JP2005056236A - Method for predicting demand - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problems that it is unnecessary for each user to manage the consumption time limit of foodstuffs by himself (or herself) because the consumption time limit is automatically acquired and managed by a refrigerator in conventional technology, but this technology insistently depends on individual information management, and for instance, a store of foodstuffs can not grasp consumer's demands by using individual information. <P>SOLUTION: The demand predicting method comprises: a receiving step for receiving the consumption time limit information of stocks in refrigerators; an updating step for updating a stock table in which the consumption time limit information of stocks in respective refrigerators is stored on the basis of the consumption time limit information received in the receiving step; a demand predicting step for calculating the demand prediction value of each commodity on the basis of the information stored in the stock table updated by the updating step; and a notification step for notifying respective stores of the demand prediction value calculated by the demand prediction step. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a demand prediction method for grasping inventory information of a refrigerator installed in a home, a small store, or the like, and predicting a demand for a product from the inventory information.

Conventionally, the expiration date of the food in the refrigerator has been managed visually by the user. As a mechanism to automate this work, when the refrigerator enters the warehouse, it reads the barcode attached to the food and the refrigerator itself manages the expiration date of each food, and warns if there is a food that is about to expire There is a technique of performing (Patent Document 1).
JP-A-5-288456

  Since the above-described conventional technology automatically acquires and manages the expiration date on the refrigerator side, it is not necessary for the user to manage the expiration date of the food. However, this technology is merely personal information management. For example, a technology that enables a grocery store to grasp consumer demand using this information has not been proposed.

  The demand prediction method according to the present invention includes a reception step of receiving expiration date information of inventory in a refrigerator, and an inventory table storing expiration date information of inventory in each refrigerator based on the expiration date information received in the reception step. An update step for updating the demand, a demand prediction step for calculating a demand prediction value for each product based on the information in the inventory table updated in the update step, and a demand prediction value calculated in the demand prediction step. And a notification step of notifying to.

  By configuring in this way, the inventory information in the refrigerator, which has been limited to the use only in each home until now, is managed collectively in the information center, and this information is aggregated and analyzed. Demand information can be distributed to grocery stores.

  In addition, the demand prediction method according to the present invention is configured to perform demand prediction on the location unit of the refrigerator, thereby enabling more detailed demand prediction.

  In addition, the demand prediction method according to the present invention refers to a store table that stores the trade area information of each store, and is configured to notify the demand forecast value limited to the trade area of each store, thereby, It is possible to distribute demand forecast information limited to the.

  In addition, the demand prediction method according to the present invention refers to a threshold value table that stores replacement threshold values for each product, and uses a date obtained by subtracting the replacement threshold value from the expiration date of the product stored in the inventory table as a demand occurrence point. By configuring so as to calculate the demand forecast value of each product, it becomes possible to distribute demand forecast information based on the demand occurrence point before the expiration date.

  As described above, the present invention makes it possible to distribute demand forecast information useful for a sales plan of a grocery store by collecting and analyzing inventory information in the refrigerator of each household at the information center.

  An overview of a system according to an embodiment of the present invention will be described with reference to FIG. First, as a premise in the present embodiment, the refrigerator 121 of each household 120 has a product name of a food product stored in the refrigerator, such as a refrigerator with an article management function disclosed in JP-A-5-288456. And a mechanism for managing expiry dates, etc. In this publication, the information on the barcode attached to the food is captured when the food is stored. However, since the present invention relates to the demand prediction method in the information center, The information acquisition method of the refrigerator 121 is not limited. The refrigerator 121 is connected to a network 140 such as the Internet, and can communicate with the information center server 100 via the network 140.

  Next, the information center server 100 that implements the demand prediction method of the present invention will be described. The information center server 100 is connected to the refrigerator 121 of each household via the network 140, makes a demand prediction based on the received inventory information, and similarly the server 131 of each store 130 connected via the network 140. The demand forecast information is distributed to. This demand prediction method is a processing method for a group of programs operating on the information center server 100. The program group includes a reception program 101 for receiving inventory information from the refrigerator 121 in each household, and information received by the reception program 101. The inventory update program 102 for updating the inventory information managed by the information center server 100 based on the information, the demand forecast program 103 for forecasting the demand based on the inventory information, and the demand forecast information predicted by the demand forecast program 103 for each dealer. A transmission program 104 to be distributed to the server 131. These programs are stored in an external storage device (not shown) of the information center server 100, and are read into the internal storage device when each program is executed.

  Furthermore, in the external storage device of the information center server 100, a location table 110 that stores location information of the refrigerator 121 in each household, an inventory table 111 that stores inventory information in the refrigerator 121 in each home, and information on each store Store table 112, threshold table 113 for storing alarm transmission timing information of each foodstuff, demand prediction table 114 for storing demand prediction results, and receiving program 101, inventory update program as necessary 102, the demand prediction program 103, and the transmission program 104 are referred to or updated.

  In addition, a server 131 that receives information is installed in the store 130 to which the demand prediction information is distributed, and this server 131 can communicate with the information center server 100 via the network 140. Yes.

  Next, an outline of processing according to an embodiment of the present invention will be described with reference to FIG. First, the reception program 101 of the information center server 100 receives inventory information from the refrigerator 121 in each home (S201). The information to be received is information such as the IP address of the refrigerator, the product name, and the expiration date. Note that the receiving method in this case may be transmitted periodically by the refrigerator 121 in each home, or the information center server 100 may periodically inquire information about the refrigerator 121 in each home. In the latter case, the information center server 100 stores location information such as the location table 301 in FIG. 3, and records the registered IP address of the refrigerator 121 and the home location corresponding to the IP address in association with each other. Therefore, the inventory information is inquired toward the IP address registered in the location table 301. The location table 301 is necessary even in the former case because the information is used by the demand prediction program 103.

  Next, based on the inventory information received by the reception program 101, the inventory update program 102 executes an inventory update process (S202) for updating the inventory table 111. Then, based on the inventory information of the inventory table 111 that has been updated in the inventory update process, the demand prediction program 103 executes the demand prediction process (S203). Finally, the transmission program 104 executes a transmission process (S204) in which the demand prediction information is distributed to the server 131 of each store based on the result of the demand prediction process.

  As described above, by executing each process as in the process flow of FIG. 2, demand prediction information useful for each store can be automatically distributed.

  Next, the inventory update processing (S202), demand prediction processing (S203), and transmission processing (S204) described in FIG. 2 will be described in detail with reference to the drawings.

  First, the inventory update process (S202) will be described in detail using the flow of FIG. When the inventory update program 102 completes the reception of the inventory information in S201, the inventory update program 102 updates the inventory information of the corresponding refrigerator based on the received inventory information. Specifically, first, the record of the corresponding IP address in the inventory table 111 is deleted (S401). This inventory table 111 is illustrated in the inventory table 501 of FIG. The stock table 501 is composed of an IP address that is an IP address of the refrigerator, a product name in stock, and a freshness guarantee that represents the expiration date. The IP address is unique for each refrigerator, but since there are a plurality of food items stocked in the refrigerator, the IP address is duplicated in a plurality of records. In some cases, there may be a plurality of the same product in the same refrigerator, and thus the product name may be duplicated in a plurality of records. For this reason, in S401, all records matching the IP address of the corresponding refrigerator are deleted and replaced with newly received inventory information (S402).

  Thereafter, it is determined whether there is inventory information received from another refrigerator (S403). If there is, the process returns to S401, and if not, the inventory update process (S202) is terminated.

  Next, demand forecast processing (S203) is explained in detail using the flow of FIG. First, the demand prediction program 103 reads the inventory table 501 (S601). As described above, the stock table 501 stores the product names of food items stocked in each refrigerator and the date of freshness guarantee. This record is read one by one, and it is determined whether or not each food item is at the time of replacement by purchase (demand generation point) (S602). This determination can be made by comparing the freshness guarantee date in the inventory table with the processing date to determine whether the freshness guarantee date has passed at the time of processing, but in this embodiment, the calculation is performed more accurately. Therefore, the demand generation point using the replacement threshold for each product is used.

  For example, foods that must always be kept at home, such as sauces and ketchup, should not be purchased after the expiration date has passed. In many cases. Therefore, a threshold value is set for how many days before the expiration date for each product, and the value is stored in the threshold value table 113. This threshold value table 113 is illustrated in the threshold value table 701 of FIG. The threshold value table 701 stores product names and the above-described threshold values in association with each other. Here, by setting the threshold to 0 days, it is possible to process a product that does not use the replacement threshold for each product described above.

  As a result of the determination in S602, if the processing date exceeds the demand generation point considering the threshold, the process proceeds to S604, and if not, the process proceeds to S605 (S603).

  For example, in the case of the first record in the inventory table 501, it can be seen that the freshness guarantee date of the OO sauce of the refrigerator with the IP address 123 is October 9, 2003. Assume that the processing date is July 1, 2003. Under this premise, first, in S602, the threshold value table 701 is searched using the XX source as the product name as a key. From the extracted information, it can be seen that the replacement threshold for the OO sauce is 30 days. Therefore, the replacement threshold 30 days is subtracted from the above-mentioned freshness guarantee date October 9, 2003 to obtain a replacement point, that is, a demand occurrence point September 9, 2003. Therefore, the demand generation point September 9, 2003 is compared with the processing date July 1, 2003, and it is determined that the demand generation point is not yet exceeded as of the processing date. In this case, the determination in S603 is N, and the process proceeds to S605.

  Further, for example, in the case of the fourth record in the inventory table 501, it can be seen that the freshness guarantee date of XX ketchup of the refrigerator with the IP address 789 is July 31, 2003. Assume that the processing date is July 1, 2003. Under this premise, first, in S602, the threshold value table 701 is searched using the product name xxketchup as a key. From the extracted information, it can be seen that the replacement threshold for xx source is 60 days. Therefore, the replacement threshold 60 days is subtracted from the aforementioned freshness guarantee date July 31, 2003, and a replacement point, that is, a demand occurrence point, June 1, 2003 is obtained. Therefore, the demand generation point June 1, 2003 is compared with the processing date July 1, 2003, and it is determined that the demand generation point has already been exceeded as of the processing date. In this case, the determination in S603 is Y, and the process proceeds to S604.

  As described above, when it is determined that the demand generation point is exceeded, it is determined that there is a replacement purchase demand for the product, and is written in the demand prediction table 114 (S604). This demand prediction table 114 is illustrated in the demand prediction table 801 of FIG. The demand prediction table 801 is a table that stores a demand forecast value for each product for each location, and includes a product name, a location name, and a quantity that is a demand value. For example, in the case of the fourth record in the inventory table 501, the demand is written in this demand prediction table. In that case, the location table 301 is first searched using the IP address 789 as a key. From the extracted information, it can be seen that the location where the demand is occurring is Idasanmaicho, Nakahara-ku, Kawasaki City. Therefore, the demand forecast table 801 is searched using the product name xx ketchup and the location name Imai Sanmai-cho, Nakahara-ku, Kawasaki city as a key, and 1 is added to the number of matched records. In this search, if no matching record is found, a new record is generated. In this way, the demand forecast value of the product for each location is accumulated.

  When the writing to the demand prediction table 801 is completed in S604, the process proceeds to S605 as in the case where N is determined in S603, and it is determined whether or not the processing is completed for all the records in the inventory table 501. If the process for all the records has not been completed, the process returns to S601. If the process for all the records has been completed, the demand prediction process (S203) is terminated.

  Next, the transmission process (S204) will be described in detail using the flow of FIG. First, the transmission program 104 reads the store table 112 (S901). The store table 112 stores information on stores that have contracted the demand prediction information distribution service of this information center. This store table 112 is illustrated in the store table 1001 of FIG. The store table 1001 is composed of store names, e-mail addresses, and trade areas. As for the trade area, it is assumed that an address is stored in this example.

  Once the store table is read in S901, the demand prediction table 801 is searched using the trade area of the read record as a key (S902). Through this process, demand prediction information that matches the trade area of the store to be processed can be extracted. Specifically, in the case of the first record in the store table 1001, the supermarket trade areas are Shimo-Odanaka, Nakahara-ku, Kawasaki-shi, Sanmai-cho, Ida-sanmai-cho, Nakahara-ku, Kawasaki-city, Kamiodanaka, Nakahara-ku, Kawasaki-city. The demand forecast table 801 is searched using these place names as keys. As a result, it can be seen that there is demand forecast information that there are 52 XX sources in Shimoodanaka, Nakahara-ku, Kawasaki City, and 105 xx ketchups in Ida-sanmai-cho, Nakahara-ku, Kawasaki City.

  Therefore, based on this information, an e-mail for distributing the demand forecast value is edited (S903). This delivery mail is exemplified in the notification mail 1101 in FIG. The body of the email is created based on the extraction result of the demand prediction table 801, and the destination (XX supermarket) is created based on the store table 1001.

  The edited e-mail is distributed to the e-mail address stored in the e-mail address of the store table 1001 (S904). Thereafter, it is determined whether or not the processing has been completed for all the records in the store table 1001 (S905). If the processing for all the records has not been completed yet, the processing returns to S901, and if the processing for all the records has been completed, this transmission processing (S204) is terminated.

  In the above-described embodiment, the trade area of each store is performed using an address. However, as another method, it is also possible to grasp the trade area using latitude and longitude. In this case, latitude and longitude are stored in the location table 110 and the store table 112, a predetermined range is determined from the store latitude and mild numerical values, and refrigerators installed at locations within the range are subject to aggregation. Will be.

  By performing such a series of processing, the demand prediction information is distributed to the server 131 of the store, and the store 130 can obtain useful demand prediction information for making a sales plan.

(Appendix 1)
A demand prediction method by a server that performs demand prediction from refrigerator inventory information,
A receiving step of receiving expiration date information of the inventory in the refrigerator;
Based on the expiration date information received in the reception step, an update step for updating an inventory table storing the expiration date information of each refrigerator stock;
Based on the information in the inventory table updated in the updating step, a demand prediction step for calculating a demand prediction value of each product;
A notification step of notifying the store of the demand prediction value calculated in the demand prediction step;
Demand forecasting method characterized by including.

(Appendix 2)
The demand prediction method according to claim 1, wherein the demand prediction step performs demand prediction for each unit of a refrigerator.

(Appendix 3)
3. The demand prediction method according to claim 2, wherein the notifying step refers to a store table storing the trade area information of each store and notifies a demand prediction value limited to the trade area of each store.

(Appendix 4)
The demand prediction step refers to a threshold value table that stores a replacement threshold value for each product, and uses the date obtained by subtracting the replacement threshold value from the expiry date of the product stored in the inventory table as a demand occurrence point. The demand prediction method according to claim 1, wherein a value is calculated.

(Appendix 5)
An inventory table that stores inventory information for each product,
Receiving means for receiving refrigerator inventory information;
Updating means for updating the inventory table based on the inventory information received by the receiving means;
Based on the information in the inventory table updated by the updating unit, a demand prediction unit that calculates a demand prediction value of each product;
A notification means for notifying the store of the demand prediction value calculated by the demand prediction means;
A demand prediction apparatus comprising:

It is a system configuration figure of an embodiment. It is a processing flow of an embodiment. It is a figure which shows an example of a location table. It is a flowchart which shows the flow of an inventory update process. It is a figure which shows an example of an inventory table. It is a flowchart which shows the flow of a demand prediction process. It is a figure which shows an example of a threshold value table. It is a figure which shows an example of a demand prediction table. It is a flowchart which shows the flow of a transmission process. It is a figure which shows an example of a shop table. It is a figure which shows an example of a notification mail.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Information center server 101 Reception program 102 Inventory update program 103 Demand prediction program 104 Transmission program 110 Location table 111 Stock table 112 Store table 113 Threshold table 114 Demand prediction table 120 Household 121 Refrigerator 130 Store 131 Server

Claims (3)

A demand prediction method by a server that performs demand prediction from refrigerator inventory information,
A receiving step of receiving expiration date information of the inventory in the refrigerator;
Based on the expiration date information received in the reception step, an update step for updating an inventory table storing the expiration date information of each refrigerator stock;
Based on information in the inventory table updated in the updating step, a demand prediction step for calculating a demand prediction value of each product;
A notification step of notifying the store of the demand prediction value calculated in the demand prediction step;
Demand forecasting method characterized by including.   The demand prediction method according to claim 1, wherein the demand prediction step performs demand prediction for each location of the refrigerator. An inventory table that stores inventory information for each product,
Receiving means for receiving refrigerator inventory information;
Updating means for updating the inventory table based on the inventory information received by the receiving means;
Based on the information in the inventory table updated by the updating unit, a demand prediction unit that calculates a demand prediction value of each product;
A notification means for notifying the store of the demand prediction value calculated by the demand prediction means;
A demand prediction apparatus comprising:

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