Supply chain management apparatus, supply chain management method, and supply chain management system

Abstract

To provide a supply chain management apparatus, a supply chain management method, and a supply chain management system configured to accurately assess risks to a supply chain by assessing the risks to the supply chain according to data sources that are more significant for each user.SOLUTION: A supply chain management apparatus includes: a data read section 11 which acquires data sources as data related to risks that affect a supply chain; and a priority data extraction section 12 which selects target data for assessing the risks from the data sources according to significance of the data sources and user's acceptance of the data sources and assesses the risks to the supply chain according to the target data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a supply chain management device, a supply chain management method, and a supply chain management system, and more particularly to a supply chain management device and the like that can evaluate risks to a supply chain. [Background technology]

[0002] In recent years, the globalization of corporate activities has created a need for resilience to deal with a wide variety of risks. To strengthen supply chains, it is necessary to visualize the entire supply chain and understand the risks.

[0003] Patent Document 1 describes a system and method for sharing and manipulating supply chain data by assigning attributes to the data and creating hierarchies, calendars, filters, and freeze profiles. Data manipulation can be achieved through assignment, integration, and transformation using predefined relationships and rules. Selective sharing of data can be achieved through predefined partnerships and filters. System users can selectively view data in customized and desired formats. Patent Document 2 describes an information extraction support device that includes a first acquisition unit, a determination unit, a selection unit, and an extraction unit. The first acquisition unit acquires a document from which an attribute indicating a desired type of information can be extracted as an analysis target. The determination unit determines whether the attribute is valid and obtains attributes determined to be valid as attribute candidates. The selection unit selects an attribute to be used for analysis from the attribute candidates as a selected attribute. The extraction unit extracts expressions belonging to the selected attribute from the document as attribute expressions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2004-511842 [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-99741 Summary of the Invention [Problem to be solved by the invention]

[0005] The supply chain risks that must be considered vary depending on the characteristics of each company, such as industry, region, and size. This requires risk assessments tailored to each company's characteristics using a wide variety of data sources. On the other hand, even for the same event (for example, weather or incident), there are multiple data sources (for example, observation points or news). Therefore, the results of the assessment will vary depending on which data source is used to assess the risk. The present invention aims to provide a supply chain management device, a supply chain management method, and a supply chain management system that can more accurately evaluate risks to a supply chain by evaluating risks to a supply chain based on data sources that are more meaningful to each user. [Means for solving the problem]

[0006] To solve the above problems, the supply chain management device of the present invention includes a data acquisition unit that acquires data sources as data related to risks affecting the supply chain, a target data selection unit that selects target data for risk assessment from the data sources in accordance with the significance of the data sources and the user's acceptance of the data sources, and a risk assessment unit that assesses risks to the supply chain based on the target data.In this case, a supply chain management device can be provided that can more accurately assess risks to the supply chain by assessing risks to the supply chain based on data sources that are more significant to each user.

[0007] Here, for example, the significance of a data source and the degree of user acceptance are expressed by weights defined for the data sources, making it easier to express the significance of a data source and the degree of user acceptance. Furthermore, for example, the target data selection unit selects, from among the data sources, data sources with a large weight representing the significance of the data source for an item corresponding to the user's satisfaction as target data. In this case, it is possible to select data sources that are important to each user. Furthermore, for example, the weight for the significance of the data source and the weight for the user's acceptance are the proportion of data sources that contain descriptions related to a predetermined keyword, which makes it easier to set the weight. Furthermore, for example, the user's degree of satisfaction is expressed as a weight for an item that the user considers important among the data sources, expressed as a priority. In this case, the data source that is important to the user can be selected based on the priority. Then, for example, the risk assessment unit assesses the risk to the supply chain using a score calculated from the weights, which makes it easier to assess the risk. Furthermore, for example, the risk assessment unit corrects the score based on feedback input by the user as the user's assessment of the risk to the supply chain. In this case, it becomes possible to calculate a score tailored to each user.

[0008] Furthermore, the supply chain management method of the present invention has a processor execute a program recorded in memory to acquire data sources as data related to risks affecting the supply chain, select target data for risk assessment from the data sources according to the significance of the data sources and the user's acceptance of the data sources, and assess the risks to the supply chain based on the target data. In this case, by assessing the risks to the supply chain based on the data sources that are more significant to each user, it is possible to provide a supply chain management method that can more accurately assess risks to the supply chain.

[0009] Furthermore, a supply chain management system of the present invention includes a supply chain management device that evaluates risks to the supply chain and a visualization device that visualizes risks to the supply chain, and the supply chain management device includes a data acquisition unit that acquires data sources as data related to risks affecting the supply chain, a target data selection unit that selects target data for risk assessment from the data sources in accordance with the significance of the data sources and the user's acceptance of the data sources, and a risk assessment unit that evaluates risks to the supply chain based on the target data. In this case, a supply chain management system that can more accurately assess risks to the supply chain can be provided by assessing risks to the supply chain based on data sources that are more significant to each user.

[0010] Here, for example, the visualization device displays the points that may affect the supply chain, as well as the assessment of the risk to the supply chain in association with the points, allowing the user to grasp the risks associated with the points that may affect the supply chain. Furthermore, for example, the visualization device receives a user's evaluation of the risk assessment for the supply chain as feedback, and in this case, it becomes possible to calculate a score tailored to each user. [Effects of the Invention]

[0011] According to the present invention, it is possible to provide a supply chain management device, a supply chain management method, and a supply chain management system that can more accurately evaluate risks to a supply chain by evaluating risks to a supply chain based on data sources that are more meaningful to each user. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram showing the overall configuration of a supply chain management system according to an embodiment of the present invention. [Figure 2] FIG. 10 is a diagram showing an example of a data characteristics table. [Figure 3] FIG. 10 is a diagram illustrating an example of a user priority definition table. [Figure 4] 10 is a flowchart illustrating the operation of a priority data extraction unit. [Figure 5] FIG. 5 is a diagram illustrating a method in which the priority data extraction unit calculates a risk score in S404 of FIG. [Figure 6] 10 is a flowchart illustrating the operation of a data characteristic evaluation unit. [Figure 7] 7 is a flowchart illustrating in detail the process of calculating weights by the data characteristic evaluation unit in S604 of FIG. 6. [Figure 8] 10 is a flowchart illustrating the operation of a user priority management unit. [Figure 9] 10 is a flowchart illustrating the operation of an extracted data display unit. [Figure 10] 10(a) to 10(c) are diagrams showing the processing results displayed by the extracted data display unit. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. <Overall explanation of supply chain management system 1> FIG. 1 is a block diagram showing the overall configuration of a supply chain management system 1 according to this embodiment. The illustrated supply chain management system 1 includes a data selection server 10 and a client server 20.

[0014] The data selection server 10 is an example of a supply chain management device that evaluates risks to the supply chain. In this case, the data selection server 10 evaluates risks to the supply chain and outputs the processing results to the client server 20.

[0015] The data selection server 10 includes a data reading unit 11, a priority data extraction unit 12, a data characteristic evaluation unit 13, a user-specific priority management unit 14, an accumulation DB (database) 15, a data characteristic table DB (database) 16, and a user-specific priority definition table DB (database) 17.

[0016] The data reading unit 11 is an example of a data acquisition unit, and acquires data sources as data related to risks affecting the supply chain. Here, the data reading unit 11 collects data sources affecting the supply chain, for example, by using a group of data providers. The group of data providers provides data such as trade statistics, news from news media, and weather data in the form of web services, etc. Here, a case is shown in which news data A, news data B, news data C, weather data X, weather data Y, etc. are acquired from the group of data providers. The data reading unit 11 checks for updates to these data sources, acquires the necessary data sources, and stores them in the accumulation DB 15.

[0017] The priority data extraction unit 12 is an example of a target data selection unit that selects target data for risk assessment from data sources according to the significance of the data sources and the user's acceptance of the data sources. The priority data extraction unit 12 is also an example of a risk assessment unit that assesses risks to the supply chain based on the target data.

[0018] "Data source significance" represents the reliability or likelihood of a data source. It can also be said that this represents the quality of the data source. As will be described in detail later, in this embodiment, the significance of a data source is represented by a weight defined for the data source. The significance of a data source is summarized in a data characteristics table (Table 1, which will be described in detail later) and stored in the data characteristics table DB16.

[0019] "User satisfaction" represents the value of a data source to the user. It can also be said that this is a characteristic that the user judges to be significant for a data source. It can also be said that this is an item that the user considers important among data sources in relation to the supply chain. It can also be said that this is an item that the user considers to be a priority among data sources that will have an impact on the supply chain. As will be described in detail later, in this embodiment, the user satisfaction is represented by a weight that is defined for the data source based on a priority that the user first specifies. The user satisfaction is summarized in a user priority definition table (Tables 2a to 2d) that will be described in detail later, and is stored in the user priority definition table DB 17. Because user satisfaction differs for each user, the user priority management unit 14 manages the user priority definition table for each user.

[0020] Furthermore, the priority data extraction unit 12 evaluates the risk to the supply chain using a score calculated from the weights, as will be described in detail later. Hereinafter, this score may be referred to as the "risk score." The risk score represents the degree of risk to the supply chain, and in the present invention, the higher the risk score value, the greater the risk.

[0021] The data characteristic evaluation unit 13 calculates weights for the characteristics of the data source for which the user has specified priorities, and then updates a data characteristic table that lists the weights.

[0022] The user-specific priority management unit 14 manages a user-specific priority definition table, which will be described in detail later. The user-specific priority management unit 14 updates the user-specific priority definition table for correcting the risk scores based on feedback entered by the user. In other words, the extracted data display unit 21 of the client server 20 displays to the user the risk scores for the supply chain determined by the data selection server 10 as processing results, and at this time the user can further evaluate the processing results. This evaluation result is returned to the data selection server 10 as feedback. The user-specific priority management unit 14 then corrects the user-specific priority definition table (Table 2d, which will be described later) based on these results.

[0023] The client server 20 is an example of a visualization device that visualizes risks to the supply chain, and runs an application that visualizes the processing results output by the data selection server 10 and provides them to the user. The user can view the processing results of risks to the supply chain, for example, on a browser screen running on a terminal device that the user owns. The client server 20 includes an extracted data display unit 21. The extracted data display unit 21 visualizes the processing results using an application and creates an image to be provided to the user.

[0024] The data selection server 10 and the client server 20 are computer devices, and are referred to as server computers here. However, they are not limited to this and may be PCs (Personal Computers), mobile computers, smartphones, tablets, etc.

[0025] The data selection server 10 and the client server 20 each include a processor such as a CPU (Central Processing Unit) as a computing means, and a main memory as a storage means. The processor executes various software such as an OS (operating system) and applications (application software). The main memory is a storage area for storing various software and data used to run the software. The data selection server 10 and the client server 20 each include a storage such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) as an auxiliary storage device, and a communication interface for communicating with the outside. They may also include input devices such as a mouse and keyboard, and output devices such as a display.

[0026] Although the data selection server 10 and the client server 20 are shown here as separate devices, they do not necessarily have to be separate devices. For example, the data selection server 10 and the client server 20 may be integrated into a single device for processing. The data selection server 10 and the client server 20 may also be configured as separate devices. Furthermore, the accumulation DB 15, the data characteristics table DB 16, and the user priority definition table DB 17 are shown here as separate DBs, but they do not necessarily have to be separate DBs. For example, they may be configured as a single DB in the data selection server 10.

[0027] <Explanation of Data Characteristics Table> FIG. 2 is a diagram showing an example of a data characteristics table. The data characteristics table (Table 1) shown in the figure sets the data ID, data name, data type, and characteristic weight (weight) for each data source. Feature weights are set for each of the characteristics: region, sector, topic, and popularity. The update frequency indicates how often the characteristic weights are updated. Daily, weekly, monthly, and Q indicate that the characteristic weights are updated daily, weekly, monthly, and quarterly, respectively. Feature weights are set for these items. These characteristic weights are numerical values ​​that represent the reliability and likelihood of the data source. Here, characteristic weights are expressed as values ​​between 0 and 1. The larger the value, the higher the reliability and likelihood of the data source. For example, for a data source with a data ID of Src001, the data name is News Data A and the data type is News. The table also shows that the characteristic weights for this data source are set to 0.70 for the Japan region and 0.10 for the US region.

[0028] This characteristic weight is, for example, the percentage of data sources that contain descriptions of a specific keyword. For a region, the keyword could be a place name. If the data source range is, for example, the most recent year, the characteristic weight is the percentage of data sources in which a specific place name appears within this period. The same is true for sectors and topics. Note that a single data source may contain keywords related to a region, sector, and topic. In this case, this keyword is counted for each region, sector, and topic. For example, in the case of Global Forecast X with data ID Src004, it is expected that many regions will be mentioned, so 1.00 will be entered in multiple places as described above. In this case, the weight for the significance of a data source can be said to be the percentage of data sources that contain descriptions of a predetermined keyword.

[0029] Furthermore, when the characteristic is popularity, the characteristic weight can be expressed as a percentage of users of the supply chain management system 1, as specified in the user priority definition table. For example, if there are 1,000 users at a given time, and 80 of them have specified popularity in the user priority definition table, the weight is expressed as 0.08. Furthermore, the characteristic weight can be calculated as a numerical value when the parameter is narrowed down by similar users (for example, users in the same sector or topic).

[0030] The characteristic weights are periodically reviewed for the period specified in the update frequency. For example, if the update frequency is daily, the characteristic weights are updated daily. This causes the characteristic weight values ​​to change over time.

[0031] <Explanation of user priority definition table> FIG. 3 is a diagram showing an example of a user priority definition table. The illustrated user priority definition table is represented by four tables, Tables 2a to 2d. Table 2a is a table that sets weights for characteristics. In this example, the characteristics are Automobile, Japan, US, Africa, Human Rights, and Environment. For each, ID, type, and weight are set. The characteristics correspond to the region, sector, topic, popularity, etc. in Figure 2. These are characteristics for which the user specifies priorities.

[0032] This weight can be specified as a numerical value that indicates the proportion of data sources in which the characteristic appears. For example, if the characteristic appears in 80% of data sources, the weight is set to 0.80. Here, the weight is expressed as a numerical value between 0 and 1. The larger the numerical value, the higher the importance to the user. Weights can also be set in place of priorities. In this case, for example, priority 1 corresponds to a value of 0.80 or more (80% or more). This means that data sources with a value above that number will be targeted. Note that priority 1 has a higher priority than priority 2. In the case of Table 2a, the weight for user persuasion can also be said to be the proportion of data sources that contain descriptions related to predetermined keywords.

[0033] Table 2b is a table that sets weights for combinations of characteristics and data names. Here, an ID and weight are set for each combination of characteristics. For example, this table shows that the priority for the combination of characteristics Japan and Environment is 1.

[0034] Table 2c is a table that sets weights for data names. Here, an ID, type, and weight are set for each data name. For example, this table shows that the priority for the data name News Data A is 2.

[0035] Table 2d is a user-specific priority definition table for correcting risk scores based on feedback entered by the user. Here, an ID and a score correction value are set for each characteristic combination. For example, this table shows that the score correction value for the combination of news data C and Japan is -0.050. In the case of Tables 2b to 2d, it can be said that the user's degree of satisfaction is expressed as a weight given to items that the user considers important in the data source, expressed as a priority.

[0036] <Explanation of the operation of the priority data extraction unit 12> FIG. 4 is a flowchart illustrating the operation of the priority data extraction unit 12. The priority data extraction unit 12 acquires the data characteristics table (Table 1) from the data characteristics table DB 16 (S401). Next, the priority data extraction unit 12 acquires the user priority definition tables (Tables 2a to 2d) from the user priority definition table DB 17 (S402). Next, the priority data extraction unit 12 selects target data for risk evaluation for each data type from the data characteristics table (Table 1) and the user priority definition tables (Tables 2a to 2c) (S403).

[0037] Furthermore, the priority data extraction unit 12 reads the target data, evaluates the supply chain risk, and extracts the risk score and the target data from the accumulation DB 15 (S404). Then, the priority data extraction unit 12 corrects the risk score in accordance with the user priority definition table (Table 2d) (S405). In this case, it can also be said that the priority data extraction unit 12 corrects the risk score based on feedback input by the user as the user's assessment of the risk assessment for the supply chain. Note that the processing of S403 and S405 can also be performed at the same time. Furthermore, the priority data extraction unit 12 passes the corrected risk score to the extracted data display unit 21 (S406).

[0038] In S403 of Fig. 4, the priority data extraction unit 12 selects target data, for example, as follows. Here, the priority data extraction unit 12 selects, from among the data sources, data sources with a large weight indicating the significance of the data source for the item corresponding to the user's satisfaction as target data. Specifically, the target data is selected from the data characteristics table (Table 1) using the data characteristics table (Table 1) shown in Fig. 2 and the user priority definition table (Tables 2a to 2c) shown in Fig. 3. Note that if there is no data that matches all the items, priority is given to data that matches in some combinations.

[0039] For example, the following data IDs are extracted from Table 1 as data sources that match the definition of the target characteristics in Table 2a.

[0040] Type is sector, matches A001: Src001 Type is region, matches A001 and A002: Src001, Src004 Type is region and matches A002: Src003, Src005, Src007 Type is region and matches A003: Src002, Src008 Type is topic and matches A005 and A006: Src007 Type is topic and matches A005: Src001 Type is topic and matches A006: Src008

[0041] Next, by checking the definition of the target property in Table 2c, Src001 and Src008 are selected. Type is topic and matches A006: Src008

[0042] Also, according to the definition of the target characteristics in Table 2b, the following is true: Matches P001: N / A (Not Available) Matches P002: Src001

[0043] As a result, the priority data extraction unit 12 selects, for example, the data sources with data IDs Src001 and Src008 as target data.

[0044] FIG. 5 is a diagram illustrating a method in which the priority data extraction unit 12 calculates the risk score in S404 of FIG. The priority data extraction unit 12 inputs the data characteristics table (Table 1), the user priority definition table (Tables 2a to 2c), and the data sources stored in the storage DB 15 as Input to LLMs (Large Language Models). As a result, the LLM outputs text as Output. Here, for example, the following text is shown: "In Region A, due to the effects of a storm, it is expected that the takeoff and landing of aircraft and ships will be affected for approximately seven days (from Src001 Score=0.750)." In this example, "from Src001" indicates that the data source with the data ID Src001 in the data characteristics table (Table 1) originates. Furthermore, "Score=0.750" indicates the risk score in this case.

[0045] <Explanation of the operation of the data characteristic evaluation unit 13> FIG. 6 is a flowchart illustrating the operation of the data characteristic evaluation unit 13. First, the data characteristic evaluation unit 13 determines whether any of the following has occurred: a data source has been added, a data source has been updated, or an item in the user priority definition table has been added or updated (S601). As a result, if neither of these conditions occurs (No in S601), the process returns to S601. On the other hand, if either of these occurs (Yes in S601), the data characteristic evaluation unit 13 acquires the data characteristic table (Table 1) from the data characteristic table DB 16 (S602). Next, the data characteristic evaluation unit 13 acquires the user priority definition tables (Tables 2a to 2d) from the user priority definition table DB 17 (S603). Then, the data characteristic evaluation unit 13 reads the data source from the storage DB 15, and calculates (adds or updates) the weight for the characteristic for which the user has specified priority based on the user priority definition table (S604). Furthermore, the data characteristic evaluation unit 13 updates the data characteristic table (Table 1) (S605). The updated data characteristic table (Table 1) is stored in the data characteristic table DB 16.

[0046] FIG. 7 is a flowchart illustrating in detail the process of calculating the weight by the data characteristic evaluation unit 13 in S604 of FIG. The data characteristic evaluation unit 13 performs weighting based on keywords related to data characteristics (S701), for example, by the proportion of data sources that contain descriptions of related keywords among the data sources. Next, the data characteristic evaluation unit 13 performs weighting based on the degree of match with the factual information (S702). This can be done, for example, by comparing the data source with the factual information and based on the numerical precision of amounts, numbers, etc. Furthermore, the data characteristic evaluation unit 13 performs weighting by popularity (S703). This can be expressed, for example, as a percentage specified in a user priority definition table among users of the supply chain management system 1. Also, if the data source is a paper, blog, etc., it can be expressed by the number of citations.

[0047] <Explanation of the operation of the user priority management unit 14> FIG. 8 is a flowchart illustrating the operation of the user priority management unit 14. First, the user priority management unit 14 receives feedback input by the user from the extracted data display unit 21 (S801). Next, the user priority management unit 14 determines whether the fed back characteristics are registered in the user priority definition table (Table 2d) (S802). As a result, if the characteristic is not registered (No in S802), the fed back characteristic is additionally registered in the user priority definition table (Table 2d) (S803). On the other hand, if it is registered (Yes in S802), the score correction value in the user priority definition table (Table 2d) is updated (S804).

[0048] Then, the user priority management unit 14 stores the user priority definition table (Table 2d) in the user priority definition table DB 17 (S805). Furthermore, the user priority management unit 14 determines whether the priority set for the weight exceeds a predetermined threshold value (S806). As a result, if it is exceeded (Yes in S806), the priority is updated and the original priority is deleted (S807). In this case, the user priority definition tables (Tables 2b and 2c) are updated.

[0049] <Explanation of the operation of the extracted data display unit 21> FIG. 9 is a flowchart illustrating the operation of the extracted data display unit 21. The extracted data display unit 21 outputs the processing result acquired from the priority data extraction unit 12 (S901). This output is performed, for example, by displaying the processing result to the user. Next, the extracted data display unit 21 receives the evaluation result of the user's evaluation of the processing result as feedback (S902). Then, the extracted data display unit 21 sends the evaluation results by the user to the user priority management unit 14 (S903).

[0050] 10(a) to 10(c) are diagrams showing the processing results displayed by the extracted data display unit 21. FIG. Here, an example is shown in which the extracted data display unit 21 visualizes the risk score and displays it on the screen. 10(a) shows the processing results displayed on screen G1. On screen G1 shown in the figure, a map drawing area is provided on the left side, displaying points that may affect the supply chain. Here, these are displayed as points 1 to 7.

[0051] When the user selects the display information list provided on the upper right side of the screen G1 by clicking with the mouse, the screen transitions to the screen G2 shown in FIG. 10(b). Screen G2 shows the risk score, data name, and article content for location 1 displayed in a table. Here, the risk score (score) for location 1 is 0.820, and the data name of the data source used to calculate this risk score is Weather Data A. The article content then displays information that will affect the supply chain. In this case, it indicates that "In region A, the storm is expected to affect the arrival and departure of aircraft and ships for approximately seven days." The article content is the text output as Output in Figure 5. In this case, it can also be said that the extracted data display unit 21 displays the locations that may affect the supply chain, as well as the assessment of the risk to the supply chain, in association with the locations.

[0052] Furthermore, when the user selects the display information property provided on the lower right side of the screen G1 by clicking with the mouse, the screen transitions to the screen G3 shown in FIG. 10(c). Screen G3 displays the characteristics for which risk scores have been calculated, along with the table displayed on screen G2. Here, it shows that the risk score has been calculated for the characteristic being an automobile. A field for accepting feedback from the user is displayed at the bottom of screen G3. Here, the user can select a positive or negative evaluation as the evaluation result using check box 1001, and after checking this check box, the user can enter feedback by pressing register button 1002. In this case, it can also be said that the extracted data display unit 21 receives the user's evaluation of the risk to the supply chain as feedback.

[0053] According to the data selection server 10 described above in detail, risks to the supply chain can be evaluated more accurately by evaluating the risks to the supply chain based on a data source that is more meaningful to each user.

[0054] <Explanation of supply chain management methods> The processing performed by the data selection server 10 is realized by the cooperation of software and hardware resources. That is, a processor such as a CPU provided in the data selection server 10 loads into main memory and executes programs that realize each function of the data selection server 10, thereby realizing each function. Therefore, the processing performed by the data selection server 10 described above can be considered to be a supply chain management method in which a processor executes a program recorded in memory to acquire data sources as data related to risks affecting the supply chain, select target data for risk assessment from the data sources according to the significance of the data sources and the user's acceptance of the data sources, and assess risks to the supply chain based on the target data. This makes it possible to provide a supply chain management method that can more accurately assess risks to the supply chain by assessing risks to the supply chain based on data sources that are more significant to each user.

[0055] Furthermore, the program running on the data selection server 10 can be considered to be a program that causes a computer processor to execute the program recorded in memory, thereby realizing the following functions: acquiring data sources as data related to risks affecting the supply chain; selecting target data for assessing risk from the data sources according to the significance of the data sources and the user's acceptance of the data sources; and assessing risks to the supply chain based on the target data. This allows the computer to realize the function of more accurately assessing risks to the supply chain by assessing risks to the supply chain based on data sources that are more meaningful to each user.

[0056] The program for realizing this embodiment can be provided not only by communication means but also by being stored on a recording medium such as a CD-ROM.

[0057] Although the present embodiment has been described above, the technical scope of the present invention is not limited to the scope of the above embodiment. It is clear from the claims that various modifications and improvements to the above embodiment are also included in the technical scope of the present invention. [Explanation of symbols]

[0058] 1...Supply chain management system, 10...Data selection server, 11...Data reading unit, 12...Priority data extraction unit, 13...Data characteristic evaluation unit, 14...User-specific priority management unit, 15...Storage DB, 16...Data characteristic table DB, 17...User-specific priority definition table DB, 20...Client server, 21...Extracted data display unit, G1 to G3...Screens

Claims

1. a data acquisition unit that acquires data sources as data on risks affecting the supply chain; a target data selection unit that selects target data for risk evaluation from the data sources according to the significance of the data sources and the user's acceptance of the data sources; a risk assessment unit that assesses risks to the supply chain based on the target data; A supply chain management device comprising:

2. 2. The supply chain management apparatus according to claim 1, wherein the significance of the data source and the user's acceptance are represented by weights defined for the data sources.

3. The supply chain management device according to claim 2, wherein the target data selection unit selects, from the data sources, data that has a large weight representing the significance of the data source for an item corresponding to the user's satisfaction as the target data.

4. 3. The supply chain management device according to claim 2, wherein the weight for the significance of the data source and the weight for the user's acceptance are the proportion of the data source that contains a description relating to a predetermined keyword.

5. 3. The supply chain management device according to claim 2, wherein the user's degree of satisfaction is expressed as a weight for an item that the user considers important in the data source, expressed as a priority.

6. The supply chain management device according to claim 2 , wherein the risk assessment unit assesses the risk to the supply chain based on a score calculated from the weight.

7. The supply chain management device according to claim 6 , wherein the risk assessment unit corrects the score based on feedback input by a user as the user's assessment of the risk to the supply chain.

8. The processor executes the program stored in the memory. Obtain data sources for data on risks affecting the supply chain, Selecting target data for risk assessment from the data sources according to the significance of the data sources and the user's acceptance of the data sources; Based on the target data, assess the risk to the supply chain; Supply chain management methods.

9. a supply chain management device for assessing risks to the supply chain; A visualization device that visualizes risks to the supply chain; Equipped with The supply chain management device includes: a data acquisition unit that acquires data sources as data on risks affecting the supply chain; a target data selection unit that selects target data for risk evaluation from the data sources according to the significance of the data sources and the user's acceptance of the data sources; a risk assessment unit that assesses risks to the supply chain based on the target data; A supply chain management system that includes:

10. The supply chain management system according to claim 9 , wherein the visualization device displays points that may affect the supply chain, as well as an assessment of the risk to the supply chain, in association with the points.

11. The supply chain management system according to claim 9 , wherein the visualization device receives, as feedback, a user's assessment of the risk to the supply chain.

Images