KR20050014813A - Distribution system, distribution device, and distribution method - Google Patents

Abstract

The distribution processing computer 10 uses the transaction DB 36 to rank each store in relation to the product to be distributed. This rank grant is performed using the sales results of the group (product attribute group) to which the product in each store belongs, and each store is divided into ranks S, A, B, and C. To which stores and how much to distribute the product to be distributed is determined by the processing to be executed based on the rank of each store. Based on the result of this distribution simulation, goods are distributed to each store. In addition, in the case of additional distribution, it determines by the process performed based on the sales performance of the said product in each store.

Description

Distribution processing system, distribution processing unit and distribution processing method {DISTRIBUTION SYSTEM, DISTRIBUTION DEVICE, AND DISTRIBUTION METHOD}

Conventionally, how to distribute goods to a plurality of stores has been made by trial and error based on the experience of the distributor and the experience of the person in charge of each store. In contrast, Japanese Patent Laid-Open No. 2-23464 discloses an article stockpiling system for a product at a store. In order to realize a more optimal stockpile at each store, this is based on "characteristics of the store (location conditions, business forms, etc.), characteristics of the product to be stored at each store", "characteristics of the store, and each individual store." It is to automatically determine the products to be displayed at each store in consideration of the sales results of the products.

However, in such a system, the following problem occurred. First, when a product to be distributed is sold for the first time in a store (new product, etc.), there is no sales performance of the product in each store. As such, the method cannot be used.

Second, when the product which is going to distribute is not sold very much in the whole store, or when it is a rare product, the sales result about the said product is few. Therefore, even if the said method is used, the sales results of each store are almost meaningless, and the sales efficiency at each store cannot be improved.

Third, since the data of the sales results themselves in a plurality of stores is difficult for the operator to grasp, it is difficult for the operators themselves to determine the distribution method in consideration of the sales results. That is, the intention of the operator cannot be easily reflected in the distribution method.

An object of the present invention is to provide a distribution processing system, an apparatus, and a method which can solve such a problem, improve the handling efficiency at a distribution destination, and can easily reflect the intention of an operator in a distribution method. It is done. For example, if you want to distribute a product to more than one store, you might be able to sell better for products that don't have a sales record, or for products with less sales results. By distributing to existing stores, the sales efficiency at each store can be improved.

<Start of invention>

(1) The distribution processing system of the present invention includes a plurality of distribution destination terminal devices, a distribution processing device, and a distribution execution device. The distribution destination terminal device records handling history information of an object, and the distribution processing device includes: Each distribution destination terminal device receives and records the handling result information of the object in each distribution destination, classifies each distribution scheduled destination on the basis of the handling result information corresponding to the distribution object to be newly distributed, and distributes it to be newly distributed. Receiving the total amount of the object, using the classification determined for each distribution destination, generating distribution data indicating how to distribute the distribution object to each distribution destination, generating the distribution data, and transmitting to the distribution execution device. Is a process for receiving the distribution data and distributing the distribution object to a distribution destination based on the distribution data. Characterized in that the execution.

(2) to (4) The distribution processing apparatus of the present invention is a device for generating distribution data for determining how to distribute a distribution object to each distribution destination. Based on the handling result information corresponding to the distribution object to be distributed, classify each distribution target destination, receive the total amount of the distribution object to be newly distributed, and use the classification determined for each distribution destination to perform each distribution plan. The distribution data, which is configured to indicate how to distribute the distribution object, is characterized in that it is generated.

Therefore, since the distribution method is determined using the classification based on the handling performance information at each distribution destination, the handling efficiency of a future distribution object can be improved. For example, when a product is to be distributed among a plurality of stores, by using a classification based on the sales results of the corresponding product of each store, the sales efficiency of the corresponding product of the entire store can be increased, resulting in increased sales. have.

Further, the operator or the like can specify the classification to select the distribution destination, or set weighting for each classification to determine the distribution amount, so that the intention of the operator or the like can be easily reflected in the distribution method. In addition, by using the classification, the distribution method can be easily changed in accordance with various circumstances.

(5) The distribution processing device of the present invention is characterized by generating the distribution data indicating which distribution target object is to be distributed or how much distribution target object is to be distributed to which distribution destination. Thereby, the distribution amount of the distribution object of each store, etc. can be determined based on classification.

(6) In the distribution processing apparatus of this invention, the handling result information corresponding to the said distribution object is the handling history information in the past of the said distribution object. It is characterized by the above-mentioned. Thereby, when the handling performance information of a distribution object exists suitably, by using the handling history information, a distribution object can be supplied more optimally in each distribution destination.

(7) In the distribution processing apparatus of this invention, the handling performance information corresponding to the said distribution object is the past handling performance information about the whole group to which the said distribution object belongs. It is characterized by the above-mentioned.

Thereby, even if there is little or no handling performance information of the distribution object, by using the handling history information of the group to which the distribution object belongs, the distribution object can be supplied more optimally at each distribution destination. For example, when distributing a new product or a rare product to a plurality of stores, by using the sales results of the group to which the product belongs, it is possible to realize more optimal stockpile at each store.

(8) The distribution processing apparatus of the present invention is characterized by generating the distribution data indicating how much to distribute the object to be distributed to the selected distribution destination selected using the classification determined for the distribution destination. Thereby, by selecting a classification, it is possible to determine to which distribution destination to distribute.

(9) The distribution processing apparatus of the present invention is each according to the ratio of the handling result information corresponding to the distribution target object of the respective selection distribution destination with respect to the handling result information corresponding to the distribution target object of the entire selection distribution destination. And the distribution data indicating how much to distribute the object to be distributed to the selective distribution destination. Thereby, the distribution amount of a distribution target object can be determined based on the ratio of the handling performance information which objected only to the selection distribution destination.

(10) The distribution processing apparatus of the present invention distributes a predetermined amount of the distribution object to each of the selection distribution destinations, and corresponds to the distribution objects of the entire selection distribution destination with respect to the remaining distribution object after the distribution. The distribution data indicating how much to distribute to each selected distribution destination is generated according to the ratio of the handling performance information corresponding to the distribution target object of each selection distribution destination with respect to the handling performance information.

Thereby, even if there are few distribution objects, since at least a predetermined amount is distributed to each selective distribution destination, there is no situation that any one of the selective distribution destinations is not distributed at all.

(11) The distribution processing device of the present invention is characterized in that the distribution data indicating how to distribute the object to be distributed is generated based on the distribution amount or distribution ratio set for the classification.

Thereby, it can distribute evenly to each distribution destination of the same classification. In addition, the operator can reflect the operator's intention by arbitrarily setting the allocation amount for each classification.

(12) The distribution processing apparatus of the present invention is based on the handling capability per group structure calculated from the handling performance information corresponding to the distribution object of the group to which the distribution object belongs, and the number of types of group members belonging to the group. Characterized in that the distribution data indicating whether to distribute the distribution object as much as possible. Therefore, by considering the handling capacity per group member, the distribution object can be supplied more optimally at each distribution destination.

(13) The distribution processing device of the present invention is characterized in that the distribution data indicating how much to distribute the distribution object is generated based on the sales forecast value calculated from the handling performance information corresponding to the distribution object. Thereby, for example, it can distribute so that sales efficiency can be made high in each distribution destination and a sales can be extended as a result.

(14) The distribution processing device of the present invention is characterized in that the distribution data indicating how much to distribute the object to be distributed is generated based on the sales prediction value in consideration of the weekend index. As a result, it is possible to preferentially distribute to stores having a high weekend sales ratio.

(15) In the distribution processing device of the present invention, the handling history information includes at least sales information.

(16) In the distribution processing apparatus of this invention, the said handling performance information contains the operation rate computed from at least sales information and stock information. It is characterized by the above-mentioned. Thereby, for example, when it is going to distribute the product which is going to sell in each store to a plurality of stores, how the said product based on the classification determined based on the sales result of the said product in each store, a stock result You can decide whether to distribute

(17) The distribution processing method of the present invention receives the handling result information of the object in each distribution destination using a computer, and based on the handling result information corresponding to the distribution object to be newly distributed, the distribution distribution destination is selected. Evaluating, receiving the total amount of distribution targets to be newly distributed, and generating distribution data indicating how to distribute the distribution targets to the respective distribution targets using the evaluation determined for each distribution target. do. Therefore, since the distribution method is determined using the evaluation based on the handling result information at each distribution destination, the handling efficiency of a future distribution object can be improved. For example, in the case where a product is to be distributed among a plurality of stores, by using an evaluation based on the sales results of the corresponding product of each store, the sales efficiency of the corresponding product of the whole store can be increased, resulting in increased sales. have.

In addition, in this invention, the "distribution processing apparatus" corresponds to the distribution processing computer 10 in the following embodiment. The "distribution execution device" corresponds to the distribution center computer 40 in the following embodiment. The "distribution destination terminal device" corresponds to the store computers 50, 52, and 54 in the following embodiments.

A "distribution object" is an article to be distributed to a distribution destination. In the following embodiment, the SKU to which the headquarters intends to distribute to each store corresponds. Moreover, it is a concept including the data etc. which intend to distribute by a telecommunication line etc. with respect to a distribution destination. The "group to which the distribution object belongs" is grouped based on the characteristics of the distribution object, etc. In the following embodiment, the product attribute group to which the SKU which the headquarters intends to distribute to each store belongs. This "product attribute group" is grouped based on the characteristic of a product. In the following embodiment, what grouped by the item (for example, a jacket, a pullover, etc.) and a size is applicable. Also included are items grouped using item subs (eg, knits, fabrics, jerseys, etc.) together with items and sizes.

"Handling performance information" is performance information handled by a distribution office, and in the following embodiment, a sale amount of money, the number of purchases, and an SKU operation rate correspond to this. For example, when the distribution destination is a factory (when distributing parts for production, etc.), the amount of production also corresponds. The concept is not limited to the past but also includes the future (for example, sales forecasting, planned production, etc.).

"Distribution destination" and "distribution destination" are places where a distribution target object is distributed, and each store corresponds to this in the following embodiment. Moreover, a factory, a sales office, etc. are included, and when a distribution object is data, it is a concept which also includes the computer and the store in a shopping mall. "Selective distribution destination" means a thing selected as a distribution destination from among the distribution destinations which can be distributed. In the following embodiment, the store of rank S, A selected by the operator as a distribution destination selected using the classification determined about the distribution schedule destination corresponds to this. Moreover, when all ranks S-C are selected, all the stores correspond.

The "remaining distribution object" is a distribution object remaining after distributing a predetermined amount to each selective distribution destination. In the following embodiment, the distribution object after distributing one sheet to each of the stores of rank S and A in the 1: 100 distribution method. (SKU) is this.

The "group structure" corresponds to an SKU belonging to a product attribute group or an SKU (movable SKU) sold over a predetermined amount.

"Handling capacity per group structure" is calculated from the handling result information of a group, and the number of types of group structures, and corresponds to the number of purchases / SKUs and the number of purchases / movable SKUs in the following embodiment.

"Sales forecast value" is a concept that includes not only sales forecasts such as goods but also demand forecasts such as electricity usage.

The "handling performance information in the past with respect to the whole group to which a distribution object belongs" corresponds to the sales amount of the product attribute group to which the SKU which is going to distribute belongs in the following embodiment.

"Receiving the total amount of the object to be newly distributed" is a concept that includes not only the case where the total amount is acquired from an operator or another computer, but also the case where the information for calculating the total amount is acquired.

"Evaluation determined for each distribution scheduled destination" is determined based on handling performance information at each distribution scheduled destination. In the following embodiment, reference values calculated for each of the ranks S to C set at the store and stores (math Equation 7), "Buy / Buy SKUs", "Buy / Buy SKUs" calculated for each store, and the like are applicable.

The term "recording medium on which a program is recorded" is a concept including a flexible disk, CD-ROM, hard disk, memory card, ROM, punch card, tape, and the like. In addition to recording media on which programs can be executed directly by a computer, not only recording media on which programs can be executed once installed on other recording media (hard disks, etc.), or recording media on which encrypted or compressed programs are recorded. It is a concept to include. A "program" is a concept that includes not only a program that can be executed directly by the CPU, but also a program in a source format, a compressed program, an encrypted program, and the like.

The present invention relates to a system, apparatus and method for determining how to distribute a distribution object to a plurality of distribution destinations. In particular, in the case where the same product is sold at a plurality of stores or the like, it is for determining how to distribute the product to the plurality of stores.

1 is a diagram illustrating a configuration of a distribution processing system.

2 is a flowchart showing an outline of a process of a distribution processing system.

3 is a diagram illustrating a relationship between an SKU and a product attribute group.

4 is a diagram illustrating hardware configurations of the distribution processing computer 10 and the database server 30.

5 is a diagram showing data of a merchandise DB 34 and a transaction DB 36;

6 is a flowchart showing a store rank assigning process.

7 is a diagram illustrating a store rank setting screen.

8 is a flowchart showing distribution simulation at the first distribution.

9 is a diagram showing a distribution simulation screen;

10 is a flowchart illustrating a normal distribution process.

11 is a flowchart showing a 1: 100 distribution process.

12 is a flowchart showing a product rank distribution process.

FIG. 13 is a diagram illustrating a distribution number setting screen for each rank. FIG.

Fig. 14 is a flowchart showing distribution simulation during additional distribution.

15 is a diagram illustrating a distribution simulation screen;

Fig. 16 is a flowchart showing distribution processing by the configuration ratio of the number of sheets of sales.

17 is a graph showing the relationship between the number of purchases and the SKU operation rate, the number of purchases, and the amount of sales at each store.

FIG. 18 is a diagram illustrating a distribution number setting screen for each rank. FIG.

19 is a table which shows an example of the distribution target object and the handling performance information corresponding to it.

20 is a flowchart showing data aggregation processing.

<Best Mode for Carrying Out the Invention>

1. Outline of system configuration and processing

As one Embodiment in FIG. 1, the structure of the distribution processing system at the time of distributing apparel goods with the same brand to several stores is shown. The distribution processing computer 10 and the aggregation processing computer 20 are provided in the headquarters which distribute | distribute goods, and the database server 30 is a database which stores a store master and a product master (store DB 32, Commodity DB 34), transaction DB 36 that stores sales data, inventory data, and the like for each store. The distribution center computer 40 is installed in the distribution center which is in charge of shipment of goods and storage. The store computers 50, 52, and 54 are installed in a plurality of stores in charge of the sale of the goods distributed by the headquarters. Each computer can communicate via the Internet. Below, the outline | summary of the process of this system is demonstrated.

First, the distribution processing computer 10 uses the transaction DB 36 to rank each store in relation to a product to be distributed ("store rank assigning process") (step S10 in FIG. 2). This rank grant is performed using the sales results of the product attribute group to which the product in each store belongs, and each store is divided into ranks S, A, B, and C. For example, if the product is "Jacket (article number A, size A, color A)", the product attribute group is "Jacket, size A".

In the following description, the product specified by the article number, size, and color is called SKU, and the relationship between the SKU and the product attribute group is shown in FIG. This product attribute group is grouped by items and sizes which are types of products. This item is, for example, a jacket, a pullover, and a cardigan.

Then, to which stores and how much to distribute the SKU to be distributed is determined by the processing executed by the distribution processing computer 10 based on the rank of each store (distribution simulation at the first distribution) (Fig. Step S12 of 2).

Based on the results of this distribution simulation, SKUs are distributed to each store (step S14 in FIG. 2), and SKUs distributed at each store are sold (step S16 in FIG. 2). In this way, the store computer 50 or the like records the sales data. In addition, the aggregation processing computer 20 receives the sales data and the like from the store computer 50 and the like, receives the incoming and outgoing data and the like from the distribution center computer 40, and the transaction DB 36 of the database server 30. Is accumulated (data aggregation processing) (step S18 in Fig. 2).

In addition, when a certain amount of SKUs are received in the distribution center, the headquarters determines whether or not to further distribute the SKUs to which stores and how much additional distributions of the SKUs are based on the sales performance of the product attribute group to which the SKU belongs. It determines by the process to perform (distribution simulation at the time of additional distribution) (step S20 of FIG. 2). Based on the result of this distribution simulation, SKUs are further distributed to each store, and the SKUs additionally distributed at each store are sold (steps S22, S24, and S26 in Fig. 2).

Moreover, the distribution processing method by distribution simulation at the time of a first distribution includes (1) normal distribution, (2) 1: 100 distribution, and (3) product rank distribution. In addition, the distribution processing method by distribution simulation at the time of additional distribution includes (4) sales prediction distribution, and (5) distribution by the composition ratio of the number of sheets.

2. Hardware Configuration of Each Computer

A hardware configuration of the distribution processing computer 10 is shown in A of FIG. The distribution processing computer 10 includes a CPU 102, a hard disk 104, a display 106, a communication unit 108, a memory 110, a keyboard / mouse 112, a CD-ROM drive 114, The flexible disk drive 116 etc. are provided.

The communication unit 108 communicates with the database server 30, the distribution center computer 40, and the like. In the hard disk 104, a distribution processing program, an operating system (OS), and the like for executing a store rank assigning process and a distribution simulation are recorded. The distribution processing program is installed from the CD-ROM 118 or the like through the CD-ROM drive 114 or downloaded through the communication line.

4B shows a hardware configuration of the database server 30. The database server 30 includes a CPU 302, a hard disk 304, a communication unit 306, a memory 308, a CD-ROM drive 310, a flexible disk drive 312, and the like. The above-mentioned store DB 32, product DB 34, transaction DB 36, etc. are recorded on the hard disk 304. As shown in FIG. In the product DB 34 shown in A of FIG. 5, the brand, product number, list price, sale year, season, and the like are recorded for each SKU. In transaction DB 36 shown in B of FIG. 5, for each SKU at each store, the cumulative total of the total amount of sales of the week (the total of the total amount of sales from Monday), the total of the number of purchases of the week, the number of purchases, the number of stocks, etc. This is recorded.

The hardware configuration of the aggregation processing computer 20 is basically the same as that of the distribution processing computer 10 shown in A of FIG. 4. However, an aggregate processing program for receiving sales data from the store computer 50 or the like and recording the transaction data in the transaction DB 36 of the database server 30 is recorded on the hard disk.

3. Processing of distribution processing computer 10

The processing by the distribution processing computer 10 will be described below, divided into the first distribution time (3.1) and the additional distribution time (3.2).

3.1 Initial Distribution

(1) store rank provision process and (2) distribution simulation by the distribution processing computer 10 at the time of a first distribution are demonstrated.

(1) Store rank grant processing

6 shows a flowchart of the store rank assigning process. The CPU 102 displays a setting period input screen (not shown) on the display 106 and prompts the operator to set the "setting period, brand, sales year, season" (step S50 of FIG. 6). ). This setting period is a period of the sales amount of money extracted from the transaction DB 36.

The CPU 102 inputs a store master such as sales data for each product attribute group at each store from the database server 30 when the setting period "37th to 40th week in 2000" is input by the operator. The product master is extracted and stored in the hard disk 104 (steps S51 and S52 in Fig. 6). Then, a product attribute group setting screen (not shown) is displayed on the display 106, prompting the operator to set "item and size" (step S53 in FIG. 6). Here, it is assumed that "item (jacket) and size A" is set by the operator (step S54 of FIG. 6).

The CPU 102 extracts, from the data acquired in step S52 of FIG. 6, the amount of sales of the corresponding setting period in each store for the set product attribute group (jacket, size A) (step S55 of FIG. 6). In addition, the sum of the sales amount of the set period at each store (hereinafter referred to as the period sales amount), the SKU operation rate represented by Equation 1, and the sales operation magnification represented by Equation 2 are calculated, and these are shown in FIG. It displays in the performance data column of the rank setting screen (step S55, S56 of FIG. 6).

SKU operation rate = SKU number of list price sales / SKU number of store stock

Sales operation multiplier = period sales amount of money (or the number of periods purchase) X SKU operation rate

In the above formula, the number of SKUs (the number of movable SKUs) of the list price sale is the number of types of SKUs sold at the store, and the number of SKUs of the store inventory is the number of types of SKUs that are inventory at the store. In other words, the SKU utilization rate is an indicator of the sales force at the store.

The operator selects the period attribute and the sales operation magnification in the attribute setting field 510. The pull-down menu of the period attribute determines, as "period purchase amount of money" or "period purchase number of pieces", which of the store ranks should be considered. The pull-down menu of the sales operation magnification is "valid" or "invalid", and determines whether to consider the SKU operation rate in ranking the store.

Here, the operator selects the period attribute "period sales amount of money" and sales operation magnification "invalid", and clicks the analysis button 520 (step S58 of FIG. 6). The CPU 102 ranks the stores according to the period selling amount (steps S60 and S62 in FIG. 6). That is, the rank of each store is determined by the amount of money sold for each store, and the top 40% is ranked S, the top 40 to 70% rank A, the top 70 to 90% rank B, and the bottom 10% rank. At C, rank is given to each store. The rank of each store is displayed in the rank column of the store rank setting screen shown in FIG. 7 (step S66 in FIG. 6).

In addition, the case where store rank provision is performed by "period purchase amount of money", "period purchase amount of money and SKU operation rate", and "period purchase number and SKU operation rate" is mentioned later.

(2) distribution simulation

In FIG. 8, the distribution simulation by the distribution processing computer 10 at the time of a first distribution is divided and described by the distribution processing method (a) normal distribution, (b) 1: 100 distribution, and (c) product rank distribution. Moreover, you may use the distribution method at the time of the additional distribution mentioned later as a distribution method at the time of initial distribution.

(a) normal distribution

The CPU 102 displays the distribution simulation screen shown in FIG. 9 on the display 106 to prompt the operator to input the SKU to be distributed (step S100 in FIG. 8). The operator inputs SKUs to be distributed in the SKU input field 600. The product attribute group to which this SKU belongs is "Jacket, size A" set above.

The CPU 102 distributes, from the transaction DB 36 and the product DB 34, the sales data (such as the number of sales of the set period), the store rank, and the corresponding SKU of the product attribute group (jacket, size A) to which the corresponding SKU belongs. The total amount possible (the total number of sheets), the SKU detailed information, and the item "JK (jacket)" corresponding to the corresponding SKU are extracted and displayed (steps S102 and S104 in FIG. 8). SKU detailed information includes product image display, "material", "the list price" that is the standard selling price of the shop, the number of storage (PK) by the distribution center, the number which deducted the number of arrivals to the distribution center among the order number (order remaining) (Refer to SKU product image display field 610 and SKU detail information field 620).

The total number of distribution becomes "PK 30 + order remaining 60 = 90". The item "JK (jacket)" corresponding to the SKU is displayed in the item column 630.

The operator sets the total distribution number, distribution target store or distribution processing method (step S106 in FIG. 8). The total distribution number can be set by the operator by clicking the distribution number changing button 640 and inputting a numerical value into the total number of distribution number setting field 650. The distribution target store can be selected from "S store only, S store A, S store A, B store, all store distribution" by the pull-down menu of the store selection column 660. The distribution processing method can be changed to 1: 100 distribution and product rank distribution by clicking on the 1: 100 distribution command button 670 or the product rank button 680. Here, the operator sets only the distribution target store "S-A store" without changing the total distribution number "90 sheets".

When the distribution target store "S-A store" is selected, the CPU 102 shifts to the normal distribution processing shown in FIG. 10 (steps S108 and S110 in FIG. 8). If the 1: 100 distribution command button 670 and the product rank button 680 are not clicked, it is determined that the distribution is normal.

CPU 102 is a ratio of period sales amount of money to corresponding product attribute group of each store of rank S, A to period sales amount of money for product attribute group "jacket, size A" of the whole store of rank S, A (Composition ratio of period sales amount of money) is calculated (step S200 of FIG. 10). According to the composition ratio of period sales amount of money, the total number of distribution number of sheets "90 sheets" is distributed, and the number of distribution to each store is determined, It displays in the distribution number column of each store shown in FIG. 9 (step S202, S204 of FIG. 10). In other words, the number of sheets distributed to any store is a numerical value obtained by rounding the calculated value a in the expression (3) to an integer.

Composition ratio = output value a of period purchase amount of money of the total number of distribution X applicable stores

Moreover, the composition ratio of the period sales amount of money of each store is calculated from the period sales amount of money only for distribution target store (ranks S and A), not the whole store (rank S-C). In addition, the numerical value of the lower column of the total distribution number setting column 650 is a total value of the distribution number for each store.

As described above, the distribution number for each store is determined. In addition, in the above, when the total distribution number supplied and the total distribution number differ from each other, the total number of distribution numbers is used. However, by the calculation processing of the CPU 102, the number of distribution to each store may be adjusted so that the total of the number of distribution is equal to the total number of distribution (the same applies to the following example).

(b) 1: 100 allocation

Fig. 11 shows a flowchart of the 1: 100 distribution processing of step S112 in Fig. 8. When the 1: 100 distribution command button 670 is clicked in the state of total distribution number "90 sheets" and distribution target store "S-A store", the CPU 102 displays the 1: 100 distribution processing shown in FIG. The process proceeds to (steps S106, S108, S111, S112 in Fig. 8).

The CPU 102 distributes each of the stores of the ranks S and A one by one from the upper rank (step S300 in FIG. 11). It is judged whether or not the remaining allocation number (total number of distribution-number of stores of rank S and A) is zero (step S302 in Fig. 11). In this embodiment, if the remaining allocation number is not 0, the corresponding product attribute of each store of rank S, A with respect to the period sales amount of money for the product attribute group "jacket, size A" of the whole store of rank S, A The ratio of the period sales amount (composition ratio of the period sales amount) for the group is calculated (step S304 in Fig. 11). By distributing the remaining number of distributions according to the composition ratio of the amount of money sold in this period, the number of distributions to each store is determined and displayed in the distribution number column of each store shown in FIG. 9 (steps S306 and S308 in FIG. 11). . In other words, the number of sheets distributed to a certain store is a numerical value obtained by rounding the calculated value b in the expression (4) to an integer.

Composition ratio = output value b of period purchase amount of money of the number of 1+ residual allocation number of times X store

(c) Product rank distribution

12, the flowchart of the product rank distribution process of step S114 of FIG. 8 is shown. When the product rank instruction button 680 is clicked in the state of total distribution number "90 sheets" and distribution target store "S-A store", CPU 102 shifts to the product rank distribution process shown in FIG. (Steps S106, S108, S114 in FIG. 8).

The CPU 102 displays the rank-numbered distribution number setting screen shown in A of FIG. 13, prompting the operator to set the number-of-rank distribution number (step S400 of FIG. 12). Here, the operator sets distribution factor "5" in rank S, distribution factor "4" in rank A, distribution factor "0" in rank B, and distribution factor "0" in rank C.

When the total value calculation button is clicked by the operator (step S402 in Fig. 12), the CPU 102 calculates the total distribution number when the distribution is distributed to each store based on the set distribution number for each rank, based on Equation (5). The window shown in FIG. 13B is displayed (step S404 in FIG. 12).

Total number of distribution = Number of stores of S = 5 + A Number of stores x 4 + B Number of stores x 0 + C Number of stores x 0

When the distribution execution instruction "0K" is clicked by an operator (step S406 of FIG. 12), the CPU 102 distributes the number of distributions for each store according to the set rank distribution number for each store shown in FIG. It displays in a column (step S408 in Fig. 12). Thereby, it can distribute evenly to the store of the same rank.

In this distribution method, the operator adjusts the distribution number so that the sum of the number of distribution numbers coincides with (or approximates) the total allocation number. In addition, a combination of the number of distributions in each rank may be prepared in advance, and the CPU 102 may select and present the number of distributions that most closely matches the total number of distributions.

3.2 Additional allocation

The result (distribution data) of the distribution simulation at the time of said first distribution is transmitted to the distribution center computer 40. The distribution center outputs distribution data using the distribution center computer 40, and performs a shipment operation for distributing SKUs to each store based on the output data. Each store sells SKUs distributed from distribution centers.

Thereby, the aggregation processing computer 20 receives the sales data etc. from the store computer 50 etc., and accumulates it in the transaction DB 36 of the database server 30 (data aggregation process). Below, the distribution simulation by the distribution processing computer 10 at the time of additional distribution is demonstrated, divided into (a) sales prediction distribution, (b) distribution by the composition ratio of the number of sheets. Moreover, you may use the distribution method at the time of the first distribution mentioned above as a distribution method at the time of further distribution.

(a) Sales forecast allocation

14, the flowchart of distribution simulation at the time of further distribution is shown. When the SKUs to be distributed are input by the operator (steps S500 and S502 in FIG. 14), the CPU 102 extracts the number of purchases for a predetermined period for the corresponding SKU from the transaction DB 36, and is expressed in equation (6). The reference value shown in equation (7) is calculated from the sales prediction value shown in the corresponding week, and this is displayed together with the sales data and the like (see the reference value display column of the distribution simulation screen in Fig. 15) (steps S504 and S506 in Fig. 14). Moreover, the distribution number which rounded up the reference value by the integer is displayed in the distribution number column of each store. The numerical value displayed in this distribution number of sheets becomes the allocation number for each store.

In this distribution method, the number which totaled the distribution number of each store becomes the total distribution number. Moreover, when the total distribution number is supplied, you may make the total distribution number distribute to each store according to the ratio of the distribution number of each store computed above.

Forecast of sales for the week

= {Pre1 × a + Pre2 × b + Pre3 × c + Pre4 × d} / (a + b + c + d)

Pre1: Weekly Buy 1 week ago a: Weight of Pre1

Pre2: Number of purchases in the week 2 weeks ago b: Weight of Pre2

Pre3: Purchases of the week 3 weeks ago c: Weight of Pre3

Pre4: Purchases of weeks before 4 weeks d: Weight of Pre4

Reference value = corresponding week's sales forecast / digestion rate-(stocks + shipments + weekly sales)

Here, the digestibility is a rate obtained by dividing the estimated total number of sales of the SKU at each store by the accumulated number of purchases. In addition, a red conveyance is the number of sheets which were not received as order completion of the said SKU in each store.

In addition, the reference value may be calculated after calculating the corresponding weekly sales forecast value in consideration of the weekend index (the ratio of the number of purchases on Saturday and Sunday among the number of purchases during the week) which is the sales ratio on Saturday and Sunday during the set period. (See Equation 8). Thereby, it is possible to raise the priority of the store with a high sales ratio of weekends, and to preferentially distribute to the store.

Forecast of sales for the week

= {Pre1 × a + Pre2 × b + Pre3 × c + Pre4 × d} / (a + b + c + d) × Weekend Index

Pre1: Weekly Buy 1 week ago a: Weight of Pre1

Pre2: Number of purchases in the week 2 weeks ago a: Weight of Pre2

Pre3: Buying weekly 3 weeks ago a: Weight of Pre3

Pre4: Purchase of Weeks 4 Weeks a: Weight of Pre4

The weights a to d such as Pre1 and the digestibility may be set arbitrarily by an operator.

(b) Distribution by Composition Ratio of Purchase Number

16, the flowchart of the distribution process by the composition ratio of the sales number of sheets of step S508 of FIG. 14 is shown. The CPU 102 is the ratio of the total number of purchases for five weeks including the corresponding week for the corresponding SKU of each store (period for each store), with respect to the corresponding SKU in the entire store to which the SKU was distributed at the time of the first distribution. The composition ratio of the number of sheets sold) (step S600 of FIG. 16). The distribution number for each store is determined by distributing the additional distribution number according to the composition ratio of the number of purchases for the period, and displayed in the distribution number column of each store on the distribution simulation screen shown in FIG. 15 (step of FIG. 16). S602, S604). In other words, the number of sheets distributed to a certain store is a numerical value obtained by rounding the calculated value c in the expression (9) to an integer.

Composition ratio = output value c of period purchase number of sheets for additional number of copies X applicable store

The result (distribution data) of the distribution simulation at the time of this additional distribution is transmitted to the distribution center computer 40. The distribution center outputs distribution data using the distribution center computer 40, and performs a shipment operation for further allocating SKUs to each store based on the output data. Each store sells SKUs further distributed from the distribution center.

4. Other Examples

4.1 Other Example of Store Rank Assignment Processing

(1) In the above embodiment, the store is given a rank based on the "period purchase amount" of the product attribute group to which the SKU belongs, but the "period purchase amount", "period purchase amount and SKU operation rate" of the product attribute group of the said product attribute group. And "period purchase number and SKU operation rate". A case of ranking the store based on the "period number of purchases and the SKU operation rate" will be described with reference to FIG. 17A. Each store is plotted by taking the number of periods of purchase of the corresponding product attribute group of each store on the horizontal axis and the SKU operation rate of the corresponding product attribute group of each store on the vertical axis. We rank each store by sales operation multiplier (period number of purchases X SKU operation rate), and rank 40, more than top 40% rank A, top 70-90% rank A, top 70-90% rank B, lower 10% Let rank be C, and rank is given to each store. Moreover, curves 800, 802, 804, and 806 represent the sales operation magnifications between the ranks. In addition, in order to compare with this, the case where the rank of a store is given to B of FIG. 17 based on "the number of period purchases" is shown.

Considering the utilization rate of SKUs, stores with a higher number of purchases but lower SKU utilization rate are lowered, whereas stores with a higher number of purchases but higher SKU utilization rate rank higher. That is, since the latter has a high sales efficiency, the sales can be increased by increasing the distribution amount with respect to the store.

In addition, the vertical axis "number of sales SKUs" of B of FIG. 17 is the number of types of SKUs in which a sale occurred in the set period. In addition, you may rank the store using inventory turnover etc. instead of SKU operation rate.

(2) In the above embodiment, the store is ranked based on the amount of sales of the product attribute group to which the SKU to be distributed belongs, and the like. However, it is not limited to this. For example, you may rank a store based on the sales amount of the said SKU, etc. Moreover, you may rank a store based on the sales amount of SKU similar to the said SKU, and the like. In addition, you may rank a store based on the sales amount of a product attribute group similar to the product attribute group which the said SKU belongs, etc.

(3) In the above embodiment, "S store only, S store A, S store A, B store, all store distribution" by pull-down menu of the store selection column 660 of the distribution simulation screen shown in FIG. Distribution target store can be selected from. However, it is not limited to this, For example, you may select only the store of rank B.

4.2 Another Example of Distribution Simulation (First Distribution, Additional Distribution)

(1) In the above embodiment, in the normal distribution processing and 1: 100 distribution processing, the number of distributions for each store is determined based on the sales amount (composition ratio of period sales amount) of the product attribute group to which the SKU to be distributed belongs. It calculates (step S202 of FIG. 10, step S306 of FIG. 11). However, you may calculate the distribution number for each store based on the number of purchases (composition ratio of the number of period purchases) of the product attribute group to which the SKU to be distributed belongs.

In addition, you may calculate the distribution number for each store based on the purchase amount of the SKU to be distributed or the number of purchases. Moreover, you may calculate the distribution number for each store based on the purchase amount of the SKU similar to the said SKU, or the number of purchases. Moreover, you may calculate the distribution number for each store based on the sales amount of the product attribute group similar or related to the product attribute group to which the SKU to be distributed belongs, or the number of purchases.

(2) In the embodiment described above, in the sales prediction distribution process and the distribution process by the composition ratio of the number of purchases, the distribution to each store is made using a reference value based on the number of purchases of the SKU to be distributed or the composition ratio of the number of purchases of the period. The number of sheets is calculated (step S506 of FIG. 14, step S602 of FIG. 16). However, you may calculate the distribution number for each store based on the sales amount of the SKU to distribute.

In addition, you may calculate the distribution number for each store based on the purchase number or the purchase amount of the product attribute group to which the SKU to be distributed belongs. Moreover, you may calculate the distribution number for each store based on the purchase number or the purchase amount of the product attribute group similar or related to the product attribute group to which the SKU to be distributed belongs.

(3) In the above embodiment, in the 1: 100 distribution process, each of the stores of the ranks S and A selected by the operator is distributed one by one from the upper rank (step S300 in FIG. 11), but is distributed by a predetermined amount. You may also Moreover, you may distribute | distribute in order of the purchase amount of money or the number of purchases, not distributing from a higher rank.

(4) In the above-described embodiment, in the product rank distribution process, the operator for each rank is inputted to the rank distribution number setting screen shown in FIG. 13A (step S400 in FIG. 12). However, the rank-specific distribution number setting screen shown in FIG. 18 may be used. This divides the rank-specific distribution number in advance and allows the operator to select a desired item. Moreover, it may be not the distribution number for each rank but the distribution ratio for each rank.

(5) In the above-mentioned embodiment, "distribution by composition ratio of normal distribution, 1: 100 distribution, product rank distribution, sales prediction distribution, and the purchase number of sheets" is demonstrated as an example of the distribution processing method at the time of a first time and addition. have. However, it is not limited to these. For example, the number of purchases per one SKU (sales) using the number of period sales of the product attribute group to which the SKUs to be distributed belongs and the number of SKUs (SKUs) (see FIG. 3) belonging to the product attribute group. The number of sheets / number of SKUs) may be calculated for each store, and it may be disassembled based on this calculated value.

In addition, the number of purchases per SKU1 type (the number of purchases / the operation), using the number of types (operating SKUs) of the SKUs belonging to the product attribute group and sold more than a predetermined amount (for example, sold more than one sheet). The number of SKUs) may be calculated for each store, and the number of SKUs may be digested based on this calculated value. Thereby, the distribution process which considered the selling power per SKU1 type can be implement | achieved. Moreover, you may perform the distribution simulation using "the number of purchases / the number of SKUs", and the "the number of the purchases / the number of movable SKUs", without performing rank rank of a store.

(6) In the above embodiment, the case where the operator himself does not set the total number of distribution sheets is described. However, in the distribution simulation screen shown in FIG. 9, you may set by clicking the distribution number change button 640 and inputting a numerical value into the total distribution number setting field 650. FIG.

(7) In the above-described embodiment, a case in which the number of purchases for five weeks including the week is used as an example will be described in the distribution of the sales forecast at the time of additional distribution and the distribution by the composition ratio of the number of purchases. However, the present invention is not limited to this period, and the operator may arbitrarily set the period. It is also possible to accumulate the total number of sales until now without setting the period.

(8) In the above-described embodiment, the number of distributions for each store is calculated using the reference value calculated by the equation (7), but instead of the reference value, the corresponding main distribution prediction value (= corresponding main sales prediction value / Digestion rate) may be used to calculate the number of sheets distributed to each store.

(9) In the above-mentioned embodiment, the distribution simulation using the reference value is executed without giving the rank of the store at the time of the additional distribution. However, the distribution simulation may be performed for the ranked store by using the reference value.

(10) In the above-mentioned embodiment, the numerical value which rounded up the calculated value of Formula (3) and Formula (4) as an integer is made into the allocation number for each store. However, a numerical value obtained by lowering the calculated value to an integer or an integer value closest to the calculated value may be the number of distributions for each store. Similarly, the reference number calculated in equation (7) may be a number of allocations for each store, not a numerical value rounded up to an integer, but a numerical value rounded down to an integer.

4.3 Other embodiments

(1) In the above embodiment, the product attribute group is grouped by the item (for example, jacket, pullover, cardigan, etc.) and the size. However, the present invention is not limited to this and may be grouped using other elements (for example, colors). Moreover, you may group with item and size using item sub (for example, knit, fabric, jersey, etc.). By using this item sub, it is possible to form a product attribute group having a characteristic close to the SKU to be distributed.

In addition, the product attribute group may be formed of only items, only item subs, or only sizes. For example, when trying to distribute a "Jersey" type SKU, the product attribute group formed by the item sub "Judge" is used, without setting an item.

Further, a plurality of product attribute groups may be set. For example, by setting a plurality of items or item subs, the sales data of the product attribute group may be increased and useful.

(2) In the embodiment described above, the sales of the product attribute group (jacket, size A) is made to correspond to the SKU that is the product specified by the item number, the size, and the color as the distribution object, and the handling result information corresponding to the distribution object. The amounts are explained correspondingly. However, as the handling performance information corresponding to the object to be distributed, the handling history information of an object having a characteristic related to or similar to other characteristics such as the kind, nature, and function of the object to be distributed may be used. 19 shows an example of handling result information corresponding to a distribution object and a distribution object.

(3) In the embodiment described above, the case of determining how much to distribute the goods to which store in the distribution simulation is described. However, the present invention can also be applied to the case of determining only to which store (distribution destination) to distribute or to determining only how much of the product to distribute (distribution amount).

(4) In the above-described embodiment, a case of physically distributing a product to each of a plurality of stores is described as an example. However, the present invention is also used when a data or program is delivered as a distribution target using a telecommunication line. Can be applied. The present invention can also be applied to distributing a product to a plurality of product arrangement regions within the same store or the like. For example, it is a case where the same goods are distributed to 1st floor-5th floor in the same store.

(5) In the above-described embodiment, data aggregation processing is performed using a separate computer (aggregation processing computer 20) from the distribution processing computer 10, but the distribution processing computer 10 performs data aggregation processing. You may do it.

(6) In the above embodiment, the case where the apparel goods are distributed to each store is explained as an example, but the present invention can be applied to general merchandise (food, electrical appliances, etc.). Incidentally, the object to be distributed is not limited to the product to be sold at each store. It can also be applied to products traded between companies.

(7) Although the above-mentioned embodiment explains the store as an example as a distribution destination, it is not limited to this. For example, it may be a factory, a sales office, a distribution center, or the like.

(8) In the above embodiment, the distribution simulation result (distribution data) is transmitted to the distribution center computer 40. However, you may transmit to the store computers 50, 52, and 54. In this case, each store may make a shipment instruction of goods to a distribution center etc. based on distribution data. In addition, within the range of the number of distribution to each store shown in distribution data, each store may perform the shipment instruction of a product.

(9) In the above-described embodiment, the distribution center computer 40 transmits the incoming and outgoing data to the counting processing computer 20 of the headquarters, but may send the document that outputs the incoming and outgoing data to the headquarters. Similarly, the store computers 50, 52, and 54 transmit the sales data to the aggregation processing computer 20 of the headquarters, but may send the document output the sales data to the headquarters.

(10) In the above embodiment, the distribution processing computer 10 is used together with the aggregation processing computer 20, the distribution center computer 40, the store computer 50, and the like, so that the above-mentioned distribution simulation and store rank assigning process are performed. The case of executing is described as an example. However, the distribution processing computer 10 alone may perform the distribution simulation and the store rank assigning process.

(11) In the above embodiment, the case where the apparel products having the same brand are distributed is described as an example. However, the brand is not limited to the same case.

4.4 Processing of the Aggregate Processing Computer 20

20 shows a flowchart of data aggregation processing by the aggregation processing computer 20 shown in FIG. 1. The CPU of the tally processing computer 20 stores the sales data (the number of SKUs sold) from the store computers 50, 52, and 54 of each store, and the incoming and outgoing data from the distribution center computer 40 (which SKU It receives how many pieces have been received and how many have been collected, and obtains ordering data (which SKUs have been ordered) from the operator, and stores them in the transaction DB 36 of the database server 30 (step S800 of FIG. 20). . This process is repeated until it reaches a predetermined time (step S802 of FIG. 20).

When the predetermined time comes, the sales data of each store is counted for each SKU, and recorded in the transaction DB 36 of the database server 30 as the number of sales on the day (step S804 in FIG. 20). Similarly, the goods receipt data and the goods issue data are aggregated for each SKU and recorded in the transaction DB 36. As a result, daily sales data and the like are accumulated.

In addition, for each SKU at each store, the cumulative total of the total amount of sales of the corresponding week (the total of the total amount of sales from Monday), the total of the total number of purchases of the week, the number of purchases, the number of stocks, etc. are also calculated (step S806 of FIG. 20), and the database server. The transaction is recorded in the transaction DB 36 (see B in FIG. 5) at 30 (step S808 in FIG. 20).

Claims (55)

A distribution processing system comprising a plurality of distribution destination terminal devices, a distribution processing device, and a distribution execution device, The distribution terminal device records the handling result information of the object, Distribution processing device, Receive and record the handling result information of the object in each distribution destination from each distribution destination terminal device, Based on the handling result information corresponding to the distribution object to be newly distributed, each distribution destination is classified, Receives the total amount of distribution targets to be newly distributed, generates distribution data indicating how to distribute the distribution targets to each distribution target destination using the classification determined for each distribution target destination, and sends them to the distribution execution device. and, The distribution execution apparatus receives the distribution data, and executes a process for distributing the distribution object to the distribution destination based on the distribution data. The method of claim 1, And the distribution data indicating which distribution target object is to be distributed or how much distribution object is to be distributed to which distribution destination. The method according to claim 1 or 2, The distribution record information corresponding to the distribution object is the distribution record information in the past of the distribution object. The method according to claim 1 or 2, The handling result information corresponding to the said distribution object is the distribution handling system information in the past with respect to the whole group to which the said distribution object belongs. The method according to claim 1 or 2, And the distribution data indicating how much to distribute the object to be distributed to the selected distribution destination selected using the classification determined for the distribution destination. The method of claim 5, According to the ratio of the handling result information corresponding to the said distribution object of each said selection distribution destination with respect to the handling result information corresponding to the said distribution object of the whole said allotment distribution destination, how much distribution object is distributed with respect to each selection distribution destination. And the distribution data indicating whether or not to be generated. The method of claim 5, The predetermined amount is distributed to the respective selected distribution destinations, and to the remaining distribution objects after the distribution, the selected distribution information on the handling result information corresponding to the distribution objects of the entire selection distribution destination. And the distribution data indicating how much to distribute to each selected distribution destination in accordance with the ratio of the handling performance information corresponding to the distribution target of the destination. The method according to claim 1 or 2, And the distribution data indicating how to distribute the object to be distributed based on the distribution amount or distribution ratio set for the classification. The method according to claim 1 or 2, The distribution data indicating how much to distribute the distribution object based on the handling performance information corresponding to the distribution object of the group to which the object to be distributed belongs and the handling capacity per group composition calculated from the number of types of group members belonging to the group. Distributing processing system, characterized in that to produce. The method according to claim 1 or 2, And the distribution data indicating how much to distribute the object to be distributed based on the sales forecast value calculated from the handling performance information corresponding to the object to be distributed. The method of claim 10, And the distribution data indicating how much to distribute the object to be distributed, based on the sales prediction value considering the weekend index. The method according to claim 1 or 2, The handling history information includes at least sales information. The method according to claim 1 or 2, The handling performance information includes an operation rate calculated from at least sales information and stock information. A distribution processing apparatus for generating distribution data for determining how to distribute a distribution object to each distribution destination, Receive the handling result information of the object in each distribution destination, classify each distribution scheduled destination based on the handling result information corresponding to the distribution object to be newly distributed, Distribution processing characterized by receiving the total amount of distribution objects to be newly distributed, and using the classification determined for each distribution destination, generating distribution data indicating how to distribute the distribution object to each distribution destination. Device. The method of claim 14, And the distribution data indicating which distribution target object is to be distributed or how much to distribute to which distribution target object. The method according to claim 14 or 15, The handling result information corresponding to the said distribution object is the distribution performance information in the past of the said distribution object. The distribution processing apparatus characterized by the above-mentioned. The method according to claim 14 or 15, The handling result information corresponding to the said distribution object is distribution handling apparatus information in the past with respect to the whole group to which the said distribution object belongs. The method according to claim 14 or 15, And the distribution data indicating how much to distribute the object to be distributed to the selected distribution destination selected by using the classification determined for the distribution destination. The method of claim 18, According to the ratio of the handling result information corresponding to the said distribution object of each said selection distribution destination with respect to the handling result information corresponding to the said distribution object of the whole said allotment distribution destination, how much distribution object is distributed with respect to each selection distribution destination. And the distribution data indicating whether or not to be generated. The method of claim 18, The predetermined amount is distributed to the respective selected distribution destinations, and to the remaining distribution objects after the distribution, the selected distribution information on the handling result information corresponding to the distribution objects of the entire selection distribution destination. And the distribution data indicating how much to distribute to each selected distribution destination in accordance with the ratio of the handling result information corresponding to the distribution target of the destination. The method according to claim 14 or 15, And the distribution data indicating how to distribute the object to be distributed based on the distribution amount or distribution ratio set for the classification. The method according to claim 14 or 15, The distribution data indicating how much to distribute the distribution object based on the handling performance information corresponding to the distribution object of the group to which the object to be distributed belongs and the handling capacity per group composition calculated from the number of types of group members belonging to the group. Distributing processing apparatus, characterized in that the production. The method according to claim 14 or 15, And the distribution data indicating how much to distribute the object to be distributed based on the sales forecast value calculated from the handling performance information corresponding to the object to be distributed. The method of claim 23, wherein And the distribution data indicating how much to distribute the object to be distributed, based on the sales prediction value considering the weekend index. The method according to claim 14 or 15, The handling history information includes at least sales information. The method according to claim 14 or 15, The handling history information includes an operation rate calculated from at least sales information and stock information. Receive the handling result information of the object in each distribution destination, classify each distribution scheduled destination based on the handling result information corresponding to the distribution object to be newly distributed, Receiving the total amount of distribution targets to be newly distributed, and using the classification determined for each distribution target destination, perform processing on the computer to generate distribution data indicating how to distribute the distribution target object to each distribution target destination. Program to make. The method of claim 27, And the distribution data indicating which distribution target object is to be distributed or how much to distribute to which distribution target. The method of claim 27 or 28, The handling history information corresponding to the said distribution object is the handling history information in the past of the said distribution object. The program characterized by the above-mentioned. The method of claim 27 or 28, The handling performance information corresponding to the said distribution object is a program of the handling history information in the past with respect to the whole group to which the said distribution object belongs. The method of claim 27 or 28, And causing a computer to execute processing for generating the distribution data indicating how much to distribute the object to be distributed to the selected distribution destination selected using the classification determined for the distribution destination. The method of claim 31, wherein According to the ratio of the handling result information corresponding to the said distribution object of each said selection distribution destination with respect to the handling result information corresponding to the said distribution object of the whole said allotment distribution destination, how much distribution object is distributed with respect to each selection distribution destination. And causing a computer to execute a process of generating the distribution data indicating whether or not. The method of claim 31, wherein The predetermined amount is distributed to the respective selected distribution destinations, and to the remaining distribution objects after the distribution, the selected distribution information on the handling result information corresponding to the distribution objects of the entire selection distribution destination. And a computer that executes a process of generating the distribution data indicating how much to distribute to each selected distribution destination in accordance with the ratio of the handling performance information corresponding to the distribution target of the destination. The method of claim 27 or 28, And a computer to execute a process of generating the distribution data indicating how to distribute the object to be distributed, based on the distribution amount or distribution ratio set for the classification. The method of claim 27 or 28, The distribution data indicating how much to distribute the distribution object based on the handling performance information corresponding to the distribution object of the group to which the object to be distributed belongs and the handling capacity per group composition calculated from the number of types of group members belonging to the group. A program comprising causing a computer to execute a processing to be generated. The method of claim 27 or 28, And a computer to execute a process of generating the distribution data indicating how much to distribute the distribution object based on the sales forecast value calculated from the handling performance information corresponding to the distribution object. The method of claim 36, And a computer that executes a process of generating the distribution data indicating how much to distribute the object to be distributed, based on the sales prediction value in consideration of the weekend index. The method of claim 27 or 28, The handling history information includes at least sales information. The method of claim 27 or 28, The handling performance information includes an operation rate calculated from at least sales information and stock information. Receive the handling result information of the object in each distribution destination, classify each distribution scheduled destination based on the handling result information corresponding to the distribution object to be newly distributed, Receiving the total amount of distribution targets to be newly distributed, and using the classification determined for each distribution target destination, perform processing on the computer to generate distribution data indicating how to distribute the distribution target object to each distribution target destination. Recording medium recording a program for making. The method of claim 40, And a program for causing a computer to execute a process for generating the distribution data indicating which distribution target object is to be distributed to or to which distribution target object is to be distributed. 42. The method of claim 40 or 41, The handling result information corresponding to the said distribution object is the handling history information in the past of the said distribution object. The recording medium characterized by the above-mentioned. 42. The method of claim 40 or 41, The handling history information corresponding to the said distribution object is the past handling performance information about the whole group to which the said distribution object belongs, The recording medium characterized by the above-mentioned. 42. The method of claim 40 or 41, And the distribution data indicating how much to distribute the object to be distributed to the selected distribution destination selected using the classification determined for the distribution destination. The method of claim 44, According to the ratio of the handling result information corresponding to the said distribution object of each said selection distribution destination with respect to the handling result information corresponding to the said distribution object of the whole said allotment distribution destination, how much distribution object is distributed with respect to each selection distribution destination. A program for causing a computer to execute a process of generating the distribution data indicating whether or not to be recorded. The method of claim 44, The predetermined amount is distributed to the respective selected distribution destinations, and to the remaining distribution objects after the distribution, the selected distribution information on the handling result information corresponding to the distribution objects of the entire selection distribution destination. A recording medium storing a program for causing a computer to execute a process of generating the distribution data indicating how much to distribute to each selected distribution destination in accordance with the ratio of the handling result information corresponding to the distribution target of the destination. . 42. The method of claim 40 or 41, And a program for causing a computer to execute a process of generating the distribution data indicating how to distribute the object to be distributed, based on the distribution amount or distribution ratio set for the classification. 42. The method of claim 40 or 41, The distribution data indicating how much to distribute the distribution object based on the handling performance information corresponding to the distribution object of the group to which the object to be distributed belongs and the handling capacity per group composition calculated from the number of types of group members belonging to the group. A program for recording a program for causing a computer to execute processing to be generated. 42. The method of claim 40 or 41, And a program for causing a computer to execute a process of generating the distribution data indicating how much to distribute the distribution object based on the sales forecast value calculated from the handling performance information corresponding to the distribution object. The method of claim 49, And a program for causing a computer to execute a process of generating the distribution data indicating how much to distribute the object to be distributed, based on the sales prediction value in consideration of the weekend index. 42. The method of claim 40 or 41, The handling history information includes at least sales information. 42. The method of claim 40 or 41, And said handling performance information includes an operation rate calculated from at least sales information and stock information. Using a computer, Receiving the handling result information of the object in each distribution destination, evaluating each distribution scheduled destination based on the handling result information corresponding to the distribution object to be newly distributed, A distribution process characterized by receiving the total amount of distribution objects to be newly distributed, and using the evaluation determined for each distribution destination, generating distribution data indicating how to distribute the distribution object to each distribution destination. Way. A distribution processing system comprising a plurality of distribution destination terminal devices, a distribution processing device, and a distribution execution device, Each distribution terminal device A first storage means for recording the handling result information of the object; The distribution processing device, Second storage means for receiving and recording handling result information of the object in each distribution destination from each distribution destination terminal device; Classification means for classifying each distribution scheduled destination based on handling result information corresponding to a distribution object to be newly distributed, stored by the second storage means; A generation that receives the total amount of distribution objects to be newly distributed and generates distribution data indicating how to distribute the distribution objects to each distribution destination by using the classification determined for each distribution destination by the classification means. Sudan, Transmitting means for transmitting distribution data generated by said generating means to a distribution execution device, The distribution execution device, And execution means for receiving distribution data transmitted by the transmission means and executing a process for distributing the distribution object to the distribution destination based on the distribution data. A distribution processing apparatus for determining how to distribute a distribution object to each distribution destination, Reception means which receives the handling result information of the object in each distribution office, Classification means for classifying each distribution scheduled destination based on handling result information corresponding to a distribution object to be newly distributed, received by the reception means; A generation that receives the total amount of distribution objects to be newly distributed and generates distribution data indicating how to distribute the distribution objects to each distribution destination by using the classification determined for each distribution destination by the classification means. Distributing apparatus comprising a means.