Production risk analysis device and production risk analysis method

Abstract

This enables improved efficiency in risk analysis related to product production. [Solution] The production risk analysis device 100 includes: a risk scenario pattern acquisition unit 112 that acquires risk scenario patterns related to the production of a product using a language model (see language model server 200) based on product-related information 121 which includes at least one of the product specifications and production process information; a causal model generation unit 113 that generates a causal model 130 by extracting the causal relationships between factors and results included in the risk scenario patterns; and a detailed risk scenario acquisition unit 114 that acquires detailed risk scenarios related to the production of a product using a language model based on the product-related information 121 and the causal model 130.

Description

Technical Field

[0001] The present invention relates to a production risk analysis apparatus and a production risk analysis method for assisting risk analysis of product production.

Background Art

[0002] In the manufacturing industry, the external environment is becoming more complex and diverse, such as an increase in geopolitical risks such as disputes and epidemics, recalls due to quality standards, the progress of labor shortages, the shortening of the product life cycle, and the diversification of product varieties. Therefore, it is becoming difficult to predict the future external environment, consider the configuration of the production line, and formulate an investment plan.

[0003] As a risk assessment of a manufacturing system, there is a method described in Patent Document 1. In this method, a manufacturing system is modeled by a resource graph defining the physical resources of the system, a process graph defining the services executed by the system, and the mapping between these resource graphs and process graphs. By performing simulations of service performance with multiple sets of operation parameters according to the resource graph and the process graph, performance indicators of the manufacturing system are modeled. Based on the modeled performance indicators, risks can be identified. A manufacturing risk indicates a change in a performance indicator that exceeds a predetermined threshold. And a set of operation parameters corresponding to a result without manufacturing risk can be identified. Based on this finding, changes to the manufacturing system can be determined, and the manufacturing system can be optimized to avoid risks.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The method described in Patent Document 1 identifies risks by obtaining resource graphs and process graphs that include causal relationships. Creating resource graphs and process graphs that include causal relationships requires knowledge from a wide range of fields, including economics, politics, society, technology, and the environment, and is therefore very costly. This invention was made in view of the above background, and aims to provide a production risk analysis device and a production risk analysis method that enable improved efficiency in risk analysis related to product production. [Means for solving the problem]

[0006] To solve the above-mentioned problems, the production risk analysis device according to the present invention comprises: a risk scenario pattern acquisition unit that acquires risk scenario patterns related to the production of a product using a language model based on product-related information including at least one of product specifications and production process information; a causal model generation unit that generates a causal model by extracting the causal relationships between factors and results included in the risk scenario patterns; and a detailed risk scenario acquisition unit that acquires detailed risk scenarios related to the production of a product using the language model based on the product-related information and the causal model. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide a production risk analysis apparatus and a production risk analysis method that enable improved efficiency in risk analysis related to product production. Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments. [Brief explanation of the drawing]

[0008] [Figure 1] This is a functional block diagram of the production risk analysis device according to this embodiment. [Figure 2] This diagram illustrates the overall flow of the production risk analysis process according to this embodiment. [Figure 3] An example of a risk scenario pattern acquisition prompt according to this embodiment is shown. [Figure 4]This is an example of a causal model according to this embodiment. [Figure 5] An example of a detailed risk scenario acquisition prompt according to this embodiment is shown. [Figure 6] This is a flowchart of the production risk analysis process according to this embodiment. [Figure 7] This is a hardware configuration diagram showing an example of a computer that implements the functions of the production risk analysis device according to the above embodiment. [Modes for carrying out the invention]

[0009] <Overview of Production Risk Analysis Equipment> The following describes a production risk analysis device in an embodiment for carrying out the present invention. The production risk analysis device according to this embodiment queries a language model (language model server) based on product-related information such as product specifications, configuration, and production process to obtain a risk scenario (first risk scenario) related to product production. Next, the production risk analysis device performs text analysis on the first risk scenario to generate a causal model that includes risk factors and consequences.

[0010] Next, the production risk analysis system queries the language model based on product-related information and causal models to obtain risk scenarios related to product production (second risk scenarios). Because the language model is queried including the causal model generated based on the first risk scenario, the second risk scenarios contain more detailed information than the first risk scenarios. For this reason, below, the first risk scenarios will also be referred to as risk scenario patterns, and the second risk scenarios as detailed risk scenarios.

[0011] The production risk analysis system collects information related to the risk factors included in the risk scenario. It also performs simulations of response costs and production costs in the event of a risk occurrence, and calculates the impact on costs.

[0012] Such production risk analysis devices enable efficient risk analysis related to product production. For example, they reduce the need to analyze the causal relationships between risks and factors, and allow for risk analysis that includes complex causal relationships where causal relationships are linked in a chain. They also enable risk analysis that includes a wide range of perspectives, such as economic, political, and environmental factors. In addition, they enable quantitative risk assessment.

[0013] <<Configuration of the Production Risk Analysis System>> Figure 1 is a functional block diagram of the production risk analysis device 100 according to this embodiment. The production risk analysis device 100 is a computer and comprises a control unit 110, a storage unit 120, and an input / output unit 180. User interface devices such as a display, keyboard, and mouse are connected to the input / output unit 180. The input / output unit 180 also has a communication device and is capable of sending and receiving data with the language model server 200. The language model server 200 is a server that provides a service that returns answers to inquiries called prompts using a language model. An example of the language model server 200 is an artificial intelligence chatbot.

[0014] ≪Production Risk Analysis Device: Memory Unit≫ The memory unit 120 is composed of memory devices such as ROM (Read Only Memory), RAM (Random Access Memory), and SSD (Solid State Drive). The memory unit 120 stores product-related information 121, risk scenario information 122, causal model 130, causal factor information 123, information resource information 124, evaluation information 125, and program 128. The program 128 contains a description of the processing to be executed by the functional unit provided in the control unit 110, which will be described later. The various contents of the memory unit 120 may be stored in an external storage device such as a cloud server and read as needed.

[0015] ≪Production Risk Analysis System: Control Unit≫ The control unit 110 is configured to include a CPU (Central Processing Unit), and is provided with a reception unit 111, a risk scenario pattern acquisition unit 112, a causal model generation unit 113, a detailed risk scenario acquisition unit 114, a causal factor information acquisition unit 115, and an evaluation unit 116. The control unit 110 may be configured to include a GPU (Graphics Processing Unit), an NPU (Neural (network) Processing Unit), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), etc.

[0016] FIG. 2 is a diagram for explaining the overall flow of the production risk analysis process according to the present embodiment. While referring to FIG. 2, the stored content stored in the storage unit 120 and the functional units provided in the control unit 110 will be described.

[0017] ≪Reception Unit - Product - Related Information≫ The reception unit 111 receives information related to the product to be produced, which is input by the user of the production risk analysis apparatus 100, and stores it in the product - related information 121. The product - related information 121 is information such as the specifications, configuration, production process, production line, etc. of the product input by the user. Examples of information on the production process include "In the case where the process is the assembly of hard parts, both the operator and the robot are candidates." "In the case of difficult parts such as wire harnesses, since it is difficult to handle with a robot, the operator is a candidate." etc.

[0018] ≪Risk Scenario Pattern Acquisition Unit - Risk Scenario Pattern (Risk Scenario Information)≫ The risk scenario pattern acquisition unit 112 generates a risk scenario pattern acquisition prompt 510 (see FIG. 3 to be described later) and transmits it to the language model server 200. The risk scenario pattern acquisition unit 112 stores the risk scenario pattern 410, which is the risk scenario returned by the language model server 200, in the risk scenario information 122.

[0019] Figure 3 shows an example of the risk scenario pattern acquisition prompt 510 according to this embodiment. Element 511 indicates the product specifications, configuration, production process, etc., which are included in the product-related information 121. Examples of risk scenario pattern 410 include: "Production site A has a risk of conflict and a risk of supply chain disruption," and "Production site A tends to have a labor shortage."

[0020] As described above, the production risk analysis device 100 includes a risk scenario pattern acquisition unit 112 that acquires a risk scenario pattern 410 related to the production of the product using a language model (see language model server 200) based on product-related information 121 which includes at least one of the product specifications and production process information.

[0021] ≪Causal Model Generation Unit · Causal Model≫ Returning to Figure 2, we will continue the explanation of the control unit 110 and the storage unit 120. The causal model generation unit 113 generates a causal model 130 by performing text analysis on the risk scenario pattern 410. Figure 4 is an example of a causal model 130 according to this embodiment. A causal model (causal relationship model) shows the relationship between causes (factors, elements) and effects (damage, risk). In Figure 4, the relationship is shown by an arrow with the cause as the starting point and the effect as the ending point. The causal model 130 is not limited to a graph consisting of a starting point, arrows, and an ending point, but may also be shown with text such as, for example, "There is a possibility that a risk of labor shortage will occur due to population decline as a factor." Note that although Figure 4 shows one linked graph, the results of the text analysis may also be multiple linked graphs.

[0022] As described above, the production risk analysis device 100 includes a causal model generation unit 113 that extracts the causal relationships between factors and outcomes included in the risk scenario pattern 410 and generates a causal model 130. The causal model generation unit 113 performs a text analysis of the risk scenario pattern 410 to extract causal relationships.

[0023] ≪Detailed Risk Scenario Acquisition Section · Detailed Risk Scenario (Risk Scenario Information)≫ Returning to Figure 2, we continue the explanation of the control unit 110 and the storage unit 120. The detailed risk scenario acquisition unit 114 generates a detailed risk scenario acquisition prompt 520 (see Figure 5 below) and sends it to the language model server 200. The detailed risk scenario acquisition unit 114 stores the detailed risk scenario 420 returned by the language model server 200 in the risk scenario information 122.

[0024] Figure 5 shows an example of the detailed risk scenario acquisition prompt 520 according to this embodiment. Element 521 shows the product specifications, configuration, production process, etc., found in product-related information 121. Element 522 shows the causal model 130 in text format. An example of a detailed risk scenario 420 is as follows:

[0025] Example 1: Production site A faces the risk of conflict and supply chain disruption. As a result, the production line may be shut down for a certain period. If product production stops, production costs will increase and opportunity costs will be lost. Example 2: There is a risk of rising material costs in production site A. Therefore, there is a risk of increased production costs. Example 3: Production site A is experiencing a labor shortage. This poses a risk of increased labor costs and higher production costs. Additionally, there is a risk of decreased production yield due to an increase in early worker retirements and a decline in skills.

[0026] As described above, the production risk analysis device 100 includes a detailed risk scenario acquisition unit 114 that acquires a detailed risk scenario 420 related to the production of the product, based on product-related information 121 and a causal model 130, using a language model (see language model server 200).

[0027] ≪Causal factor information acquisition unit / Causal factor information≫ Returning to Figure 2, let's continue the explanation of the control unit 110 and the storage unit 120. The causal factor information acquisition unit 115 performs a text analysis of the detailed risk scenario 420 to obtain the causes (factors) of risk occurrence. Next, the causal factor information acquisition unit 115 refers to the information resource information 124 to obtain information related to the causes and stores it in the causal factor information 123. The information resource information 124 stores reliable information resources outside the production risk analysis device 100. Examples of information resources include news provision sites, newspapers, broadcasting stations, and news agencies. The information resource information 124 may also store the reliability level of each information resource. The causal factor information acquisition unit 115, for example, accesses the server provided by the information resource, searches using the cause as a keyword, and obtains highly relevant information or recently provided information to make it the causal factor information 123.

[0028] As described above, the production risk analysis device 100 includes a causal factor information acquisition unit 115 that acquires factors included in the detailed risk scenario 420 and acquires external information (see causal factor information 123) that serves as the basis for those factors.

[0029] ≪Evaluation Department / Evaluation Information≫ The evaluation unit 116 evaluates the risks shown in the detailed risk scenario 420. More specifically, the evaluation unit 116 extracts risks by text analysis of the detailed risk scenario 420 and stores them as evaluation information 125. Next, the evaluation unit 116 calculates the countermeasure costs, production costs, and the increase (increase rate) of production costs when the risk occurs, using production simulation, and stores them as evaluation information 125 in relation to the risks. Note that the countermeasure costs and the increase in production costs may be considered losses as they are new costs resulting from the occurrence of the risk.

[0030] Production costs include material costs and labor costs. Countermeasure costs are, for example, the costs of changing the production line. When a risk occurs, the production line is changed to minimize production costs. The evaluation unit 116 calculates the optimal product production line configuration after the risk occurs, as well as the costs of changing the production line. The following references describe techniques for changing the production line, and by using these techniques, it is possible to calculate the changes and their costs. References: Daiki Kajita et al., Development of Integrated Automated Design Technology for Robot Production Lines that Can Rapidly Respond to Changes in the Production Environment, Journal of the Japan Society for Precision Engineering, vol.87, no.2, pp.160-163, 2021.

[0031] When multiple risks are identified, the evaluation unit 116 calculates the countermeasure costs and production costs for each risk. The evaluation unit 116 may also calculate the countermeasure costs and production costs according to the degree (severity) of the risk. For example, if the risk of increased labor costs is identified, the evaluation unit 116 may calculate the countermeasure costs and production costs by setting the degree (severity) of the increase, such as a 10% increase or a 20% increase.

[0032] The evaluation unit 116 performs a sensitivity analysis to determine the degree to which a risk affects production costs. For example, the evaluation unit 116 calculates the increase in production costs when labor costs increase by 10%. The evaluation unit 116 calculates the importance of the risk according to the sensitivity and stores it as evaluation information 125 in association with the risk. The evaluation unit 116 may also calculate a higher importance of the risk if the increase (loss) in countermeasure costs and production costs is large.

[0033] The evaluation unit 116 may calculate the importance of a risk as its probability of occurrence increases. The evaluation unit 116 may calculate the probability of a risk occurring based on the likelihood of the risk included in the causal factor information 123, the number of pieces of information related to the risk included in the causal factor information 123, the number of information resources from which the information was acquired, and the probability of similar risks occurring.

[0034] The evaluation unit 116 displays the risks in the evaluation information 125, the scenarios in which the risks occur (see detailed risk scenario 420), the causal factor information of the risks (causal factor information 123), the response costs when the risks occur, the production costs, the increment in production costs, and the importance on a display connected to the input / output unit 180.

[0035] As described above, the production risk analysis device 100 includes an evaluation unit 116 that calculates the importance of a risk based on the amount of loss (countermeasure costs, increments in production costs) that would occur if a risk included in the detailed risk scenario 420 were to occur, and the amount of external information related to the factors of the risk (see causal factor information 123).

[0036] The evaluation unit 116 calculates the loss if the risks included in the detailed risk scenario 420 occur. The evaluation unit 116 calculates the optimal product production line configuration in the event that the risks included in the detailed risk scenario 420 occur. The evaluation unit 116 calculates the importance of a risk based on the results of a sensitivity analysis of losses to the degree of risk when a risk included in the detailed risk scenario 420 occurs.

[0037] <Production Risk Analysis Processing> Figure 6 is a flowchart of the production risk analysis process according to this embodiment. At the start of the production risk analysis process, it is assumed that the user has already entered product-related information 121.

[0038] In step S11, the risk scenario pattern acquisition unit 112 generates a risk scenario pattern acquisition prompt 510 (see Figure 3) and sends it to the language model server 200 to acquire the risk scenario pattern 410. In step S12, the causal model generation unit 113 performs a text analysis of the risk scenario pattern 410 to generate a causal model 130.

[0039] In step S13, the detailed risk scenario acquisition unit 114 generates a detailed risk scenario acquisition prompt 520 (see Figure 5) and sends it to the language model server 200 to acquire the detailed risk scenario 420. In step S14, the causal factor information acquisition unit 115 performs a text analysis of the detailed risk scenario 420 to obtain the causes (factors) of the risk and acquire information related to those causes.

[0040] In step S15, the evaluation unit 116 performs a text analysis of the detailed risk scenario 420 to extract risks. In step S16, the evaluation unit 116 calculates the cost of countermeasures and production costs when the risks identified in step S15 occur.

[0041] In step S17, the evaluation unit 116 calculates the increment in production costs due to the occurrence of the risk, the sensitivity of losses to the risk (countermeasure costs, increment in production costs), and the importance of the risk. In step S18, the evaluation unit 116 outputs the risk, the risk occurrence scenario, risk factor information, the cost of responding to the risk occurrence, the production cost, the increment of the production cost, and the importance.

[0042] Features of the Production Risk Analysis Device The production risk analysis device 100 acquires risk scenario patterns 410 using the language model server 200 based on product-related information 121. The production risk analysis device 100 generates a causal model 130 based on the risk scenario patterns 410 and further acquires detailed risk scenarios 420 using the language model server 200.

[0043] The production risk analysis device 100 acquires information related to the factors (causes) of risks included in the detailed risk scenario 420. The production risk analysis device 100 also calculates the response cost, production cost, increment in production cost, and importance of the risk when it occurs.

[0044] The production risk analysis device 100 includes the generated causal model 130 in the detailed risk scenario acquisition prompt 520 and queries the language model server 200 to acquire the detailed risk scenario 420. By using this detailed risk scenario 420, it is possible to reduce the amount of causal relationship analysis between risks and factors required for risk analysis, and to perform risk analysis that includes complex causal relationships where causal relationships are linked in a chain. Furthermore, it becomes possible to perform risk analysis that includes a wide range of perspectives, including economic, political, and environmental factors. As a result, users of the production risk analysis device 100 can analyze production risks efficiently. Moreover, quantitative risk assessment becomes possible.

[0045] ≪Variations≫ Although several embodiments of the present invention have been described above, these embodiments are merely illustrative and do not limit the technical scope of the present invention. For example, the production risk analysis device 100 utilizes an external language model server 200. Alternatively, the production risk analysis device 100 may use a language model to generate responses (e.g., risk scenario patterns 410) to prompts (e.g., risk scenario pattern acquisition prompts 510).

[0046] The present invention can take on various other embodiments, and furthermore, various modifications such as omissions and substitutions can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention as described herein, and are also included in the scope of the invention and its equivalents as described in the claims.

[0047] Hardware Configuration The production risk analysis device 100 according to the above embodiment is implemented by a computer 900 having a configuration such as that shown in Figure 7. Figure 7 is a hardware configuration diagram showing an example of a computer 900 that implements the functions of the production risk analysis device 100 according to the above embodiment. The computer 900 includes a CPU 901, ROM 902, RAM 903, SSD 904, and an input / output interface 905 (labeled as input / output I / F (Interface) in Figure 7). Furthermore, the computer 900 includes a communication interface 906 (labeled as communication I / F in Figure 7) and a media interface 907 (labeled as media I / F in Figure 7). The computer 900 may be equipped with an HDD (Hard Disk Drive) instead of the SSD 904, or it may be equipped with an HDD in addition to the SSD 904.

[0048] The CPU 901 operates based on programs stored in the ROM 902 or SSD 904 and is controlled by the control unit 110 in Figure 1. The ROM 902 stores boot programs executed by the CPU 901 when the computer 900 starts up, as well as programs related to the computer 900's hardware.

[0049] The CPU 901 controls input devices 910, such as a mouse and keyboard, and output devices 911, such as a display and printer, via the input / output interface 905. The CPU 901 acquires data from the input devices 910 and outputs the generated data to the output devices 911 via the input / output interface 905.

[0050] SSD904 stores programs executed by CPU901 and data used by those programs. Communication interface906 receives data from other devices (e.g., language model server200) not shown via the communication network and outputs it to CPU901, and also transmits data generated by CPU901 to other devices via the communication network.

[0051] The media interface 907 reads a program or data stored in the recording medium 912 and outputs it to the CPU 901 via the RAM 903. The CPU 901 loads the program from the recording medium 912 onto the RAM 903 via the media interface 907 and executes the loaded program. The recording medium 912 can be an optical recording medium such as a DVD (Digital Versatile Disk), a magneto-optical recording medium such as an MO (Magneto Optical Disk), a magnetic recording medium, a conductive memory tape medium, or a semiconductor memory.

[0052] For example, when the computer 900 functions as the production risk analysis device 100 according to the above embodiment, the CPU 901 of the computer 900 realizes the functions of the production risk analysis device 100 by executing the program 128 (see Figure 1) loaded on the RAM 903. The CPU 901 reads the program from the recording medium 912 and executes it. In addition, the CPU 901 may read the program from another device via a communication network, or it may install the program 128 from the recording medium 912 to the SSD 904 and execute it. [Explanation of Symbols]

[0053] 100 Production Risk Analysis Device 111 Reception Department 112 Risk Scenario Pattern Acquisition Unit 113 Causal Model Generation Unit 114 Detailed Risk Scenario Acquisition Section 115 Causal factor information acquisition unit 116 Evaluation Department 121 Product-related information 122 Risk Scenario Information 130 Causal Models 123 Causal factor information 124 Information Resources 125 Review Information 200 Language Model Servers (Language Models) 410 Risk Scenario Patterns 420 Detailed Risk Scenarios

Claims

1. A risk scenario pattern acquisition unit acquires risk scenario patterns related to the production of the product using a language model based on product-related information, which includes at least one of the product specifications and production process information. A causal model generation unit that extracts the causal relationships between factors and outcomes included in the aforementioned risk scenario pattern and generates a causal model, The system includes a detailed risk scenario acquisition unit that acquires a detailed risk scenario related to the production of the product using the language model based on the product-related information and the causal model. Production risk analysis device.

2. The system further includes a causal factor information acquisition unit that acquires factors included in the detailed risk scenario and acquires external information that serves as the basis for those factors. The production risk analysis apparatus according to claim 1.

3. The system further includes an evaluation unit that calculates the importance of a risk based on the loss incurred if the risks included in the detailed risk scenario occur, and the amount of external information related to the factors of that risk. The production risk analysis apparatus according to claim 2.

4. The system further includes an evaluation unit that calculates the loss if any of the risks included in the detailed risk scenario described above occur. The production risk analysis apparatus according to claim 1.

5. The aforementioned causal model generation unit, The aforementioned risk scenario patterns are subjected to text analysis to extract the causal relationships. The production risk analysis apparatus according to claim 1.

6. The system further includes an evaluation unit that calculates the optimal production line configuration for the product in the event that any of the risks included in the detailed risk scenario described above occur. The production risk analysis apparatus according to claim 1.

7. The system further includes an evaluation unit that calculates the importance of a risk based on the results of a sensitivity analysis of the loss to the degree of the risk when a risk included in the detailed risk scenario occurs. The production risk analysis apparatus according to claim 1.

8. Production risk analysis equipment, A step of obtaining risk scenario patterns related to the production of the product using a language model based on product-related information, which includes at least one of the product specifications and production process information. The steps include: extracting the causal relationships between factors and outcomes included in the aforementioned risk scenario pattern to generate a causal model; Based on the product-related information and the causal model, the language model is used to obtain a detailed risk scenario related to the production of the product. Production risk analysis methods.

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