Information processing device, information processing method, and information processing program

Abstract

The present invention provides an information processing device that can satisfy both the needs of users who desire a final product and the needs of manufacturers who want to inform users that they are capable of manufacturing the final product desired by the user. [Solution] An information processing device is provided, comprising: a prediction unit that inputs desired conditions for a product specified by a user into a pre-built prediction model and obtains output from the prediction model to predict manufacturing conditions that satisfy the desired conditions; and a manufacturing location determination unit that determines a manufacturing location capable of manufacturing the product from among a plurality of manufacturing locations based on the manufacturing conditions predicted by the prediction unit.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Techniques for inferring the physical properties of a resin composition from the manufacturing condition information of the resin composition have been disclosed (see Patent Documents 1 to 3).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although the product forms of chemical products are various, the final product always requires a compounding process or a formulation process (a process of physically mixing). Users who desire the final product have a need to find a manufacturer who can sell the final product under conditions such as a desired delivery date and price. In addition, manufacturers who produce chemical products have a need to convey to users that they can produce the final products desired by the users.

[0005] The present disclosure has been made in view of the above points, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program capable of satisfying the needs of users who desire a final product and the needs of manufacturers who want to convey to users that they can produce the final products desired by the users.

Means for Solving the Problems

[0006] According to one aspect of this disclosure, an information processing device is provided, comprising: a prediction unit that inputs desired conditions for a product specified by a user into a pre-built prediction model and obtains output from the prediction model to predict manufacturing conditions that satisfy the desired conditions; and a manufacturing location determination unit that determines a manufacturing location capable of manufacturing the product from among a plurality of manufacturing locations based on the manufacturing conditions predicted by the prediction unit.

[0007] The manufacturer determination unit may further determine a manufacturer capable of producing the product based on data that does not involve the prediction model.

[0008] The manufacturer determination unit may determine a manufacturer capable of producing the product based on data relating to the feasibility of producing the product, without going through the prediction model.

[0009] The manufacturer determination unit may determine a manufacturer capable of producing the product based on data relating to the feasibility of producing the product, specifically, at least one of the following: the quantity of the product, quality standards, delivery date, and materials.

[0010] The manufacturer determination unit may determine which manufacturers are capable of manufacturing the product based on data provided by each of the manufacturers as data related to determining whether or not the product can be manufactured.

[0011] The manufacturer determination unit may determine which manufacturers are capable of manufacturing the product based on data provided by each manufacturer, specifically data relating to the equipment owned by each manufacturer.

[0012] The manufacturer determination unit may determine which manufacturers are capable of manufacturing the product based on data relating to the assets of each manufacturer, as data provided by each manufacturer.

[0013] The manufacturer determination unit may determine which manufacturers are capable of producing the product based on data provided by each manufacturer, specifically data regarding the material inventory held by each manufacturer.

[0014] The manufacturing destination determination unit may determine a manufacturing destination capable of producing the product based on data relating to the product's logistics, as data not obtained through the prediction model.

[0015] The manufacturer determination unit may determine a manufacturer capable of producing the product based on data that does not involve the prediction model, specifically data on the amount of carbon dioxide emitted during the transportation of the product from the manufacturer to the user.

[0016] According to one aspect of this disclosure, an information processing method is provided in which a processor inputs desired conditions for a product specified by a user into a pre-built predictive model, obtains output from the predictive model to predict manufacturing conditions that satisfy the desired conditions, and, based on the predicted manufacturing conditions, determines a manufacturing location capable of manufacturing the product from among a plurality of manufacturing locations.

[0017] According to one aspect of this disclosure, an information processing program is provided that causes a computer to input desired conditions for a product specified by a user into a pre-built predictive model, obtain output from the predictive model to predict manufacturing conditions that satisfy the desired conditions, and then determine a manufacturing location capable of manufacturing the product from among multiple manufacturing locations based on the predicted manufacturing conditions. [Effects of the Invention]

[0018] According to this disclosure, it is possible to provide an information processing device, an information processing method, and an information processing program that can satisfy the needs of users who desire a final product and the needs of manufacturers who want to inform users that they can manufacture the final product desired by the user. [Brief explanation of the drawing]

[0019] [Figure 1] This figure shows a schematic configuration of a chemical product ordering system having an information processing device according to an embodiment of the disclosed technology. [Figure 2]It is a block diagram showing the hardware configuration of an information processing apparatus. [Figure 3] It is a block diagram showing an example of the functional configuration of an information processing apparatus. [Figure 4] It is a flowchart showing the flow of information processing by an information processing apparatus.

Embodiments for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may be different from the actual ratios.

[0021] FIG. 1 is a diagram showing a schematic configuration of a chemical product ordering system having an information processing apparatus according to the present embodiment. The information processing apparatus 10 acquires information regarding a chemical product desired by a user from a terminal 20 used by the user who desires the chemical product as the final product ((1) in FIG. 1), and based on the acquired information, provides the user with information such as what chemical products are available and where the manufacturers capable of manufacturing the chemical product are. In FIG. 1, three manufacturers 31, 32, and 33 are shown as manufacturers, but the number of manufacturers is not limited to such an example.

[0022] The information processing apparatus 10 uses a pre-generated learned model 30 in order to obtain information on the formulation, composition, and materials of the chemical product from the information regarding the chemical product desired by the user ((2) in FIG. 1). The information processing apparatus 10 performs descriptor conversion as a preprocessing before giving information to the learned model 30. Descriptor conversion is a process of converting the configuration of a monomer into a character string, for example, converting a benzene ring into a numerical value and an alphabet. Since the information input by the user may be highly abstract, the information processing apparatus 10 converts the information input by the user into a format that can be input into the pre-generated learned model 30 in advance.

[0023] The information processing device 10 inputs information about the chemical product desired by the user into a trained model 30 and obtains information about the formulation, composition, and materials of the chemical product from the trained model 30. Once the information processing device 10 obtains the information about the formulation, composition, and materials of the chemical product from the trained model 30, it provides this information to manufacturers 31, 32, and 33 and inquires whether they can accept the manufacture of the chemical product, and if so, the price and delivery date (Figure 1 (3)). The information processing device 10 then obtains from manufacturers 31, 32, and 33 whether they can accept the manufacture of the chemical product, and if so, the price and delivery date (Figure 1 (4)). The information processing device 10 also obtains information about materials held by each manufacturer and other information about the manufacture of chemical products from manufacturers 31, 32, and 33 in real time.

[0024] The pre-trained model 30 is a model that has been pre-machine-trained to output information on the formulation, composition, and materials of a chemical product when given information on that chemical product, and is an example of a predictive model in this disclosure. The pre-trained model 30 is also a model that has been pre-machine-trained to output information on the formulation, composition, and materials of a chemical product, as well as candidate manufacturers capable of producing that chemical product, when given information on that chemical product.

[0025] The information processing device 10 obtains information from manufacturers 31, 32, and 33 regarding whether they can accept the manufacture of chemical products, and if so, the price and delivery date. It then provides the information obtained from manufacturers 31, 32, and 33 to the terminal 20 (Figure 1 (5)). At this time, the information processing device 10 may suggest a manufacturer to the user from among manufacturers 31, 32, and 33. The user uses the terminal 20 to instruct the information processing device 10 to place an order for chemical products with the manufacturer (Figure 1 (6)). The information processing device 10 places an order for the chemical products desired by the user with the manufacturer specified by the user (for example, manufacturer 32). The specified manufacturer 32 manufactures the chemical products desired by the user and delivers the manufactured chemical products to the user.

[0026] The information processing device 10 can exchange information between the user and the manufacturer, thereby fulfilling the manufacturer's need to inform the user that they can manufacture the final product desired by the user.

[0027] Figure 2 is a block diagram showing the hardware configuration of the information processing device 10.

[0028] As shown in Figure 2, the information processing device 10 includes a CPU (Central Processing Unit) 11, ROM (Read Only Memory) 12, RAM (Random Access Memory) 13, storage 14, input unit 15, display unit 16, and communication interface (I / F) 17. Each component is connected to the others via a bus 19 so that they can communicate with each other.

[0029] The CPU 11 is a central processing unit that executes various programs and controls various parts. Specifically, the CPU 11 reads a program from the ROM 12 or storage 14 and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various calculations according to the program recorded in the ROM 12 or storage 14. In this embodiment, the ROM 12 or storage 14 stores an information processing program that determines the manufacturer of the final product desired by the user and provides the user with information about the manufacturer.

[0030] ROM12 stores various programs and data. RAM13 temporarily stores programs or data as a working area. Storage14 consists of a storage device such as an HDD (Hard Disk Drive), SSD (Solid State Drive), or flash memory, and stores various programs, including the operating system, and various data.

[0031] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used for various types of input.

[0032] The display unit 16 is, for example, a liquid crystal display and displays various information. The display unit 16 may also function as an input unit 15 by employing a touch panel system.

[0033] The communication interface 17 is an interface for communicating with other devices such as the terminal 20, and standards such as Ethernet®, FDDI, and Wi-Fi® are used.

[0034] When executing the above information processing program, the information processing device 10 uses the above hardware resources to implement various functions. The functional configuration implemented by the information processing device 10 will now be described.

[0035] Figure 3 is a block diagram showing an example of the functional configuration of the information processing device 10.

[0036] As shown in Figure 3, the information processing device 10 has a functional configuration consisting of a prediction unit 101, a manufacturer determination unit 102, and an estimation unit 103. Each functional configuration is realized by the CPU 11 reading and executing an information processing program stored in the ROM 12 or storage 14.

[0037] The prediction unit 101 inputs information about the chemical product desired by the user into the trained model 30 and obtains information about the chemical product's formulation, composition, and materials from the trained model 30 to predict what kind of chemical product the user desires. Information about the chemical product desired by the user includes, for example, the material, durability, and quantity of the chemical product. More specifically, it obtains information such as the flexural elasticity and MFR (Melt Flow Rate) of the resin used as a raw material for the chemical product from the terminal 10. The prediction unit 101 can predict the chemical product desired by the user by obtaining information about the chemical product's formulation, composition, and materials from the trained model 30.

[0038] The pre-trained model 30 is trained to take information about a chemical product desired by the user as input and output information about the chemical product's formulation, composition, and materials. Furthermore, it is also trained to output manufacturers capable of producing the chemical product desired by the user. Because the pre-trained model 30 is trained using information about the production of chemical products held by each manufacturer, when information about a chemical product desired by the user is input, it can output candidate manufacturers capable of producing that chemical product.

[0039] It should be noted that the information regarding chemical products desired by the user is not limited to the examples mentioned above. For example, only the physical properties of the raw material resin, only the raw materials used (monomers, additives, etc.), the raw materials used and the final physical properties, or only the final physical properties may be entered into the terminal 20 as information regarding chemical products desired by the user.

[0040] If the user inputs only the physical properties of the raw resin into the terminal 20, the prediction unit 101 uses the information input by the user, such as the flexural elasticity and MFR of the resin used as the raw material, as explanatory variables to provide to the trained model 30. In this case, if the user also inputs the final physical properties (target variable) of the compound into the terminal 20, the prediction unit 101 may determine whether there is a difference in the output from the trained model 30. If there is a difference of a predetermined amount or more between the final target physical properties input by the user and the output from the trained model 30, the prediction unit 101 may change the composition ratio, etc., and perform physical property prediction again using the trained model 30.

[0041] If the user only inputs raw materials (monomers, additives, etc.) into the terminal 20, the structure of the raw material compounds can be described using strings such as SMILES notation, SMARTS notation, or InChI notation. Therefore, the prediction unit 101 may perform descriptor conversion as a preprocessing step before providing information to the trained model 30. The prediction unit 101 may then provide the converted strings to the trained model 30 to perform physical property prediction using the trained model 30.

[0042] When the user inputs the raw materials and final physical properties to the terminal 20, the prediction unit 101 may perform descriptor conversion as a preprocessing step before providing information to the trained model 30, similar to when only the raw materials (monomers, additives, etc.) to be used are input. The prediction unit 101 may then perform physical property prediction by the trained model 30 by providing the converted string to the trained model 30 at a certain composition ratio. If there is a difference of a predetermined amount or more between the final physical properties input by the user and the output from the trained model 30, the prediction unit 101 may change the composition ratio, etc., and perform physical property prediction again using the trained model 30.

[0043] If the user inputs only the final physical properties into the terminal 20, the prediction unit 101 may predict materials and formulations that satisfy the final physical properties by providing the final physical properties to the trained model 30.

[0044] The manufacturer selection unit 102 determines which manufacturer to commission to produce the chemical product from among multiple manufacturers, based on information about the chemical product's formulation, composition, and materials obtained by the prediction unit 101 from the trained model 30. For example, if the chemical product desired by the user is a combination of materials A, B, and C, the manufacturer selection unit 102 determines which manufacturer is capable of producing the chemical product using these materials. Furthermore, the manufacturer selection unit 102 determines which manufacturer to commission to produce the chemical product from among multiple manufacturers, based on candidate manufacturers obtained by the prediction unit 101 from the trained model 30.

[0045] The manufacturer determination unit 102 may further determine which manufacturer to commission to manufacture the chemical product from among multiple manufacturers based on data that does not go through the trained model 30. Data that does not go through the trained model 30 includes, for example, data possessed by each manufacturer, in other words, data that depends on each manufacturer. Data that depends on each manufacturer includes, for example, the manufacturing capacity of each manufacturer. The manufacturer is determined. The manufacturing capacity of each manufacturer includes, for example, capacity, material, temperature control (controllable temperature, kettle heating method, cooling method), agitator (motor output, agitator blade shape, corresponding viscosity), vacuum level, etc. Data that depends on each manufacturer includes, for example, the availability of assets at each manufacturer, such as the availability of equipment and the availability of operators. Data that depends on each manufacturer includes, for example, the inventory of materials at each manufacturer.

[0046] If the chemical product desired by the user is a combination of materials A, B, and C, the manufacturer determination unit 102 determines the manufacturer of the chemical product by considering factors such as whether the manufacturer has the necessary equipment to produce the chemical product, whether it has the capacity to produce it if it is possible, and whether it has inventory of each material if it is possible to produce it.

[0047] Data that does not go through the trained model 30 includes, for example, user-dependent data, specifically data entered by the user into terminal 20. Data entered by the user into terminal 20 includes the desired quantity of product, quality standards, delivery date, and materials. For example, if the user specifies the quantity of product, the manufacturer determination unit 102 extracts manufacturers capable of producing the desired quantity of chemical product as candidate requests. For example, if the user specifies the quality of the product, the manufacturer determination unit 102 extracts manufacturers capable of producing chemical product of the desired quality as candidate requests. For example, if the user specifies the delivery date of the product, the manufacturer determination unit 102 extracts manufacturers capable of producing chemical product by the desired delivery date as candidate requests. For example, if the user specifies the materials for the product, the manufacturer determination unit 102 extracts manufacturers capable of producing chemical product using the desired materials as candidate requests.

[0048] Data that does not go through the trained model 30 includes, for example, data that depends on either the manufacturer or the user. Data that depends on either the manufacturer or the user includes, for example, data related to logistics. Data related to logistics includes, for example, the transportation method and cost when transporting chemical products manufactured by the manufacturer to the user, the transportation network from the manufacturer to the user's delivery destination, road conditions during transportation from the manufacturer to the user's delivery destination (such as whether there are road closures), and carbon dioxide emissions during transportation from the manufacturer to the user's delivery destination.

[0049] The manufacturer determination unit 102 may prioritize data that does not go through the trained model 30 when determining a manufacturer capable of producing chemical products. For example, if the manufacturer determination unit 102 is set to prioritize the cost of transporting the chemical products produced by the manufacturer to the user, if two or more manufacturers are capable of producing chemical products, the manufacturer with the lowest transportation cost to the user may be determined as the manufacturer capable of producing chemical products. Alternatively, if the manufacturer determination unit 102 is set to prioritize the delivery time when transporting the chemical products produced by the manufacturer to the user, if two or more manufacturers are capable of producing chemical products, the manufacturer with the earliest delivery time to the user may be determined as the manufacturer capable of producing chemical products.

[0050] In this embodiment, the chemical product ordering system is preferable to allow as many manufacturers as possible to be involved, rather than having the system repeatedly request manufacturing from the same manufacturer. It is also preferable that the manufacturer selection unit 102 does not repeatedly select the same manufacturer. If two or more manufacturers are capable of manufacturing the chemical product, the manufacturer selection unit 102 may select the manufacturer with the longest history since the last request as the capable manufacturer. Each manufacturer's system stores the details of the request from the information processing device 10, the number of requests, the frequency of requests, information on the price and delivery date for requests for which manufacturing was possible and orders were received, and information on the price and delivery date for requests for which manufacturing was possible but orders were not received, and this information is shared with the information processing device 10. The manufacturer selection unit 102 may then refer to the information held in each manufacturer's system to determine the manufacturer.

[0051] Once the manufacturer selection unit 102 determines a manufacturer for the chemical product specified by the user, it inquires with the selected manufacturer whether they can accept the order for the manufacture of the chemical product. Upon receiving a response from the manufacturer indicating that they can accept the order, along with information on the manufacturing costs and delivery date, the manufacturer selection unit 102 transmits the manufacturer's estimated costs and delivery date for the chemical product to the user's terminal 20. If the user accepts these conditions, the terminal 20 places an order for the chemical product.

[0052] The information that the manufacturing location determination unit 102 notifies the user of may include, for example, that the product can be processed and there are no problems with logistics or delivery time; that the product can be processed but the manufacturing site is far away, making delivery difficult, requiring the procurement of materials, and there are limitations on the quantity (minimum or maximum); or that the product can be processed but requires nighttime service, which will increase costs.

[0053] It is possible that there are multiple manufacturers that can fulfill a single order from a user. For example, one manufacturer may not be able to meet the user's order quantity, but two or more manufacturers may be able to. Also, since the manufacturing capacity of a manufacturer changes constantly, depending on the timing of the order, even a manufacturer that can normally meet the user's order may not be able to meet a single order from a user. In such cases, the manufacturer determination unit 102 may determine two or more manufacturers for the chemical product specified by the user. When the manufacturer determination unit 102 determines two or more manufacturers for the specified chemical product, it may also determine the production quantity of the specified chemical product by combining the production quantities of each manufacturer based on the manufacturing capacity of each manufacturer that has been obtained in advance.

[0054] The estimation unit 103 estimates the costs of manufacturing chemical products based on information obtained in advance from the manufacturer. For example, the estimation unit 103 estimates the costs of manufacturing chemical products based on the usage time of equipment available at the manufacturer, the work time before and after equipment use (including process planning costs, setup costs, etc.), the amount and unit price of materials required, the hourly rates for equipment costs and labor costs, logistics costs (distance between the processing site and the delivery site, product form (liquid or solid), transportation method), etc.

[0055] Next, the operation of the information processing device 10 will be explained.

[0056] Figure 4 is a flowchart showing the flow of information processing by the information processing device 10. Information processing is performed when the CPU 11 reads an information processing program from the ROM 12 or storage 14, loads it into the RAM 13, and executes it.

[0057] In step S101, the CPU 11 inputs the desired product specifications specified by the user into the trained model 30. The desired product specifications specified by the user include the material, durability, and quantity of the product.

[0058] Following step S101, in step S102, the CPU 11 obtains the output from the trained model 30 to predict the manufacturing conditions for a chemical product that meets the desired conditions for the product specified by the user. The manufacturing conditions for a chemical product include information on the chemical product's formulation, composition, and materials.

[0059] Following step S102, in step S103, the CPU 11 determines a manufacturing facility capable of producing the chemical product from among several facilities, based on the manufacturing conditions of the chemical product predicted by obtaining the output from the trained model 30. If the chemical product desired by the user is a combination of materials A, B, and C, the CPU 11 determines a manufacturing facility capable of producing the chemical product using these materials.

[0060] While embodiments of the present disclosure have been described in detail above with reference to the attached drawings, the technical scope of the present disclosure is not limited to these examples. It is clear that a person with ordinary skill in the art of the present disclosure may conceive of various modifications or alterations within the scope of the technical idea set forth in the claims, and these modifications or alterations are also understood to fall within the technical scope of the present disclosure.

[0061] Furthermore, the effects described in the above embodiments are descriptive or illustrative, and are not limited to those described in the above embodiments. In other words, the technology relating to this disclosure may produce other effects that would be obvious to a person of ordinary skill in the art of this disclosure from the descriptions in the above embodiments, in addition to or in lieu of the effects described in the above embodiments.

[0062] Furthermore, the information processing that the CPU reads and executes in each of the above embodiments may be executed by various processors other than the CPU. Examples of such processors include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacturing, and dedicated electrical circuits that are processors with circuit configurations specifically designed to execute specific processing, such as ASICs (Application Specific Integrated Circuits). In addition, the information processing may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (for example, multiple FPGAs, and a combination of a CPU and an FPGA). More specifically, the hardware structure of these various processors is an electrical circuit that combines circuit elements such as semiconductor elements.

[0063] Furthermore, while the above embodiments describe a configuration in which the information processing program is pre-stored (installed) in ROM or storage, the invention is not limited thereto. The program may be provided in a form recorded on a non-transitory recording medium such as a CD-ROM (Compact Disk Read Only Memory), DVD-ROM (Digital Versatile Disk Read Only Memory), or USB (Universal Serial Bus) memory. The program may also be provided in a form that can be downloaded from an external device via a network. This disclosure may also be applied to program products. [Explanation of symbols]

[0064] 10 Information Processing Devices 20 devices 30 pre-trained models

Claims

1. A prediction unit that inputs the desired conditions for a product specified by the user into a pre-built prediction model and obtains the output from the prediction model to predict manufacturing conditions that satisfy the desired conditions, Based on the manufacturing conditions predicted by the prediction unit, the manufacturer determination unit determines a manufacturer capable of manufacturing the product from among multiple manufacturers, An information processing device equipped with the following features.

2. The information processing apparatus according to claim 1, wherein the manufacturer determination unit further determines a manufacturer capable of manufacturing the product based on data that does not involve the prediction model.

3. The information processing apparatus according to claim 2, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data relating to the determination of whether or not the product can be manufactured, as data not obtained through the prediction model.

4. The information processing apparatus according to claim 3, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on at least one of the quantity, quality standards, delivery date, and materials of the product as data relating to the determination of whether or not the product can be manufactured.

5. The information processing apparatus according to claim 3, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data provided by each of the manufacturers as data relating to the determination of whether or not the product can be manufactured.

6. The information processing apparatus according to claim 5, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data relating to the equipment owned by each manufacturer, as data provided by each manufacturer.

7. The information processing apparatus according to claim 5, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data relating to the assets of each manufacturer, as data provided by each manufacturer.

8. The information processing apparatus according to claim 5, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data relating to the material inventory held by each manufacturer, as data provided by each manufacturer.

9. The information processing apparatus according to claim 2, wherein the manufacturing destination determination unit determines a manufacturing destination capable of producing the product based on data relating to the logistics of the product, as data not obtained through the prediction model.

10. The information processing apparatus according to claim 9, wherein the manufacturer determination unit determines a manufacturer capable of manufacturing the product based on data of the amount of carbon dioxide emitted during the transportation of the product from the manufacturer to the user, as data not obtained through the prediction model.

11. The processor, By inputting the desired product conditions specified by the user into a pre-built predictive model and obtaining the output from the predictive model, the manufacturing conditions that satisfy the desired conditions are predicted. Based on the predicted manufacturing conditions, a manufacturer capable of producing the product is selected from among several manufacturers. An information processing method that executes a process.

12. On the computer, By inputting the desired product conditions specified by the user into a pre-built predictive model and obtaining the output from the predictive model, the manufacturing conditions that satisfy the desired conditions are predicted. Based on the predicted manufacturing conditions, a manufacturer capable of producing the product is selected from among several manufacturers. An information processing program that executes a process.

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