JPS62167110A - Commodity ordering recommending device - Google Patents

Abstract

PURPOSE:To improve efficiency of ordering work, by providing a control part which decides the necessity of ordering from a difference obtained by subtracting a sales amount delivered from the preceding ordering time to this ordering time, per unit commodity. CONSTITUTION:When ordering work is performed, an ordering commodity department is assigned from an operation panel 9, and if a start button is pushed, a microcomputer 2, which reads a commodity in the assigned department successively from an ordering master memory part 5 while a preceding time ordering amount about said commodity from an ordering data memory part 8 further a sales amount since preceding ordering time from a sales data memory part 4, calculates a difference between the preceding time ordering amount and the sales amount after the preceding ordering time. And the microcomputer 2, which decides whether or not ordering of said commodity is recommended by a value of said difference, performs printing of the decision result by a printer 7. Ordering data, determined by an ordering worker on the basis of this ordering recommended commodity list, are input to the microcomputer 2 from an ordering data input part 6, and a device prints an ordering slip by the printer 7 while stores the slip in the ordering data memory part 8 after collecting the ordering data per every vendor.

Description

[Detailed description of the invention] [Industrial application field] The present invention relates to a product order recommendation device in a retail store or the like.

[Conventional technology]

BACKGROUND ART Several methods have conventionally been used to order product replenishment at retail stores and the like. The first method is to look at the products on the product shelf, record the names of products whose stock on the shelf is low, and place orders based on the names. This method is direct and easy to understand, but it has the disadvantage that once the shelf inventory reaches zero, you will not know what the product is, and after the inventory reaches zero, the empty space on the shelf will be on both sides. If there are no blank spaces left due to the movement of products, it becomes impossible to visually tell that the item is out of stock, and in the worst case scenario, it also has the disadvantage that no more orders will be placed. . The second method is to attach a shelf label to a shelf near the position of the product and place an order using the shelf label. The shelf tags include the product name, price, order unit, product code, and barcode. As with the first method, the ordering process involves visually finding a product that is out of stock, recording the product name or product code written on the shelf label corresponding to the product, and placing an order based on that. Additionally, if a barcode is printed on the shelf label, a handy terminal equipped with a barcode league can be used to mechanize the ordering process by reading the barcode on the shelf label with the barcode league and specifying the product to be ordered. Sometimes. Unlike the first ordering method above, this ordering method using shelf tags will not cause the product name of an out-of-stock product to become unclear, but it may cause the blank space for an out-of-stock product to be filled with products on both sides. In such cases, even if a shelf label is attached, it is possible to miss the fact that the item is out of stock. Furthermore, in the method using shelf labels, accurate management of the shelf labels is an important condition. Specifically, shelf labels must be changed accurately in response to the addition, replacement, or removal of products, changes in product placement, changes in prices, etc. This shelf label management was quite a difficult job and placed a heavy burden on store staff. Furthermore, in reality, it is often not always accurately managed, and as a result, there is a drawback that appropriate orders are not placed.

The third method is to place replenishment orders based on an order ledger. First, a product order ledger corresponding to the products on the shelf is created. When ordering, instead of looking at the products on the shelf, customers visually read the product names in the order they are written on the ledger, find the product with that name on the shelf, and decide whether to order based on the amount in stock. A major feature of this method is that it does not miss out-of-stock items because the items in the ledger are checked sequentially. In addition, there is no need for shelf labels, so product placement can be changed to a certain extent.

There is also a mechanized method of placing orders in which the contents of the product order ledger are stored in the memory of the handheld terminal, and information corresponding to the contents of the product order ledger is sequentially displayed on the display by key operations on the handheld terminal. E-OB
(Electronic Order Booking
) is also called. The disadvantage of this method is that all products on the order ledger must be visually confirmed. Even though only a portion of all products need to be ordered, it is extremely inefficient to have to check whether or not many products that do not need to be ordered need to be ordered.

The fourth method is called the automatic ordering method, in which sales information for each item is collected using a POS scanner or by collecting product tags at the time of sale, and orders are automatically placed according to the number of sales. is processed by a computer. This method has the advantage of not requiring much labor, but depending on the product, there are many cases in which past sales numbers must be changed and ordered depending on the season, fashion, social environment, climate, recent sales trends, etc. However, inputting the conditions into the computer each time has the disadvantage of requiring more human labor.

As explained above, the conventional ordering method or the mechanized ordering device has several drawbacks.

[Purpose of the invention]

In view of these problems, the present invention provides a POS (Point
of 5ale) To provide a device that combines single item sales information and previous order information to output an order recommendation without missing an order and with high work efficiency.

[Means to solve the problem]

In order to achieve the above object, the present invention includes a single item sales information input means, a sales data storage section for storing the single item sales information obtained by the single item sales information input means, and an order data input section for inputting order information. and an order data storage unit that stores the order information, and records tα in the sales data storage unit from the previous order quantity stored in the order data storage unit.
A control unit that determines the necessity of ordering based on the difference in sales volume for each item from the previous order placed to the current order, and a control unit that makes an order recommendation based on the determination result obtained by the control unit. The system is equipped with an order recommendation means.

〔Example〕

Hereinafter, the present invention will be explained using an example in which the present invention is applied to a product replenishment order at a certain store.

FIG. 1 is a block diagram of a product order recommendation device in this embodiment.

In Figure 1, 1 is the PO as a means of inputting information on single item sales.
This is an S scanner that reads barcodes printed or affixed to products. The read barcode information is manually input to the microcomputer 2 as a control unit, and compared with the barcode information stored in the barcode product mask storage unit 3. If there is a matching product, the product name, price, etc. are displayed. Can be read. Products sold by the POS scanner 1 are stored in the sales data storage section 4 as single item data. Here, a single item means the smallest possible classification item for a product.

The product ordering mask storage unit 5 stores information necessary for ordering products, such as product name, product number, selling price, cost, supplier, department, etc.

The product order recommendation device according to this embodiment outputs the order recommendation as described below for product order work.

Referring to the flowchart in FIG. 3, when placing an order, first specify the product category to be ordered from the operation panel 9 and press the start button, and the microcomputer 2 will sequentially store the products in the specified product category as a product order mask. Read out from part 5, sales 1s since the last order for the read product
is read out from the sales data storage section 4, and the previous order quantity H of the product is read out from the order data storage section 8. Next, the difference D=H-3 between the sales amount S and the previous order amount H is calculated.

Based on the difference value, the microcomputer 2 determines whether or not to recommend ordering the product, and prints out the result. The determination in FIG. 3 is such that printing is performed when D≦0, but for example, the order recommendation output for each product is ranked according to the level of order necessity and is sent to the printing device 7 as an order recommendation means. Print. For example, rank 1 is ``If you don't place an order immediately, it will run out of stock.''
Rank 2 is ``It is better to order.'' Rank 3 is ``It is better to order if possible,'' and Rank 4 is ``There is no need to order yet.'' Products are printed separately for each rank. The recommended order product list is printed out on the device 7. In the recommended ordering product list, products are ranked and stored according to the degree of need for ordering.
The ordering worker visually checks the inventory of the products on the shelves in that order and decides whether or not to actually place the order. The ordering data thus determined by the ordering worker based on the recommendation of the recommended ordering product list is inputted into the microcomputer 2 from the ordering data input section (for example, a keyboard). The input order data is checked against the information in the product order mask storage unit 5, and an order slip is printed by the printing device 7 for each supplier, for example, or an order may be placed online through a telephone line. (Means for this purpose are not shown in FIG. 1). Further, the ordering data is stored in the ordering data storage section 8 as the ordering amount for each item as the previous ordering amount.

Next, a method for determining order recommendations will be explained. As mentioned above, the order recommendation is made based on the difference D=H-3 between the sales amount S and the previous order amount H. The meaning of the difference is as follows. In the first place, the purpose of ordering products is to ensure that the product inventory runs out on store shelves.
In other words, the goal is to prevent products from running out of stock and to reduce excess inventory. Therefore, it is necessary to calculate the amount of products in stock on the shelves and output order recommendations based on the amount of products in stock. By the way, it is not possible to determine the amount of product in stock by simply taking an inventory at a certain point in time and then calculating it from the sales volume and purchase volume. For example, if the product is stolen, the number will remain as an error forever. Also, when purchasing,
Alternatively, when arranging products on a shelf, the user has to enter the quantity, which is an extra task. If the error between the product inventory amount and its calculated value becomes large, or if there is a concern that they do not match, the work of taking inventory and actually measuring the actual product inventory amount increases. As described above, the conventional product inventory calculation method has a major drawback in that extra work is required to improve the accuracy of inventory calculation.

The previous order quantity H for the difference D=H-8 used in the embodiment of the present invention is data input in the conventional ordering work that is indispensable for store management, so no extra work is required. Further, the sales volume S can be automatically obtained in a store that has introduced a POS system using a POS scanner and performs sales analysis, and no extra work is required.

The relationship between H and S will be explained with reference to Figure 2, taking the inventory amount of products on the shelf (hereinafter referred to as Z). In the graph of FIG. 2, the vertical axis indicates the product inventory amount Z, and the horizontal axis indicates time T.

After the product inventory amount is ZO at time TO, the product inventory amount Z decreases as the product sells. At time TIO, the product inventory 1z is larger than the ordering limit ZL, so no order is placed. At the next ordering time T1, the product inventory quantity Z is smaller than the ordering limit quantity ZL, so H
Place an order for 2 pieces. After a time delay d (delivery date) after placing the order, H2 items are delivered at time T2, and as a result, the product inventory increases by H2. Similarly, by placing an order at time T3 and delivering the product at time T4, the product inventory amount Z increases as shown in FIG.

If the order quantity H2 at time T1 is the previous order quantity H=H2, then the difference at the next order time T3 is the difference D=D3=H2-33 (S3=33) from the previous sales quantity 5=33.
A+33B). By the way, 21+)12=33+2
3 holds, so from this H2”53=Z3-Zl
When this is combined with the formula of D3, D3=23-Zl is obtained. Here, since H2 and 83 are each obtained from the stored data of the product order recommendation device, D3 can be calculated.

Therefore, it turns out that Z3-Zl can also be calculated.

Here, 21 is the product inventory amount at the previous order time T1, Z
3 is the product inventory amount at the current order time T3.

Incidentally, the ordering decision at time T1 was made by the ordering worker who visually inspected the stock amount Z1 and based on the size relationship with the ordering limit IZL determined from various conditions, and the same judgment criteria were used at the current ordering time T3. is applied, it is desirable that the ordering recommendation output from the product ordering recommendation device be based on the same ordering criteria as the one adopted by the ordering worker at time TI.

Since the stock amount when the previous order was placed was Zl, it is desirable that the current order be made when the stock amount is close to or smaller than 21. That is, Z3
It is desirable to place an order when ≦21. By the way, since Z3-Zl=D3, this ordering criterion can be expressed as D3≦0. Depending on the value of D3,
The fact that the apparatus of this embodiment can output a recommendation based on the same ordering criteria as the ordering criteria of the TI ordering worker at the time of the previous ordering has the following important significance. The significance of this is that the product order recommendation device can make order recommendations without using information on the amount of products in stock on actual shelves. This means that even if you do not know this, you can output the same ordering recommendation as if you had adopted an ordering criterion close to the ordering criterion adopted by the previous ordering worker. Since inventory information itself is not required, there is no need to take inventory from time to time to measure the actual inventory, and even if the inventory decreases due to product theft, the ordering worker can judge the amount by the amount. The product order recommendation device of this embodiment does not need to know this because it is compensated for by placing an order early or by increasing the order quantity.

Ordering criteria for ordering workers are quite complex, taking into consideration, for example, the type of product, season, temperature, social environment, recent sales trends, and fashion trends.

Because of this complexity, conventional automated ordering by computer based on fixed ordering criteria has had major problems, and the reality is that it has rarely been put into practical use.

In the product order recommendation device of this embodiment, the criteria for making an order recommendation are close to the ordering criteria of the ordering worker at the time of the previous order, so for example, the ordering worker can By changing the ordering criteria,
The product order recommendation device also has an adaptive ability to change the criteria for making order recommendations accordingly.

As explained above, according to the product order recommendation device of this embodiment, a list of recommended order products created based on appropriate ordering criteria is printed, and the inventory of products is checked in order of order rank in the list.

For example, if you check the inventory of products listed in rank 1,
Most of them need to be ordered, and as you go through ranks 2, 3, and 8 times, you can decide whether to order or not, or the quantity to order, taking into account the judgment of the ordering worker.

As described above, the product order recommendation device of this embodiment makes use of the advantages of the ordering system based on the ordering ledger described in the third method of the prior art, but also has the disadvantage of having to check even products that do not need to be ordered. Highly efficient work that eliminates
It is possible to place orders without missing any orders.

As explained above, it has been found that if an ordering recommendation is output based on the value of the difference D=H-3, the ordering standard of the ordering recommendation will be close to the ordering standard held by the ordering worker. By the way, there are variations in the actual sales process at stores, and the time of ordering (day, day of the week, etc.) can vary, so
It is not realistic to output an order recommendation only when D≦0 as described above. Therefore, as described above, it is actually better to output order recommendations (printed output, etc.) after ranking them according to the level of necessity of order placement based on the value of D. There may be various criteria for D in this ranking, but for example, the ranking may be based on the ratio of the previous order quantity H to D. Specifically, calculate R=D/H, and depending on the value of R, rank 1, 0, etc. if R≦0.1.
If 1<R≦0.2, rank is 2, if 0.2<R≦0.4, rank is 3, etc. Regarding this point, the choice can be made depending on the store owner's way of thinking or the type of product.

In the above explanation, the ordering recommendation output is printed out by the printing device 7 as the ordering recommendation means, but a display device may be used as another specific example, and this can be selected depending on the usage situation.

In the above description, the ordering data input unit 6 as the ordering data inputting means is a keyboard, but as another specific example, it may be a touch panel switch attached to the display device.

In the above explanation, the single item sales information input means is POS.
Although a scanner is used, other single item sales information input means may be a tag league that collects and reads barcode tags attached to products. However, if a POS scanner is used, there will be no delay in receiving sales information, and there will be a significant labor-saving effect.

〔Effect of the invention〕

According to the present invention, it is possible to obtain ordering recommendation information based on appropriate ordering criteria that can be implemented with high work efficiency without missing product replenishment orders at retail stores.

[Brief explanation of drawings]

FIG. 1 is a block diagram of an embodiment of the present invention, and FIG. 2 is a graph showing changes in product inventory over time when the embodiment of the present invention is applied. FIG. 3 is a flow chart of an embodiment of the present invention. l... POS scanner 2... Microcomputer 4... Sales data storage section 6... Order data Jinkaro"7... Printing device 8... Order data storage section T1... Previous time Order time T3...Current order time H2...Previous order M H4...Currently ordered quantity a
figure

Claims (2)

[Claims] (1) Single item sales information input means, a sales data storage section for storing the single item sales information obtained by the single item sales information input means, an order data input section for inputting ordering information, and storing the ordering information. and an order data storage unit,
Determining the necessity of ordering based on the difference obtained by subtracting the sales volume from the previous ordering time to the current ordering time stored in the sales data storage unit for each item from the previous ordering quantity stored in the ordering data storage unit. What is claimed is: 1. A product order recommendation device comprising: a control section for making a recommendation for ordering; and an order recommendation means for making an order recommendation based on a judgment result obtained by the control section. (2) A product order recommendation device characterized in that the unit sales information input means as set forth in claim 1 is a POS scanner.