layout and rank division (see FIG. 11). In addition,
A line is a product category (snack, canned, ...
Etc.). In this table, a store group and a line code are used as search keys.

Reference value / days master: Reference value TB layout, amount rank, number of days rank, stock days in stock, stock days at ordering point, past days in the actual day (specified value of number of appearance days), stock stock order F / C (Figure 12)
reference). The holding stock order F / C indicates the presence or absence of a product to be ordered up to the holding stock number exceeding the maximum display amount of the following product master. In the example of FIG. 12, when there is no product to be ordered up to the number of stocks that exceeds the maximum display amount, the value is set to “0” (or “1” when there is). In addition, this stock holding order F
/ C is set according to the inventory turnover rate of the product (= monthly sales amount / (maximum display number × product price)). This is because products with a higher inventory turnover are more likely to be out of stock if they do not have more than the maximum number of items in the display. So as to perform the inventory holding F / C.

FIG. 13 shows a typical example of the reference value / days master. The reference value / days master uses the reference value TB layout, the money amount rank, and the days rank as search keys. Note that the reference days and days included in the master, the number of holding days (holding stock days), the number of ordering days (ordering stock days), and the number of past days (actual past days) are appropriately determined according to the characteristics of the product and line sales. It is set, but by changing these set values, the store inventory can be controlled. For example, store inventory can be increased by increasing the number of inventory days, and conversely, store inventory can be reduced by reducing the number of inventory days.

Amount / Day Rank Separation Parameter Table: Rank separation items, amount ranks (S, A, B, C, D, E) in which the amount rank separation parameter and the number of day separation parameter are stored, and the number of days rank (1) To 5) (see FIG. 14). This table uses rank division parameters (R1, R2,...) As search keys. FIG. 15 shows an example of the money amount rank separation parameter and the day number separation parameter stored in this table. For example, if the rank break is R1
In the case of the pattern No., the amount rank separation parameter of the amount rank “S” is “0% to 20%”, and the number of days rank separation parameter of the number of days rank “1” is “0% to 0.
5% ".

Amount total table: It is made up of data having items of store code, line group, and total amount VE within period (see FIG. 16). This table uses a store code and a line group as search keys. Special sale section table: It is made up of data having each item of a special sale section code and a special sale section name (special sale section name) (see FIG. 17). This table uses a special sale category as a search key.

Product master: a product code for identifying a product, a product name, a face when the product is displayed on a standard gondola (shelf) (number arranged on the front of the shelf), and a depth (arranged in the depth direction) Number), number of stacks (number of stacks), maximum number of displays (= face x depth x number of stacks), minimum number of displays (set to a value that does not degrade the appearance of stores), and delivery units (See FIG. 18).

Result DB (result database): store code of serial number 1, store name of serial number 2, store group of serial number 3, product code of serial number 4 (for identifying a product), product name of serial number 5, and serial number 6 (Categories of products that further summarize several lines: processed food, confectionery, daily necessities, miscellaneous goods, etc.), line code of serial number 7, line name of serial number 8, line group of serial number 9, line number 9-1 Reference value TB layout, rank break of serial number 9-2, start date of serial number 10 which is the start date of the POS data extraction target period, serial number 11 which is the end date of the POS data extraction target period
, The sales date of serial number 15 which is the date on which the target product was actually sold, the quantity (sales) of serial number 16, the amount (sales) of serial number 17, and the sales quantity of the POS data extraction target period. The in-period amount of the serial number 18, the in-period amount of the serial number 19, which is the sales amount in the POS data extraction target period,
Serial number 20 which is the average selling price during the POS data extraction target period
, The intra-period amount VE of the serial number 21, which is the square root of the intra-period amount of the serial number 19, and the serial number 22, which will be described later.
, The number of appearances within the period of serial number 23, which is the number of times sold during the POS data extraction target period, the composition ratio of the intra-period appearance of serial number 24 described later, and the aforementioned serial number 2
5, the rank of the number of days of the serial number 26, the stock days of the following serial number 27, the stock days of the order point of the serial number 28, 2
9, the actual number of past days (designated value of the number of appearance days), the stock inventory F / C of serial number 29 and the actual total number of sales of serial number 30 described below, the actual average sales number of serial number 32 below, and the holding of serial number 33 It is composed of data having items of the stock quantity and the order point stock quantity of the serial number 24 (see FIG. 19).

Here, the stock days in stock, the stock days in the reorder point,
The relationship between the actual number of actual sales (the actual number of sales), the number of stocks held, and the number of stocks at the ordering point will be described. These have the relationship shown in FIG. The holding stock number is the maximum stock number. Average daily sales is the average number of sales per day of sales,
The number of stocks in stock is obtained by multiplying the average number of sales in actual days by the number of stock days in stock. The ordering point days is the number of days obtained by adding the lead time required for the delivery of the product to one sales day, and the ordering point inventory quantity is obtained by multiplying the actual number of sales in actual day by the ordering point number of days.

Next, the configuration of the processing section 2 will be described. The processing unit 2 includes a POS data input unit 2a, a bargain purchase data input unit 2b, an actual shelf data input unit 2c, a POS data extraction unit 2d, a performance DB update unit 2e, a calculation unit 2f, and an optimization unit. 2g, an output unit 2h, and an ordering unit 2i.

The POS data input unit 2a acquires POS data obtained from a POS terminal installed in each retail store and stores it in the storage unit 3. Special purchase data input section 2b
Acquires data (sale purchase data) relating to a special sale item ordered separately from the standard product, and stores the data in the storage unit 3. The bargain purchase data is separately managed under the host computer and is acquired from the host computer. The actual shelf data input unit 2c acquires the actual shelf data that has been separately tabulated and stores the data in the storage unit 3.

The POS data extraction unit 2d extracts POS data of a period to be extracted and stores for each store and each product from the POS data obtained by the POS data input unit 2a. The result DB updating unit 2e updates the contents of the result DB using the extracted POS data. Details will be described later.

The computing unit 2f calculates the content of the result DB
The reference value data (see FIG. 21) is obtained, and the order quantity is further obtained. Note that examples of the reference value data shown in FIG. 21 include a store code, a store name, a store group, a product code, a product name, a large classification, a line code, an operation date, a money rank, a number of days rank, a stock inventory number, and an order point. Days of stock,
It consists of store-specific and product-specific data, with items such as F / C for holding inventory, holding stock quantity, and order point stock quantity. As described above, the reference value data includes basic data related to the order processing. The order data is data including the order amount for each store and each product, and the order amount is calculated from the following formula (see FIG. 20). (1) In the case where the setting of the inventory holding order F / C is not set so as to place an order up to the inventory amount exceeding the maximum display amount: order amount = maximum display amount−current number of (theoretical inventory) (2) When the setting of the inventory holding F / C is set to place an order up to the inventory quantity exceeding the maximum display quantity: order quantity = inventory quantity held-current inventory quantity (of theoretical inventory)

The optimizing unit 2g calculates an optimal order quantity, an order date, and the like so that the stock in the store is not out of stock. In this calculation, for example, all settings are tried, and a set value in a case where the out-of-stock is 0 is set as an optimal selection. The output unit 2h displays various data, lists, and the like on a display device or causes a printing device to print. Ordering part 2i
Sends order data including the order quantity for each store and each product calculated by the calculation unit 2f to a predetermined wholesaler (wholesale) and places an order.

The processing unit 2 may be realized by dedicated hardware. The processing unit 2 is constituted by a memory and a CPU (Central Processing Unit), and functions of the processing unit 2 are provided. The function may be realized by loading a program for realization into a memory and executing the program. Further, it is assumed that an input device, a display device, and the like (neither is shown) are connected to the automatic product ordering system 1 as peripheral devices. Here, the input device refers to an input device such as a keyboard and a mouse. A display device is a CRT (Cathode Ray)
Tube), a liquid crystal display device, or the like, or a device that outputs data to the outside by a printer.

Next, the operation of the thus configured automatic commodity ordering system 1 will be described.

In the following description, it is assumed that the POS data, the bargain purchase data, and the actual shelf data are previously input and stored in the storage unit 3. First, the user receives an input of a target period (input period) for POS data extraction. The input period (number of weeks) is set and input in a range of 4 weeks to 13 weeks. Further, the average number of business days during the week is input, and the number of business days in the input period is calculated from the following formula and the following number of weeks. Number of business days during the input period = number of weeks x average number of business days per week

Next, POS data extraction processing for the input period is performed. Here, the POS data input processing has three types of processing modes: batch processing, weekly processing, and daily processing. Target period / week parameter (input for each processing by external input) Start week to end week: 4 Period from week to 13 weeks Target period / date parameter (input for each process by external input) Start date-end date

In the case of batch processing, the results DB is initialized and
POS data for each day / store / commodity in the given target period / week parameter period is input. If there is no POS data, for example, the result DB is initially set as a stock having the number of stocks held as an initial value. In the case of weekly processing, POS data for each day, each store, and each product in the end week of the given target period / week parameter is input. In the case of daily processing, POS data for each day / store / product is input for the end year / month / day of the end parameter of the end week of the given target period / week parameter.

The extraction conditions for extracting POS data are described below. 1. Extraction by store master table: With reference to the store master table, only stores registered in the store master table are extracted. 2. Extraction by week and date: In the case of the above-described weekly processing and daily processing, extraction is performed on weekly or date data according to given parameters. 3. Zero extraction of quantity: An item whose quantity in the POS data is zero is extracted as non-target. 4. Extraction of standard products: With the special sale category in the POS data as a key, extraction is performed for “standard” registered in the special sale category table. In the example of the special sale section table of FIG. 17, when the special sale section is “00” or a space, the POS data is extracted as “standard”.

Next, using the "store group" in the store master table and the "line code" of the POS data as search keys, a line group is obtained from the line group master table.

Next, the result DB is updated using the extracted POS data. This process is performed by the result DB updating unit 2e. Details are as follows.

First, the item of the store code of serial number 1 is updated by the store code in the POS data to be extracted. Next, referring to the store master, the corresponding store name is obtained based on the store code, and the item of the store name of serial number 2 is updated. Further, a corresponding store group is obtained based on the store code, and the item of the store group of serial number 3 is updated.

Next, the merchandise code, merchandise name, major classification, and line code in the extracted POS data are respectively represented by the merchandise code of serial number 4, the merchandise name of serial number 5, the major classification of serial number 6, and the serial number of seven. Set each item of the line code. Then, by referring to the line name table, the line name is obtained based on the line code, and set in the line name item of serial number 8. Then, referring to the line group master table, a line group (reference value TB layout, rank division) is obtained based on the store group code and the line code.
Each item of the reference value TB layout of 1 and the rank division of the serial number 9-2 is set.

Next, the initially set start date, end date, start week, and end week are defined as the start date of serial number 10, the end date of serial number 11, the start week of serial number 13, and the start week of serial number 14. The end week is set for each item, and the date of the processing day is set to the operation date of serial number 12.

Then, the items of the sales date (serial number 15), (sales) quantity (serial number 16), and (sales) amount (serial number 17) for up to 92 days in the results DB are set from the first to the 91st. The data is sequentially shifted from the second to the 92nd, and the latest sales date, (sales) quantity, and (sales)
The second corresponding item is set (see FIG. 22). As described above, the performance DB stores the sales date, the (sales) quantity, and the sales amount of the past 92 days by store and product.
(Sales) Stores the history of the amount.

Next, the (sales) quantity and the (sales) amount for the period from the initially set start date to the end date are totaled, and the respective items of the serial number 18 in the period and the serial number 19 in the period are added to the respective items. Set each. Further, the average selling price in the period obtained by dividing the amount in the period by the amount in the period is represented by the serial number 20.
Set in the item of average selling price during the period. Then, the value of the square root of the intra-period amount is set as the intra-period amount VE of the serial number 21.

Next, using the store code and the code of the line group as keys, the amount VE within the period is totaled (aggregated) using the amount total table. Then, using the store code and the code of the line group as keys, the cumulative composition ratio for each product is calculated in the descending order of the amount of money VE within the period. In addition, the intra-period amount VE composition ratio is obtained by dividing the intra-period amount VE by the intra-period amount VE total in the total amount table (see FIG. 16), and further dividing by 10
Calculate as a percentage value multiplied by zero. The cumulative composition ratio calculated in this manner is set in the item of the amount VE composition ratio within the period of the serial number 22. FIG. 23 illustrates a calculation example of the intra-period amount VE composition ratio.

Next, the number of sales (number of appearances) during the period from the start date to the end date, which is initially set, is counted, and the number of appearances within the period of serial number 23 is set. Next, using the store code and the line group as keys, the in-period face appearance composition ratio is calculated for each product according to the following formula, and set in the item of the in-period face appearance composition ratio of serial number 24. Face-up composition ratio within the period = Number of face-ups within the period / Initially set business days x 1
00

Next, using the rank division of the serial number 9-2 of the result DB, a price / day rank division parameter is obtained. Then, the in-period amount VE composition ratio of the serial number 22 is compared with the amount / day rank division parameter to obtain the amount rank. For example, when the in-period amount VE composition ratio of the serial number 22 is 38.2%, “A” is set to the amount rank of the serial number 25.

Next, similarly to the money amount rank, the money amount / days rank separation parameter is obtained using the rank division of the serial number 9-2. Then, the facial appearance composition ratio in the period of the serial number 24 is compared with the amount / day rank division parameter to obtain the day rank. For example, when the face-out composition ratio within the period of serial number 24 is 78.11%, the number of days rank is “2”.
Set.

Next, using the reference value TB layout of the serial number 9-1, the money rank of the serial number 25, and the number of days rank of the serial number 26 as keys, the reference value / days master (see FIG. 12)
The number of inventory days, the number of inventory days at the ordering point, the number of days before the actual date, and the inventory number F / C are obtained.
8 order point stock days, serial number 29 actual past days, serial number 3
0 is set for each item of the inventory holding F / C.

Next, the actual number of past days of serial number 29 and serial number 23
Is compared with the number of appearances within the period, and if the number of past days on the actual day = <the number of appearances within the period, the number of past days on the actual day of serial number 29 (8 to 3
On the basis of (0 day: changed depending on the product characteristics), the sum of the quantity of serial number 16 (maximum holding of 92 pieces) for the past number of days from 1 to 29 is set as the actual total sales number of serial number 31. . Further, the formula: average number of actual sales = total number of actual sales of serial number 31 / serial number 29
The calculation result based on the past number of actual days is set to the actual number of actual sales of the serial number 32. In addition, in the above comparison, if the number of past days in the actual day> the number of appearances in the period, the in-period quantity of the serial number 18 is set to the actual day total sales number of the serial number 31. And furthermore, the formula: average number of actual sales = total number of actual sales of serial number 31 / serial number 23
The number of appearances within the period of, and the calculation result of, are set to the average number of actual sales for the number 32.

Next, a calculation result based on the following formula: Number of stocks in stock = Average number of real days sold with serial number 32 × Number of stocks in stock with serial number 27 is set in the item of “Stock number in stock” with number 33. Further, the calculation result by the formula: Ordering point stock quantity = Average actual sales number of serial number 32 × Ordering point stock number of serial number 28, is set in the item of ordering point stock quantity of serial number 33. The above processing is performed by the result DB updating unit 2e.

As described above, after updating the performance DB, the calculation unit 2f obtains reference value data (see FIG. 21) and order data from the performance DB. The order quantity included in this order data is calculated as the maximum display quantity or the current stock of theoretical stock subtracted from the maximum stock quantity or the stock held in accordance with the stock inventory order F / C as described above. In addition, since there is a discrepancy between the current inventory of the theoretical store and the inventory at the retail store, the current inventory of the theoretical inventory is periodically updated using the actual shelf data. The output unit 2h outputs the reference value data and other data, and also outputs forms such as a list of stocks held per store and line. Further, the output unit 2h uses the updated results DB to search and display data for each store using the store code as a key, and further searches data for each store and product using the product code as a key. Can be displayed. Then, the ordering unit 2i transmits the ordering data obtained by the arithmetic unit 2f to a predetermined wholesaler corresponding to the product to be ordered the day before the delivery day.

The operation of the automatic commodity ordering system 1 has been described above. FIG. 23 shows an example of a change (amount) of inventory at a retail store by an automatic order using the automatic product ordering system 1. As shown in this graph, it can be seen that the inventory held decreases every month. That is, it can be seen that the inventory automatic holding system 1 can reduce the stock held. Also, for example, in a conventional retail store where the number of deliveries is three, the number of inspections and outgoing goods can be reduced by reducing the number of deliveries. The reference value data in the case of the number of times can be obtained by simulation, and can be used for a study for improving storefront work.

In addition, the standard automatic ordering by the automatic product ordering system 1 can reduce the standard ordering cost at the store, thereby improving the work efficiency and the human productivity. This also reduces labor costs.
Similarly, by reducing out-of-stocks by avoiding human errors (missing orders or forgetting to order), it is possible to secure sales opportunities for products.
In addition, since the number of stocks held can be controlled, and the cost of burden of stock at the store can be reduced, the cash flow can be improved. In addition, by reducing the standard stock, long-term staying products on the display shelf can be reduced, and a sales floor with high freshness can be realized (the inventory turnover of the display shelf can be improved). Further, from the data obtained from the automatic product ordering system 1, the shelf layout (setting of the face, depth, and product number) can be set according to the sales trend.

Next, an embodiment (example of use) of the automatic product ordering system 1 will be described. FIG. 25 is an example of a menu selection screen of the automatic product ordering system 1.

When the store master maintenance button on the menu selection screen is clicked, a store master maintenance screen shown in FIG. 26 is displayed. From this screen, the store master data can be updated.

When the environment setting master maintenance button is clicked, an environment setting screen shown in FIG. 27 is displayed. From this screen, the reference value / days master is set. The example of FIG. 27 is an example in which the money rank and the number of days rank are five levels, but as described above, there are a plurality of patterns.

When the product master maintenance button is clicked, a product master maintenance screen shown in FIG. 28 is displayed. From this screen, the acquired data of the product master can be updated. When the product master is managed by a host computer or the like, it is imported and used.

When the database initialization button is clicked, a database initialization screen shown in FIG. 29 is displayed. From this screen, the results DB can be initialized.
When a check button in the figure (“check if the maximum number of displays is not set”) is checked, initialization (update) is performed based on the acquired POS data. If not checked, initialize (update) with the maximum number of displays as the initial value
I do.

When the POS result data import button is clicked, a sales day input screen shown in FIG. 30 is displayed. Here, the processing date (start date and end date) is input, and when the OK button is clicked, POS data for the period is input.

When the data export button is clicked, a data export menu screen shown in FIG. 31 is displayed. In the present embodiment, order quantity data,
Out-of-stock data, actual data, and stock data can be selected, and a check button corresponding to desired data can be marked on the menu screen and the EXPPORT button can be clicked to display the data. 32 to 35 show display examples of order quantity data, out-of-stock data, actual data, and stock data, respectively.

The order quantity data includes the order date, order quantity, sales date, actual sales quantity, bargain purchase quantity, average daily sales quantity, stock in stock, order point stock, out of stock, and price rank for each store and product. , And the number of days rank.

The out-of-stock data includes sales dates, out-of-stock quantities, actual sales quantities, bargain sales quantities, average daily sales quantities, stocks in stock, reorder inventory, order quantities, amount ranks, number of days, It consists of items such as selling price.

The performance data is classified by store and product.
Last update date, current update date, sales date (in the example in the figure, 9
Daily sales), sales date, order date, average daily sales, inventory held, order point inventory, out of stock, order quantity, selling price,
It consists of items such as stock in stock, stock in stock, stock at reorder point, stock at reorder point, and average selling price.

Further, the inventory data includes, for each store, total sales quantity, total sales amount, special sale purchase total quantity, merchandise M (master) inventory quantity, owned inventory quantity, owned inventory amount, reorder inventory quantity, reorder inventory quantity. The items include an amount, a processing date, a From (start date), a To (end date), an out-of-stock quantity, an out-of-stock amount, and the like.

When the checklist output button is clicked, a sales date / delivery date setting screen shown in FIG. 36 is displayed. Here, the sales date and the delivery date are set, and when the print button is clicked, a check list (a list of out-of-stock items, a sales amount total, a sales amount total, a bargain purchase total amount list, and a store-by-store total list) is shown in FIGS. -The order quantity list for each product is printed.

When the stock inquiry button is clicked, a stock inquiry screen (a screen showing stock information for each store) shown in FIG. 40 is displayed. On this screen, store code / store name, processing date (processing date, start date, end date), POS data information (total sales volume,
Total sales amount, total purchase quantity, merchandise master information (merchandise M (master) inventory quantity, merchandise M (master) inventory amount), actual DB information (inventory quantity, inventory amount, inventory amount held, inventory amount held, Respective information such as order stock quantity, order stock price), and out-of-stock information (out-of-stock quantity, out-of-stock amount, out-of-stock rate quantity (out-of-stock quantity ratio), and out-of-stock rate amount (out-of-stock amount ratio)) are shown. From this stock inquiry screen, the stock status of each store can be grasped.

A program for realizing the function of the processing section 2 in FIG. 1 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read by a computer system and executed. Automatic ordering of products may be performed.

That is, this program includes a POS data acquisition unit function, a bargain purchase data acquisition unit function, an actual shelf data acquisition unit function, a POS data extraction unit function,
The computer implements the function of the performance DB update unit, the function of the calculation unit, the function of the optimization unit, the function of the output unit, and the function of the order unit.

It is to be noted that the “computer system” here includes an OS and hardware such as peripheral devices. The “computer-readable recording medium” means a portable medium such as a floppy (registered trademark) disk, a magneto-optical disk, a ROM, a CD-ROM, and a storage device such as a hard disk built in a computer system. . Further, the “computer-readable recording medium” refers to a volatile memory (RAM) inside a computer system that becomes a server or a client when a program is transmitted through a network such as the Internet or a communication line such as a telephone line. And those holding programs for a certain period of time.

The above program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the "transmission medium" for transmitting a program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. Further, the program may be for realizing a part of the functions described above. further,
What can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes a design and the like within a range not departing from the gist of the present invention. It is.

[0075]

As described above, according to the automatic product ordering system and the automatic product ordering method of the present invention, the actual sales data of the standard product is extracted from the actual sales data for each store and for each product. Based on the actual sales data for the product,
Updates the results DB in which information required for ordering products is stored, and a reference value including information on the maximum stock set according to the sales amount of the product and the number of days of the sale date of the product and information serving as a reference for ordering・ Since information including the order quantity of the product is automatically calculated based on the number of days master, ordering the standard product using this information can reduce the ordering work of the standard product, and furthermore, this information is automatically By sending the information to a predetermined wholesaler, the ordering work for the standard product can be abolished.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating an example of a configuration of an automatic product ordering system according to an embodiment.

FIG. 2 is a diagram showing a relationship between a retail store and a wholesaler.

FIG. 3 is a diagram showing an example of a process between a retail store and a wholesaler, and a process of sending and receiving data and goods.

FIG. 4 is a diagram illustrating the assignment of money ranks.

FIG. 5 is a diagram for explaining assignment of a number of days rank.

FIG. 6 is a merchandise classification diagram based on a price rank and a number of days rank.

FIG. 7 shows a line group TB layout (A1 to A
8) is an example.

FIG. 8 shows a line group TB layout (B1 to B
8) is an example.

FIG. 9 is an example of a store master.

FIG. 10 is an example of a line name table.

FIG. 11 is an example of a line group master table.

FIG. 12 is an example of a reference value / days master.

FIG. 13 is a representative example of a reference value / days master.

FIG. 14 is an example of an amount / days rank separation parameter.

FIG. 15 is an example of a money rank division parameter and a day rank division parameter.

FIG. 16 is an example of a total amount table.

FIG. 17 is an example of a special sale division table.

FIG. 18 is an example of a product master.

FIG. 19 is a diagram showing data items of an achievement DB.

FIG. 20 is a diagram illustrating the number of stocks held, the number of stocks at an order point, the order quantity, and the like.

FIG. 21 is an example of reference value data.

FIG. 22 is a diagram for explaining updating of the result DB.

FIG. 23 is a diagram showing a transition of inventory by the automatic product ordering system.

FIG. 24 is a calculation example of an intra-period amount VE composition ratio.

FIG. 25 is an example of a menu selection screen of the automatic product ordering system 1;

FIG. 26 is an example of a store master maintenance screen.

FIG. 27 is an example of an environment setting screen.

FIG. 28 is an example of a product master maintenance screen.

FIG. 29 is an example of a database initialization screen.

FIG. 30 is an example of a sales day input screen.

FIG. 31 is an example of a data export menu screen.

FIG. 32 is an example of order quantity data to be exported.

FIG. 33 is an example of out-of-stock data to be exported.

FIG. 34 is an example of actual data to be exported.

FIG. 35 is an example of stock data to be exported.

FIG. 36 is an example of a sales date / delivery date setting screen.

FIG. 37 is a list example of the number of sold out items.

FIG. 38 is an example of a total sales quantity, a total sales amount, and a special sale purchase total quantity list.

FIG. 39 is an example of an order quantity list for each store and each product.

FIG. 40 is an example of a stock inquiry screen.

FIG. 41 is an example of a store floor layout of a retail store.

FIG. 42 is an example of a time composition ratio of work types of storefront work in a retail store.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Commodity automatic ordering system 2 ... Processing part 3 ... Storage part (storage means) 2a ... POS data input part 2b ... Bargain purchase data input part 2c ... Actual shelf data input part 2d ... POS data extraction part 2e ... Result DB update Unit 2f arithmetic unit 2g optimization unit 2h output unit 2i ordering unit

 ──────────────────────────────────────────────────の Continued on the front page (72) Inventor Shiro Fukuda 1-3-7 Honjo, Sumida-ku, Tokyo Inside Lion Corporation (72) Inventor Kenichiro Tsutsumi 1-3-7, Honjo, Sumida-ku, Tokyo F term in Lion Corporation (reference) 5B049 AA01 AA02 BB12 CC05 CC27 DD01 DD05 EE00 EE05 FF09 GG01

Claims (3)

[Claims] 1. An actual result DB storing information required for ordering a product, information on a maximum stock set according to a sales amount of the product and the number of days of the sales date of the product, and information serving as a reference for ordering. A storage means for storing information including a reference value and a number of days master, and input of actual sales data of products collected for each store,
Actual sales data input means to be stored in the storage means, Special purchase data input means to receive the input of the special purchase data, and to store the actual purchase data in the storage means, Actual shelf data to be received and stored in the storage means Input means, actual sales data extraction means for extracting actual sales data of a staple product for each store and product from the actual sales data and the bargain purchase data, and a staple product extracted by the actual sales data extraction means Means for updating the contents of the results DB based on the actual sales data and the actual shelf data, and the order quantity of the product based on the updated contents of the results DB and the reference value / days master information. And a calculating means for calculating information including the following. 2. An automatic merchandise ordering method using a computer device, wherein a result DB storing information required for merchandise ordering, and a maximum set according to the sales amount of the merchandise and the number of days of the merchandise sales day. A procedure for storing information including information relating to inventory and a reference value / days master including information serving as a reference for ordering, and receiving input of actual sales data of products collected for each store,
A procedure for storing the data in the storage means, a procedure for receiving the input of the bargain purchase data and storing the data in the storage means, a procedure for receiving the input of the actual shelf data and storing the data in the storage means, A procedure for extracting actual sales data of a standard product for each store and each product from the data, and a procedure for updating the contents of a performance DB based on the actual sales data of the standard product extracted in the above procedure and the actual shelf data And a step of calculating information including an order quantity of the product based on the updated contents of the performance DB and the information of the reference value / days master. 3. A computer-readable recording medium on which is recorded software that, when installed on a computer device, causes the device to execute the method according to claim 2.