Egg supply and demand forecasting system

Abstract

This invention provides a system that allows anyone, even without experience, to easily predict egg production and shipment. [Solution] The present invention acquires daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of laying hens. It creates a standard performance model that correlates and identifies the survival rate, egg-laying rate, and average egg weight according to the age of laying hens, and an egg weight table that identifies the egg-laying rate for each size according to the average egg weight. Using the standard performance model and the egg weight table, it is possible to predict the production quantity by size according to the age of laying hens. Furthermore, it acquires past shipment information that allows for the identification of the shipment quantity according to the shipment date for each egg size, and creates an index category that identifies a calculation method for predicting the shipment quantity according to the size of eggs based on shipment information for a predetermined period. Using the shipment information for a predetermined period and the index category, it is possible to predict the shipment quantity by size according to the scheduled shipment date.

Description

Technical Field

[0001] The present invention relates to a supply and demand prediction system for chicken eggs that predicts the expected future production and shipment quantities of chicken eggs.

Background Art

[0002] Conventionally, the production prediction of chicken eggs has been carried out based on the long-term experience of producers, and the egg production performance varies depending on the age (years) of the egg-laying hens, the environment, and the breed. For example, in summer, since the amount of feed eaten decreases, a decrease in production volume and a size reduction (from L size to M size, etc.) can be observed. Furthermore, since the egg production performance also varies depending on the breeding environment of each producer, the production prediction of chicken eggs has always been handled by skilled workers with long-term experience. In recent years, the scale of production farms has been increasing, and due to the corresponding large fluctuations, it has become difficult to predict the production of chicken eggs based on experience.

[0003] On the other hand, the shipment prediction of chicken eggs is calculated based on the long-term experience of producers by comparing the sales information of each sales destination such as retail stores and food service providers. On the producer side, supply and demand adjustments are often made to avoid shortages, which has resulted in factors leading to surplus inventory. When there is a surplus, it is necessary to sell at a lower price than usual, and in some cases, it may be inevitable to sell at a price lower than the production cost.

[0004] As described above, currently, both the production prediction and shipment prediction of chicken eggs are carried out manually by producers. However, to make long-term predictions, it is necessary to train skilled workers, and in recent years, there is a concern that supply and demand prediction cannot be comprehensively covered only by experience. Therefore, if this continues, surplus inventory and shortages may occur, leading to the risk of declining production areas and reduced demand. Thus, there has been a conventional demand for systematizing and efficiently predicting the supply and demand of chicken eggs.

[0005] By the way, several mechanisms for systematizing the supply and demand management of products have been conventionally proposed (for example, refer to Patent Documents 1 to 4). Patent Document 1 makes it possible to adjust supply and demand by determining the planting quantity based on the predicted sales quantity of agricultural products and calculating the predicted harvest quantity based on this planting quantity. Patent Document 2 describes a method for predicting demand for similar products based on sales history (sales performance) information of products sold in the past. Patent Document 3 describes a method for understanding demand by having farmers reserve the total amount they plan to purchase before sowing, and for understanding supply by receiving orders for desired shipment quantities on a daily basis before harvest and shipment, thereby managing the production and distribution of agricultural products and achieving efficient shipment adjustments without waste. Patent Document 4 describes a method for supporting the planning of crop cultivation by specifying the planting period and identifying indicators regarding the amount of planting required to achieve the target sales amount of the crop, in order to enable harvesting at the target sales period of the crop.

[0006] However, none of the conventional mechanisms or systems described above manage the uncertain supply and demand of eggs, such as the production of eggs whose size and quantity may fluctuate depending on age and season, or the shipment of eggs whose quantity may fluctuate depending on whether there are special sales or events at the sales destination. Therefore, it is difficult to apply these mechanisms or systems to supply and demand management such as forecasting egg production or shipments. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Patent No. 6509278 [Patent Document 2] Patent No. 2955081 [Patent Document 3] Japanese Patent Application Publication No. 11-175609 [Patent Document 4] International Publication No. 2015 / 173875 brochure [Overview of the project] [Problems that the invention aims to solve]

[0008] This invention has been made in view of the above-mentioned circumstances in the conventional era, and aims to provide a system that allows anyone, even without experience, to easily predict egg production and shipment. [Means for solving the problem]

[0009] To achieve the above objective, a first aspect of the present invention is a supply and demand forecasting system for eggs, comprising: a means for acquiring production performance information that acquires daily production information capable of identifying the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of eggs produced, according to the age of laying hens; a means for creating a standard performance model that associates and identifies the survival rate, egg-laying rate, and average egg weight according to the age of laying hens based on the acquired production information; a means for creating an egg weight table that identifies the egg-laying rate for each size according to the average egg weight based on the acquired production information; and a means for outputting supply forecast information that predicts the production quantity by size according to the age of laying hens based on the standard performance model and the egg weight table.

[0010] A second aspect of the present invention is that, in the first aspect described above, the production performance information acquisition means acquires the production information including the molting induction start date, the table creation means further creates a molting induction table based on the acquired production information that identifies information to correct the survival rate of the laying hens, information to correct the egg production rate, and information to correct the average egg weight according to the number of days after the start of molting induction for the laying hens, and the production forecasting means outputs supply forecasting information that predicts the production quantity by size according to the age of the laying hens, based on the standard performance model, the egg weight table, and the molting induction table.

[0011] A third aspect of the present invention is a supply and demand forecasting system for eggs, comprising: a means for acquiring past shipment information that allows for the identification of shipment quantities according to the shipment date for each size of egg; a means for creating an index category that identifies a calculation method for predicting shipment quantities according to egg size based on the acquired shipment information for a predetermined period; and a means for outputting demand forecast information that predicts shipment quantities by size according to the scheduled shipment date based on the shipment information for the predetermined period and the index category.

[0012] A fourth aspect of the present invention is a producer terminal used by a producer who raises laying hens and produces eggs, a seller terminal used by a seller who sells eggs, and a supply and demand forecasting device that predicts the production quantity and shipment quantity of eggs by size, all of which are connected via a network in a manner that enables communication, wherein the producer terminal has means for inputting daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production quantity for each size of eggs produced, according to the age of the laying hens, and means for transmitting the input production information to the supply and demand forecasting device, and chicken The seller terminal comprises means for transmitting a request for egg production quantity forecasting to the supply and demand forecasting device, and means for receiving supply forecasting information from the supply and demand forecasting device, and the seller terminal comprises means for inputting past shipment information that allows for the identification of shipment quantities according to the shipment date for each size of egg, means for transmitting the input shipment information to the supply and demand forecasting device, means for transmitting a request for egg shipment quantity forecasting to the supply and demand forecasting device, and means for receiving demand forecasting information from the supply and demand forecasting device, and the supply and demand forecasting device acquires production performance information from the producer terminal The egg supply and demand forecasting system is characterized by comprising: an acquisition means; a model creation means for creating a standard performance model that associates and identifies the survival rate, egg-laying rate, and average egg weight according to the age of the laying hen based on the acquired production information; a table creation means for creating an egg weight table that identifies the amount of eggs laid for each size according to the average egg weight based on the production information; a production forecasting means that, in response to receiving a request for egg production quantity forecasting from the producer terminal, outputs supply forecasting information that predicts the production quantity by size according to the age of the laying hen based on the standard performance model and the egg weight table; and a shipment performance information acquisition means for acquiring shipment information from the seller terminal; a classification creation means for creating an index classification that identifies a calculation method for predicting the shipment quantity according to the size of the egg based on the acquired shipment information for a predetermined period; and a shipment forecasting means that, in response to receiving a request for egg shipment quantity forecasting from the seller terminal, outputs demand forecasting information that predicts the shipment quantity by size according to the scheduled shipment date based on the shipment information for a predetermined period and the index classification.

[0013] A fifth aspect of the present invention is a producer terminal used by a producer who raises laying hens and produces eggs, a seller terminal used by a seller who sells eggs, and a supply and demand forecasting device that predicts the production quantity and shipment quantity of eggs by size, all of which are connected via a network in a manner that enables communication. The producer terminal includes means for inputting daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production quantity for each size of eggs produced, according to the age of the laying hens, means for transmitting the input production information to the supply and demand forecasting device, and means for sending egg production quantity forecasting requests to the supply and demand forecasting device. The seller terminal comprises means for transmitting to a measuring device and means for receiving supply forecast information from the supply and demand forecasting device, and the seller terminal comprises means for inputting past shipment information that allows for the identification of shipment quantities according to the shipment date for each size of egg, means for transmitting the input shipment information to the supply and demand forecasting device, means for transmitting a request for egg shipment quantity forecast to the supply and demand forecasting device, and means for receiving demand forecast information from the supply and demand forecasting device, and the supply and demand forecasting device comprises means for acquiring production performance information that acquires the production information from the producer terminal, and means for determining the age of the laying hens according to the acquired production information A model creation means for creating a standard performance model that identifies and correlates survival rate, egg-laying rate, and average egg weight; a table creation means for creating an egg weight table that identifies the amount of eggs laid for each size according to the average egg weight based on the production information, and a molting induction table that identifies information to correct the survival rate, egg-laying rate, and average egg weight of the laying hens according to the number of days after the start of molting induction of the laying hens; and in response to receiving a request for egg production quantity prediction from the producer terminal, the system uses the standard performance model, the egg weight table, and the molting induction table to generate a production The system includes a production forecasting means that outputs supply forecasting information predicting the production quantity by size according to the age of egg-laying hens, a shipment performance information acquisition means that acquires the shipment information from the seller terminal, a classification creation means that creates an index classification that specifies a calculation method for predicting the shipment quantity according to the size of the eggs based on the acquired shipment information for a predetermined period, and a shipment forecasting means that, in response to receiving a request for egg shipment quantity prediction from the seller terminal, outputs demand forecasting information predicting the shipment quantity by size according to the scheduled shipment date based on the shipment information for the predetermined period and the index classification.This is a supply and demand forecasting system for chicken eggs, characterized by having the following features.

[0014] In the first, second, fourth, or fifth embodiment described above, the means for acquiring production performance information may acquire the production information separately for each production area, or for each farm, or for each chicken coop.

[0015] Furthermore, if the production performance information acquisition means acquires the production information separately for each production area, farm, or chicken coop, the model creation means may create the standard performance model separately for each production area, farm, or chicken coop. Also, if the production performance information acquisition means acquires the production information separately for each production area, farm, or chicken coop, the table creation means may create the egg weight table separately for each production area, farm, or chicken coop.

[0016] Furthermore, if the production performance information acquisition means acquires the production information separately for each production area, farm, or chicken coop, the production forecasting means may also forecast the production quantity by size separately for each production area, farm, or chicken coop.

[0017] In the third, fourth, or fifth embodiment described above, the means for acquiring shipment performance information may acquire the shipment information for each shipping destination. Furthermore, if the means for acquiring shipment performance information acquires shipment information for each shipping destination, the means for creating classifications may create the indicator classifications for each shipping destination. Furthermore, if the shipment performance information acquisition means acquires the shipment information for each shipping destination, the shipment prediction means may predict the number of shipments by size for each shipping destination. [Effects of the Invention]

[0018] According to the present invention, it is possible to provide a system that can predict egg production and shipment without relying on individual experience or skill. [Brief explanation of the drawing]

[0019] [Figure 1] It is a schematic diagram showing the configuration of a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 2] It is a block diagram showing the hardware configuration of an apparatus constituting a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 3] It is a functional block diagram showing an example of the functional configuration of the apparatus. [Figure 4] It is a sequence diagram explaining the flow of supply prediction processing in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 5] It is a sequence diagram explaining the flow of other supply prediction processing in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 6] It is a schematic diagram showing a configuration example of a standard performance model used in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 7] It is a schematic diagram showing a configuration example of an egg weight table used in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 8] Explaining the supply prediction result in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention, (A) is a schematic diagram showing the simulation result 1 of the first stage, and (B) is a schematic diagram showing the simulation result 2 of the second stage. [Figure 9] It is a schematic diagram showing a configuration example of a molt induction table used in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 10] It is a schematic diagram showing the simulation result 3 after molt induction explaining the supply prediction result in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 11] It is a flowchart showing the operation of supply prediction processing in a supply and demand prediction apparatus. [Figure 12] It is a sequence diagram explaining the flow of demand prediction processing in a supply and demand prediction system for chicken eggs according to an embodiment of the present invention. [Figure 13]This is a schematic diagram showing an example of the configuration of indicator categories used in an egg supply and demand forecasting system according to one embodiment of the present invention. [Figure 14] This diagram illustrates shipment information for a predetermined period obtained by a supply and demand forecasting system for chicken eggs according to one embodiment of the present invention. (A) is a schematic diagram showing the first shipment record, (B) is a schematic diagram showing the second shipment record, and (C) is a schematic diagram showing the third shipment record. [Figure 15] The following diagram illustrates the shipment forecast results of an egg supply and demand forecasting system according to one embodiment of the present invention: (A) is a schematic diagram showing a shipment forecast based on indicator category 1, (B) is a schematic diagram showing a shipment forecast based on indicator category 2, and (C) is a schematic diagram showing a shipment forecast based on indicator category 3. [Figure 16] This is a flowchart showing the operation of the demand forecasting process in a supply and demand forecasting system. [Modes for carrying out the invention]

[0020] An example of an embodiment of the egg supply and demand forecasting system according to the present invention will be described below with reference to the drawings. The embodiments described below are preferred examples of the present invention and are subject to various technical limitations. However, the scope of the present invention is not limited to these forms unless otherwise specified in the following description.

[0021] <System Configuration> Figure 1 shows an example of the configuration of the egg supply and demand forecasting system according to this embodiment. As shown in Figure 1, the egg supply and demand forecasting system in this embodiment is configured such that an egg supply and demand forecasting device 10, a producer terminal 20, and a seller terminal 30 are connected to each other via a network N.

[0022] In the following explanation, the supply and demand forecasting device 10 may be referred to as a "server." Furthermore, network N is not limited to the internet; it may also be a communication network such as a LAN (Local Area Network) or a WAN (Wide Area Network).

[0023] The supply and demand forecasting device 10 shown in Figure 1 is a device used for egg supply and demand forecasting processing, which stores information received from producer terminals 20 and seller terminals 30 and forecasts production quantities and shipment quantities by egg size. This supply and demand forecasting device 10 has computational processing functions and communication functions, and functions as a server in the egg supply and demand forecasting system. The supply and demand forecasting device 10 in this embodiment is implemented by, for example, a server device or an electronic device such as a personal computer.

[0024] The producer terminal 20 is a computer operated by personnel at a facility engaged in poultry farming, where egg-laying hens are raised on a farm to produce eggs. It is a device with arithmetic processing and communication functions, and can be implemented as, for example, a desktop computer, laptop computer, tablet computer, or smartphone.

[0025] The producer terminal 20 includes, for example, means for inputting daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of the laying hens; means for transmitting the input production information to the supply and demand forecasting device 10; means for transmitting a request for egg production quantity forecast to the supply and demand forecasting device 10; and means for receiving supply forecast information from the supply and demand forecasting device 10. This production information may also include the start date of molting induction. Furthermore, the production information may include details that allow for the identification of the number of laying hens, their age in days, and the size, number, and weight of the eggs produced.

[0026] The seller terminal 30 is a computer operated by personnel at automated egg sorting and packaging facilities such as GP centers (Grading and Packaging Centers) that wash, dry, inspect, weigh, and pack eggs produced on farms, or by personnel at facilities engaged in wholesale businesses that sell eggs to retailers such as supermarkets. It is a device with arithmetic processing and communication functions, and can be implemented as, for example, a desktop computer, laptop computer, tablet computer, or smartphone.

[0027] The seller terminal 30 includes, for example, means for inputting past shipment information that allows for the identification of shipment quantities according to the shipment date for each size of egg; means for transmitting the input shipment information to the supply and demand forecasting device 10; means for transmitting a request for egg shipment quantity forecasting to the supply and demand forecasting device 10; and means for receiving demand forecasting information from the supply and demand forecasting device 10.

[0028] In the example shown in Figure 1, only one producer terminal 20 and one seller terminal 30 are shown, but their number is not particularly limited and may be multiple.

[0029] <Hardware Configuration> Figure 2 is a block diagram showing the hardware configuration of the supply and demand forecasting device 10 according to this embodiment. The supply and demand forecasting device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an output unit 16, an input unit 17, a storage unit 18, and a communication unit 19.

[0030] The CPU 11 executes various processes according to the program stored in the ROM 12 or the program loaded from the storage unit 18 into the RAM 13. In other words, the CPU 11 is a processor that controls the operation of the entire server, executing various processes according to the program. The program is, for example, an operating system (system software) for operating each part of the supply and demand forecasting device 10, or application software for realizing the functional blocks described later.

[0031] RAM13 also stores data and other information necessary for the CPU11 to perform various processes. The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14. The input / output interface 15 is connected to an output unit 16, an input unit 17, a storage unit 18, and a communication unit 19.

[0032] The output unit 16 consists of a display, speakers, etc., and outputs various information as images and sounds. The input unit 17 consists of a keyboard, mouse, microphone, camera, etc., and inputs various information according to the received instructions and operations. For example, the output unit 16 and the input unit 17 may be integrated and implemented using a touch panel.

[0033] The memory unit 18 stores various data necessary for the server 1 to perform information processing, and is composed of, for example, a hard disk or DRAM (Dynamic Random Access Memory). The communication unit 19 communicates with other devices via a network N, including the Internet.

[0034] The supply and demand forecasting device 10 may also be equipped with a drive that can be appropriately fitted with removable media such as magnetic disks, optical disks, magneto-optical disks, or semiconductor memory. This drive allows programs read from the removable media to be installed in the storage unit 18. Furthermore, the removable media can store various types of data stored in the storage unit 18, just as the storage unit 18 does.

[0035] Furthermore, the supply and demand forecasting device 10 is not limited to a standalone device; it may also be a distributed server system or a cloud server that operates cooperatively by communicating via the network N.

[0036] <Demand and supply forecasting device 10> Figure 3 is a block diagram illustrating an example configuration of the supply and demand forecasting device 10, which functions as a server in this embodiment. The supply and demand forecasting device 10 includes a production performance information acquisition unit 111, a model creation unit 112, a table creation unit 113, a production forecasting unit 114, a shipment performance information acquisition unit 115, a classification creation unit 116, and a shipment forecasting unit 117. The supply and demand forecasting device 10 also includes a production information DB 181 and a shipment information DB 182.

[0037] The production performance information acquisition unit 111 is a means that has the function of acquiring daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of the laying hens. The production performance information acquisition unit 111 may acquire direct information as production information, such as the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of the laying hens. Alternatively, it may acquire indirect information that allows for the identification of each of these factors.

[0038] Specifically, in the case of the survival rate of laying hens according to their age, indirect information can be obtained, such as start date information which identifies the start date of rearing and the number of laying hens at the start of rearing, and excluded number information which identifies the number of laying hens that were removed due to accidents, illness, death, etc. That is, the age of the laying hens can be determined by counting the number of days elapsed from the start date of rearing. Furthermore, the number of surviving laying hens can be determined by subtracting the number of excluded hens from the number of hens at the start of rearing, and the survival rate can be calculated by dividing the number of surviving hens by the number of hens at the start of rearing. In this way, the survival rate according to the age of the laying hens can be determined.

[0039] Furthermore, in the case of egg-laying rates according to the age of laying hens, indirect information such as production quantity information that can identify the total number of eggs produced can be obtained daily. That is, by dividing the production quantity by the number of surviving laying hens, which was determined when calculating the survival rate, the egg-laying rate according to the age of laying hens can be identified.

[0040] This production quantity information can also be used as size-specific production quantity information, which identifies the number of eggs produced each day for each size. In other words, by summing up the production quantities for each egg size, the total number of eggs produced each day (production quantity) can be determined. The sizes of chicken eggs can be, for example, extra small, SS, S, MS, M, L, LL, and extra large. Additionally, off-spec sizes, such as those that are too small or too large, may be included.

[0041] Furthermore, in the case of the average egg weight, indirect information can be obtained, for example, by acquiring production weight information that can identify the total weight of eggs produced each day. That is, by dividing the production weight by the production quantity (total number) obtained when calculating the egg-laying rate, the average egg weight according to the age of the laying hen can be identified.

[0042] Furthermore, in the case of egg production volume by size, indirect information can be obtained, for example, by acquiring size-specific production quantity information that identifies the number of eggs of each size produced daily. That is, by dividing the number of eggs produced by size by the total production quantity (total number) obtained when calculating the egg production rate, the egg production rate for each size of egg produced can be identified.

[0043] Therefore, the production performance information acquisition unit 111 may acquire, on a daily basis, indirect information such as information on the start date of rearing, information on the number of birds to be reared at the start of rearing, information on the number of birds to be removed, production quantity information, production weight information, and production quantity information by size, instead of directly identifying information such as the survival rate, egg-laying rate, average egg weight, and egg production quantity for each size of eggs produced according to the age of the laying hens.

[0044] Furthermore, information on the start date of rearing and the number of birds to be reared only needs to be obtained at the start of rearing; it does not need to be obtained thereafter. Similarly, information on the number of birds to be excluded only needs to be obtained when there is a change in the number of birds to be reared; it does not need to be obtained daily. In addition, the production quantity can be determined by aggregating the production quantity information by size. Therefore, the indirect information acquired daily as production information can be at least production weight information and production quantity information by size. The production information acquired by the production performance information acquisition unit 111 is stored in the production information DB 181.

[0045] Furthermore, the production performance information acquisition unit 111 is also a means that has the function of acquiring production information that includes the start date of molting induction. Furthermore, the production performance information acquisition unit 111 is also a means that has the function of acquiring production information separately for each production area (breeding region), farm, or chicken coop. In other words, the production performance information acquisition unit 111 can acquire production information including production area information, farm information, and chicken coop information, or acquire production information including classification information that allows identification of production area, farm, and chicken coop.

[0046] In this way, by dividing the sources of production information into smaller parts, the model creation unit 112 can create multiple highly accurate standard performance models with finely divided scopes of application, and the table creation unit 113 can also create multiple highly accurate egg weight tables with finely divided scopes of application.

[0047] The model creation unit 112 is a means that has the function of creating a standard performance model that associates and identifies the survival rate, egg-laying rate, and average egg weight of laying hens according to their age in days, based on the production information acquired by the production performance information acquisition unit 111. If the production information acquired by the production performance information acquisition unit 111 is indirect information as described above, the model creation unit 112 identifies the survival rate, egg-laying rate, and average egg weight of laying hens according to their age in days, based on the indirect information, and creates a standard performance model.

[0048] Furthermore, if the production performance information acquisition unit 111 acquires production information separately for each production area (breeding region), farm, or chicken coop, the model creation unit 112 may also have the function of creating a standard performance model separately for each production area, farm, or chicken coop. This makes it possible to create highly accurate standard performance models with detailed applicability divided by production area, farm, or poultry house.

[0049] Furthermore, the model creation unit 112 may create a standard performance model based on the production information for the immediate vicinity that the production performance information acquisition unit 111 has acquired, and may update it periodically. This makes it possible to regularly create highly accurate standard performance models that reflect changes in the physical condition and environment of laying hens.

[0050] The table creation unit 113 is a means that has the function of creating an egg weight table that identifies the spawning rate for each size according to the average egg weight, based on the production information acquired by the production performance information acquisition unit 111. If the production information acquired by the production performance information acquisition unit 111 is indirect information as described above, the table creation unit 113 identifies the spawning rate for each size based on the indirect information and creates the egg weight table.

[0051] Furthermore, the table creation unit 113 also has the function of creating a molting induction table that, if the production information acquired by the production performance information acquisition unit 111 includes the molting induction start date, identifies information to correct the survival rate of laying hens, information to correct the egg production rate, and information to correct the average egg weight, based on the number of days after the start of molting induction for laying hens.

[0052] Furthermore, if the production performance information acquisition unit 111 acquires production information separately for each production area (breeding region), farm, or chicken coop, the table creation unit 113 may also have the function of creating an egg weight table separately for each production area, farm, or chicken coop. This makes it possible to create highly accurate egg weight tables with detailed application areas divided by production area, farm, or chicken coop.

[0053] Furthermore, the table creation unit 113, like the model creation unit 112, may also create egg weight tables and molting induction tables based on production information for the immediate vicinity period acquired by the production performance information acquisition unit 111, and update them periodically. This makes it possible to regularly create highly accurate egg weight tables and molting induction tables that reflect changes in the laying hens' physical condition and environmental changes.

[0054] The production forecasting unit 114 is a means that has the function of outputting supply forecast information that predicts the production quantity by size according to the age of laying hens, based on the standard performance model created by the model creation unit 112 and the egg weight table created by the table creation unit 113. In other words, the production forecasting unit 114 predicts the number of surviving birds based on the survival rate of the standard performance model from the initial number of birds being raised. Next, it predicts the production (egg laying) quantity based on the egg laying rate of the standard performance model from the predicted number of surviving birds. Then, it refers to the egg weight table based on the average egg weight of the standard performance model, multiplies the predicted production quantity by the egg laying rate for each size, predicts the production quantity by size, and outputs supply forecast information.

[0055] Furthermore, the production forecasting unit 114 is also a means that has the function of outputting supply forecast information that predicts the production quantity by size according to the age of laying hens, based on the standard performance model created by the model creation unit 112, the egg weight table created by the table creation unit 113, and the molting induction table. Specifically, the production forecasting unit 114 predicts the number of surviving birds based on the survival rate of the standard performance model from the initial number of birds being raised, and further corrects the predicted number of surviving birds to the number of surviving birds after molting based on the survival rate correction information of the molting induction table. Next, it predicts the production (egg-laying) quantity based on the egg-laying rate of the standard performance model from the corrected number of surviving birds, and further corrects the predicted production quantity to the production (egg-laying) quantity after molting based on the egg-laying rate correction information of the molting induction table. Then, it corrects the average egg weight of the standard performance model by adding the average egg weight correction weight from the molting induction table to the average egg weight after molting, and based on the corrected average egg weight, it refers to the egg weight table and predicts the production quantity by size by multiplying the corrected production quantity by the egg-laying rate for each size. Therefore, the production forecasting unit 114 is designed to be able to handle situations where molting is induced.

[0056] Furthermore, if the production performance information acquisition unit 111 acquires production information separately for each production area (breeding region), farm, or chicken coop, the production forecasting unit 114 may also have the function of predicting production quantities by size separately for each production area, farm, or chicken coop. The supply forecast information predicted by the production forecasting unit 114 may be stored in the production information DB 181, for example, separated by production area, farm, or chicken coop, or it may be stored in a separate database.

[0057] The shipment performance information acquisition unit 115 is a means that has the function of acquiring past shipment information that allows for the identification of shipment quantities according to the shipment date for each size of egg. The shipment date may include the day of the week. In addition, the shipment quantity may be the number of eggs or the quantity per pack. Furthermore, the shipment performance information acquisition unit 115 also has the function of acquiring shipment information for each shipping destination. That is, the shipment performance information acquisition unit 115 accepts shipment information that includes shipping destination information, or accepts shipment information that includes classification information that can identify the shipping destination. In addition, the shipping destination information may include classification information that can identify each store (sales destination). The shipping information acquired by the shipping performance information acquisition unit 115 is stored in the shipping information DB 182.

[0058] The classification creation unit 116 is a means that has the function of creating indicator classifications that specify a calculation method for predicting the shipment quantity according to the size of the eggs, based on the acquired shipment information for a predetermined period. The predetermined period can be, for example, the most recent week, half a month, one month, three months, six months, or one year. This classification creation unit 116 may also have the function of creating indicator classifications for each shipping destination, similarly when the shipping performance information acquisition unit 115 acquires shipping information for each shipping destination.

[0059] The shipment forecasting unit 117 is a means that has the function of outputting demand forecasting information that predicts the shipment quantity by size according to the future scheduled shipment date, based on the shipment information for a predetermined period acquired by the shipment performance information acquisition unit 115 and the indicator classification created by the classification creation unit 116. If the shipment forecasting unit 115 acquires shipment information for each destination, the shipment forecasting unit 117 can similarly have the function of predicting the number of shipments by size for each destination. The demand forecast information predicted by the shipment forecasting unit 117 may be stored in the shipment information DB 182, separated by destination, or it may be stored in a separate database.

[0060] The Production Information DB 181 is a database that stores production information acquired by the Production Performance Information Acquisition Unit 111. It is a means of storing direct production information such as the survival rate, egg-laying rate, average egg weight, and egg production volume by egg size according to the age of laying hens, or indirect production information such as the start date of rearing, the number of hens at the start of rearing, the number of hens to be removed, production quantity information, production weight information, and production quantity information by size, all associated on a daily basis. The Production Information DB 181 can also store the start date of molting induction. Furthermore, the production information DB181 may store production information separately for each production area, farm, or chicken coop.

[0061] The shipping information DB182 is a database that stores shipping information acquired by the shipping performance information acquisition unit 115. For example, it is a means of storing shipping information such as the shipping date (including the day of the week), the shipping destination, and the shipping quantity (including the quantity per pack) in an associated manner. Furthermore, the shipping information DB182 may store shipping information separately for each store or sales destination.

[0062] As described above, the supply and demand forecasting device 10 according to this embodiment functions as a device including a production performance information acquisition unit 111, a model creation unit 112, a table creation unit 113, a production forecasting unit 114, a shipment performance information acquisition unit 115, a classification creation unit 116, and a shipment forecasting unit 117, when various programs (OS, applications, etc.) stored in the auxiliary storage device are loaded into the main storage device and executed by the CPU 11.

[0063] Next, we will explain the processing flow of the egg supply and demand forecasting system according to one embodiment of the present invention. Figures 4 and 5 are sequence diagrams showing an example of egg supply and demand forecasting processing according to one embodiment of the present invention. Figure 4 explains the basic egg production process flow, and Figure 5 explains the production process flow when molting induction is performed.

[0064] First, in order to take advantage of this system, each user accesses the system's website using the communication functions of the producer terminal 20 and the seller terminal 30, and logs in to the system, thereby enabling them to perform tasks using this system.

[0065] The producer terminal 20 then requests the supply and demand forecasting device 10 to input production information, and receives (receives) form information for inputting production information from the supply and demand forecasting device 10. In step S301, the producer terminal 20 displays a production information input form screen on the display device (monitor) based on the form information received from the supply and demand forecasting device 10, inputs the predetermined production information, and transmits this production information to the supply and demand forecasting device 10.

[0066] In step S302, the supply and demand forecasting device 10 acquires (receives) production information transmitted from the producer terminal 20.

[0067] In step S303, the producer terminal 20 sends a request to the supply and demand forecasting device 10 for a forecast of the future production of the laying hens it is raising. In step S304, the supply and demand forecasting device 10 receives a production quantity forecast request transmitted from the producer terminal 20.

[0068] Next, in step S305, the supply and demand forecasting device 10 identifies the age of the laying hens to be forecasted based on the production quantity forecast request from the producer terminal 20, and forecasts the production quantity by size according to the requested age, based on the standard performance model and egg weight table described later. Then, in step S306, the supply and demand forecasting device 10 outputs the predicted production quantity by size according to the predicted age in days as supply forecast information and sends it back to the producer terminal 20. Subsequently, in step S307, the producer terminal 20 receives supply forecast information transmitted from the supply and demand forecasting device 10 and displays it on the display device (monitor).

[0069] In this system, to address future anxieties and questions such as what the future egg production volume and size will be, producers effectively utilize the production information they input daily. Based on past performance data, the system references a standard performance model and egg weight table, and uses these standard performance models and egg weight tables (i.e., past performance data) to predict future production volumes by size according to the age of the chickens.

[0070] Furthermore, the flow of supply processing when molting is induced in the rearing of laying hens can be shown in Figure 5. After logging into the system, the producer terminal 20 inputs predetermined production information into the report information received from the supply and demand forecasting device 10 in step S311, and transmits this production information to the supply and demand forecasting device 10.

[0071] In step S312, the supply and demand forecasting device 10 acquires (receives) production information transmitted from the producer terminal 20.

[0072] Next, in step S313, the supply and demand forecasting device 10 creates a molting induction table based on the acquired production information, which identifies information to correct the survival rate of laying hens, information to correct the egg production rate, and information to correct the average egg weight, according to the number of days since the start of molting induction for laying hens.

[0073] In step S314, the producer terminal 20 sends a request to the supply and demand forecasting device 10 for a forecast of the future production of the laying hens it is raising. In step S315, the supply and demand forecasting device 10 receives a production quantity forecast request transmitted from the producer terminal 20.

[0074] Next, in step S316, the supply and demand forecasting device 10 identifies the age of the laying hens to be forecasted based on the production quantity forecast request from the producer terminal 20, and forecasts the production quantity by size according to the requested age, based on the standard performance model and egg weight table described later. Then, in step S317, the supply and demand forecasting device 10 outputs the predicted production quantity by size according to the predicted age in days as supply forecast information and sends it back to the producer terminal 20. Subsequently, in step S318, the producer terminal 20 receives supply forecast information transmitted from the supply and demand forecasting device 10 and displays it on the display device (monitor).

[0075] Thus, this system is designed to handle situations where molting is induced, and to address future concerns and questions such as how many eggs laying hens will produce and what size they will be after molting, it predicts future production quantities by size according to age, based on a standard performance model created from production information entered daily by producers, along with an egg weight table and a molting induction table (i.e., past performance).

[0076] Here, the standard performance model used by the supply and demand forecasting device 10 in steps S305 and S316 described above can be shown, for example, in Figure 6. Figure 6 is a schematic diagram showing an example of the configuration of a standard performance model used in an egg supply and demand forecasting system according to one embodiment of the present invention. In Figure 6, the standard performance model is shown as one that stores and associates survival rate, egg-laying rate, and average egg weight with the age of laying hens so that they can be identified.

[0077] The survival rate is the ratio of the number of surviving laying hens to the number of laying hens at the start of rearing, that is, the ratio when the number of laying hens at the start of rearing is set to 1. Specifically, if there were 1,000 hens at the start of rearing and 999 were still alive at day 133, the survival rate would be shown as "0.999". The egg-laying rate is expressed as a percentage (%) of the number of eggs produced relative to the number of laying hens being raised (surviving). Specifically, if there are 999 laying hens surviving on day 133 and 33 eggs have been produced, the egg-laying rate is shown as "3 (%)". Average egg weight indicates the average weight (in grams) of each egg produced. Specifically, if 33 eggs are produced on day 133 of age, and the total weight of all eggs produced is 1380g, the average egg weight would be "41.8(g)".

[0078] Furthermore, the egg weight table used by the supply and demand forecasting device 10 in steps S305 and S316 described above can be shown, for example, in Figure 7. Figure 7 is a schematic diagram showing an example of the configuration of an egg weight table used in an egg supply and demand forecasting system according to one embodiment of the present invention. In Figure 7, the egg weight table is shown as a system that stores and associates information so that the egg-laying rate for each size of egg produced can be identified based on the average egg weight.

[0079] The egg-laying rate for each size is expressed as a percentage (%) representing the proportion of eggs of that size to the total number of eggs produced. Specifically, if the average egg weight produced is "41.8", and 95 extra-small eggs, 150 SS-sized eggs, 50 S-sized eggs, 3 MS-sized eggs, 1 M-sized egg, 1 L-sized egg, 0 LL-sized eggs, and 0 extra-large eggs are produced, the egg-laying rates for each size are shown as: Extra-small [32(%)], SS "50(%)", S "17(%)", MS "1(%)", M "0(%)", L "0(%)", LL "0(%)", and Extra-large "0(%)".

[0080] Furthermore, the production quantities by size predicted by the supply and demand forecasting device 10 in steps S305 and S316 described above can be shown, for example, in Figure 8. Figure 8 illustrates the supply forecast results in an egg supply and demand forecasting system according to one embodiment of the present invention. (A) is a schematic diagram showing the first stage simulation result 1, and (B) is a schematic diagram showing the second stage simulation result 2.

[0081] For example, if the supply and demand forecasting device 10 receives a request from the producer terminal 20 to predict the production quantity 132 days later at the start of rearing (when the laying hen is 1 day old), it first identifies the survival rate of the laying hen at 133 days old (1 + 132) based on the standard performance model. From the standard performance model, it can be seen that the survival rate of the laying hen at 133 days old is "0.999".

[0082] Next, the producer is identified based on the login information from the producer terminal 20, the number of laying hens being raised by that producer is determined, and the number of surviving laying hens at 133 days old is calculated. For example, if the producer was raising 100,000 laying hens at the start of raising them, multiplying this by the survival rate of 0.999 identified from the standard performance model allows us to predict (calculate) that the number of surviving laying hens at 133 days old is "99,900", as shown in the field labeled I in Figure 8(A).

[0083] Next, we determine the egg-laying rate of laying hens at 133 days of age based on the standard performance model. From the standard performance model, we can see that the egg-laying rate of laying hens at 133 days of age is "3 (%)". Then, multiplying the number of surviving laying hens at 133 days of age, which was determined earlier (99,900), by the egg-laying rate of 3 (%) determined from the standard performance model, we can predict (calculate) that the number of eggs laid by laying hens at 133 days of age is "2,997 (eggs)", as shown in the field labeled II in Figure 8(A).

[0084] Furthermore, based on the standard performance model, the average egg weight of laying hens at 133 days of age is determined. From the standard performance model, it can be seen that the average egg weight of laying hens at 133 days of age is "41.8 (g)". In addition, based on the egg weight table, the egg production rate for each egg size when the average egg weight is 41.8 is determined. From the egg weight table, it can be seen that when the average egg weight is 41.8, the egg production rates for each egg size are "32 (%)" for extra small, "50 (%)" for SS, "17 (%)" for S, "1 (%)" for MS, and "0 (%)" for M, L, LL, and extra large.

[0085] Then, multiplying the previously determined number of eggs laid by a laying hen at 133 days old (2,997) by the egg-laying rate for each size identified from the egg weight table, we can predict (calculate) that, as shown in the record labeled III in Figure 8(B), the number of eggs laid by each size of laying hen at 133 days old is 959 for the smallest, 1,499 for the super-smallest, 509 for the smallest, 30 for the medium-sized eggs, and 0 for the medium, large, extra-largest, and super-largest sizes.

[0086] Therefore, the supply and demand forecasting device 10 outputs the above-mentioned forecast values ​​for each size as supply forecast information indicating the production quantity predicted 132 days later, and sends (replies to) the producer terminal 20.

[0087] Furthermore, the molting induction table created by the supply and demand forecasting device 10 in step S313 described above can be shown, for example, in Figure 9. Figure 9 is a schematic diagram showing an example of the configuration of a molting induction table used in a chicken egg supply and demand forecasting system according to one embodiment of the present invention. In Figure 9, the molting induction table is shown as a system that stores and associates the survival rate correction %, egg-laying rate correction %, and average egg weight correction % with the number of days after molting induction.

[0088] The survival rate correction percentage indicates the correction rate for the number of laying hens that survive after molting induction. It represents a value that corrects the number of laying hens obtained by multiplying the survival rate identified in the standard performance model shown in Figure 6 to fit the number of laying hens after molting induction. In Figure 9, the survival rate correction value for the first day (Day 1) after molting induction is shown as "100 (%)". This indicates that the number of laying hens obtained by multiplying the survival rate identified in the standard performance model shown in Figure 6 can be applied directly (100%).

[0089] Furthermore, the egg-laying rate correction % represents the percentage (%) of the correction to the quantity (number) of eggs produced after molting induction. It is a value that corrects the quantity of eggs produced, obtained by multiplying it by the egg-laying rate identified in the standard performance model shown in Figure 6, to suit egg-laying hens after molting induction. In Figure 9, the egg-laying rate correction value for the first day (Day 1) after molting induction is shown as "3 (%)". This means that the quantity of eggs obtained by multiplying the egg-laying rate identified in the standard performance model shown in Figure 6 is further multiplied by 3%.

[0090] Furthermore, the average egg weight correction % represents the percentage (%) of the correction to the average weight (g) of each egg produced by laying hens after molting induction. It is a value that corrects the average egg weight of eggs produced after molting induction to suit laying hens after molting. In Figure 9, the average egg weight correction value on the first day (Day 1) after molting induction is shown as "3.5 (%)". In other words, this indicates that an additional 3.5% of the weight is added to the average egg weight identified based on the age of the laying hen in the standard performance model shown in Figure 6.

[0091] Figure 10 illustrates the specific application methods for the survival rate correction%, egg-laying rate correction%, and average egg weight correction%, as shown in the molting induction table described above. Figure 10 is a schematic diagram showing simulation results 3 after molting induction, illustrating the supply forecast results in a chicken egg supply and demand forecasting system according to one embodiment of the present invention.

[0092] For example, if molting is induced when the hens are 500 days old, and the production quantity of laying hens on the first day after molting is predicted, first, the producer is identified based on the login information from the producer terminal 20, and the number of laying hens being raised by that producer is determined. Then, the number of surviving laying hens at 500 days old is calculated by multiplying this number by the survival rate identified from the standard performance model. Furthermore, in order to correct for the number of surviving laying hens at 500 days of age, we identify the survival rate correction value on the first day after molting induction (Day 1). From the molting induction table, we can see that the survival rate correction value on Day 1 of molting induction is "100 (%)".

[0093] For example, if the number of surviving laying hens for a given producer, obtained by multiplying the survival rate at 500 days of age identified from the standard performance model, is 94,884, then multiplying this by the survival rate correction value of 100% identified from the molting induction table, as shown in the field labeled IV in Figure 10, allows us to predict (calculate) that the number of surviving laying hens at 500 days from the start of rearing can be applied as is (100%), resulting in a corrected number of surviving laying hens of "94,884".

[0094] Next, we will determine the corrected value for egg-laying rate on the first day after molting induction. From the molting induction table, we can see that the corrected value for egg-laying rate on the first day of molting induction is "3 (%)". For example, if the egg-laying rate of laying hens at age 500, as determined by the standard performance model, is 82.4%, then multiplying the previously determined number of surviving laying hens at age 500 (94,884) by the egg-laying rate of 82.4 (%) determined by the standard performance model, we can predict (calculate) that the number of eggs laid by laying hens at age 500 is "78,184 (eggs)," as shown in the field labeled V in Figure 10.

[0095] Furthermore, by multiplying the predicted number of eggs laid by the laying hens, 78,184, by the egg-laying rate correction value of 3 (%) identified from the molting induction table, the corrected number of eggs laid by the laying hens at 500 days of age can be predicted (calculated) as "2,346 (eggs)," as shown in the field labeled VI in Figure 10.

[0096] Furthermore, the average egg weight correction value on the first day after molting induction is identified. From the molting induction table, it can be seen that the average egg weight correction value on the first day of molting induction is "3.5 (%)". For example, if the average egg weight at age 500, as determined by the standard performance model, is 66.4g, then we add the average egg weight correction value of 3.5%, determined from the molting induction table. That is, by adding 2.3 to 66.4, the corrected average egg weight can be predicted (calculated) as "68.7g".

[0097] Although not shown in the diagram below, similar to the above, based on the egg weight table, the egg-laying rate for each egg size when the average egg weight is 68.7 is identified. By multiplying the previously calculated corrected number of eggs laid by laying hens at 500 days old (2,346) by the egg-laying rate for each size identified from the egg weight table, the number of eggs laid by laying hens at 500 days old for each size can be predicted (calculated).

[0098] Next, we will explain the detailed flow of the egg supply and demand forecasting process in the supply and demand forecasting device 10. Figure 11 is a flowchart illustrating an example of the operation of the supply forecasting process in a supply and demand forecasting device.

[0099] In step S401, the production performance information acquisition unit 111 performs a process to determine whether or not it has acquired (received) the production information transmitted from the producer terminal 20. If the production performance information acquisition unit 111 has acquired the production information (YES in S401), it proceeds to step S402; otherwise (NO in S401), it proceeds to step S404.

[0100] Next, in step S402, the table creation unit 113 performs a process to determine whether or not the production information acquired by the production performance information acquisition unit 111 includes the molting induction start date. If the table creation unit 113 determines that the molting induction start date is included in the production information (YES in S402), the process proceeds to step S403; otherwise (NO in S402), the operation of the supply and demand forecasting device 10 ends thereafter (END).

[0101] In step S403, the table creation unit 113 further creates a molting induction table based on the production information including the molting induction start date acquired by the production performance information acquisition unit 111, which identifies information to correct the survival rate of laying hens, information to correct the egg production rate, and information to correct the average egg weight according to the number of days after the start of molting induction for laying hens. After that, the operation of the supply and demand forecasting device 10 ends (END).

[0102] Furthermore, in step S404, the production forecasting unit 114 performs a process to determine whether or not it has received a request for a chicken egg production quantity forecast. If the production forecasting unit 114 has received the production quantity forecast request (YES in S404), it proceeds to step S405; otherwise (NO in S404), the operation of the supply and demand forecasting device 10 ends thereafter (END).

[0103] In step S405, the production forecasting unit 114 performs a process to predict the production quantity by size according to the age of the laying hens, based on the standard performance model and the egg weight table. Furthermore, if the production quantity forecasting request corresponds to the period after molting induction, the production forecasting unit 114 performs a process to predict the production quantity by size according to the age of the laying hens, based on the standard performance model, the egg weight table and the molting induction table.

[0104] Then, in step S406, the production forecasting unit 114 outputs the predicted production quantity by size according to the age of the laying hens as supply forecast information (sent back to the producer terminal 20). After that, the operation of the supply and demand forecasting device 10 ends (END).

[0105] Next, we will describe the other processing steps in the egg supply and demand forecasting system according to one embodiment of the present invention. Figure 12 is a sequence diagram illustrating the flow of egg shipment processing as part of the demand forecasting process in an egg supply and demand forecasting system according to one embodiment of the present invention.

[0106] The seller terminal 30 requests the supply and demand forecasting device 10 to input shipping information from the seller, and receives (receives) the form information for inputting shipping information from the supply and demand forecasting device 10.

[0107] In step S501, the seller terminal 30 displays a form screen for inputting shipping information on the display device (monitor) based on the form information received from the supply and demand forecasting device 10, inputs the predetermined shipping information, and transmits this shipping information to the supply and demand forecasting device 10.

[0108] In step S502, the supply and demand forecasting device 10 acquires (receives) shipping information transmitted from the seller terminal 30. Next, in step S503, the supply and demand forecasting device 10 creates an indicator category that identifies a calculation method for predicting the shipment quantity according to the size of the eggs, based on the acquired shipment information for a predetermined period.

[0109] In step S504, the seller terminal 30 sends a request to the supply and demand forecasting device 10 for a forecast of egg shipment quantities, asking for the shipment quantities by size according to future scheduled shipment dates. In step S505, the supply and demand forecasting device 10 receives a shipment quantity forecast request transmitted from the seller terminal 30.

[0110] Next, in step S506, the supply and demand forecasting device 10, in response to a shipment quantity forecast request from the seller terminal 30, forecasts the shipment quantity by size according to the requested future shipment date, based on shipment information and indicator classifications for a predetermined period. Then, in step S507, the supply and demand forecasting device 10 outputs the predicted demand forecast information and sends it back to the seller terminal 30. Subsequently, in step S508, the seller terminal 30 receives the demand forecast information transmitted from the supply and demand forecasting device 10 and displays it on the display device (monitor).

[0111] In this system, to address future anxieties and questions such as the expected volume and size of eggs to be shipped, the system effectively utilizes the shipping information entered daily by sellers. It creates indicator categories from past shipping data and predicts the size-specific shipping volume for future shipping dates based on shipping data (i.e., past performance) and indicator categories for a predetermined period.

[0112] Here, in step S503 described above, the indicator categories created by the supply and demand forecasting device 10 can be shown in Figure 13, for example. Figure 13 is a schematic diagram showing an example of the configuration of indicator categories used in an egg supply and demand forecasting system according to one embodiment of the present invention. In Figure 13, the indicator categories are shown as being stored in association with the production area information and farm information that identify producers, the customer information that identifies sales destinations, and the predictive calculation category information that serves as the indicator category for each egg size that identifies the product.

[0113] This indicator category can be created, for example, from the shipment performance shown in Figure 14. Figure 14 illustrates shipment information for a predetermined period obtained by an egg supply and demand forecasting system according to one embodiment of the present invention. Figure 14(A) is a schematic diagram showing the first shipment record, Figure 14(B) is a schematic diagram showing the second shipment record, and Figure 14(C) is a schematic diagram showing the third shipment record.

[0114] Figure 14(A) shows the shipment figures for S-sized chicken eggs for a specified period, with 150 eggs shipped on Saturday, June 1, 2024; 100 eggs shipped on Sunday, June 2, 2024; 100 eggs shipped on Monday, June 3, 2024; 100 eggs shipped on Tuesday, June 4, 2024; 150 eggs shipped on Wednesday, June 5, 2024; 100 eggs shipped on Thursday, June 6, 2024; and 100 eggs shipped on Friday, June 7, 2024, totaling 7 days (one week).

[0115] In the indicator categories shown in Figure 13, the forecast calculation category for cases where there are shipments every day for 7 days is set to "1," and the calculation method involves averaging the total quantity over 7 days and using that as the forecast (calculation) for the daily shipment quantity for the next 7 days.

[0116] Furthermore, Figure 14(B) shows the shipment figures for medium-sized chicken eggs for a specified period, with 200 eggs shipped on Saturday, June 1, 2024; 0 eggs shipped on Sunday, June 2, 2024; 200 eggs shipped on Monday, June 3, 2024; 200 eggs shipped on Tuesday, June 4, 2024; 200 eggs shipped on Wednesday, June 5, 2024; 200 eggs shipped on Thursday, June 6, 2024; and 200 eggs shipped on Friday, June 7, 2024, totaling 7 days (one week).

[0117] In the indicator classification shown in Figure 13, the prediction calculation category for cases where shipments occur on 6 days out of 7 days (one week), excluding one specific day or day of the week, is designated as "2". The calculation method involves averaging the total quantity over the 6 days and predicting (calculating) the shipment quantity for the next 6 days, excluding the specific day or day of the week.

[0118] Furthermore, Figure 14(C) shows the actual shipment figures for mixed-size eggs over a specified period, specifically for 7 days (one week): Saturday, June 1, 2024: 0; Sunday, June 2, 2024: 0; Monday, June 3, 2024: 500; Tuesday, June 4, 2024: 0; Wednesday, June 5, 2024: 600; Thursday, June 6, 2024: 0; Friday, June 7, 2024: 0.

[0119] In the indicator classifications shown in Figure 13, the prediction calculation category for cases where shipments occur only on specific days or days of the week is designated as "3," and the calculation method involves predicting (calculating) the quantity of shipments by reflecting the actual shipment volume on future days or days of the week that correspond to the days or days of the week on which shipments have occurred.

[0120] Furthermore, in step S504 described above, the shipment quantities by size according to the future shipment date predicted by the supply and demand forecasting device 10 can be shown, for example, in Figure 15. Figure 15 illustrates the shipment forecast results in a supply and demand forecasting system for chicken eggs according to one embodiment of the present invention. (A) is a schematic diagram showing the shipment forecast based on indicator category 1, (B) is a schematic diagram showing the shipment forecast based on indicator category 2, and (C) is a schematic diagram showing the shipment forecast based on indicator category 3.

[0121] As mentioned above, forecasts of shipment quantities by size based on future shipment dates are made based on shipment information and indicator categories for a predetermined period. For example, if the supply and demand forecasting device 10 receives a request from the seller terminal 30 for a forecast of the shipment quantity of S-sized eggs for the next 7 days (one week), it identifies the forecast calculation category for S-sized eggs based on the indicator categories shown in Figure 13. From the indicator categories, it can be seen that the forecast calculation category for S-sized eggs is "1".

[0122] Here, if the shipment record for S-sized chicken eggs over a specified period is as shown in Figure 14(A), with shipments occurring every day for 7 days and a total quantity of 800 over those 7 days, then the average quantity over 7 days, which is 114, can be predicted (calculated) as the daily shipment quantity for the next 7 days. Specifically, this shows the shipment forecast for the next 7 days (one week), with shipment quantities of 114 on Saturday, June 8, 2024; 114 on Sunday, June 9, 2024; 114 on Monday, June 10, 2024; 114 on Tuesday, June 11, 2024; 114 on Wednesday, June 12, 2024; 114 on Thursday, June 13, 2024; and 114 on Friday, June 14, 2024.

[0123] Furthermore, for example, if the supply and demand forecasting device 10 receives a request from the seller terminal 30 for a forecast of the shipment quantity of medium-sized eggs for the next 7 days (one week), it identifies the forecast calculation category for medium-sized eggs based on the indicator categories shown in Figure 13. From the indicator categories, it can be seen that the forecast calculation category for medium-sized eggs is "2".

[0124] Here, as shown in Figure 14(B), if the shipment record for medium-sized eggs over a specified period is 1,200, with shipments occurring on 6 days out of 7 days (one week) excluding one specific day or week, then the average quantity over these 6 days, 200, can be predicted (calculated) as the shipment quantity for the next 7 days (one week) excluding one specific day or week. Specifically, this shows a shipment forecast for the next 7 days (one week), with shipments of 200 on Saturday, June 8, 2024; 0 on Sunday, June 9, 2024; 200 on Monday, June 10, 2024; 200 on Tuesday, June 11, 2024; 200 on Wednesday, June 12, 2024; 200 on Thursday, June 13, 2024; and 200 on Friday, June 14, 2024.

[0125] Furthermore, for example, if the supply and demand forecasting device 10 receives a request from the seller terminal 30 for a forecast of the shipment quantity of mixed-size eggs for the next 7 days (one week), it identifies the forecast calculation category for mixed-size eggs based on the indicator categories shown in Figure 13. From the indicator categories, it can be seen that the forecast calculation category for mixed-size eggs is "3".

[0126] Here, if the shipment record for mixed-size chicken eggs over a predetermined period is as shown in Figure 14(C), and shipments occur only on specific days or days of the week within a 7-day period (one week), then it is possible to predict (calculate) the quantity of shipments on future days or days of the week corresponding to the days or days of the week on which shipments have been made, by reflecting the quantity of shipments made on those days or days of the week. Specifically, if there were 500 shipments on Monday, June 3, 2024, 600 shipments on Wednesday, June 5, 2024, and 0 shipments on other days or days of the week, then these would be reflected in future days or days of the week, resulting in a shipment forecast for the next 7 days (one week), such as 0 on Saturday, June 8, 2024, 0 on Sunday, June 9, 2024, 500 on Monday, June 10, 2024, 0 on Tuesday, June 11, 2024, 600 on Wednesday, June 12, 2024, 0 on Thursday, June 13, 2024, and 0 on Friday, June 13, 2024.

[0127] Next, we will explain the detailed flow of other egg supply and demand forecasting processes in the supply and demand forecasting device 10. Figure 16 is a flowchart illustrating an example of the operation of the shipment forecasting process in a supply and demand forecasting device.

[0128] In step S601, the shipment performance information acquisition unit 115 performs a process to determine whether or not it has acquired (received) the shipment information transmitted from the seller terminal 30. If the shipment performance information acquisition unit 115 has acquired the shipment information (YES in S601), it proceeds to step S602; otherwise (NO in S601), it proceeds to step S603. In step S602, the classification creation unit 116 performs a process to create an indicator classification that identifies a calculation method for predicting the shipment quantity according to the size of the eggs, based on the shipment performance information acquisition unit 115 acquired for a predetermined period. After that, the operation of the supply and demand forecasting device 10 ends (END).

[0129] Furthermore, in step S603, the shipment forecasting unit 117 performs a process to determine whether or not it has received a request for a shipment quantity forecast of chicken eggs. If the shipment forecasting unit 117 has received the shipment quantity forecast request (YES in S603), it proceeds to step S604; otherwise (NO in S603), the operation of the supply and demand forecasting device 10 ends thereafter (END). In step S604, the shipment forecasting unit 117 performs a process to predict the shipment quantity by size according to the scheduled shipment date, based on shipment information and indicator classifications for a predetermined period.

[0130] Then, in step S605, the shipment forecasting unit 117 outputs the shipment quantity by size according to the predicted scheduled shipment date as demand forecast information (sending it back to the seller terminal 30). After that, the operation of the supply and demand forecasting device 10 ends (END).

[0131] As described above, this embodiment makes it possible for anyone to forecast the supply and demand of eggs anytime, anywhere, and to visualize this forecast as concrete numerical values ​​such as production quantities and shipment quantities by size. Therefore, producers can reduce the need for low-priced sales by checking future production forecasts, even if they have overproduction. This can strengthen the foundation of the domestic poultry industry and help alleviate labor shortages. On the other hand, sellers can predict shipments by checking available production areas and quantities, even when receiving special sale information from customers on weekends or at night. This can lead to a stable supply of eggs and prevent lost sales opportunities. Moreover, since supply and demand forecasts are based on actual performance data from each production area (region), farm, and poultry house, they can be made more accurate and reflect the actual situation.

[0132] The above-described series of processes are merely examples and are not particularly limited. In other words, it is sufficient for the information processing system to have the functionality to execute the series of processes described above as a whole, and the specific functional blocks used to realize this functionality are not limited to the examples given above.

[0133] Although one embodiment of the present invention has been described above, the above-described embodiment is merely an example to facilitate understanding of the present invention and is not intended to limit the present invention. Furthermore, the present invention is not limited to the embodiments described above, and any modifications, improvements, etc., that can achieve the objectives of the present invention are included in the present invention. [Industrial applicability]

[0134] This invention is expected to be used as a supply and demand forecasting system that allows producers to predict the production quantity by size according to the age of laying hens, separated by production performance information for each production area, farm, or chicken coop, and for sellers to predict the shipment quantity by size for each destination, including stores (sales outlets), by using sales performance information. Furthermore, by unifying and managing production performance information and sales performance information, it can be used as a supply and demand forecasting system that allows sellers to propose shipment quantities by size according to supply forecast information and producers to adjust production quantities by size according to demand forecast information. [Explanation of Symbols]

[0135] 10. Demand forecasting device (server) 20 Producer Terminals 30 Seller terminals 111 Production Performance Information Acquisition Department 112 Model Creation Department 113 Table Creation Section 114 Production Forecasting Department 115 Shipment Performance Information Acquisition Department 116 Classification section 117 Shipment Forecasting Department 181 Production Information Database 182 Shipping Information Database

Claims

1. A means for acquiring production performance information that can obtain daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg production volume for each size of egg produced, according to the age of egg-laying hens in days, A model creation means for creating a standard performance model that identifies and correlates the survival rate, egg-laying rate, and average egg weight of the laying hens according to their age in days, based on the acquired production information, A table creation means for creating an egg weight table that identifies the spawning rate for each size according to the average egg weight based on the acquired production information, A production forecasting means that outputs supply forecasting information predicting the production quantity by size according to the age of laying hens, based on the standard performance model and the egg weight table, A supply and demand forecasting system for chicken eggs, characterized by comprising the following features.

2. The means for acquiring production performance information acquires the production information including the start date of molting induction. The table creation means further creates a molting induction table that identifies information to correct the survival rate of the laying hens, information to correct the egg-laying rate, and information to correct the average egg weight, based on the acquired production information, according to the number of days after the start of molting induction for the laying hens. The production forecasting means outputs supply forecasting information that predicts the production quantity by size according to the age of the laying hen, based on the standard performance model, the egg weight table, and the molting induction table. The egg supply and demand forecasting system according to claim 1, characterized in that it is the same as described in claim 1.

3. A means for acquiring past shipping information that allows for the identification of the shipping quantity according to the shipping date for each size of chicken egg, A classification creation means for creating an index classification that identifies a calculation method for predicting the shipment quantity according to the size of the eggs, based on the acquired shipment information for a predetermined period, A shipping forecasting means outputs demand forecasting information that predicts the shipment quantity by size according to the scheduled shipment date, based on the shipment information for the predetermined period and the indicator classification. A supply and demand forecasting system for chicken eggs, characterized by comprising the following features.

4. A producer's terminal used by producers who raise laying hens and produce eggs, a seller's terminal used by sellers who sell eggs, and a supply and demand forecasting device that predicts the production quantity and shipment quantity of eggs by size are all connected via a network in a way that allows them to communicate with each other. The aforementioned producer terminal is A means for inputting daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg yield for each size of egg produced, according to the age of the laying hens in days, Means for transmitting the input production information to the supply and demand forecasting device, Means for transmitting a request for a forecast of the quantity of chicken egg production to the supply and demand forecasting device, Means for receiving supply forecast information from the supply and demand forecasting device, Equipped with, The aforementioned seller terminal is A means of inputting past shipping information that allows for the identification of shipping quantities according to the shipping date for each size of chicken egg, Means for transmitting the input shipment information to the supply and demand forecasting device, Means for transmitting a request for a forecast of the quantity of eggs to be shipped to the supply and demand forecasting device, means for receiving demand forecast information from the supply and demand forecasting device, Equipped with, The supply and demand forecasting device is A means for acquiring production performance information that acquires the production information from the producer terminal, A model creation means for creating a standard performance model that identifies and correlates the survival rate, egg-laying rate, and average egg weight of the laying hens according to their age in days, based on the acquired production information, A table creation means for creating an egg weight table that identifies the amount of eggs laid for each size according to the average egg weight based on the aforementioned production information, A production forecasting means that, upon receiving a request for egg production quantity prediction from the producer terminal, outputs supply forecast information predicting the production quantity by size according to the age of the laying hens, based on the standard performance model and the egg weight table, In addition to being equipped, A means for acquiring shipment performance information that acquires the shipment information from the seller terminal, A classification creation means for creating an index classification that identifies a calculation method for predicting the shipment quantity according to the size of the eggs, based on the acquired shipment information for a predetermined period, A shipment forecasting means that, in response to receiving a request for shipment quantity forecasting of eggs from the seller terminal, outputs demand forecasting information that predicts the number of eggs shipped by size according to the scheduled shipping date, based on the shipment information for the predetermined period and the indicator classification, Equipped with, A supply and demand forecasting system for chicken eggs, characterized by the following features.

5. A producer's terminal used by producers who raise laying hens and produce eggs, a seller's terminal used by sellers who sell eggs, and a supply and demand forecasting device that predicts the production quantity and shipment quantity of eggs by size are all connected via a network in a way that allows them to communicate with each other. The aforementioned producer terminal is A means for inputting daily production information that allows for the identification of the survival rate, egg-laying rate, average egg weight, and egg yield for each size of egg produced, according to the age of the laying hens in days, Means for transmitting the input production information to the supply and demand forecasting device, Means for transmitting a request for a forecast of the quantity of chicken egg production to the supply and demand forecasting device, Means for receiving supply forecast information from the supply and demand forecasting device, Equipped with, The aforementioned seller terminal is A means of inputting past shipping information that allows for the identification of shipping quantities according to the shipping date for each size of chicken egg, Means for transmitting the input shipment information to the supply and demand forecasting device, Means for transmitting a request for a forecast of the quantity of eggs to be shipped to the supply and demand forecasting device, means for receiving demand forecast information from the supply and demand forecasting device, Equipped with, The supply and demand forecasting device is A means for acquiring production performance information that acquires the production information from the producer terminal, A model creation means for creating a standard performance model that identifies and correlates the survival rate, egg-laying rate, and average egg weight of the laying hens according to their age in days, based on the acquired production information, A table creation means that creates an egg weight table that identifies the amount of eggs laid for each size according to the average egg weight based on the production information, and creates a molting induction table that identifies information to correct the survival rate of the laying hens, information to correct the egg-laying rate, and information to correct the average egg weight according to the number of days after the start of molting induction for the laying hens, In response to receiving a request for egg production quantity prediction from the producer terminal, a production prediction means outputs supply prediction information that predicts the production quantity by size according to the age of the laying hen, based on the standard performance model, the egg weight table, and the molting induction table. In addition to being equipped, A means for acquiring shipment performance information that acquires the shipment information from the seller terminal, A classification creation means for creating an index classification that identifies a calculation method for predicting the shipment quantity according to the size of the eggs, based on the acquired shipment information for a predetermined period, A shipment forecasting means that, in response to receiving a request for shipment quantity forecasting of eggs from the seller terminal, outputs demand forecasting information that predicts the number of eggs shipped by size according to the scheduled shipping date, based on the shipment information for the predetermined period and the indicator classification, Equipped with, A supply and demand forecasting system for chicken eggs, characterized by the following features.

6. The egg supply and demand forecasting system according to any one of 1, 2, 4, or 5, characterized in that the means for acquiring production performance information acquires the production information separately for each production area, farm, or chicken coop.

7. The egg supply and demand forecasting system according to claim 6, characterized in that the model creation means creates the standard performance model separately for each production area, farm, or chicken coop.

8. The egg supply and demand forecasting system according to claim 6, characterized in that the table creation means creates the egg weight table separately for each production area, farm, or chicken coop.

9. The egg supply and demand forecasting system according to claim 6, characterized in that the production forecasting means forecasts the production quantity by size separately for each production area, farm, or chicken coop.

10. The egg supply and demand forecasting system according to any one of claims 3, 4, or 5, characterized in that the means for acquiring shipment performance information acquires the shipment information for each destination.

11. The egg supply and demand forecasting system according to claim 10, characterized in that the classification creation means creates the indicator classification for each shipping destination.

12. The egg supply and demand forecasting system according to claim 10, characterized in that the shipment forecasting means predicts the number of shipments by size for each destination.

Images