Information processing system and information processing method

Abstract

To accurately evaluate the influence on business during the occurrence of a disaster.SOLUTION: An information processing system holds information indicating the correlation between the occurrence of a disaster event and the occurrence of a damage event that may occur in a facility used in business, information indicating the disaster event occurring in a disaster scenario and the magnitude of the disaster event, and a first index indicating the execution state of risk measures for reducing the damage event that may occur in the facility. The information processing system specifies the damage event correlated to the disaster event in the disaster scenario and the facility in which the disaster event may occur, and predicts the amount of resources to be consumed for recovery from the damage event on the specified facility, on the basis of at least one of the magnitude of the disaster event in the disaster scenario and the first index for the damage event that may occur in the specified facility.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing system and an information processing method. [Background technology]

[0002] In recent years, the impact of climate change on business has been increasing, with natural disasters becoming more severe and flooding damage caused by rising sea levels. Companies and local governments need to implement risk countermeasures in preparation for natural disasters, social disasters, and other disasters, but because the budget available for risk countermeasures is limited, there is a need for technology to quantitatively evaluate the impact of disasters on business in order to determine which risk countermeasures should be prioritized within the limited budget.

[0003] Background art in this technical field is the technology described in Japanese Patent Laid-Open No. 2009-53977 (Patent Document 1), which states that "a business risk calculation system for calculating the impact of an earthquake on a company that continues its business while receiving supplies from multiple supply chains comprises earthquake risk assessment period input means for inputting an earthquake risk assessment period, supply chain status input means for inputting the location, building characteristics, current status of lifelines, and number of days of inventory for each supply chain, allowable operation suspension period calculation means for calculating the allowable operation suspension period for each supply chain, supply chain supply network setting means for setting a supply network from the supply chain to the company, operation suspension probability calculation means for calculating the probability of operation suspension due to damage caused by an earthquake, and business interruption probability calculation means for calculating the probability of business interruption at the company" (see abstract). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-53977 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology described in Patent Document 1 calculates the probability of a company's business interruption due to an earthquake from factory inventory levels and supply chain substitutability, and evaluates the impact on the business based on the probability of supply chain operations being suspended. However, the technology described in Patent Document 1 does not calculate the probability of business interruption using damage events caused by natural disasters to multiple facilities necessary for business continuity, nor does it evaluate the financial impact of such damage events on those facilities, so there is a risk that it will not be possible to accurately evaluate the impact on a business of a natural disaster.

[0006] Therefore, one aspect of the present invention is to evaluate with high accuracy the impact on business when a disaster occurs. [Means for solving the problem]

[0007] One aspect of the present invention for solving the above problem employs the following configuration: An information processing system includes a processor and a memory, wherein the memory stores correlation information indicating a correlation between the occurrence of a disaster event and the occurrence of a damage event that may occur at a facility used in a business, disaster scenario information indicating a disaster event that occurs in a disaster scenario and the magnitude of the disaster event, and a first indicator indicating a status of implementation of risk countermeasures for mitigating the damage event that may occur at the facility, and the processor refers to the correlation information to identify a damage event that is correlated with the disaster event in the disaster scenario and a facility at which the damage event may occur, and executes a prediction process to predict the amount of resources to be spent on recovery from the damage event for the identified facility based on at least one of the magnitude of the disaster event in the disaster scenario and the first indicator for the damage event that may occur at the identified facility. [Effects of the Invention]

[0008] According to one aspect of the present invention, it is possible to evaluate with high accuracy the impact on business when a disaster occurs.

[0009] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram illustrating an example of a hardware configuration of a risk impact assessment system according to a first embodiment. [Figure 2] 1 is a block diagram illustrating an example of a functional configuration of a risk influence degree assessment system according to a first embodiment. [Figure 3] 10 is a flowchart illustrating an example of a risk influence degree evaluation process according to the first embodiment. [Figure 4] FIG. 3 is a diagram illustrating an example of a data configuration of disaster record information according to the first embodiment. [Figure 5] FIG. 2 is a diagram illustrating an example of a data configuration of damage record information according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a data configuration of facility information according to the first embodiment. [Figure 7] FIG. 4 is a diagram illustrating an example of a data configuration of correlation information according to the first embodiment. [Figure 8] 10 is a flowchart illustrating an example of a correlation analysis process in the first embodiment. [Figure 9] FIG. 3 is a diagram illustrating an example of a data configuration of disaster scenario information according to the first embodiment. [Figure 10A] FIG. 2 is an explanatory diagram showing an example of a water transport route when water is supplied to farmland in Example 1. [Figure 10B] FIG. 2 is an explanatory diagram showing an example of a transport route of seedlings when the seedlings are supplied to farmland in Example 1. [Figure 11] FIG. 3 is a diagram illustrating an example of a data configuration of facility usage information according to the first embodiment. [Figure 12] 10 is a flowchart illustrating an example of a disaster scenario acquisition process according to the first embodiment. [Figure 13] FIG. 3 is a diagram illustrating an example of a data configuration of risk countermeasure information according to the first embodiment. [Figure 14] FIG. 2 is a diagram illustrating an example of a data configuration of risk countermeasure implementation information according to the first embodiment. [Figure 15] FIG. 10 is a diagram illustrating an example of a data configuration of recovery-related prediction model information according to the first embodiment. [Figure 16] FIG. 2 is a diagram illustrating an example of a data configuration of recovery-related information according to the first embodiment. [Figure 17] FIG. 4 is a diagram illustrating an example of a data configuration of operation suspension period information according to the first embodiment. [Figure 18] FIG. 10 is a diagram illustrating an example of a data configuration of business suspension period information according to the first embodiment. [Figure 19A] 10 is a flowchart illustrating an example of an operation suspension period prediction process according to the first embodiment. [Figure 19B] 10 is a flowchart illustrating an example of an operation suspension period prediction process according to the first embodiment. [Figure 20] FIG. 3 is a diagram illustrating an example of a data configuration of product information according to the first embodiment. [Figure 21] FIG. 3 is a diagram illustrating an example of a data configuration of shipping information in the first embodiment. [Figure 22] FIG. 10 is a diagram showing an example of financial impact prediction information in the first embodiment. [Figure 23] 10 is a flowchart illustrating an example of a financial impact prediction process according to the first embodiment. [Figure 24A] FIG. 10 is an explanatory diagram showing an example of the screen configuration of a risk influence degree evaluation result output screen in the first embodiment. [Figure 24B] FIG. 10 is an explanatory diagram showing an example of the screen configuration of a risk influence degree evaluation result output screen in the first embodiment. [Figure 25] FIG. 10 is a diagram illustrating an example of a data configuration of part information according to the second embodiment. [Figure 26] 11 is a flowchart illustrating an example of a part of an operation downtime prediction process according to the second embodiment. [Figure 27] 10 is a flowchart illustrating an example of a financial impact prediction process according to the second embodiment. [Figure 28] FIG. 11 is a diagram illustrating an example of a data configuration of business information according to a third embodiment. [Figure 29] FIG. 11 is a diagram illustrating an example of the data configuration of business continuity determination criteria information according to the third embodiment. [Figure 30]13 is a flowchart illustrating an example of an operation suspension period prediction process according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. Note that the following embodiment is merely an example of the present invention, and the present invention is not limited to the configuration shown in the drawings. Note that in this embodiment, an example in which the present invention is applied to a specific industry or disaster will be described, but the present invention can also be applied to other industries or disasters. [Example]

[0012] In Example 1, an example of a risk impact assessment system for assessing the impact of a natural disaster on a business run by a company is shown. In Example 1, a heavy rain disaster is assumed as a natural disaster, and an example is shown in which the impact assessment system assesses the impact of a heavy rain disaster on a farmer's business.

[0013] <Example of hardware configuration for risk impact assessment system> 1 is a block diagram showing an example of the hardware configuration of a risk impact assessment system. The risk impact assessment system 100, which is an example of an information processing system, evaluates the impact of disaster risk on a business. The risk impact assessment system 100 is configured by a computer having a CPU (Central Processing Unit) 101, memory 102, auxiliary storage device 103, communication device 104, input device 105, and output device 106, which are connected to each other by an internal communication line such as a bus.

[0014] The CPU 101 is an example of a processor, and executes programs stored in the memory 102. The memory 102 includes a ROM (Read Only Memory), which is a nonvolatile storage element, and a RAM (Random Access Memory), which is a volatile storage element. The ROM stores unchanging programs (e.g., a BIOS (Basic Input / Output System)). The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the CPU 101 and data used when the programs are executed.

[0015] The auxiliary storage device 103 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD (Hard Disk Drive)) or a flash memory (SSD (Solid State Drive)), and stores programs executed by the CPU 101, data used when the programs are executed, and data generated by the execution of the programs. In other words, the programs are read from the auxiliary storage device 103, loaded into the memory 102, and executed by the CPU 101.

[0016] In addition, part or all of the program executed by CPU 101 may be provided to risk impact assessment system 100 via a network from a removable medium (CD-ROM, flash memory, etc.) which is a non-transitory storage medium, or from an external computer equipped with a non-transitory storage device, and stored in non-volatile auxiliary storage device 103 which is a non-transitory storage medium.

[0017] The communication device 104 is a network interface device that connects to the network 120 via a wired or wireless connection in accordance with a predetermined protocol and controls communication with other devices. Information required for processing by the CPU 101 may be acquired via the network 120 using the communication device 104 as an interface. The communication device 104 may also include a serial interface such as a USB (Universal Serial Bus).

[0018] The network 120 may be a public communication network such as a LAN (Local Area Network) or the Internet, or may be a communication network that partially uses a general public line such as a VPN (Virtual Private Network). The risk impact assessment system 100 may be connected to a terminal 110 or the like via the network 120.

[0019] The input device 105 is a device such as a keyboard or a mouse that receives input from a user, and is used to input information necessary for processing by the CPU 101. Note that if the information necessary for processing by the CPU 101 can be obtained via the network 120, the risk impact assessment system 100 does not need to have the input device 105.

[0020] The risk impact assessment system 100 is a computer system configured on one physical computer or on multiple logically or physically configured computers, and may operate in separate threads on the same computer, or on a virtual computer constructed on multiple physical computer resources.

[0021] The output device 106 is a device such as a display device or a printer that outputs the execution results of a program in a format that can be viewed by a user. Note that the input device 105 and the output device 106 may be integrated into one device, such as a touch panel. Note that if the terminal 110 can output information via the network 120, the risk impact assessment system 100 does not need to have the output device 106.

[0022] The terminal 110 is a computer such as a PC (Personal Computer), a server, a smartphone, or a tablet terminal. The terminal 110 may be connected to input devices such as a keyboard, a mouse, and a touch panel, as well as output devices such as a display device and a printer, and can output the results of program execution in a format that can be confirmed by the user and receive input from the user.

[0023] <Example of functional configuration of risk impact assessment system> 2 is a block diagram showing an example of the functional configuration of the risk impact assessment system 100. The risk impact assessment system 100 includes, for example, a correlation analysis unit 221, a disaster scenario acquisition unit 222, an operation downtime prediction unit 223, and a financial impact prediction unit 224, all of which are functional units included in the CPU 101.

[0024] The correlation analysis unit 221 analyzes the correlation between the occurrence of a disaster event and the occurrence of a damage event in order to identify damage events that may occur due to a disaster. The disaster scenario acquisition unit 222 acquires disaster scenario information that may occur in an area where a facility used by a company subject to risk assessment for business is located, and identifies damage events that may occur at the facility due to a disaster.

[0025] The operation outage duration prediction unit 223 predicts the operation outage duration of the facility and the business outage duration associated with the operation outage of the facility in the event of a disaster event indicated in the disaster scenario. The financial impact prediction unit 224 predicts the amount of sales loss and the amount of recovery costs of the risk assessment target company associated with the business outage in the event of a disaster event indicated in the disaster scenario.

[0026] For example, the CPU 101 functions as a correlation analysis unit 221 by operating in accordance with a correlation analysis program loaded into the memory 102, and functions as a disaster scenario acquisition unit 222 by operating in accordance with a disaster scenario acquisition program loaded into the memory 102. The same relationship between programs and functional units applies to the other functional units included in the risk impact assessment system 100.

[0027] Note that some or all of the functions of the functional units included in the risk impact assessment system 100 may be realized by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0028] The risk impact assessment system 100 holds, for example, disaster history information 211, damage history information 212, facility information 213, disaster scenario information 214, company information 215, facility usage information 220, risk countermeasure information 216, risk countermeasure implementation information 217, product information 218, shipping information 219, correlation information 231, recovery-related prediction model information 232, recovery-related information 233, operation downtime period information 234, business downtime period information 235, and financial impact prediction information 236.

[0029] The company information 215 holds, for example, a company name, which is an example of identification information for a company subject to risk assessment, and a business name, which is an example of identification information for a business run by the company subject to risk assessment. Note that a single company subject to risk assessment may run a single business or multiple businesses. Details of information other than the company information 215 will be described later.

[0030] All of the information used by the risk impact assessment system 100 is stored, for example, in the auxiliary storage device 103. Also, some or all of the information used by the risk impact assessment system 100 may be stored in the memory 102, or may be stored in an external database connected to the risk impact assessment system 100.

[0031] In this embodiment, the information used by the risk impact assessment system 100 does not depend on the data structure and may be expressed in any data structure. In this embodiment, the information is expressed in a table format, but the information can be stored in a data structure appropriately selected from, for example, a list, a database, or a queue.

[0032] <Example of risk impact assessment procedure> FIG. 3 is a flowchart showing an example of a risk impact assessment process performed in the risk impact assessment system 100. As shown in FIG.

[0033] The correlation analysis unit 221 determines whether the current date and time is the timing to perform the correlation analysis (step S301). The timing to perform the correlation analysis is, for example, a timing for every predetermined period, such as a predetermined time on the first day of each month. However, the timing to perform the correlation analysis is not limited to this.

[0034] Furthermore, the timing for executing the correlation analysis may be predetermined, or the correlation analysis process may be started in accordance with a correlation analysis execution instruction input by the user via the input device 105 or the terminal 110 (in this case, the timing for executing the correlation analysis is the timing for receiving the correlation analysis execution instruction).

[0035] If the correlation analysis unit 221 determines that the current date and time is the timing to perform correlation analysis (step S301: YES), it starts the processing of step S302. If the correlation analysis unit 221 determines that the current date and time is not the timing to perform correlation analysis (step S301: NO), the disaster scenario acquisition unit 222 starts the processing of step S303.

[0036] The correlation analysis unit 221 executes a correlation analysis process (step S302). In order to identify damage events that may occur due to a disaster, the correlation analysis unit 221 analyzes the correlation between the occurrence of a disaster event and the occurrence of a damage event in the disaster analysis process, and records the analysis result in the correlation information 231.

[0037] The disaster scenario acquisition unit 222 determines whether the current date and time is the timing to execute the risk impact assessment (step S303). The timing to execute the risk impact assessment may be determined in advance, or the risk impact assessment process may be started in accordance with an instruction to execute the risk impact assessment input by a user of each company to be assessed via the input device 105 or the terminal 110 (in this case, the timing to execute the risk impact assessment is the timing when the instruction to execute the risk impact assessment is received).

[0038] In addition, in order for the processing from step S304 onwards to be executed, the correlation analysis processing in step S302 must have been executed at least once, or correlation information 231 must have been generated.In other words, if the disaster scenario acquisition unit 222 determines that the correlation analysis processing in step S302 has not been executed even once, or that there is no correlation information 231, it determines that the current date and time is not the right time to execute a risk impact assessment.

[0039] If the disaster scenario acquisition unit 222 determines that the current date and time is not the timing for executing the risk impact evaluation (step S303: NO), it ends the risk impact evaluation process.

[0040] When the disaster scenario acquisition unit 222 determines that the current date and time is the timing to perform a risk impact assessment (step S303: YES), it extracts one combination of the company name of the company to be assessed and the business name of the business to be assessed that is run by the company to be assessed from the company information 215, and then executes the processing from step S304 onwards. Note that the combination is specified by input from the user of each company to be assessed via, for example, the input device 105 or the terminal 110.

[0041] Here, if the company information 215 stores multiple combinations of the company name of the company being assessed for risk and the business name of the business conducted by the company being assessed for risk, the risk impact assessment system 100 may perform the processing from step S304 to step S306 for each of the multiple combinations.

[0042] The disaster scenario acquisition unit 222 executes a disaster scenario acquisition process (step S304). In the disaster scenario acquisition process, the disaster scenario acquisition unit 222 acquires disaster scenario information that may occur in the area where the facilities used by the company in the business that is the target of risk assessment are located. After the process of step S304 is completed, the disaster scenario acquisition unit 222 transmits the combination of the company name that is the target of risk assessment and the business name that is the target of assessment that was extracted in step S303 to the operation downtime period prediction unit 223.

[0043] The operation downtime prediction unit 223 executes an operation downtime prediction process (step S305). In the operation downtime prediction process, the operation downtime prediction unit 223 records a model for predicting the time and cost required for recovery from a damage event when a disaster occurs in the recovery-related prediction model information 232. Note that the recovery time and recovery cost required for recovery from a damage event are both examples of resources spent on recovery from a damage event; in other words, the model can be used to predict the amount of resources. In addition, in the operation downtime prediction process, the operation downtime prediction unit 223 records prediction results, such as recovery time and recovery cost in a disaster scenario, facility operation downtime period, and business downtime period, based on the prediction model in the recovery-related information 233, operation downtime period information 234, and business downtime period information 235. Note that in this embodiment, an example is described in which both recovery time and recovery cost are predicted; however, only one of the recovery time and recovery cost may be predicted. In this case, only a model for predicting that one of the above-mentioned prediction models is generated.

[0044] After the process of step S305 is completed, the operation downtime prediction unit 223 transmits the combination of the company name and business name of the risk assessment target received in step S304 to the financial impact prediction unit 224.

[0045] The financial impact prediction unit 224 executes the financial impact prediction process (step S306) and ends the risk impact assessment process. In the financial impact prediction process, the financial impact prediction unit 224 predicts the amount of sales loss and recovery costs of the company that will be incurred due to the business suspension of the risk assessment target in the event of a disaster scenario occurring, and records the prediction results in the financial impact prediction information 236.

[0046] The correlation analysis process in step S302, the disaster scenario acquisition process in step S304, the operation downtime period prediction process in step S305, and the financial impact prediction process in step S306 will be described in detail later.

[0047] <Data configuration example of Disaster Record Information 211> 4 is a diagram showing an example of the data configuration of the disaster history information 211. The disaster history information 211 is information used in the correlation analysis process in step S302, and is information in which the history of disasters that have occurred in the past is recorded.

[0048] The disaster record information 211 has data items such as a disaster occurrence date 2111, which is the date on which the disaster occurred, a disaster event 2112, which is the name of the disaster event that occurred (an example of identification information), a disaster magnitude 2113, which indicates an index indicating the magnitude of the disaster that occurred and the magnitude of the disaster, and a disaster-occurred area 2114, which indicates the area where the disaster occurred. Note that the disaster-occurred area 2114 may be indicated by an address, or by latitude and longitude, etc.

[0049] For example, if the disaster event 2112 is heavy rain, an index that measures the magnitude of the heavy rain, such as the amount of rainfall per day, is an example of an index of the disaster magnitude 2113, and the actual amount of rainfall is an example of the magnitude of the disaster magnitude 2113. Note that the type of index and the type of magnitude of the disaster magnitude 2113 may be different or the same depending on the type of disaster event 2112.

[0050] <Data configuration example of Damage Record Information 212> 5 is a diagram showing an example of the data configuration of the damage record information 212. The damage record information 212 is information used in the correlation analysis process in step S302, and is information in which the damage record that occurred in the facility in the past is recorded.

[0051] The damage record information 212 has data items such as a damage occurrence date 2121 which is the date when the damage occurred, a damage event 2122 which is the name of the damage event that occurred (hereinafter, a name is an example of identification information), a facility name 2123 which is the name of the facility where the damage occurred, location information 2124 which indicates the location of the facility where the damage occurred, a recovery time record value 2125 which indicates the length of time it took to recover from the damage event at the facility, and a recovery cost record value 2126 which indicates the cost it took to recover from the damage event at the facility. Note that the location information 2124 may be indicated by an address, or by latitude, longitude, etc.

[0052] In principle, information on damage events that affect business continuity and require restoration work to resume use of facilities is recorded in the damage record information 212. In the case of farmland, which is an example of a facility, if a damage event occurs such as flooding of farmland or collapse of levees, farmers will not be able to continue farming unless restoration from the damage event is carried out.

[0053] Furthermore, in the case of roads, which are an example of facilities, if a landslide causes sediment to accumulate or the road collapses, the road will become unusable unless recovery from the damage event is carried out. In addition, information on the breakdown or damage of facilities necessary for business continuity may also be recorded. When such a damage event occurs, the record information of these damage events is recorded in the damage record information 212.

[0054] <Example of facility information 213 data configuration> 6 is a diagram showing an example of the data configuration of the facility information 213. The facility information 213 is information used in the correlation analysis process in step S302, and is information in which location information of each facility and the owner of the facility are recorded.

[0055] The facility information 213 includes data items such as the facility name 2123, a facility type code 2131 indicating the type of facility, location information 2124 indicating the location of the facility, land risk information 2132 indicating the disaster risk on the facility's land, and owner 2133 indicating the name of the person who owns the facility.

[0056] The land risk information 2132 indicates, for example, data on flood risk and landslide risk on the land of the facility. Specifically, for example, the land risk information 2132 includes information on the flood inundation depth, which is the estimated depth of inundation when a river overflows due to heavy rain, the inland water inundation depth, which is the estimated depth of inundation when heavy rain exceeds the drainage capacity of sewers and the like, and landslide risk, which indicates the risk of landslides. However, examples of land risks are not limited to these. Furthermore, information on hazard maps may be recorded in the land risk information 2132. The facility type code 2131 and the land risk information 2132 are both examples of attribute information of a facility.

[0057] <Data configuration example of correlation information 231> 7 is a diagram showing an example of the data configuration of the correlation information 231. The correlation information 231 is information output by the correlation analysis process in step S302, and is information in which damage events that are likely to occur due to a disaster are recorded.

[0058] The correlation information 231 has data items such as a disaster event 2112, which is the name of the disaster event; a damage event 2122, which is the name of a damage event that may occur due to a disaster; a facility type code 2131 of a facility where the damage event 2122 may occur; land risk information 2132 for the land of the facility where the damage event 2122 may occur; and a recording date 2311, which indicates the date on which the information of the record was recorded in the correlation analysis process of step S302.

[0059] The top record in the example of correlation information 231 in Figure 7 indicates that if "heavy rain" occurs as a disaster, there is a high possibility that "flooding of farmland" will occur as a damage event caused by the "heavy rain" for the facility "farmland" with land risks of flood inundation depth of "less than 5m-10m," inland water inundation depth of "less than 5m-10m," landslide risk of "none," etc.

[0060] <Example of correlation analysis processing procedure> 8 is a flowchart showing an example of the correlation analysis process in step S302. It is assumed that the disaster record information 211, damage record information 212, and facility information 213 have already been set before the correlation analysis process starts.

[0061] The correlation analysis unit 221 repeatedly executes the processes from step S801 to step S810 until it selects all records of the disaster history information 211. The correlation analysis unit 221 selects one unselected record from the disaster history information 211 (step S801).

[0062] Note that, among the records of the disaster history information 211, only records whose disaster occurrence date 2111 falls within a predetermined period may be used in the correlation analysis process. For example, if the predetermined range is a period going back a predetermined time from the current date and time, the correlation analysis unit 221 can record the correlation analysis result calculated based on the latest disaster history in the correlation information 231.

[0063] Furthermore, only records in which the magnitude of the index of the disaster magnitude 2113 is equal to or greater than a predetermined threshold may be used in the correlation analysis process among the records of the disaster history information 211. This enables the correlation analysis unit 221 to identify damage events associated with disaster events that cause great damage.

[0064] The correlation analysis unit 221 identifies the disaster-stricken area 2114 of the record being selected in step S801, and extracts all records from the damage record information 212 whose location information 2124 is included in the identified disaster-stricken area 2114 (step S802).

[0065] The correlation analysis unit 221 extracts from the facility information 213 all records that include the facility name 2123 indicated by the record extracted in step S802, identifies all combinations of values ​​of the facility type code 2131 and land risk information 2132 indicated by the extracted records, and repeatedly executes the processes from step S803 to step S809 until all of the combinations are selected (because the facility type code 2131 and land risk information 2132 are both examples of facility attribute information, each time the process of step S803 is executed, a new combination of facility attribute information is fixed, and then the processes of step S803 to step S809 are executed). The correlation analysis unit 221 selects one unselected combination of the facility type code 2131 and land risk information 2132 (step S803).

[0066] For example, suppose that in step S802 the correlation analysis unit 221 extracts from the damage record information 212 records that include "farmland A" in the facility name 2123 and records that include "irrigation pond X." In this case, the correlation analysis unit 221 identifies, from the facility information 213 in Fig. 6, (facility type code, flood inundation depth, inland water inundation depth, landslide risk, ) = (farmland, less than 5m-10m, less than 5m-10m, none, ) as a combination of values ​​of the facility type code 2131 and land risk information 2132 corresponding to the record that includes "farmland A" in the facility name 2123, and identifies (facility type code, flood inundation depth, inland water inundation depth, landslide risk, ) = (irrigation pond, less than 5m-10m, less than 5m-10m, none, ) as the combination corresponding to the record that includes "irrigation pond X" in the facility name 2123.

[0067] In addition, since there may be no difference in the damage events that can occur at a facility depending on the facility's land risk information 2132, the processing from step S803 to step S809 may be repeated until all facility type codes 2131 corresponding to the facility names 2123 indicated by the records extracted in step S802 in the facility information 213 are selected.

[0068] The correlation analysis unit 221 extracts from the facility information 213 records whose location information 2124 is included in the disaster occurrence area 2114 of the records extracted in step S801 and whose combination of values ​​of facility type code 2131 and land risk information 2132 is the same as the combination of values ​​selected in the most recent step S803, and calculates a first facility count, which is the number of facility names 2123 included in the extracted records (step S804). In other words, the first facility count indicates the number of facilities located within a certain disaster occurrence area whose facility type and land risk values ​​are the same as the values ​​selected in step S803.

[0069] The correlation analysis unit 221 identifies the damage event 2122 that corresponds to the facility name 2123 corresponding to the combination of the facility type code 2131 and land risk information 2132 values ​​selected in the most recent step S803, in the record extracted in step S802, and repeatedly executes the processes from step S805 to step S808 until it has selected all of the identified damage events 2122. The correlation analysis unit 221 selects one unselected damage event from the identified damage events 2122 (step S805).

[0070] The correlation analysis unit 221 searches for records including the damage event 2122 selected in the most recent step S805 from among the records extracted in step S802 that include the facility name 2123 corresponding to the combination of the facility type code 2131 and land risk information 2132 values ​​selected in the most recent step S803. Furthermore, the correlation analysis unit 221 extracts from the search results records whose damage occurrence date 2121 falls within a predetermined period from the disaster occurrence date 2111 of the record selected in the most recent step S801, and calculates a second number of facilities, which is the number of facility names 2123 included in the extracted records (step S806). In other words, the second number of facilities indicates the number of facilities located in a certain disaster-prone area that have the same facility type and land risk values ​​as the values ​​selected in step S803 and are presumed to have occurred as a result of the disaster.

[0071] The predetermined period in step S806 is, for example, but not limited to, several days or one week. The predetermined period may be set in advance, or may be specified by the user via the input device 105 or the terminal 110 in the correlation analysis process.

[0072] The correlation analysis unit 221 may limit the facilities to be searched in steps S804 and S806 to facilities for which risk countermeasures have not been sufficiently implemented. Specifically, for example, the correlation analysis unit 221 refers to the risk countermeasure implementation information 217 (specific examples of the risk countermeasure implementation information 217 will be described later), and determines that facilities for which the proportion of risk countermeasures implemented at the target facility by the disaster occurrence date 2111 is equal to or less than a predetermined value, among all risk countermeasures corresponding to the facility type and the damage event, are facilities for which risk countermeasures have not been sufficiently implemented. This enables the correlation analysis unit 221 to record damage events that may occur in a disaster when risk countermeasures are not sufficiently implemented.

[0073] The correlation analysis unit 221 calculates a first ratio, which is the ratio of the number of second facilities calculated in the most recent step S806 to the number of first facilities calculated in the most recent step S804 (step S807). In step S807, the correlation analysis unit 221 calculates, as the first ratio, the ratio of facilities in which the target damage event 2122 occurred, among facilities that are located in the disaster occurrence area 2114 extracted in step S801 and have the same conditions related to the facility type code 2131 and the land risk information 2132. As described above, the first ratio is calculated for a combination of (the record of) the disaster record, the facility type code, the value of the land risk information, and the damage event.

[0074] The correlation analysis unit 221 returns to step S805 if there are any unselected damage events 2122 among the damage events 2122 to be selected in step S805, and proceeds to step S809 if all damage events 2122 have been selected (step S808). The correlation analysis unit 221 returns to step S803 if there are any unselected combinations among the combinations of values ​​of the facility type code 2131 and the land risk information 2132 to be selected in step S803, and proceeds to step S810 if all combinations have been selected (step S809). The correlation analysis unit 221 returns to step S801 if there are any unselected records in the disaster history information 211, and proceeds to step S811 if all records in the disaster history information 211 have been selected (step S810).

[0075] The correlation analysis unit 221 identifies all disaster events 2112 included in the disaster record information 211, and repeatedly executes the processes from step S811 to step S815 until it selects all combinations of the disaster event 2112, the damage event 2122 that is the selection target in step S805, and (combinations of values ​​of) the facility type code 2131 and land risk information 2132 that are the selection targets in step S803. The correlation analysis unit 221 selects one unselected combination from the above combinations (step S811).

[0076] The correlation analysis unit 221 identifies the disaster history record in the disaster history information 211 that corresponds to the disaster event 2112 indicated by the combination selected in the most recent step S811, and calculates the average value of the first ratio corresponding to the combination of the identified disaster history record, the facility type code indicated by the combination selected in the most recent step S811, the value of the land risk information, and the damage event (step S812).

[0077] The correlation analysis unit 221 determines whether the average value calculated in the most recent step S812 is equal to or greater than a predetermined threshold (step S813). If the correlation analysis unit 221 determines that the average value is equal to or greater than the threshold (step S813: YES), the process proceeds to step S814. If the correlation analysis unit 221 determines that the average value is less than the threshold (step S813: NO), the process proceeds to step S815 and subsequent steps. The threshold value is, for example, 0.6 or 0.7. The threshold value may be set in advance, or may be specified by the user via the input device 105 or the terminal 110 during the correlation analysis process.

[0078] The correlation analysis unit 221 records the combination of the disaster event 2112, facility type code 2131, land risk information 2132 (value), and damage event 2122 selected in the most recent step S811 in the correlation information 231, and records the date on which the processing of step S814 was executed as the recording date 2311 of the correlation information 231 (step S814). That is, a combination with a high occurrence rate of a damage event is recorded in the correlation information 231. Furthermore, the correlation analysis unit 221 may record the value calculated in step S812 in the correlation information 231 as the occurrence rate of a damage event.

[0079] If there are any combinations that have not been selected among the combinations to be selected in step S811, the correlation analysis unit 221 returns to step S811, and if all combinations have been selected, the correlation analysis process ends (step S815).

[0080] <Data structure example of disaster scenario information 214> 9 is a diagram showing an example of the data configuration of the disaster scenario information 214. The disaster scenario information 214 is information used in the disaster scenario acquisition process in step S304, and records disaster scenarios. The disaster scenario includes, for example, disaster events that may occur in each area in the future, the period during which the disaster will occur, and the scale of the disaster.

[0081] The disaster scenario information 214 includes data items such as a disaster scenario code 2140, which is an example of identification information for the disaster scenario; a predicted disaster occurrence period 2141, which indicates the start and end dates of the period in which a disaster is predicted to occur in the disaster scenario; a disaster event 2112, which is predicted to occur in the disaster scenario; a disaster magnitude 2113, which indicates an index measuring the magnitude of the disaster predicted to occur in the disaster scenario and the magnitude of the disaster; and a disaster occurrence area 2114, which indicates the area in which a disaster is predicted to occur in the disaster scenario.

[0082] Here, the risk impact assessment system 100 may acquire disaster prediction information from an external device or via input from the input device 105 or the terminal 110, and create the disaster scenario information 214 based on the acquired prediction information, or may generate the disaster scenario information 214 by predicting future disasters and the duration of occurrence using a technique such as machine learning using the past disaster record information 211. Note that the method of generating the disaster scenario information 214 is not limited to this.

[0083] <Examples of facilities necessary for a company to continue its business> Figures 10A and 10B are explanatory diagrams showing an example of facilities that a farmer would need to continue his farming business, assuming that the farmer runs a business growing crops on farmland A1003.

[0084] Fig. 10A is an explanatory diagram showing an example of a water transportation route when water is supplied to farmland. In the example of Fig. 10A, in order for water to be supplied to farmland A1003, the water must pass through reservoir A1001 and irrigation canal A1002 in that order. In this case, if at least one of reservoir A1001, irrigation canal A1002, and farmland A1003 stops operating, it will be difficult for the farmer to continue his business.

[0085] 10B is an explanatory diagram showing an example of a transportation route for seedlings when they are supplied to farmland. In the example of FIG. 10B, in order for seedlings to be supplied to farmland A1003, the seedlings must pass through supplier A1004, public road A1005, farm road bridge 1006, and farm road A1007 in that order. In this case, if at least one of supplier A1004, public road A1005, farm road bridge 1006, farm road A1007, and farmland A1003 stops operating, it will be difficult for the farmer to continue his business unless he himself has a sufficient stock of seedlings.

[0086] In addition, if there are alternative transportation routes, it will be difficult for farmers to continue their business if at least one facility on each of those transportation routes ceases operation.

[0087] <Example of data configuration for facility usage information 220> 11 is a diagram showing an example of the data configuration of the facility usage information 220. The facility usage information 220 is information used in the disaster scenario acquisition process in step S304, and is information in which the names of facilities where each company produces products in its business and the names of facilities used to continue its business are recorded.

[0088] Facility usage information 220 includes data items such as business name 2201, which is the name of the business run by the company, company name 2202, which is the name of the company running the business, production facility name 2203, which is the name of the facility where products are produced in the business, and facility name used 2204, which is the name of the facility used to continue the business.

[0089] The first to third records in the example of facility usage information 220 in Figure 11 indicate that "Farmer A" produces products on "Farmland A" as part of "Farming operations on Farmland A," and that the facilities used in "Farming operations on Farmland A" include "Farmland A," "Reservoir X," and "Farm road P."

[0090] The facility usage information 220 may further include data items such as a flag indicating whether each facility (each production facility and usage facility) is essential for business continuity, and the names of products sold in each business.

[0091] <Example of disaster scenario acquisition procedure> 12 is a flowchart showing an example of the disaster scenario acquisition process in step S304. It is assumed that the disaster scenario information 214 and facility usage information 220 have already been set before the correlation analysis process starts.

[0092] The disaster scenario acquisition unit 222 searches the facility usage information 220 for records containing the business name 2201 indicated by the combination extracted in step S303 (the company name of the company being assessed at risk and the business name of the business being assessed conducted by the company being assessed at risk), and extracts all facility usage names 2204 of the records included in the search results (step S1201).

[0093] In this embodiment, it is assumed that the business name 2201 is unique among companies, but if different companies carry out businesses with the same business name 2201, the disaster scenario acquisition unit 222 searches for a record that includes the business name 2201 and company name 2202 indicated by the combination extracted in step S303.

[0094] The disaster scenario acquisition unit 222 repeatedly executes the processes from step S1202 to step S1208 until it has selected all of the facility names 2204 extracted in step S1201. The disaster scenario acquisition unit 222 selects one facility name 2204 that has not been selected from the facility names 2204 extracted in step S1201 (step S1202).

[0095] The disaster scenario acquisition unit 222 extracts from the facility information 213 all location information 2124 linked to facility names 2123 that have the same name as the facility name 2204 selected in the most recent step S1202, and determines whether at least one of the locations indicated by the extracted location information 2124 is included in at least one of the disaster occurrence areas 2114 indicated by the disaster scenario information 214 (step S1203).

[0096] If the disaster scenario acquisition unit 222 determines that at least one of the locations indicated by the extracted location information 2124 is included in at least one of the disaster-occurring areas 2114 indicated by the disaster scenario information 214 (step S1203: YES), it extracts the facility type code 2131 and land risk information 2132 linked in the facility information 213 to the facility name 2123 whose location is included in the disaster-occurring area 2114, and then proceeds to processing of step S1204.

[0097] If the disaster scenario acquisition unit 222 determines that none of the locations indicated by the extracted location information 2124 are included in any of the disaster occurrence areas 2114 indicated by the disaster scenario information 214 (step S1203: NO), it proceeds to the processing of step S1208.

[0098] The disaster scenario acquisition unit 222 identifies, from the disaster scenario information 214, disaster scenario codes 2140 linked to the disaster-stricken area 2114 that included any of the locations indicated by the location information 2124 in step S1203, and repeatedly executes the processes from step S1204 to step S1207 until it has selected all of the identified disaster scenario codes 2140. The disaster scenario acquisition unit 222 selects one unselected disaster scenario code 2140 from the identified disaster scenario code 2140 (step S1204).

[0099] The disaster scenario acquisition unit 222 extracts from the disaster scenario information 214 the disaster event 2112 linked to the disaster scenario code 2140 selected in the most recent step S1204, and extracts from the correlation information 231 the damage event 2122 that may occur due to the extracted disaster event 2112 for the facility name 2204 selected in the most recent step S1202 (step S1205). Specifically, the disaster scenario acquisition unit 222 extracts from the correlation information 231 the damage event 2122 linked to the facility type code 2131 and land risk information 2132 extracted in the most recent step S1203 and the disaster event 2112 selected in the most recent step S1204.

[0100] The disaster scenario acquisition unit 222 transmits to the operation downtime prediction unit 223 a combination of the facility name 2123 (the same as the facility name 2204 used) selected in the most recent step S1202, the disaster scenario code 2140 selected in the most recent step S1204, and the damage event 2122 extracted in the most recent step S1205 (step S1206). Note that after the processing of step S1208, the disaster scenario acquisition unit 222 may transmit all of the combinations obtained by the loop from step S1202 to step S1207 to the operation downtime prediction unit 223.

[0101] If there are any disaster scenario codes 2140 that are to be selected in step S1204 that have not been selected, the disaster scenario acquisition unit 222 returns to step S1204, and if all disaster scenario codes 2140 have been selected, it proceeds to processing of step S1208 (step S1207).

[0102] If there are any unselected facility names 2204 to be selected in step S1202, the disaster scenario acquisition unit 222 returns to step S1202, and if all facility names 2204 to be selected have been selected, the disaster scenario acquisition unit 222 terminates the disaster scenario acquisition process (step S1208).

[0103] <Data configuration example of risk countermeasure information 216> 13 is a diagram showing an example of the data configuration of the risk countermeasure information 216. The risk countermeasure information 216 is information used in the operation downtime prediction process in step S305, and is information that records a list of risk countermeasures that can be implemented to mitigate each damage event.

[0104] The risk countermeasure information 216 has data items such as a risk countermeasure code 2161, which is identification information for the risk countermeasure, a risk countermeasure content 2162, which indicates the specific content of the risk countermeasure, a base type code 2163, which indicates the type of base at which the risk countermeasure is implemented, and a damage event 2122, which is the name of a damage event that can reduce damage to the base by risk countermeasures.

[0105] For example, as shown in Figure 13, risk measures to reduce the risk of "flooding of farmland," which is a damage event to "farmland," a facility, can be implemented for the "farmland" itself, which is a facility that the risk assessment target company may have for conducting its business, and can also be implemented for locations other than the facilities where the risk assessment target company conducts its business, such as "irrigation channels" and "rivers." In other words, the concept of a base includes not only facilities but also locations other than facilities.

[0106] Furthermore, if one risk countermeasure can be implemented at multiple types of bases, multiple base type codes 2163 may be linked to one risk countermeasure code 2161 in the risk countermeasure information 216. Furthermore, if one risk countermeasure can reduce damage caused by multiple damage events, multiple damage events 2122 may be linked to one risk countermeasure code 2161 in the risk countermeasure information 216.

[0107] Furthermore, if the degree to which damage can be mitigated by risk countermeasures can be quantified, a data item called "damage mitigation level" can be added. This makes it possible to calculate the recovery time and recovery costs of a damage event according to the damage mitigation level of the risk countermeasures implemented at the site.

[0108] <Data configuration example of risk countermeasure implementation information 217> 14 is a diagram showing an example of the data configuration of the risk countermeasure implementation information 217. The risk countermeasure implementation information 217 is information used in the operation downtime prediction process in step S305, and is information that records a list of risk countermeasures that are being implemented at each base or are assumed to be being implemented at each base when a risk impact assessment is performed.

[0109] The risk countermeasure implementation information 217 has data items such as the base name 2171 of the target base where risk countermeasures are being implemented, the base type code 2163 of the target base, location information 2124 indicating the location of the target base, the risk countermeasure code 2161 of the risk countermeasure being implemented or assumed to be implemented at the target base, the damage event 2122 of the damage event where damage to the target base can be mitigated by the risk countermeasure being implemented or assumed to be implemented, and the disaster area 2172 indicating the area where a damage event may occur when the base is affected by a disaster and where the damage may be mitigated by implementing risk countermeasures at the base.

[0110] For example, when a flood occurs at a base called "River A" due to "heavy rain," the area where a damage event called "flooding of farmland" may occur (the area where the damage event will be mitigated if the risk countermeasure indicated by the risk countermeasure code 2161 is taken) is recorded in the disaster area 2172 of the risk countermeasure implementation information 217. The location information 2124 of the risk countermeasure implementation information 217 may be indicated by an address, or by latitude and longitude, etc.

[0111] <Data configuration example of recovery-related prediction model information 232> 15 is a diagram showing an example of the data configuration of the recovery-related prediction model information 232. The recovery-related prediction model information 232 is information that is output by and used in the operation downtime prediction process in step S305, and is information in which a prediction model of recovery time and recovery cost for a damage event is recorded.

[0112] The recovery-related prediction model information 232 has data items such as a disaster event 2112, a facility type code 2131, a damage event 2122, a recovery time prediction model 2321, and a recovery cost prediction model 2322. In other words, the operation downtime prediction unit 223 refers to the recovery-related prediction model information 232 and formulates a recovery time prediction model 2321 and a recovery cost prediction model 2322 for each combination of the disaster event 2112, the damage event 2122, and the facility type code 2131.

[0113] In addition, the operation downtime prediction unit 223 may formulate a recovery time prediction model 2321 and a recovery cost prediction model 2322 for each combination of disaster event 2112, damage event 2122, facility type code 2131, and facility land risk information 2132, in which case the recovery-related prediction model information 232 further has data items for land risk information 2132.

[0114] For example, the operation downtime prediction unit 223 may formulate a recovery time prediction model 2321 and a recovery cost prediction model 2322 for each combination of any two data items from the disaster event 2112, the damage event 2122, and the facility type code 2131. The operation downtime prediction unit 223 may also add other data items and formulate a recovery time prediction model 2321 and a recovery cost prediction model 2322 for each combination.

[0115] <Example of data configuration for recovery-related information 233> 16 is a diagram showing an example of the data configuration of the recovery-related information 233. The recovery-related information 233 is information output by the operation downtime prediction process in step S305, and is information in which predicted results of recovery time and recovery costs for each facility when each damage event occurs in the disaster scenario are recorded.

[0116] The recovery-related information 233 includes data items such as a disaster scenario code 2140, a disaster event 2112 assumed in the disaster scenario, a facility name 2123 of a facility where a disaster event may occur, a damage event 2122 that may occur at the facility due to a disaster, a recovery time prediction value 2331 indicating the predicted number of days for the facility to recover from the damage event (the number of days required for recovery work to be performed on the facility affected by the damage event), and a recovery cost prediction value 2332 indicating the predicted cost for the facility to recover from the damage event (the cost required for recovery work to be performed on the facility affected by the damage event). Note that the recovery time prediction value 2331 may record a predicted value indicating the recovery start date and recovery completion date instead of the number of days or months.

[0117] <Data configuration example of operation downtime information 234> 17 is a diagram showing an example of the data configuration of operation downtime period information 234. Operation downtime period information 234 is information output by the operation downtime period prediction process in step S305, and is information in which the predicted results of operation downtime periods for each facility in the event of a disaster event indicated by a disaster scenario occurring are recorded.

[0118] The outage period information 234 includes data items such as a disaster scenario code 2140 of the target disaster scenario, a disaster event 2112 assumed in the disaster scenario, a facility name 2123 of a facility where the disaster event may occur, and an outage period forecast value 2341 (e.g., start date and end date) indicating the period during which the facility will be out of operation due to the occurrence of the disaster event.

[0119] <Data configuration example of business suspension period information 235> 18 is a diagram showing an example of the data configuration of the business suspension period information 235. The business suspension period information 235 is information output by the operation suspension period prediction process in step S305, and is information in which the predicted results of the business suspension period for each company in the event of a disaster event indicated in the disaster scenario occurring are recorded.

[0120] The business suspension period information 235 includes data items such as a disaster scenario code 2140 of the target disaster scenario, a disaster event 2112 assumed in the disaster scenario, a company name 2202 of the company operating the business that is subject to the business suspension period, a business name 2201 of the business that is subject to the business suspension period, and a predicted business suspension period value 2352 indicating the period during which the target business will be suspended due to the occurrence of the disaster event.

[0121] <Example of operation downtime prediction procedure> 19A and 19B are flowcharts showing an example of the operation downtime prediction process in step S305. The process of FIG. 19B is executed following the process of FIG. 19A. In other words, the processes of FIG. 19A and FIG. 19B are a series of processes. It is assumed that the risk countermeasure information 216 and the risk countermeasure implementation information 217 have already been set before the operation downtime prediction process starts.

[0122] The operation downtime prediction unit 223 repeatedly executes the processes from step S1901 to step S1905 until it selects all records in the damage record information 212. The operation downtime prediction unit 223 selects one unselected record from the damage record information 212 (step S1901).

[0123] Note that the records to be selected in the damage record information 212 in step S1901 may be only records whose damage occurrence date 2121 is within a predetermined time period (for example, within the last few years) from the current date and time. This allows the operation downtime period prediction unit 223 to formulate a prediction model for recovery time and recovery cost based on the latest damage record.

[0124] The operation downtime prediction unit 223 extracts the damage occurrence date 2121, the damage event 2122, the facility name 2123, the location information 2124, the actual restoration time value 2125, and the actual restoration cost value 2126 from the record selected in the most recent step S1901 (step S1902).

[0125] The operation downtime prediction unit 223 extracts the magnitude of the disaster event that caused the actual damage (step S1903). Specifically, as the process of step S1903, the operation downtime prediction unit 223 executes the following process.

[0126] The operation downtime prediction unit 223 extracts a facility type code 2131 and land risk information 2132 linked to the facility name 2123 extracted in step S1902 from the facility information 213. The operation downtime prediction unit 223 extracts a disaster event 2112 linked to the combination of the extracted facility type code 2131, the extracted land risk information 2132, and the damage event 2122 extracted in step S1902 from the correlation information 231.

[0127] The operation downtime prediction unit 223 extracts from the disaster history information 211 records that have the extracted disaster event 2112, that have a disaster occurrence date 2111 that is a predetermined number of days before the damage occurrence date 2121 extracted in step S1902 (for example, any one of 0 days before and 3 days before), and that include the location indicated by the location information 2124 extracted in step S1902 in the disaster occurrence area 2114, and extracts the disaster magnitude 2113 from the extracted records. Here, when multiple records with the same disaster event 2112 are extracted from the disaster history information 2111, the disaster magnitude 2113 with the largest value among the extracted records may be extracted.

[0128] The operation suspension period prediction unit 223 calculates an index indicating the risk countermeasure implementation status for the base related to the damage record (step S1904). The risk countermeasure implementation status affects the extent of damage to the facility. Below, an example of calculating a risk countermeasure implementation rate will be described as an example of an index indicating the risk countermeasure implementation status in step S1904.

[0129] The operation suspension period prediction unit 223 extracts from the risk countermeasure implementation information 217 all records (bases) that indicate the damage event 2122 extracted in step S1902 and whose location indicated by the location information 2124 extracted in step S1902 is included in the disaster area 2172, and calculates the number of risk countermeasure codes 2161 being implemented for each base indicated by the extracted records (i.e., the number of types of risk countermeasures being implemented or assumed to be implemented at the base in order to reduce damage to facilities located in the disaster area due to the extracted damage event 2122 (hereinafter also referred to as the first number of types corresponding to the base)).

[0130] Furthermore, the operation suspension period prediction unit 223 refers to the risk countermeasure information 216, identifies the base type code 2163 corresponding to each of the bases indicated by the extracted record, and calculates the number of risk countermeasure codes 2161 linked to each of the identified base type codes 2163 (i.e., for each type of base, the number of types of risk countermeasures that can be implemented at the base in order to reduce damage to facilities located within the disaster area due to the extracted damage event 2122 (hereinafter also referred to as the second number of types corresponding to the base)).

[0131] For each extracted base, the operation downtime prediction unit 223 calculates the risk countermeasure implementation rate by dividing the number of first types corresponding to that base by the number of second types corresponding to that base. Furthermore, the operation downtime prediction unit 223 calculates the average value of the calculated risk countermeasure implementation rates for bases having the same base type code. Through the above processing, the operation downtime prediction unit 223 calculates the risk countermeasure implementation rate for each base type code in step S1904.

[0132] If there are any records of the damage record information 212 that are the selection targets in step S1901 that have not been selected, the operation downtime prediction unit 223 returns to the processing of step S1901, and if all records have been selected, proceeds to the processing of step S1906 (step S1905).

[0133] The operation downtime prediction unit 223 uses the data extracted in steps S1901 to S1905 to formulate a prediction model for recovery time and a prediction model for recovery cost for each combination of the disaster event 2112, the damage event 2122, and the facility type code 2131, and records the formulated prediction models in the recovery-related prediction model information 232 (step S1906). A specific example of the processing in step S1906 will be described below.

[0134] The operation downtime prediction unit 223 determines the data to be the learning data, which is the combination of the disaster magnitude 2113 extracted in step S1903, the risk countermeasure implementation rate for each base type code calculated in step S1904, and the actual recovery time value 2125 and actual recovery cost value 2126 extracted in step S1902, classified by combination of disaster event 2112, damage event 2122, and facility type code 2131.

[0135] In other words, when a combination of a disaster event 2112, a damage event 2122, and a facility type code 2131 is fixed, for example, the following data is included in the learning data: (1) The magnitude of the disaster 2113 extracted in step S1903 for the disaster event 2112 linked to the damage event 2122 indicated by the combination. (2) The risk countermeasure implementation rate for each base type code calculated in step S1904 corresponding to the damage event 2122 indicated by the combination. (3) The actual recovery time value 2125 and actual recovery cost value 2126 extracted in step S1902 corresponding to the damage event 2122 indicated by the combination and the facility with the facility type code 2131 indicated by the combination.

[0136] The operation downtime prediction unit 223 may determine as learning data data that is obtained by classifying a combination of the disaster magnitude 2113 extracted in step S1903, the risk countermeasure implementation rate for each base type code calculated in step S1904, and the actual recovery time value 2125 and actual recovery cost value 2126 extracted in step S1902 into combinations of the disaster event 2112, the damage event 2122, the facility type code 2131, and the land risk information 2132 extracted in step S1902.

[0137] This enables the operation suspension period prediction unit 223 to formulate a prediction model according to the land risk of the facility, and ultimately enables highly accurate prediction of recovery time and recovery cost even when the recovery time and recovery cost vary significantly depending on the land risk of the facility.

[0138] The downtime period prediction unit 223 uses a predetermined algorithm such as regression analysis or deep learning to generate a recovery time prediction model for each combination of disaster event 2112, damage event 2122, and facility type code 2131, with the scale of the disaster and the risk countermeasure implementation rate included as explanatory variables and recovery time as the objective variable.

[0139] Similarly, the operation downtime prediction unit 223 uses a predetermined algorithm such as regression analysis or deep learning for each combination of the disaster event 2112, the damage event 2122, and the facility type code 2131 to generate a recovery cost prediction model that includes the magnitude of the disaster for each disaster event and the risk countermeasure implementation rate for each base type code as explanatory variables and uses the recovery cost as a response variable.

[0140] The operation downtime prediction unit 223 records the combination of the disaster event 2112, the damage event 2122, and the facility type code 2131, as well as the recovery time prediction model 2321 and the recovery cost prediction model 2322 generated for the combination, in the recovery-related prediction model information 232. The operation downtime prediction unit 223 may generate a prediction model that includes, as explanatory variables, a binary variable indicating whether each disaster event 2112 has occurred, a binary variable indicating whether each damage event 2122 has occurred, and a binary variable indicating whether each facility type code 2131 corresponds to the type of the target facility; that is, it may generate a prediction model that is common to all combinations of the disaster event 2112, the damage event 2122, and the facility type code 2131.

[0141] For example, when a prediction model is generated using regression analysis, an example of a prediction model f1ABC of recovery time at a facility with disaster event A, damage event B, and facility type code C is shown in Equation 1 below, and an example of a prediction model f2ABC of recovery cost is shown in Equation 2 below. Note that βiABC below is a regression coefficient. Here, base type code D exists for each base type code of the risk countermeasure implementation rate determined as learning data in step S1906 when generating a prediction model for each disaster event 2112, damage event 2122, and facility type code 2131.

[0142] Recovery time = f1ABC(scale of disaster, risk countermeasure implementation rate for base type code D) = β0ABC + β1ABC × disaster magnitude + β2ABC × risk countermeasure implementation rate for base type code D (Equation 1)

[0143] Recovery cost = f2ABC(scale of disaster, implementation rate of risk measures for base type code D) = β3ABC + β4ABC × disaster magnitude + β5ABC × risk countermeasure implementation rate for base type code D (Equation 2)

[0144] The magnitude of the disaster and the risk countermeasure implementation rate (an example of the risk countermeasure implementation status) are both indicators that affect the degree of damage caused by a damage event. The example of the prediction model described above includes both the magnitude of the disaster and the risk countermeasure implementation rate, which are indicators that affect the degree of damage caused by a damage event, as explanatory variables, but it is also possible to include either the magnitude of the disaster or the risk countermeasure implementation rate as an explanatory variable.

[0145] Furthermore, the explanatory variables included in the prediction model are not limited to these. For example, instead of or in addition to the risk countermeasure implementation rate, i.e., the proportion of risk countermeasures implemented at bases, a true / false value indicating whether each risk countermeasure is being implemented or the proportion of bases that are implementing the countermeasure may be used as an explanatory variable.

[0146] Furthermore, the operation downtime prediction unit 223 does not have to perform the process of generating a prediction model from step S1901 to step S1906 every time a risk impact assessment process is performed, but may update the prediction model at a predetermined timing and record it in the recovery-related prediction model information 232, and refer to the prediction model at the timing when the risk impact assessment process is performed to predict the recovery time and recovery cost.

[0147] The operation downtime prediction unit 223 repeatedly executes the processes from step S1907 to step S1920 until it selects all disaster scenario codes 2140 included in the combination received in step S1206. The operation downtime prediction unit 223 selects one unselected disaster scenario code 2140 from the disaster scenario code 2140 included in the combination (step S1907).

[0148] The operation downtime prediction unit 223 repeatedly executes the processes from step S1908 to step S1917 until it selects all facility names 2123 linked in the combination received in step S1206 to the disaster scenario code 2140 selected in the most recent step S1907 (i.e., facilities where the disaster scenario indicated by the disaster scenario code 2140 selected in the most recent step S1907 may occur).The operation downtime prediction unit 223 references the combination received in step S1206 and selects one unselected facility name 2123 linked to the disaster scenario code 2140 selected in the most recent step S1907 (step S1908).

[0149] The downtime period prediction unit 223 repeatedly executes the processing from step S1909 to step S1914 until it selects all damage events 2122 linked to both the disaster scenario code 2140 selected in the most recent step S1907 and the facility name 2123 selected in the most recent step S1908 in the combination received in step S1206 (i.e., damage events that may occur at the facility selected in the most recent step S1908 based on the disaster scenario indicated by the disaster scenario code 2140 selected in the most recent step S1907).

[0150] The downtime period prediction unit 223 refers to the combination received in step S1206 and selects one unselected damage event 2122 linked to both the disaster scenario code 2140 selected in the most recent step S1907 and the facility name 2123 selected in the most recent step S1908 (step S1909).

[0151] The operation downtime prediction unit 223 extracts the disaster-occurred area 2114 linked to the disaster scenario code 2140 selected in the most recent step S1907 from the disaster scenario information 214. Furthermore, the operation downtime prediction unit 223 extracts from the risk countermeasure implementation information 217 all records that include the damage event 2122 selected in the most recent step S1909, whose location indicated by the location information 2124 is included in the extracted disaster-occurred area 2114, and whose location indicated by the location information 2124 of the facility selected in the most recent step S1908 is included in the disaster affected area 2172.

[0152] Hereinafter, the bases with the base name 2171 indicated by each record extracted from the risk countermeasure implementation information 217 are also referred to as related bases. A related base is a base that is located within the disaster occurrence area of ​​the disaster event indicated by the disaster scenario code (selected in the most recent step S1907), and is implementing risk countermeasures to mitigate the damage event (selected in the most recent step S1909) that occurs to the facility (selected in the most recent step S1908) due to the occurrence of the disaster event.

[0153] The operation downtime prediction unit 223 repeatedly executes the processes from step S1910 to step S1912 until it has selected all of the associated bases' base names 2171. The operation downtime prediction unit 223 selects one unselected associated base's base name 2171 (step S1910).

[0154] The operation downtime prediction unit 223 calculates the risk countermeasure implementation rate at the associated base selected in the most recent step S1910 (step S1911). Specifically, for example, the operation downtime prediction unit 223 calculates the number of types of risk countermeasure codes 2161 corresponding to the base name 2171 selected in the most recent step S1910 (the above-mentioned first number of types corresponding to the selected associated base) from the records extracted from the risk countermeasure implementation information 217 to identify the associated base in the above-mentioned processing.

[0155] Furthermore, the operation downtime prediction unit 223 calculates the number of types of risk countermeasure codes 2161 (the second number of types corresponding to the selected related base) linked to the damage event 2122 selected in the most recent step S1909 and the base type code 2163 corresponding to the base name 2171 selected in the most recent step S1910, from the risk countermeasure information 216. The operation downtime prediction unit 223 calculates the value obtained by dividing the first number of types corresponding to the selected related base by the second number of types corresponding to the selected related base as the risk countermeasure implementation rate at the selected related base.

[0156] If there are any unselected base names 2171 of the related bases that are the selection targets in step S1910, the operation downtime prediction unit 223 returns to the processing of step S1910, and if all of the related base names 2171 have been selected, it proceeds to the processing of step S1913 (step S1912).

[0157] The operation downtime prediction unit 223 refers to the recovery-related prediction model information 232, predicts the recovery time and recovery cost, and records the predictions in the recovery-related information 233 (step S1913). Specifically, the operation downtime prediction unit 223 executes the following process.

[0158] The operation downtime prediction unit 223 extracts the facility type code 2131 linked to the facility name 2123 selected in the most recent step S1908 from the facility information 213. The operation downtime prediction unit 223 extracts the disaster event 2112 and the disaster scale 2113 linked to the disaster scenario code 2140 selected in the most recent step S1907 from the disaster scenario information 214.

[0159] The downtime period prediction unit 223 extracts from the recovery-related prediction model information 232 a recovery time prediction model 2321 and a recovery cost prediction model 2322 that are linked to the extracted facility type code 2131, the extracted disaster event 2112, and the damage event 2122 selected in the most recent step S1908.

[0160] The operation downtime prediction unit 223 calculates the average value for the risk countermeasure implementation rates of the related bases calculated in step S1911 for each base type code. The operation downtime prediction unit 223 substitutes the extracted disaster magnitude 2113 and the calculated risk countermeasure implementation rate for each base type code into the extracted recovery time prediction model 2321 and recovery cost prediction model 2322, respectively. Here, if there is no risk countermeasure implementation rate for base type code D set as an explanatory variable for the recovery time prediction model 2321 and the recovery cost prediction model 2322, it may be assumed that there is no damage that will affect the base on the target facility, and the risk countermeasure implementation rate may be set to 1. However, this is not limited to this.

[0161] As a result, the operation downtime prediction unit 223 can calculate the predicted recovery time value 2331 and the predicted recovery cost value 2332 corresponding to the disaster scenario code 2140 selected in the most recent step S1907, the facility name 2123 selected in the most recent step S1908, and the damage event 2122 selected in the most recent step S1909, and records the combination of these values ​​in the recovery-related information 233.

[0162] If there are any unselected damage events 2122 among the damage events 2122 to be selected in step S1909, the operation downtime prediction unit 223 returns to the processing of step S1909, and if all damage events 2122 have been selected, proceeds to the processing of step S1915 (step S1914).

[0163] The operation downtime prediction unit 223 calculates the number of days required for recovery from all damage events that may occur at the facility selected in the most recent step S1908 (step S1915). Specifically, if it is predetermined that all recovery work can be carried out simultaneously, the operation downtime prediction unit 223 extracts the maximum value of the recovery time prediction values ​​2331 for each damage event calculated in step S1913 as the number of days required for recovery of the facility.

[0164] On the other hand, if it is predetermined that some recovery work cannot be performed simultaneously due to circumstances such as a limit on the number of recovery contractors, the operation outage period prediction unit 223 assigns recovery work for each damage event to the recovery contractors performing the recovery work, and creates a recovery plan so that recovery work for the next damage event begins as soon as the first recovery work is completed, and calculates the number of days until recovery work for all damage events at the facility is completed as the number of days for recovery of the facility. Note that constraints on the order of recovery work may be predetermined, and in this case, the operation outage period prediction unit 223 creates a recovery plan so that recovery work is performed in the order indicated by the constraints.

[0165] The operation downtime prediction unit 223 calculates the operation downtime of the facility selected in the most recent step S1908, and records the calculated downtime in the operation downtime information 234 (step S1916). Specifically, the operation downtime prediction unit 223 executes the following process.

[0166] The operation downtime prediction unit 223 extracts, from the disaster scenario information 214, the disaster event 2112 linked to the disaster scenario code 2140 selected in the most recent step S1907, and the start date and end date of the predicted disaster occurrence period 2141. It is determined that recovery work will begin after the end date of the disaster (for example, the day after the end date of the disaster), and the operation downtime prediction unit 223 calculates the end date of the operation downtime period for the facility by adding the number of days for recovery for the facility calculated in step S1915 to the end date of the extracted predicted disaster occurrence period 2141.

[0167] Furthermore, the operation downtime period prediction unit 223 links the disaster scenario code 2140 selected in the most recent step S1907, the extracted disaster event 2112, and the facility name 2123 selected in the most recent step S1908, and records the start date of the extracted predicted disaster occurrence period 2141 as the start date of the operation downtime period prediction value 2341 and the end date of the calculated operation downtime period as the end date of the operation downtime period prediction value 2341 in the operation downtime period information 234.

[0168] If there are any facility names 2123 that are the selection targets in step S1908 that have not been selected, the operation suspension period prediction unit 223 returns to the processing of step S1908, and if all facility names 2123 have been selected, it proceeds to the processing of step S1918 (step S1917).

[0169] The operation downtime prediction unit 223 calculates the business downtime period of the business being evaluated based on the operation downtime periods of the facilities (step S1918). For example, it is assumed that it is determined that it is difficult to continue the business while even one facility used in the business being evaluated of the risk assessment target company is out of operation. In this case, the operation downtime prediction unit 223 calculates the union of the operation downtime period prediction values ​​2341 of each facility recorded in step S1916 as the business downtime period prediction value 2352 of the business being evaluated of the risk assessment target company.

[0170] Here, if there are facilities that can be substituted for the facilities that have stopped operating, the operation downtime prediction unit 223 calculates the union of the operation downtime of each substitutable facility and all other essential facilities (non-substitutable facilities), and calculates the intersection of these unions as the business downtime. In other words, the period when all substitutable facilities are out of operation and essential facilities are also out of operation is calculated as the business downtime period.

[0171] For example, if "Farmer A" uses "irrigation canal A" and "irrigation canal B," and "Farmer A" can continue its business if either of the irrigation canals is operating, the union of the predicted downtime period 2341 between "irrigation canal A" and all other facilities essential for "Farmer A's" business continuity and the union of the predicted downtime period 2341 between "irrigation canal B" and all other facilities essential for "Farmer A's" business continuity are calculated, and the product of the unions is calculated as the predicted business downtime period 2352. It is to be noted that, for example, information indicating which facilities among the facilities indicated by the facility usage name 2204 are substitutable for each other may be stored in the facility usage information 220.

[0172] Note that in cases where business can only be resumed from a specific month in a year, such as in agriculture, where sowing and raising seedlings starts in April each year, the operation suspension period prediction unit 223 may set the day before the earliest date on which business can be resumed that is after the end date of the business suspension period predicted value 2352 calculated above, as a new end date for the business suspension period predicted value 2352. Furthermore, in cases where a recovery period before business resumption is required after the operation suspension period, the operation suspension period prediction unit 223 may include the recovery period in the business suspension period.

[0173] The operation downtime period prediction unit 223 links the disaster scenario code 2140 selected in the most recent step S1907, the disaster event 2112 extracted in the most recent step S1916, the company name 2202 and business name 2201 of the business being evaluated that are the subject of risk assessment received in step S303, and the start date and end date of the business downtime period prediction value 2352 calculated in the most recent step S1918, and records them in the business downtime period information 235 (step S1919).

[0174] If there are any disaster scenario codes 2140 that are to be selected in step S1907 that have not been selected, the operation downtime prediction unit 223 returns to the processing of step S1907, and if all disaster scenario codes 2140 have been selected, it terminates the operation downtime prediction processing (step S1920).

[0175] <Example of data structure for product information 218> 20 is a diagram showing an example of the data configuration of the product information 218. The product information 218 is information used in the financial impact prediction process in step S306, and is information in which the unit prices of products sold by each company to shipping destinations are recorded.

[0176] Product information 218 includes data items such as product name 2181, which is the name of the product, company name 2202 of the company selling the product, business name 2201 of the business selling the product, and unit price 2182, which indicates the selling price per unit of the product.

[0177] <Data configuration example of shipping information 219> 21 is a diagram showing an example of the data configuration of the shipping information 219. The shipping information 219 is information used in the financial impact prediction process in step S306, and is information in which the number of shipments and shipping frequency of products produced by each company are recorded.

[0178] The shipping information 219 includes data items such as product name 2181, shipper 2191, which is the name of the facility from which the product is shipped, destination 2192, which is the name of the facility to which the product is shipped, shipping quantity 2193, which indicates the amount of product shipped in one shipment, shipping frequency 2194, which indicates the frequency of product shipments, and shipping period 2195, which indicates the start and end dates of the period for shipping the product.

[0179] The weight of the product to be shipped or the quantity of the product to be shipped may be recorded in the shipping amount 2193. In the example of Fig. 21, it is assumed that shipments are made only during a predetermined period of one year, and only the month and day are recorded in the shipping period 2195, but in consideration of cases where shipments are made only during a specific year, the year, month, and day may also be recorded in the shipping period 2195.

[0180] <Data configuration example of Financial Impact Forecast Information 236> 22 is a diagram showing an example of the financial impact prediction information 236. The financial impact prediction information 236 is information output by the financial impact prediction process in step S306, and is information in which the degree of financial impact on the business run by each company in a disaster scenario is recorded.

[0181] The financial impact prediction information 236 has data items such as disaster scenario code 2140 of the target disaster scenario, company name 2202 of the company for which the financial impact in the disaster scenario is to be calculated, business name 2201 of the business for which the financial impact in the disaster scenario is to be calculated, total recovery cost 2361 indicating the predicted total recovery cost required to resume the business in the disaster scenario, recovery cost burden amount 2362 indicating the recovery cost to be borne by the company out of the total recovery cost, and sales loss amount 2363 indicating the amount of sales loss due to the suspension of the business in the disaster scenario.

[0182] <Example of financial impact forecast processing procedure> 23 is a flowchart showing an example of the financial impact prediction process in step S306. It is assumed that the product information 218 and shipping information 219 have already been set before the financial impact prediction process starts.

[0183] The financial impact prediction unit 224 repeatedly executes the processes from step S2301 to step S2314 until it selects all of the disaster scenario codes 2140 included in the disaster scenario information 214. The financial impact prediction unit 224 selects one of the disaster scenario codes 2140 included in the disaster scenario information 214 that has not been selected (step S2301).

[0184] The financial impact prediction unit 224 extracts from the business suspension period information 235 a record linked to the disaster scenario code 2140 selected in the most recent step S2301 and the company name 2202 of the business being evaluated for risk and the business name 2201 of the business being evaluated received in step S303, and extracts from the record the start date and end date of the business suspension period prediction value 2352 (step S2302).

[0185] The financial impact prediction unit 224 calculates the estimated shipping volume that would have been shipped during the business suspension period if the disaster indicated by the disaster scenario code 2140 selected in the most recent step S2301 had not occurred (step S2303). Specifically, the financial impact prediction unit 224 extracts, from the product information 218, the product name 2181 that is linked to the company name 2202 that is the target of risk assessment and the business name 2201 that is the target of assessment. Furthermore, the financial impact prediction unit 224 extracts, from the shipping information 219, the shipping volume 2193, shipping frequency 2194, and shipping period 2195 that are linked to the extracted product name 2181.

[0186] Furthermore, the financial impact prediction unit 224 calculates the total number of days of the extracted shipping period 2195 included in the business suspension period predicted value 2352 extracted in the most recent step S2302. The financial impact prediction unit 224 divides the total number of days by the extracted shipping frequency 2194, multiplies the result by the extracted shipping volume 2193, and calculates the estimated shipping volume that would have been shipped during the business suspension period if there had been no disaster. Note that if the total number of days is not divisible by the extracted shipping frequency 2194 within the range of integers, the financial impact prediction unit 224 may obtain a quotient of the division (the largest natural number that does not result in a negative remainder), and calculate the estimated shipping volume that would have been shipped during the business suspension period if there had been no disaster by multiplying the obtained quotient by the shipping volume.

[0187] The financial impact prediction unit 224 extracts the unit price 2182 linked to the product name 2181 extracted in step S2303 from the product information 218, and calculates the amount of sales loss by multiplying the extracted unit price 2182 by the estimated shipping volume that would have been shipped during the business suspension period if there had been no disaster, calculated in step S2304 (step S2304).

[0188] An example of the formula for calculating the amount of sales loss during the business suspension period is shown in Equation 3 below. P is the product sold by the business being evaluated.

[0189] Lost sales = unit price of product P × daily shipment volume of product P × number of days for shipment of product P during business suspension period ÷ shipment frequency (Equation 3)

[0190] Here, the financial impact prediction unit 224 may output a time series of sales for each predetermined cycle during a certain period (for example, a period specified by the user) that includes the business suspension period. An example of a formula for calculating sales for each cycle for the business being evaluated is shown in Equation 4 below. Note that since product P is not shipped during the business suspension period, the number of days during which product P is shipped in a cycle is the number of days in the period included in the cycle minus the business suspension period. Here, if there is inventory of product P that was produced before the business suspension period, the number of days during which product P is shipped in a cycle may include the number of days during which the inventory is shipped.

[0191] Sales in a cycle = unit price of product P × shipping volume of product P per day × number of days product P is shipped in a cycle ÷ shipping frequency (Equation 4)

[0192] The predetermined period may be, for example, a month or a week, but is not limited to these. For example, the financial impact prediction unit 224 outputs a time series of monthly sales for one year. Furthermore, the financial impact prediction unit 224 may calculate sales for each period not only for the business name 2201 to be evaluated received in step S303, but also for all businesses operated by the company to be risk evaluated, and may output the total sales for each period for all businesses of the company to be risk evaluated by summing up the calculated sales for each period.

[0193] The financial impact prediction unit 224 repeatedly executes the processes from step S2305 to step S2311 until it has selected all facility names 2123 used by the risk assessment target company in the business being assessed. The financial impact prediction unit 224 extracts from the facility usage information 220 a record including the company name 2202 being the risk assessment target and the business name 2201 being the assessment target received in step S303, and identifies the facility name 2123 included in the extracted record as the facility name 2123 used by the risk assessment target company in the business being assessed. The financial impact prediction unit 224 selects one unselected facility name 2123 from the facility names 2123 used by the risk assessment target company in the business being assessed (step S2305).

[0194] The financial impact prediction unit 224 repeatedly executes the processes from step S2306 to step S2308 until all damage events 2122 that may occur to the facility due to a disaster are selected. The financial impact prediction unit 224 extracts, from the recovery-related information 233, a record that includes the disaster scenario code 2140 selected in the most recent step S2301 and the facility name 2123 selected in the most recent step S2305, and identifies the damage event 2122 included in the extracted record as the damage event 2122 that may occur to the facility due to a disaster. The financial impact prediction unit 224 selects one unselected damage event 2122 from the damage events 2122 that may occur to the facility due to a disaster (step S2306).

[0195] The financial impact prediction unit 224 extracts from the recovery-related information 233 the recovery cost prediction value 2332 associated with the disaster scenario code 2140 selected in the most recent step S2301, the facility name 2123 selected in the most recent step S2305, and the damage event 2122 selected in the most recent step S2306 (step S2307).

[0196] If there are any unselected damage events 2122 among the damage events to be selected in step S2306, the financial impact prediction unit 224 returns to the processing of step S2306, and if all damage events 2122 have been selected, it proceeds to the processing of step S2309 (step S2308).

[0197] The financial impact prediction unit 224 determines whether the owner 2133 linked in the facility information 213 to the facility name 2123 selected in the most recent step S2305 is the same as the company name of the company being assessed for risk (step S2309). If the financial impact prediction unit 224 determines that the owner 2133 is the same as the company name of the company being assessed for risk (step S2309: YES), it proceeds to processing of step S2310. If the financial impact prediction unit 224 determines that the owner 2133 is different from the company name of the company being assessed for risk (step S2309: NO), it proceeds to processing of step S2311.

[0198] The financial impact prediction unit 224 determines the recovery cost prediction value 2332 extracted in step S2307 for all damage events 2122 that occur at the facility selected in the most recent step S2305 as the recovery cost burden for the facility of the company being risk assessed (step S2310).

[0199] If there are any facility names 2123 that are the selection targets in step S2305 that have not been selected, the financial impact prediction unit 224 returns to the processing of step S2305, and if all facility names 2123 have been selected, it proceeds to the processing of step S2312 (step S2311).

[0200] The financial impact prediction unit 224 calculates the total of the predicted recovery costs 2332 calculated in step S2307 and the total of the burden on the companies being assessed for risk calculated in step S2310 for all facility names 2123 selected in step S2305 (step S2312).

[0201] Here, the financial impact prediction unit 224 may output a time series of costs including the total recovery costs and / or the recovery cost burden of the risk assessment target company for each predetermined cycle of a period that includes the business suspension period (for example, a period specified by the user). In this time series, the timing at which recovery costs are incurred is a predetermined timing during the period in which recovery work is being performed, such as the start date or end date of recovery work. Note that the date on which recovery costs are incurred is determined, for example, in accordance with actual operations, and the financial impact prediction unit 224 adds the recovery costs in the cycle that includes the date on which recovery costs are incurred.

[0202] The predetermined period may be, for example, a month or a week, but is not limited to these. For example, the financial impact prediction unit 224 outputs a time series of costs for each month in a year. Furthermore, the financial impact prediction unit 224 may calculate the costs for each period not only for the business name 2201 to be evaluated received in step S303, but also for all businesses operated by the company to be risk evaluated, and may output the costs for each period for all businesses of the company to be risk evaluated by adding up the calculated costs for each period.

[0203] Furthermore, the financial impact prediction unit 224 may calculate profit, which is the difference between the sales of the risk assessment target company for each predetermined period calculated in step S2304 and the costs, including recovery costs, borne by the risk assessment target for each predetermined period calculated in step S2312 (the necessary costs for each predetermined period other than recovery costs are predetermined). The financial impact prediction unit 224 may also calculate the amount of lost profit by taking the difference between the profit, which is the difference between the sales that would have occurred during the business suspension period if the business had not been suspended (if the disaster had not occurred) minus costs (other than recovery costs) (e.g., procurement costs), and the profit, which is the difference between the sales during the business suspension period if the business is suspended minus costs including recovery costs. Here, the sales during the business suspension period if the business is suspended may be the sales amount if the inventory of products produced before the business suspension is sold, but if the inventory is not sold or there is no inventory, the sales amount will be zero.

[0204] In the financial impact prediction information 236, the financial impact prediction unit 224 links the disaster scenario code 2140 selected in the most recent step S2301 with the company name 2202 of the company being assessed for risk, and the business name 2201 being assessed, and records the sales loss amount calculated in step S2304 as sales loss amount 2363, the total recovery costs calculated in step S2312 as total recovery costs 2361, and the total amount borne by the company being assessed for risk calculated in step S2312 as recovery cost burden amount 2362 (step S2313).

[0205] If there are any disaster scenario codes 2140 that are selection targets in step S2301 that have not been selected, the financial impact prediction unit 224 returns to the processing of step S2301, and if all disaster scenario codes 2140 have been selected, it terminates the financial impact prediction processing (step S2314).

[0206] In this embodiment, an example has been described in which the amount of recovery costs borne by the risk assessment target company is calculated in the financial impact prediction process, but the amount borne by the local government may also be calculated. In this case, the financial impact prediction unit 224 may extract the amount of profit loss due to a disaster scenario for all businesses with production facilities in the local government's jurisdiction (information indicating the jurisdiction is set in advance), and calculate the amount of lost profit by multiplying, for example, a predetermined tax rate as the amount of lost tax revenue for the local government. Furthermore, the financial impact prediction unit 224 may also calculate the recovery costs borne by the local government by calculating the total amount of recovery costs for facilities owned by the local government.

[0207] <Example of the screen layout for the risk impact assessment result output screen> 24A and 24B are explanatory diagrams showing an example of the screen configuration of the risk impact evaluation result output screen. Note that the risk impact evaluation result output screen 2400 is divided into Fig. 24A and Fig. 24B and drawn, but the risk impact evaluation result output screen 2400 includes the display contents shown in Fig. 24A and Fig. 24B. The risk impact evaluation result output screen 2400 is displayed on the output device 106 or terminal 110 of the risk impact evaluation system 100.

[0208] The risk impact evaluation result output screen 2400 includes, for example, a display area 2410, a display area 2420, a display area 2431, a display area 2440, and a display area 2450.

[0209] For example, in the event of the occurrence of a disaster indicated in the disaster scenario, the display area 2410 displays the predicted operation shutdown period of facilities used by the risk assessment target company in the business being assessed. Specifically, for example, based on the data of the disaster scenario information 214 and the operation shutdown period information 234, the display area 2410 displays a Gantt chart for each facility of the facility name 2123, with the predicted disaster occurrence period 2141 indicated by an arrow 2411 and the period of the predicted operation shutdown period 2341 other than the predicted disaster occurrence period 2141 indicated by an arrow 2412.

[0210] For example, in the event of the occurrence of a disaster indicated by a disaster scenario, predicted results of recovery time and recovery costs for facilities used by a company subject to risk assessment in the business being assessed are displayed in display area 2420. Specifically, for example, based on disaster scenario information 214 and recovery-related information 233, display area 2420 displays predicted disaster occurrence period 2141 and disaster event 2112 of the disaster scenario, facility names 2123 used by the company, damage events 2430 of the disaster scenario, and predicted recovery time values ​​2331 and predicted recovery costs 2332 for the facilities in response to the damage events.

[0211] For example, in the event of a disaster indicated by the disaster scenario, a map is displayed in which shaded areas indicate facilities that will be out of operation among the facilities used by the risk assessment target company in the business being assessed, and that will experience an outage period. When a shaded area is selected on the map in display area 2431, information indicating, for example, the facility name and outage period of the facility corresponding to that area is displayed.

[0212] For example, the predicted results of the financial impact on the business being assessed of the risk assessment target company are displayed in display area 2440. Specifically, for example, based on disaster scenario information 214, business suspension period information 235, and financial impact prediction information 236, display area 2440 displays the predicted disaster occurrence period 2141 of the disaster scenario, disaster event 2112, business name 2201 for which the financial impact is to be calculated, predicted business suspension period due to the disaster 2352, predicted recovery cost burden amount 2362 that the company will bear due to the disaster, and predicted sales loss amount 2363 of the business due to the disaster.

[0213] For example, the display area 2450 displays the time-series simulation results of the sales and recovery costs of the business being assessed of the risk assessment target company. Specifically, for example, the display area 2450 displays a bar graph for each month of sales 2451 for the period including the business suspension period. In addition, for example, the display area 2450 displays a bar graph for each month of recovery costs 2452 (months in which recovery cost burden amount 2362 is incurred) for the period including the business suspension period.

[0214] As described above, the risk impact assessment system 100 of the first embodiment can estimate the correlation between a natural disaster and a damage event that may occur as a result of the natural disaster. Furthermore, the risk impact assessment system 100 identifies damage events that may occur in the event of a disaster at facilities that are necessary for a company's business continuity based on the correlation, and calculates the time and cost required for the facility to recover from the damage event, thereby making it possible to quantitatively evaluate the period of suspension of operations of facilities, including public infrastructure, and the period of suspension of business of a company after a disaster, as well as the financial impact on sales, tax revenue, costs, etc.

[0215] Furthermore, the risk impact assessment system 100 evaluates the financial impact on a business when various risk countermeasures are implemented at a base, enabling companies and local governments to identify cost-effective risk countermeasures and preventive measures and to support the formulation of implementation plans for countermeasures. Furthermore, for example, by providing information indicating the financial impact to financial institutions such as insurance companies and banks in addition to companies and local governments, it can support risk management at financial institutions, such as setting insurance premiums and adjusting loan terms such as loan amounts and loan periods. It can also support business improvements for companies that use financial services such as insurance and loans. [Example]

[0216] In Example 1, a farming company is assumed to be the company to be assessed for risk, and the risk impact assessment system 100 quantitatively assesses the degree of financial impact on the farming company's business after a disaster occurs. In Example 2, a manufacturing company is assumed to be the company to be assessed for risk, and the risk impact assessment system 100 assesses the degree of financial impact, taking into consideration the impact on the business of the company to be assessed for risk due to the suspension of the supplier's business. In Example 2, differences from Example 1 will be mainly explained, and the same configurations and processes as in Example 1 will be assigned the same reference numerals, and their explanation will be omitted as appropriate.

[0217] <Example of functional configuration of risk impact assessment system 100 in Example 2> The risk impact assessment system 100 of the second embodiment further includes part information 201. The part information 201 is stored in, for example, the memory 102, the auxiliary storage device 103, or an external database connected to the risk impact assessment system 100.

[0218] <Data configuration example of part information 201> 25 is a diagram showing an example of the data configuration of part information 201. The part information 201 is information in which the dependency relationship between a product and a part is recorded. Specifically, the part information 201 is information in which the relationship between a parent product and a child part required to manufacture the parent product is recorded.

[0219] The part information 201 has data items such as a parent company name 2011 which is the name of the company that manufactures the parent product, a parent product name 2012 which is the name of the parent product, a child company name 2013 which is the name of the company that manufactures the child part, a child part name 2014 which is the name of the child part, and a quantity 2015 which indicates the quantity (number) of child parts required to manufacture the parent product.

[0220] <Example of Disaster Scenario Acquisition Processing Procedure in Second Embodiment> The disaster scenario acquisition process in step S304 of the second embodiment will be described with reference to the disaster scenario acquisition process in step S304 of the first embodiment shown in Fig. 12. Note that in the second embodiment, it is assumed that the component information 201 has already been set before the start of the disaster scenario acquisition process in step S304.

[0221] In step S1201, the disaster scenario acquisition unit 222 searches the facility usage information 220 for records that include the business name 2201 indicated by the combination extracted in step S303 (the company name of the company being assessed and the business name of the business being assessed, which is run by the company being assessed), and extracts all facility names 2204 used from the records included in the search results (step S1201). Note that in this embodiment, it is assumed that the business name 2201 is unique among companies, but if different companies carry out businesses with the same business name 2201, the disaster scenario acquisition unit 222 searches for records that include the business name 2201 and company name 2202 indicated by the combination extracted in step S303.

[0222] In step S1201, the disaster scenario acquisition unit 222 extracts all facility names used by suppliers when producing parts related to the business being evaluated. Specifically, the disaster scenario acquisition unit 222 extracts the product name 2181 linked to the business name 2201 from the product information 218, extracts records in which the extracted product name 2181 is the parent product name 2012 from the part information 201, and extracts the child company name 2013 and the child part name 2014 from the extracted records.

[0223] Furthermore, in step S1201, the disaster scenario acquisition unit 222 extracts from the product information 218 records whose product name 2181 matches the extracted child part name 2014 and whose company name 2202 matches the extracted child company name 2013, extracts the business name 2201 from the extracted record, and extracts all facility names 2204 linked to the extracted business name 2201 from the facility usage information 220.

[0224] The disaster scenario acquisition unit 222 repeatedly executes the processing from step S1202 to step S1208 until it selects all combinations of the business name 2201 of the risk assessment target company or subsidiary company extracted in step S1201 and the facility name 2123 used by the company in the business of the business name 2201. In step S1202, the disaster scenario acquisition unit 222 selects one unselected combination from the combination of the business name 2201 and the facility name 2123.

[0225] The processing of steps S1203 to S1205 is the same as in Example 1. In step S1206, the disaster scenario acquisition unit 222 transmits to the operation downtime prediction unit 223 a combination of the business name 2201 and facility name 2123 selected in the most recent step S1202, the disaster scenario code 2140 selected in the most recent step S1204, and the damage event 2122 extracted in the most recent step S1205.

[0226] The processing of step S1207 is the same as in Example 1. In step S1208, if there are any unselected combinations of the business name 2201 and facility name 2123, which are the nicknames selected in step S1202, the disaster scenario acquisition unit 222 returns to the processing of step S1202, and if all such combinations have been selected, ends the disaster scenario acquisition processing.

[0227] <Example of operation downtime prediction processing procedure in the second embodiment> Fig. 26 is a flowchart showing an example of a part of the operation downtime prediction process in step S306 of the second embodiment. The process in Fig. 26 is executed following the process shown in Fig. 19A. That is, the processes from step S1901 to step S1906 are the same as those in the first embodiment.

[0228] In the second embodiment, step S1921, which will be described later, is added between step S1907 and step S1908, and step S1922, which will be described later, is added between step S1919 and step S1920.

[0229] The operation downtime prediction unit 223 repeatedly executes the processing from step S1907 to step S1920 (in this embodiment, steps S1921 and S1922 are also included) until all disaster scenario codes 2140 included in the combination received in step S1206 are selected.

[0230] The operation downtime prediction unit 223 repeatedly executes the processing from step S1921 to step S1922 until it selects all of the business names 2201 of the risk evaluation target company and its subsidiary companies received in step S1206. The operation downtime prediction unit 223 selects one unprocessed business name from the business names 2201, and proceeds to the processing of step S1908 (step S1921).

[0231] The operation suspension period prediction unit 223 repeatedly executes the processing from step S1908 to step S1917 until it selects all facility names 2123 that are linked in the combination received in step S1206 to the disaster scenario code 2140 extracted in the most recent step S1907, which are facility names 2123 of facilities used in the business of the business name 2201 selected in the most recent step S1921.

[0232] In step S1908, the operation downtime prediction unit 223 refers to the combination received in step S1206 and selects one unselected facility name 2123 linked to the disaster scenario code 2140 selected in the most recent step S1907 and the business name 2201 selected in the most recent step S1921.

[0233] The downtime period prediction unit 223 repeatedly executes the processing from step S1909 to step S1914 until it selects all of the damage events 2122 associated with the disaster scenario code 2140 selected in the most recent step S1907, the facility name 2123 selected in the most recent step S1921, and the facility name 2123 selected in the most recent step S1908.

[0234] In step S1909, the operation downtime prediction unit 223 refers to the combination received in step S1206 and selects one unselected damage event 2122 linked to the disaster scenario code 2140 selected in the most recent step S1907, the facility name 2123 selected in the most recent step S1921, and the facility name 2123 selected in the most recent step S1908.

[0235] The processing from step S1910 to step S1918 is the same as in Example 1. In step S1919, the operation downtime period prediction unit 223 links the disaster scenario code 2140 selected in the most recent step S1907, the disaster event 2112 extracted in the most recent step S1916, the business name 2201 selected in the most recent step S1921, the company name 2202 linked to the business name 2201 in the facility usage information 220, and the start date and end date of the business downtime period prediction value 2352 calculated in the most recent step S1918, and records these in the business downtime period information 235 (step S1919).

[0236] If there are any unselected business names 2201 among the business names 2201 to be selected in step S1921, the operation downtime prediction unit 223 returns to the processing of step S1921, and if all business names 22021 have been selected, the operation downtime prediction unit 223 proceeds to the processing of step S1920 (step S1922). The processing of step S1920 is the same as in the first embodiment.

[0237] <Example of financial impact forecasting procedure in Example 2> Fig. 27 is a flowchart showing an example of the financial impact prediction process in step S306. The financial impact prediction process of the second embodiment differs from the first embodiment in that the processes of steps S2601 to S2607 are added between steps S2304 and S2305. Note that the processes of steps S2301 to S2303 and the processes of step S2306 and after are also executed in the second embodiment, but the description of these processes is omitted in Fig. 27.

[0238] Following the process of step S2304, the financial impact prediction unit 224 proceeds to the process of step S2601. The financial impact prediction unit 224 repeatedly executes the processes from step S2601 to step S2606 until all related businesses of the supplier are selected.

[0239] Specifically, the financial impact prediction unit 224 extracts the product name 2181 linked to the business name 2201 that is the target of risk impact evaluation from the product information 218. The financial impact prediction unit 224 extracts records from the part information 201 in which the extracted product name 2181 matches the parent product name 2012, and extracts all child part names 2014 from the extracted records.

[0240] Furthermore, the financial impact prediction unit 224 extracts records from the product information 218 whose product name 2181 matches the extracted child part name 2014, extracts all business names 2201 contained in the extracted records, and determines the businesses with the extracted business names 2201 as related businesses of the supplier.

[0241] The financial impact prediction unit 224 selects one unselected business name 2201 from the extracted business names 2201 of the related business of the supplier (step S2601). The financial impact prediction unit 224 extracts, from the business suspension period information 235, a record linked to the disaster scenario code 2140 selected in the most recent step S2301 and the business name 2201 of the related business of the supplier selected in the most recent step S2601, and extracts the start date and end date of the business suspension period forecast value 2352 from the extracted record (step S2602).

[0242] The financial impact prediction unit 224 calculates the shipment volume of products (sub-components) from the supplier that is estimated to have been shipped during the business suspension period if there had been no disaster (step S2603). In step S2603, the financial impact prediction unit 224 executes the following process.

[0243] The financial impact prediction unit 224 extracts the item name 2181 of the product (child part) linked to the business name 2201 of the related business of the supplier selected in the most recent step S2601 from the product information 218. The financial impact prediction unit 224 extracts the shipping quantity 2193, shipping frequency 2194, and shipping period 2195 linked to the extracted item name 2181 from the shipping information 219.

[0244] The financial impact prediction unit 224 calculates the total number of days of the extracted shipping period 2195 in the business suspension period prediction value 2352 extracted in step S2602, divides the total number of days by the extracted shipping frequency 2194, multiplies the result by the extracted shipping volume 2193, and calculates the amount of product (sub-component) shipments from the supplier that are estimated to have been able to be shipped during the business suspension period if there had been no disaster.

[0245] The financial impact prediction unit 224 calculates the shipping volume of the product (parent product) that the risk assessment target company can ship during the supplier's business suspension period (S2604). In step S2604, the financial impact prediction unit 224 executes the following process.

[0246] The financial impact prediction unit 224 refers to the part information 201 and determines whether the parent product name 2012 is Records are extracted from the parts information 201 that match the product name 2181 of the product (parent product) (linked to the company name 2202 being the risk assessment target and the business name 2201 being the assessment target) extracted in step S2303, and whose child part name 2014 matches the product name 2181 of the product (child part) linked to the business name 2201 of the supplier's related business extracted in the most recent step S2603, and the quantity 2015 is extracted from the extracted record.

[0247] The financial impact prediction unit 224 divides the shipment volume of the supplier's products (child parts) that is estimated to have been shipped during the business suspension period if there had been no disaster, calculated in step S2603, by the extracted number 2015, and calculates this as the shipment volume of the product (parent product) that the company being assessed for risk can ship during the supplier's business suspension period.

[0248] In addition, if the shipment volume of the supplier's products (child parts) that is estimated to have been shipped during the business suspension period if there had been no disaster cannot be divided evenly within the range of integers by the extracted number of items 2015, the financial impact prediction unit 224 may obtain the quotient of the division (the largest natural number that does not result in a negative remainder) and calculate the obtained quotient as the shipment volume of the product (parent product) that the company being assessed for risk can ship during the supplier's business suspension period.

[0249] The financial impact prediction unit 224 calculates the amount of sales loss due to the supplier's business suspension (step S2605). Specifically, the financial impact prediction unit 224 extracts the unit price 2182 of the product name 2181 of the risk assessment target company extracted in step S2303 from the product information 218, and calculates the amount of sales loss due to the supplier's business suspension by multiplying the extracted unit price 2182 by the shipping volume of the product (parent product) that the risk assessment target company can ship during the supplier's business suspension period calculated in step S2604.

[0250] If there are any unselected business names 2201 of the related businesses selected in step S2601, the financial impact prediction unit 224 returns to the processing of step S2601, and if all business names 2201 of the related businesses have been selected, it proceeds to the processing of step S2607 (step S2606).

[0251] The financial impact prediction unit 224 calculates the total amount of sales loss due to the business suspension of the supplier calculated in step S2605 for each business name 2201 selected in step S2601. Furthermore, the financial impact prediction unit 224 adds the amount of sales loss due to the business suspension of the risk assessment target company itself calculated in step S2304 and the total amount of sales loss due to the business suspension of the supplier calculated in step S2604, and calculates this as the total amount of sales loss due to the business suspension of the risk assessment target company and the supplier (step S2607). Following the processing of step S2607, the financial impact prediction unit 224 proceeds to the processing of step S2305 and subsequent steps.

[0252] The processing from step S2305 onwards is the same as in Example 1. However, in step S2313, the financial impact prediction unit 224 links the disaster scenario code 2140 selected in the most recent step S2301 with the company name 2202 of the risk assessment target company and the business name 2201 of the assessment target in the financial impact prediction information 236, and records the total amount of sales loss due to business suspension of the risk assessment target company and supplier calculated in step S2607 as sales loss amount 2363, the total amount of recovery costs calculated in step S2312 as total recovery cost 2361, and the total amount of burden of the risk assessment target company calculated in step S2312 as recovery cost burden 2362.

[0253] As described above, the risk impact assessment system 100 of Example 2, when assessing the financial impact of a disaster occurring in the manufacturing industry, is able to quantitatively assess the financial impact on the business of the company being assessed by taking into account the impact on the company being assessed of the suspension of business at its suppliers due to the disaster. [Example]

[0254] In the first and second embodiments, the processing of the risk impact assessment system 100 was explained on the assumption that a company will resume business after temporarily suspending business due to the occurrence of a disaster. However, it is conceivable that some companies will go out of business after the occurrence of a disaster. Therefore, in the third embodiment, the risk impact assessment system 100 determines whether the company can continue business after the occurrence of a disaster, and then calculates the business suspension period of the company. In the third embodiment, differences from the first embodiment will be mainly explained, and the same configurations and processing as those in the first embodiment will be assigned the same reference numerals, and their explanation will be omitted as appropriate.

[0255] <Example of functional configuration of risk impact assessment system 100 in Example 3> The risk impact assessment system 100 of the third embodiment further includes business information 202 and business continuity criteria information 203. The business information 202 and the business continuity criteria information 203 are stored in, for example, the memory 102, the auxiliary storage device 103, or an external database connected to the risk impact assessment system 100.

[0256] <Data configuration example for Business Information 202> 28 is a diagram showing an example of the data configuration of the business information 202. The business information 202 is information used in the operation downtime prediction process in step S305, and is information in which information related to the business run by a company is recorded.

[0257] The business information 202 has data items such as a business name 2201 of the business, a company name 2202 of the company operating the business, an industry name 2021 indicating the type of the business, main business information 2022 indicating a truth value of whether the business is the company's main business, successor information 2023 indicating a truth value of whether the business has a successor, and annual income information 2024 indicating the company's annual income from the business. The industry name 2021, main business information 2022, successor information 2023, and annual income information 2024 are all examples of characteristic information indicating the characteristics of the business.

[0258] <Data configuration example of business continuity criteria information 203> 29 is a diagram showing an example of the data configuration of the business continuity determination criteria information 203. The business continuity determination criteria information 203 is information used in the operation downtime period prediction process in step S305, and is information in which an upper limit value of the business downtime period according to the characteristics of the company or business is recorded.

[0259] The business continuity determination criteria information 203 includes data items indicating conditions related to characteristic information such as the business industry name 2021, main business information 2022 related to the business, successor information 2023 related to the business, and annual income determination information 2031 indicating the truth value of whether the annual income of the business is above a predetermined threshold, as well as data items such as business suspension period upper limit 2032 indicating the upper limit of the business suspension period during which the business can be resumed.

[0260] The business suspension period upper limit 2032 indicates the upper limit of the period that a company that conducts a business that satisfies the conditions related to the characteristic conditions including the corresponding industry name 2021, main business information 2022, successor information 2023, and annual income determination information 2031 can endure in order to resume the business (that is, if the business is suspended for a period exceeding the upper limit, the business cannot be resumed). Here, the business suspension period upper limit 2032 may be a value predicted based on performance information related to business continuity and business closure in the past when a disaster occurred, or may be a value set by the user.

[0261] For example, if statistical data shows that businesses with successors tend to be able to endure longer periods before resuming operations after a disaster than businesses without successors, assuming that other characteristic information is the same, then the upper limit of business suspension period 2032 will be higher. Also, if statistical data shows that businesses run as main businesses tend to be able to endure longer periods before resuming operations after a disaster than businesses run as side businesses, assuming that other characteristic information is the same, then the upper limit of business suspension period 2032 will be higher. Also, if statistical data shows that businesses with higher annual income from the business tend to be able to endure longer periods before resuming operations after a disaster, assuming that other characteristic information is the same, then the upper limit of business suspension period 2032 will be higher. Note that if there are other data items (other characteristic information) included in the conditions for determining the upper limit of the business suspension period, the business information 202 may include the other data items, and the business continuity criteria information 203 may include information indicating the conditions according to the other data items.

[0262] <Example of operation downtime prediction processing procedure in the third embodiment> 30 is a flowchart showing an example of the operation downtime prediction process in step S305. The operation downtime prediction process of the third embodiment differs from that of the first embodiment in that the processes of steps S2901 to S2904 are added between steps S1918 and S1919.

[0263] In the third embodiment, the processes from step S1901 to step S1917 and the processes from step S1920 onwards are also executed, but these processes are omitted from Fig. 30. In the third embodiment, it is assumed that the business information 202 and the business continuity criteria information 203 have already been set before the start of the operation downtime period prediction process in step S305.

[0264] Following the processing of step S1918, the operation downtime prediction unit 223 proceeds to the processing of step S2901. The operation downtime prediction unit 223 refers to the business information 202, and extracts business characteristic information such as the industry name 2021, main business information 2022, successor information 2023, and annual income information 2024 that are linked to the company name 2202 and business name 2201 that are the subject of risk assessment received in step S303 (step S2901).

[0265] The operation downtime prediction unit 223 extracts the business downtime upper limit 2032 corresponding to the characteristic information extracted in step S2901 from the business continuity determination criteria information 203 (step S2902). Specifically, the operation downtime prediction unit 223 determines whether the annual income information 2024 extracted in step S2901 is equal to or greater than a predetermined threshold, and if it determines that the annual income information 2024 is equal to or greater than the predetermined threshold, determines that the annual income determination information 2031 is TRUE, and if it determines that the annual income information 2031 is less than the predetermined threshold, determines that the annual income determination information 2031 is FALSE. The operation downtime prediction unit 223 refers to the business continuity determination criteria information 203 and extracts the business downtime upper limit 2032 corresponding to the industry name 2021, main business information 2022, and successor information 2023 extracted in step S2901 and the determined annual income determination information 2031.

[0266] The operation downtime prediction unit 223 determines whether the business downtime prediction value calculated in the most recent step S1918 is equal to or less than the business downtime upper limit value 2032 extracted in the most recent step S2902 (step S2903).

[0267] If the operation downtime prediction unit 223 determines that the business downtime prediction value is less than or equal to the business downtime upper limit value 2032 (step S2903: YES), it determines that the target business can continue even after the occurrence of the disaster indicated by the disaster scenario code selected in the most recent step S1907, and proceeds to processing of step S1919.

[0268] If the operation suspension period prediction unit 223 determines that the business suspension period prediction value exceeds the business suspension period upper limit value 2032 (step S2903: NO), it determines that the target business will go out of business after the occurrence of the disaster indicated by the disaster scenario code selected in the most recent step S1907, and proceeds to processing of step S2904.

[0269] The operation downtime prediction unit 223 changes the end date of the business downtime prediction value calculated in step S1918 to NULL (step S2904), and proceeds to the processing of step S1919. In other words, a business for which the end date of the business downtime prediction value is NULL is a business that cannot be continued after a disaster occurs, and a business for which the end date of the business downtime prediction value is not NULL is a business that can be continued after a disaster occurs. Note that in the processing described above, the operation downtime prediction unit 223 determines whether or not to continue the business using the upper limit value of the business downtime period, but the determination of whether or not to continue the business may be made using financial impacts such as the amount of sales loss and recovery costs instead of or in addition to the upper limit value of the business downtime period.

[0270] <Example of financial impact forecasting procedure in Example 3> The financial impact prediction process in step S306 of the third embodiment will be described with reference to the financial impact prediction process in step S306 of the first embodiment shown in Fig. 23. However, the description of processes similar to those in Fig. 23 will be omitted as appropriate.

[0271] In step S2302, as in Example 1, the financial impact prediction unit 224 extracts from the business suspension period information 235 records linked to the disaster scenario code 2140 selected in the most recent step S2301 and the company name 2202 and business name 2201 of the risk assessment target received in step S303, and extracts the start date and end date of the business suspension period prediction value 2352.

[0272] If the financial impact prediction unit 224 determines that the end date of the extracted business suspension period forecast value 2352 is NULL, it assumes, for example, the end date of a predetermined period (e.g., several years) for calculating the amount of lost sales as the end date of the business suspension period, and then calculates the amount of lost sales in step S2304. Furthermore, if the financial impact prediction unit 224 determines that the end date of the extracted business suspension period forecast value 2352 is NULL, it may not calculate the amount of lost sales in steps S2303 to S2304, but may calculate only the sales for a predetermined cycle in a chronological order for a predetermined period in step S2304.

[0273] If the financial impact prediction unit 224 determines that the end date of the extracted business suspension period predicted value 2352 is not NULL, it executes the processes of steps S2303 and S2304 similar to those in the first embodiment.

[0274] If the financial impact prediction unit 224 determines that the end date of the business suspension period prediction value 2352 extracted in step S2302 is NULL, it assumes, for example, that restoration work on facilities used in the business owned by the risk assessment target company will not be carried out, and in step S2310 determines the burden amount of the risk assessment target company to be 0.

[0275] If the financial impact prediction unit 224 determines that the end date of the business suspension period prediction value 2352 extracted in step S2302 is not NULL, in step S2310, the financial impact prediction unit 224 determines the recovery cost prediction value 2332 for all damage events 2122 extracted in step S2307 as the amount to be borne by the company being risk assessed.

[0276] The risk impact assessment system 100 of the third embodiment can determine whether a company will continue its target business after a disaster occurs, based on the characteristics of each company, and then calculate the business suspension period. This makes it possible to determine, for example, whether a business with production facilities located in the jurisdiction of a local government will continue its business after a disaster occurs. This allows the risk impact assessment system 100 to assist the local government in determining which risk countermeasures should be prioritized.

[0277] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0278] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0279] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0280] 100 Risk impact assessment system, 101 CPU, 102 memory, 103 auxiliary storage device, 104 communication device, 105 input device, 106 output device, 110 terminal, 201 parts information, 202 business information, 203 business continuity determination criteria information, 211 disaster record information, 212 damage record information, 213 facility information, 214 disaster scenario information, 215 company information, 216 risk countermeasure information, 217 risk countermeasure implementation information, 218 product information, 219 shipping information, 220 facility usage information, 221 correlation analysis unit, 222 disaster scenario acquisition unit, 223 operation outage period prediction unit, 224 financial impact prediction unit, 231 correlation information, 232 recovery-related prediction model information, 233 recovery-related information, 235 operation outage period information, 236 financial impact prediction information

Claims

1. An information processing system, a processor and a memory, The memory includes: Correlation information indicating the correlation between the occurrence of a disaster event and the occurrence of a damage event that may occur in facilities used in the business; disaster scenario information indicating disaster events that occur in the disaster scenario and the magnitude of the disaster events; a first index indicating the implementation status of risk countermeasures for mitigating the damage event that may occur in the facility; The processor: By referring to the correlation information, a damage event that is correlated with the disaster event in the disaster scenario and a facility where the damage event may occur are identified; An information processing system that performs a prediction process to predict the amount of resources to be spent on recovery from a damage event at the identified facility based on at least one of the magnitude of the disaster event in the disaster scenario and the first indicator for the damage event that may occur at the identified facility.

2. 2. The information processing system according to claim 1, The memory includes: Disaster record information indicating disaster events that have occurred in the past, and the dates and areas where the disaster events occurred; Damage history information indicating damage events that have occurred in the facility in the past, the dates of occurrence of the damage events, and the location of the facility; Facility information indicating attributes of the facility is stored; The processor: Classifying the facilities by the attributes; For each classification of the facilities, calculate the percentage of facilities in which a damage event occurred, the damage occurrence date of which is within a predetermined period from the disaster occurrence date, among the facilities whose facility locations are included in the disaster occurrence area; determining whether the disaster event and the damage event are correlated for each facility classification based on the calculated ratio; An information processing system that associates the disaster event and the damage event that are determined to be correlated with the attribute indicated by the classification of the facility and stores the correlation information.

3. 2. The information processing system according to claim 1, The memory includes: Disaster record information indicating disaster events that have occurred in the past, the magnitude of the disaster events, and the disaster occurrence dates and disaster occurrence areas of the disaster events; Damage record information indicating damage events that have occurred in the facility in the past, the dates of occurrence of the damage events, the location of the facility, and the actual value of the amount of the resources spent on recovery from the damage events for the facility; Facility information indicating attributes of the facility is stored; The correlation information indicates the correlation for each attribute of the facility where the damage event occurred, The processor: extracting a disaster event that corresponds in the correlation information to the damage event indicated by the damage record information and an attribute indicated by the facility information of the facility where the damage event occurred; Identifying, from the disaster history information, a disaster event that is the same as the extracted disaster event, that is included in the period from the damage occurrence date indicated by the disaster occurrence date to a predetermined time before, and that the disaster occurrence area includes the facility location, and extracting the magnitude of the disaster that corresponds to the identified disaster event in the disaster history information; generating learning data that classifies at least one of the magnitude of the extracted disaster and the first index, and the actual value for each combination of the disaster event, the damage event, and the attribute of the facility; generating a prediction model for each combination based on the learning data, the prediction model outputting the amount of resources when a value of an explanatory variable including at least one of the magnitude of the disaster event and the first index is input, and storing the prediction model in the memory; Identifying the prediction model corresponding to the magnitude of the disaster event in the disaster scenario, the damage event correlated with the disaster event in the disaster scenario, and the attribute of the identified facility; An information processing system that predicts the amount of resources to be spent on recovery from the damage event at the identified facility based on at least one of the magnitude of the disaster event in the disaster scenario and the first indicator for the damage event that may occur at the identified facility, and the identified prediction model.

4. 2. The information processing system according to claim 1, The memory includes: risk countermeasure information indicating risk countermeasures that can be implemented at the base to mitigate the damage event; retaining risk countermeasure implementation information indicating risk countermeasures being implemented or assumed to be implemented at the base and disaster-stricken areas in which the damage event will be mitigated if the risk countermeasures are implemented; The processor: By referring to the risk countermeasure implementation information, a base whose location information of the identified facility is included in the disaster area is identified; Identifying the number of risk countermeasures that can be implemented for the identified base to mitigate the damage event from the risk countermeasure information; Identifying the number of risk countermeasures that are being implemented or are expected to be implemented at the identified base from the risk countermeasure implementation information; An information processing system that calculates, as the first index, a risk countermeasure implementation rate of the base based on the number of risk countermeasures implemented and the number of risk countermeasures.

5. 2. The information processing system according to claim 1, The resources include a recovery time required for the identified facility to recover from the damage event; the disaster scenario information indicates a disaster occurrence period of a disaster event in the disaster scenario; The processor: In the prediction process, a recovery time for the identified facility is calculated for each damage event that may occur in the facility; In the calculation of the operation suspension period, Calculating a total recovery time required for the identified facility to recover from all damage events that may occur to the facility based on each of the calculated recovery times; An information processing system that calculates a period of downtime for the identified facility based on the disaster occurrence period and the total recovery time.

6. 6. The information processing system according to claim 5, The processor generates data for displaying a Gantt chart showing the disaster occurrence period in the disaster scenario and the operation suspension period of each of the identified facilities.

7. 6. The information processing system according to claim 5, the memory holds facility information indicating facilities that are interchangeable among the facilities; The processor: In the prediction process, a recovery time for each of the identified facilities is calculated for each of the damage events that may occur in the facility; Execute the operation suspension period calculation process for each of the identified facilities; An information processing system that calculates a business suspension period of the business based on the operation suspension period of each of the identified facilities and the operation suspension period of each of facilities that can be substituted for the identified facilities, as indicated by the facility information.

8. 8. The information processing system according to claim 7, The memory includes: business information indicating characteristics of the business, including at least one of the type of business, information indicating whether the business is a main occupation, information indicating whether the business has a successor, and income from the business; retaining business continuity determination information indicating conditions related to the characteristics and an upper limit of the business suspension period for a business for which the conditions are satisfied; The processor: Identifying the conditions satisfied by the characteristics of the business indicated by the business information from the business continuity determination information; obtaining an upper limit value corresponding to the specified condition in the business continuity determination information; An information processing system that determines whether or not the business can be continued based on a comparison result between the calculated business suspension period and the acquired upper limit value.

9. 8. The information processing system according to claim 7, The memory includes: Shipping information indicating a shipping period for products shipped by the business and a shipping volume of the products during the shipping period; product information indicating the unit price of the product; The processor: calculating an estimated shipping volume of the products that would have been shipped during the business suspension period if the disaster event in the disaster scenario had not occurred, based on the shipping period and the shipping volume during the business suspension period; An information processing system that calculates the amount of sales loss to the business due to the disaster event in the disaster scenario based on the estimated shipping volume and the unit price.

10. 10. The information processing system according to claim 9, The resources include a recovery cost required for the identified facility to recover from the damage event; The processor calculates the amount of profit loss, which is the difference between the profit when the business suspension period occurs and the profit when the business suspension period does not occur, based on the estimated shipping volume, the unit price, the costs incurred in the business including the recovery costs predicted in the prediction process, the shipping period, and the shipping volume.

11. 11. The information processing system according to claim 10, The memory includes: Facility use information indicating facilities used in the business; maintain information indicating facilities within the jurisdiction of a local government; The processor: extracting, from the calculated amount of lost profit, the amount of lost profit for the business that uses the facility included in the jurisdiction; An information processing system that calculates the amount of tax revenue loss of the local government based on the extracted amount of profit loss.

12. 10. The information processing system according to claim 9, said product contains components necessary for the manufacture of other products; The memory stores part information indicating a product corresponding to the part and a product for which the part is used in the manufacture thereof; Facility usage information indicating facilities used in the business is stored; The processor: Accept the designation of the project to be evaluated, Identifying products shipped by the business to be evaluated from the shipping information; Identifying parts used in manufacturing the identified product from the parts information; Identifying a business to which the identified parts will be shipped from the shipping information; Identifying facilities used in the project to be evaluated and facilities used in the identified project from the facility usage information; Calculating the business suspension period for each of the evaluation target businesses and the specified businesses based on the operation suspension period for each of the specified facilities and the operation suspension period for each of the facilities that can be substituted for the specified facilities indicated by the facility information; calculate a first estimated shipping volume that is estimated to have been shipped by the products of the business to be evaluated during the business suspension period if the disaster event in the disaster scenario had not occurred, based on the shipping period and the shipping volume during the business suspension period of the business to be evaluated; calculate a second estimated shipment volume that is estimated to have been shipped by the specified business during the business suspension period if the disaster event in the disaster scenario had not occurred, based on the shipment period and the shipment volume during the business suspension period of the specified business; An information processing system that calculates the amount of sales loss of the business being evaluated due to the disaster event in the disaster scenario based on the first estimated shipping volume, the second estimated shipping volume, and the unit price.

13. 10. The information processing system according to claim 9, The processor: The resources include a recovery cost required for the identified facility to recover from the damage event; calculating sales for each predetermined cycle during a specified period including the business suspension period based on the business suspension period, the shipping period, the shipping volume, and the unit price; calculating the recovery cost for each predetermined cycle of the specified period based on the recovery cost predicted in the prediction process and the end date of the disaster occurrence period in the disaster scenario; An information processing system that generates data for displaying a time series of sales for each of the predetermined cycles during the specified period and recovery costs or costs incurred in the business including recovery costs for each of the predetermined cycles during the specified period.

14. 2. The information processing system according to claim 1, The resources include recovery time and recovery costs required for the identified facility to recover from the damage event; The processor: An information processing system that generates data for displaying information that links together information indicating disaster events in the disaster scenario, information indicating the identified damage events, information indicating the identified facilities, information indicating recovery time predicted in the prediction process, and information indicating recovery costs predicted in the prediction process.

15. An information processing method by an information processing system, the information processing system includes a processor and a memory; The memory includes: Correlation information indicating the correlation between the occurrence of a disaster event and the occurrence of a damage event that may occur in facilities used in the business; disaster scenario information indicating disaster events that occur in the disaster scenario and the magnitude of the disaster events; a first index indicating the implementation status of risk countermeasures for mitigating the damage event that may occur in the facility; The information processing method includes: the processor refers to the correlation information to identify a damage event that is correlated with the disaster event in the disaster scenario and a facility in which the damage event may occur; An information processing method, in which the processor performs a prediction process to predict the amount of resources to be spent on recovery from a damage event at the identified facility based on at least one of the magnitude of the disaster event in the disaster scenario and the first indicator for the damage event that may occur at the identified facility.

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