JP2007128475A - Merchandise management method, merchandise management device and program thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a merchandise management method capable of predicting with high reliability the number of sold merchandise in a plurality of stores of management targets. <P>SOLUTION: A computer 40 of a management corporation 14 divides pharmacies 10_1 to 10_n into a plurality of groups and predicts the number of sold merchandise each in a group unit. Then, the computer 40 totals the predicted numbers of sold merchandise of all of the groups to perform order processing to a maker 12. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a product management method, a product management device, and a program thereof that can specify the total value of the number of products to be delivered with high reliability when delivering products to a plurality of stores.

For example, there are businesses that make contracts with stores such as a plurality of pharmacies and deliver products to these stores.
Such business operators predict the total number of products sold at all contracted stores based on the past sales performance of each store for each product and each period, and based on the predicted value, manufacturers Place an order for a product. In this case, the business operator can use a volume discount or the like by ordering a large amount of products, and can purchase the products at low cost.

  JP 2003-337915 A

By the way, in the business of the above-mentioned business operators, it is possible to predict how accurately the total number of products sold at all contracted stores can be predicted for each product and for each period, such as price cost reduction and inventory reduction. Very important from.
However, if the past sales performance of all stores is viewed only by the total value, there is a problem that the reliability of the prediction is low.

  SUMMARY OF THE INVENTION In order to solve the above-described problems of the prior art, an object of the present invention is to provide a product management method, a product management device, and a program thereof that can predict the number of products sold at a plurality of stores to be managed with high reliability. To do.

  In order to solve the above-described problems of the prior art and achieve the above-described object, the product management method of the first aspect of the invention is a product management method executed by a computer, and includes a plurality of product delivery destinations in the past. Based on the product sales performance data indicating the number of product sales, the total number of product sales of product delivery destinations belonging to the group can be predicted, and the plurality of product delivery so that one product delivery destination belongs to one group Group determination step for grouping destinations, and the total number of product sales of product delivery destinations belonging to the group for each of the plurality of groups, and the number of products delivered to each group based on the prediction result A group delivery product number determining step for determining a product, an ordering process for performing product ordering based on the number of products determined in the group delivery product number determining step, and the group delivery product Based on the number of products determined in the determination step and the product sales performance data, for each of the plurality of product delivery destinations, a product delivery destination shipping product number determination step for determining the number of products delivered to the product delivery destination, Have.

In the merchandise management method of the present invention, it is preferable that the group determination step includes time-series data indicating a total time of merchandise sales of a plurality of merchandise delivery destinations belonging to the group in each of the plurality of groups. When a predetermined relationship is established between the pattern and the pre-stored feature pattern, it is determined that the prediction is possible.
In the product management method of the present invention, it is preferable that the group determination step sequentially changes the grouping mode until the predetermined relationship is established.
In the product management method of the present invention, it is preferable that the group determination step determines the predetermined relationship when it is determined that the predetermined relationship is not established for all the forms of the plurality of predetermined groupings. The grouping mode is sequentially changed until the relationship is established.
Further, the product management method of the present invention is based on the inventory acquisition process of acquiring inventory quantity data indicating the inventory quantity of the delivered product at the product delivery destination, and the inventory quantity data acquired in the inventory acquisition process. And a product delivery determining step of determining a product delivery pattern among a plurality of product delivery destinations in each group.

  In addition, the product management device of the present invention can predict the total value of product sales of product delivery destinations belonging to a group based on product sales performance data indicating past product sales of a plurality of product delivery destinations. Group determination means for grouping the plurality of product delivery destinations so that one product delivery destination belongs to one group, and for each of the plurality of groups, the total number of product sales of the product delivery destinations belonging to the group A group delivery product number determination unit that predicts and determines the number of products to be delivered to each group based on the prediction result, and performs a product ordering process based on the product number determined by the group delivery product number determination unit For each of the plurality of product delivery destinations, the product delivery destination based on the ordering means, the number of products determined by the group delivery product number determination means and the product sales record data To determine the items that delivery to and a product delivery destination dispatch items determination means.

  Further, the program of the present invention is a program executed by a computer that performs product delivery processing, and is based on product sales performance data indicating the number of past product sales of a plurality of product delivery destinations. The group determination procedure for grouping the plurality of product delivery destinations so that the total value of the number of product sales can be predicted and one product delivery destination belongs to one group, and for each of the plurality of groups, Predict the total value of the product sales of the product delivery destination to which it belongs, determine the number of products to be delivered to each group based on the prediction results, and determine the number of products to be delivered to the group and the group delivery product number determination procedure An ordering process for ordering products based on the number of products made, the number of products determined in the group delivery product number determining procedure, and the product sales performance data, Based, for each of the plurality of product delivery destination, to execute the product delivery destination dispatch items determining procedure for determining the items to be delivered to the product delivery destination to the computer.

  ADVANTAGE OF THE INVENTION According to this invention, the merchandise management method, merchandise management apparatus, and program which can predict the merchandise sales number in the some management object store with high reliability can be provided.

Hereinafter, a product delivery management system according to an embodiment of the present invention will be described.
FIG. 1 is a diagram for explaining an environment in which a product delivery management system 1 according to an embodiment of the present invention is used, and FIG. 2 is an overall configuration diagram of the product delivery management system 1 of the present embodiment.
As shown in FIG. 1, the merchandise delivery management system 1 is configured such that the management company 14 of the merchandise delivery management system 1 individually makes a contract with a plurality of independent pharmacies 10_1 to 10_n (n is an integer of 2 or more). The goods purchased at a low price using a volume discant from 12 etc. are delivered to the pharmacies 10_1 to 10_n.

As illustrated in FIG. 2, the merchandise delivery management system 1 includes, for example, computers 20_1 to 20_n, a computer 30, and a computer 40.
The computers 20_1 to 20_n, the computer 30, and the computer 40 are connected via a network such as the Internet, for example.

[Computers 20_1 to 20_n]
The computers 20_1 to 20_n are used by the pharmacies 10_1 to 10_n, respectively.
The person in charge of the pharmacies 10_1 to 10_n inputs the sales status of each product (date and time when each product was sold, the number) in the pharmacy to the computers 20_1 to 20_n.
The computers 20 </ b> _ <b> 1 to 20 </ b> _n generate product sales record data indicating the sales situation described above, for example, at predetermined time intervals, and transmit this to the computer 40.

[Computer 30]
The computer 30 is used, for example, by the manufacturer 12 or the like.
The manufacturer 12 receives a product order request from the computer 40 and performs a product shipping process to the management company 14 based on the order quantity for each product indicated by the product order request.
The manufacturer 12 sells the product to the management company 14 at a low cost by volume discount according to the ordering process from the management company 14.

[Computer 40]
The computer 40 generates a product order request based on the product sales record data used by the management company 14 and input from the computers 20_1 to 20_n, and transmits this to the computer 30.
In this embodiment, the management company 14 individually contracts with each pharmacy 10_1 to 10_n as shown in FIG. Here, the contract contents are, for example, a contract period (for example, one year), a product type, a minimum number of purchased products, a purchase price, and the like.
In other words, as shown in FIG. 3, the management company 14 performs the function of the headquarters in the chain store organization for a plurality of pharmacies 10_1 to 10_n that are not related to each other.
As shown in FIG. 3, the computer 40 assigns each of the pharmacies 10_1 to 10_n to one of a plurality of groups.
In the present embodiment, it is not necessary to notify the pharmacies 10_1 to 10_n from the management company 14 which group each of the pharmacies 10_1 to 10_n belongs to. The group is used for merchandise ordering processing for the manufacturer 12 by the management company 14.
Further, for example, as shown in FIG. 4, the computer 40 also performs management for delivering products from other pharmacies when the number of products in stock falls below the standard in any of the pharmacies 10_1 to 10_n.

FIG. 5 is a hardware configuration diagram of the computer 40 shown in FIG.
As illustrated in FIG. 5, the computer 40 includes, for example, an interface 51, a display 52, an operation unit 53, a memory 54, and a CPU 55, which are connected via a data line 50.
The interface 51 inputs and outputs data and requests between the pharmacies 10_1 to 10_n and the computer 30 shown in FIG. The display 52 displays a screen corresponding to the display signal from the CPUC 55.
The operation unit 53 is a keyboard, a mouse, a touch panel, or the like, and outputs an operation signal corresponding to a user operation to the CPU 55.
The memory 54 stores a program PRG (program of the present invention) executed by the CPU 55 and data used for processing of the CPU 55.
The CPU 55 executes the program PRG read from the memory 54 and controls the operation of the computer 40 in an integrated manner.

Hereinafter, an operation example of the computer 40 will be described focusing on the processing of the CPU 55.
In the present specification, each step executed by the CPU 55 may be executed in parallel or individually without necessarily being processed in time series, as well as processing performed in time series.
Each procedure shown below is described in the program PRG.

[First operation example]
In this operation example, a case where the computer 40 newly determines a group to which the pharmacies 10_1 to 10_n belong will be described.
FIG. 6 is a flowchart for explaining the operation example.
Step ST3 shown in FIG. 6 is an example of the group determination process / procedure of the present invention.

Step ST1:
The CPU 55 monitors an operation signal input via the operation unit 53 and determines whether or not a new group creation instruction has been input by the management company 14.
If the CPU 55 determines that a new group creation instruction has been input, the process proceeds to step ST2, and if not, the process of step ST1 is repeated.

Step ST2:
For example, the CPU 55 transmits a request for product sales record data to the computers 20_1 to 20_n via the interface 51.
In response to the request, the computers 20_1 to 20_n transmit the product sales record data regarding the pharmacies 10_1 to 10_n to the computer 40, respectively.
The CPU 55 writes the merchandise sales performance data input from the computers 20_1 to 20_n via the interface 51 in the memory 54.
Note that the computers 20_1 to 20_n may themselves transmit the product sales performance data to the computer 40 without a request from the computer 40 at predetermined time intervals.
Alternatively, the management company 14 may make an inquiry to the pharmacies 10_1 to 10_n by telephone or the like, and the management company 14 may input the sales result data of the pharmacies 10_1 to 10_n to the computer 40.

Step ST3:
The CPU 55 performs a process of determining a plurality of groups and the pharmacies 10_1 to 10_n belonging to the groups so that the sales performance of each group can be predicted based on the product sales performance data input from the computers 20_1 to 20_n.
Specifically, the CPU 55 randomly groups the pharmacies 10_1 to 10_n, and generates time-series data indicating the state of product sales (products and the number of sales) by the pharmacies 10_1 to 10_n belonging to each group in time series. To do. The time series data is data indicating the number of sales for each product in units of time such as days, weeks, or months.
Then, the CPU 55 determines whether or not a predetermined feature exists in the time series data. The predetermined feature is, for example, a constant value of the total number of each product sold at a pharmacy belonging to the group in the above time unit. Or it changes with a fixed pattern.
The CPU 55 performs a comparison process between the generated time-series data pattern and the feature data pattern indicating the feature stored in the memory 54 in advance, and the error is within a predetermined reference (approximate). In this case, it is determined that the feature is present in the time series data.

CPU55 performs the process which assigns the pharmacy mentioned above to a group, changing the pharmacy which belongs to the said group sequentially until all the pharmacies 10_1 to 10_n belong to any group.
The CPU 55 changes the group division itself when the allocation satisfying the above-described conditions cannot be performed for a predetermined group group.
If the CPU 55 does not satisfy the above-described conditions even if all the grouping methods are changed, the CPU 55 gradually reduces the error and the approximation criterion and performs the allocation process again.
The CPU 55 generates group data indicating the pharmacies 10 </ b> _ <b> 1 to 10 </ b> _n belonging to each of the plurality of finally determined groups, and writes this in the memory 54.
Note that the CPU 55 may determine a group as it is even if there is a group that does not satisfy the above condition among the plurality of determined groups. In this case, for a group that does not satisfy the above condition, a prediction process is performed based on the unique feature data of the group. The feature data may be generated based on data regarding the group. That is, the CPU 55 may predict the number of merchandise sales based on a unique standard for each group.

[Second operation example]
In this operation example, a case where a product is delivered to each store according to a group will be described.
FIG. 7 is a flowchart for explaining the operation example.
Step ST16 shown in FIG. 7 is an example of the group delivery product number determination process / procedure of the present invention. Moreover, step ST17 is an example of the product delivery destination dispatch product number determination process / procedure of the present invention. Step ST18 is an example of an ordering process / procedure of the present invention.

Step ST11:
The CPU 55 monitors an operation signal input via the operation unit 53 and determines whether or not an ordering instruction to the manufacturer is input by the management company 14.
If the CPU 55 determines that an ordering instruction to the manufacturer has been input, the process proceeds to step ST12. If not, the process of step ST11 is repeated.

Step ST12:
The CPU 55 reads past product sales record data and group data of the pharmacies 10_1 to 10_n stored in the memory 54, and generates sales record data of each group based on these data.

Step ST13:
The CPU 55 reads from the memory 54 prediction data of the merchandise sales contents of each group used to generate group merchandise shipping data indicating the merchandise shipping contents for each group determined last time.
Then, the CPU 55 needs to update the group when the read prediction data is compared with the sales performance data generated in step ST12 corresponding to the read prediction data and the error exceeds a predetermined standard. The process proceeds to step ST14, otherwise proceeds to step ST16.
For example, the CPU 55 calculates, for each predetermined product, the difference between the number of products indicated by the prediction data and the number of products indicated by the sales performance data, and the difference exceeds a predetermined threshold value. A number is specified, and when the number exceeds a predetermined threshold, it is determined that the error exceeds a predetermined reference.

Step ST14:
The CPU 55 reads past product sales record data of the pharmacies 10_1 to 10_n stored in the memory 54.

Step ST15:
The CPU 55 performs the same processing as step ST3 of FIG. 6 described above based on the past product sales record data read in step ST14, generates new group data, and writes this into the memory 54.

Step ST16:
The CPU 55 predicts the product sales contents of each group in the next period based on the latest group data read from the memory 54 and the feature data read from the memory 54, and based on the prediction data indicating the prediction result. Then, group product delivery data indicating the contents of product shipment to each group is generated.
The CPU 55 writes the prediction data in the memory 54.

Step ST17:
Based on the group merchandise delivery data generated in step ST16 and the merchandise sales performance data of the pharmacies 10_1 to 10_n read from the memory 54, the CPU 55 calculates the total number of deliveries of each merchandise in each group as the group merchandise delivery data. As shown in the figure, the content of the product delivery to each of the pharmacies 10_1 to 10_n is determined, and store product delivery data indicating it is generated.

Step ST18:
Based on the group product delivery data determined in step ST16, the CPU 55 generates order data indicating the total number of products delivered to all groups for each product.
The CPU 55 transmits the order data to the computer 30 via the interface 51.
When the computer 30 inputs the ordering data from the computer 40, the computer 30 performs volume ordering based on a predetermined standard and performs order processing so as to sell the product to the management company 14.

  After receiving the product from the manufacturer 12, the CPU 55 performs a delivery process for delivering the product to each of the pharmacies 10_1 to 10_n based on the store product delivery data generated in step ST17.

[Third operation example]
The computer 40 identifies the stock quantity of each product in the pharmacy 10_1 to 10_n based on the sales performance data input from the computers 20_1 to 20_n, and based on the identified stock quantity, the product 40 between the pharmacies 10_1 to 10_n. Perform delivery processing.
For example, the computer 40 delivers products between the stores so that the stores have the safety stock quantity shown in FIG. In addition, the computer 40 determines the number of items delivered to each store so that each store has as much stock as possible as shown in FIG.

  As described above, according to the merchandise delivery management system 1, as described above, the computer 40 classifies the pharmacies 10_1 to 10_n into a plurality of groups in which the total merchandise sales can be predicted, Prediction data for the number of products sold in the store is generated, and the number of products ordered from the manufacturer 12 is determined for each product. Therefore, the reliability of prediction data can be improved compared with the past.

  Moreover, according to the merchandise delivery management system 1, by managing the pharmacies 10_1 to 10_n for each group in the computer 40, it is possible to predict the number of merchandise sales for each group based on a unique standard, and the prediction accuracy can be improved. .

According to the merchandise delivery management system 1, as described above, the number of merchandise sold at the pharmacies 10_1 to 10_n can be predicted with high accuracy, so that a large quantity of merchandise purchased from the manufacturer 12 with a volume discount is not generated. That is, it is possible to use volume discount even for pharmacies that sell a small number of products. Further, the manufacturer 12 can automatically reduce the burden on the management company 14 by automatically performing the ordering process by the computer 40. Therefore, the management cost of the management company 14 can be reduced, and the selling price of the product from the management company 14 to the pharmacies 10_1 to 10_n can be further reduced.
Moreover, according to the merchandise delivery management system 1, it is possible to avoid running out of stock in the pharmacy by transferring the merchandise between the pharmacies 10_1 to 10_n according to their stock status.
Moreover, according to the merchandise delivery management system 1, it is possible to predict the number of merchandise sales in the pharmacies 10_1 to 10_n with high accuracy, thereby avoiding that some pharmacies 10_1 to 10_n have a large amount of inventory. In addition, the management company 14 can easily set an annual budget and can stabilize management. Further, the management company 14 can increase sales opportunities for other products by making contracts with a large number of pharmacies.

Further, according to the product delivery management system 1, by defining the group G as described above, inventory management and inventory leveling within the group can be simplified. In addition, it is possible to distribute the center inventory amount to each group to reduce the center inventory and to efficiently use the inventory space of each store.
Moreover, according to the product delivery management system 1, the number of stocks of each store can be estimated based on the number of product delivery to each store and the product sales record data of each store, and can be used for management. Thereby, the stock preservation period of the management company 14 or the distribution center can be shortened.

The present invention is not limited to the embodiment described above.
That is, those skilled in the art may make various modifications, combinations, subcombinations, and alternatives regarding the components of the above-described embodiments within the technical scope of the present invention or an equivalent scope thereof.
For example, in the above-described embodiment, the case where one group is defined for a plurality of products sold at pharmacies belonging to the group is exemplified, but a group may be uniquely defined for each of a plurality of products.
In addition, for example, as shown in FIG. 9A, the feature pattern matching method employs a change data matching method that extracts changes in the sample period in a certain total period and produces characteristics unrelated to the time axis. May be. In this example, the pattern U → NC → D → NC → NC → U → D → D. Then, grouping is performed by matching these feature patterns.
Further, as shown in FIG. 9B, a total data matching method may be employed in which accumulation is performed in a certain period and matching of accumulated patterns is performed. This matching is performed by a method in which the accumulated pattern itself is performed within an error range (the time axis is the same), and a method in which the accumulated pattern is applied to the change data matching method.
For example, in the above-described embodiment, a pharmacy is illustrated as an example of the commodity delivery destination of the present invention, but other stores may be used.

  FIG. 1 is a diagram for explaining an environment in which a product delivery management system according to an embodiment of the present invention is used.   FIG. 2 is an overall configuration diagram of the commodity delivery management system according to the embodiment of the present invention.   FIG. 3 is a diagram for explaining a merchandise delivery mode between stores in the merchandise delivery management system according to the embodiment of the present invention.   FIG. 4 is a diagram for explaining grouping of stores.   FIG. 5 is a hardware configuration diagram of the computer 40 shown in FIG.   FIG. 6 is a flowchart for explaining a case where the computer 40 shown in FIG. 5 newly determines a group to which the pharmacies 10_1 to 10_n belong.   FIG. 7 is a flowchart for explaining a case where a product is delivered to each store according to a group.   FIG. 8 is a diagram for explaining the stock quantity of each store.   FIG. 9 is a diagram for explaining a feature pattern matching method.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Merchandise delivery management system, 10_1-10_n ... Pharmacy, 12 ... Manufacturer, 14 ... Management company, 20_1-20_n ... Computer, 30 ... Computer, 40 ... Computer, 51 ... Interface, 52 ... Display, 53 ... Operation part, 54 ... Memory, 55 ... CPU

Claims (7)

A product management method executed by a computer,
Based on product sales performance data indicating the number of past product sales of multiple product delivery destinations, the total value of product sales of product delivery destinations belonging to a group can be predicted, and one product delivery destination belongs to one group A group determination step for grouping the plurality of product delivery destinations,
A group delivery product number determination step of predicting a total value of product sales of product delivery destinations belonging to the group for each of the plurality of groups, and determining the number of products delivered to each group based on the prediction result; ,
An ordering process for ordering products based on the number of products determined in the group delivery product number determining step;
Product delivery destination shipping product for determining the number of products delivered to the product delivery destination for each of the plurality of product delivery destinations based on the product number determined in the group delivery product number determination step and the product sales performance data A product management method comprising: a number determining step. In the group determination step, in each of the plurality of groups, a time series data pattern indicating the total number of product sales of a plurality of product delivery destinations belonging to the group in time series and a pre-stored feature pattern are stored in advance. The merchandise management method according to claim 1, wherein when the determined relationship is established, it is determined that the prediction is possible. The product management method according to claim 2, wherein the group determination step sequentially changes the grouping mode until the predetermined relationship is established. When it is determined that the predetermined relationship is not established for all the forms of the plurality of predetermined groupings, the group determination step changes the content of the predetermined relationship until the relationship is established The merchandise management method according to claim 3, wherein the grouping mode is sequentially changed. An inventory acquisition step of acquiring inventory quantity data indicating the inventory quantity of the delivered goods at the commodity delivery destination;
The product delivery determination step of determining a delivery pattern of products among a plurality of product delivery destinations in each group based on the inventory quantity data acquired in the inventory acquisition step. Management method. Based on product sales performance data indicating the number of past product sales of multiple product delivery destinations, the total value of product sales of product delivery destinations belonging to a group can be predicted, and one product delivery destination belongs to one group Group determining means for grouping the plurality of product delivery destinations,
A group delivery product number determining means for predicting a total value of product sales of product delivery destinations belonging to the group for each of the plurality of groups, and determining the number of products delivered to each group based on the prediction result; ,
Ordering means for ordering products based on the number of products determined by the group delivery product number determining means;
Product delivery destination shipping product for determining the number of products delivered to the product delivery destination for each of the plurality of product delivery destinations based on the product number determined by the group delivery product number determination means and the product sales record data A product management device having number determining means. A program executed by a computer that performs merchandise delivery processing,
Based on product sales performance data indicating the number of past product sales of multiple product delivery destinations, the total value of product sales of product delivery destinations belonging to a group can be predicted, and one product delivery destination belongs to one group A group determination procedure for grouping the plurality of product delivery destinations,
A group delivery product number determination procedure for predicting the total value of product sales of product delivery destinations belonging to the group for each of the plurality of groups, and determining the number of products delivered to each group based on the prediction result; ,
An ordering procedure for ordering products based on the number of products determined in the group delivery product number determining procedure;
Product delivery destination shipping product for determining the number of products delivered to the product delivery destination for each of the plurality of product delivery destinations based on the product number determined in the group delivery product number determination procedure and the product sales record data A program for causing the computer to execute a number determination procedure.

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