Supply chain risk evaluation device and method

Abstract

The present invention makes it possible to more appropriately evaluate an incident that may occur in a supply chain with respect to a risk.　A supply chain risk evaluation device 1000 comprising: an information collection management unit 1100 for collecting restoration period information 2060 of an incident occurring in a supply chain, restoration pattern information 2120, and supply capability information of a product which is the object of the supply chain; an input / output interface unit 1600 for receiving a countermeasure plan for the incident; an incident influence scenario generation unit 1300 for generating an influence scenario indicating a degree of influence caused by the incident, from the occurrence to the restoration of the incident, on the capability of the supply chain by using the restoration period information, the restoration pattern information, and the supply capability information; and a supply chain supply-demand optimization calculation unit 1400 for identifying an evaluation value of the countermeasure plan that corresponds to a period for executing the countermeasure plan.

Description

Supply chain risk assessment device and method

[0001] The present invention relates to a technique for assessing supply chain risks.

[0002] Background art in this technical field is Patent Literature 1. Patent Literature 1 describes the system as including "a handling item information acquisition means, a model setting means for setting supply chain models that correspond individually to steady-state conditions and to conditions when a risk event occurs, an estimated data derivation means for deriving estimated data that estimates a time-series situation in the supply chain, a performance data acquisition means for chronologically acquiring performance data from the supply chain, a risk determination means for determining whether or not a risk event has occurred based on the ratio between the estimated data and the performance data, and a risk system derivation means for, when a risk event occurs, performing a simulation using a risk response supply chain model and deriving a supply chain system and risk costs that minimize risk losses that occur when the risk event occurs."

[0003] JP 2011-227852 A

[0004] To improve business continuity, companies must prepare for disasters and geopolitical risks even during normal times. From the perspective of supply chain risk in particular, it is important to take measures that take into account the impact of supply disruptions from suppliers, increased transport lead times, and the recovery time required. Furthermore, because the time required to implement each measure varies, it is important to consider the time required for implementation when formulating measures.

[0005] In response to this, Patent Document 1 describes a mechanism for estimating the time-series state of a supply chain, determining whether a risk will occur by comparing it with actual supply chain data, and deriving a system and risk costs to minimize losses due to the risk when the risk occurs. However, while the method described in Patent Document 1 makes it possible to formulate countermeasures after a risk has occurred, it does not enable anticipating risks that may occur in the supply chain during normal times and taking countermeasures against those risks in advance.

[0006] Therefore, an object of the present invention is to enable more appropriate risk assessment for incidents that may occur in a supply chain, regardless of whether or not a risk occurs. Note that, in this application, an incident is an occurrence that may affect the supply chain and require countermeasures, and includes accidents and events.

[0007] To solve the above problems, the present invention evaluates incident countermeasure proposals using supply chain recovery period information, recovery pattern information, and supply capacity information. Note that evaluation in this invention refers to specifying an evaluation value for the countermeasure proposal, and while it is desirable to calculate the amount of opportunity loss as the evaluation value, it also includes calculation of the amount of damage, the postponement period, etc.

[0008] More specifically, the supply chain risk assessment device assesses a supply chain in transactions between organizations, and includes an information collection and management unit that collects recovery period information indicating the recovery period for an incident that occurs in the supply chain, recovery pattern information indicating a pattern of recovery for the incident, and supply capacity information indicating the supply capacity of the products that are the target of the supply chain; an input / output interface unit that accepts countermeasure proposals for the incident; an incident impact scenario generation unit that uses the recovery period information, the recovery pattern information, and the supply capacity information to generate an impact scenario that indicates the degree of impact that the incident will have on the capacity of the supply chain from the occurrence of the incident to recovery; and an evaluation value identification unit that identifies an evaluation value for the countermeasure proposal according to the period for implementing the countermeasure proposal.

[0009] The present invention also includes a supply chain risk assessment method using a supply chain risk assessment device, a supply chain risk assessment program that causes the supply chain risk assessment device to function as a computer, and a storage medium that stores the program.Furthermore, a supply chain risk assessment system that includes the supply chain risk assessment device and a method using the system are also aspects of the present invention.

[0010] According to the present invention, it is possible to objectively evaluate each incident that is expected to occur at a supplier. Furthermore, it is possible to formulate more effective countermeasures against supplier risks even during normal times when no incidents have occurred. Issues, configurations, and effects other than those described above will become clear from the description of the following embodiments.

[0011] 1 is a diagram showing an example of functional blocks of a supply chain risk assessment device 1000 according to a first embodiment. FIG. 2 is a diagram showing an example of the hardware configuration of a supply chain risk assessment system including the supply chain risk assessment device 1000 according to the first embodiment. FIG. 3 is a flowchart showing the overall processing flow of the supply chain risk assessment device 1000 according to the first embodiment. FIG. 4 is a flowchart showing the flow of a risk scenario generation process (step S10) according to the first embodiment. FIG. 5 is a flowchart showing the flow of an incident supplier impact scenario generation process (step S20) according to the first embodiment. FIG. 6 is a flowchart showing the flow of a supply chain supply and demand optimization calculation process (step S30) according to the first embodiment. FIG. 7 is a diagram showing an example of handling item information 2010 used in the first embodiment. FIG. 8 is a diagram showing an example of supplier location information 2020 used in the first embodiment. FIG. 9 is a diagram showing an example of incident occurrence risk information 2030 used in the first embodiment. FIG. 10 is a diagram showing an example of incident occurrence risk information 2030 used in the first embodiment. FIG. 11 is a diagram showing an example of risk occurrence probability information 2040 used in the first embodiment. FIG. 12 is a diagram showing an example of risk scenario information 2050 used in the first embodiment. FIG. 13 is a diagram showing an example of recovery period information 2060 used in the first embodiment. 1. A diagram showing an example of transportation lead time information 2070 used in the first embodiment. 2. A diagram showing an example of additional transportation lead time information 2080 used in the first embodiment. 3. A diagram showing an example of available inventory information 2090 used in the first embodiment. 4. A diagram showing an example of production capacity information 2100 used in the first embodiment. 5. A diagram showing an example of assumed incident occurrence time point information 2110 used in the first embodiment. 6. A diagram showing an example of an input screen for recovery pattern information 2120 in the first embodiment. 7. A diagram showing an example of transportation lead time impact scenario information 2130 in the first embodiment. 8. A diagram showing an example of inventory impact scenario information 2140 in the first embodiment. 9. A diagram showing an example of production capacity impact scenario information 2150 in the first embodiment. 10. A diagram showing an example of item price information 2160 in the first embodiment. 11. A diagram showing an example of BOM information 2170 in the first embodiment. 12. A diagram showing an example of demand information 2180 in the first embodiment. 13. A diagram showing an example of actual inventory information 2190 in the first embodiment. 14. A diagram showing an example of a countermeasure plan information input screen, which is an input screen for countermeasure plan information 2200 in the first embodiment. 10 is a diagram showing an example of estimated opportunity loss information 2210 in the first embodiment. FIG. 11 is a diagram showing an example of an output screen of a risk countermeasure proposal evaluation result display unit 1500 in the first embodiment.

[0012] An embodiment of the present invention will be described below. In this embodiment, incident impact scenarios for components of a supplier's supply capacity, such as the supplier's production capacity, transportation lead time, and inventory volume, are generated from publicly available information, such as supplier location information and hazard map information. Furthermore, for each incident impact scenario and each proposed countermeasure for the incident, a supply chain supply and demand optimization calculation is performed that takes into account supply and demand disruptions and recovery in the supply chain, and the company's estimated opportunity loss is calculated. Then, based on the expected value of the estimated opportunity loss for each risk scenario and proposed countermeasure and the total amount (total value) of the countermeasure costs, the system supports the selection of the most cost-effective countermeasure for the risk.

[0013] More preferably, the supply chain risk assessment device for assessing a supply chain in transactions between organizations includes an information collection and management unit that collects recovery period information indicating a recovery period for an incident that occurs in the supply chain, recovery pattern information indicating a recovery pattern for the incident, and supply capacity information indicating the supply capacity of the product targeted by the supply chain, an input / output interface unit that accepts countermeasure proposals for the incident, an incident impact scenario generation unit that generates an impact scenario indicating the degree of impact of the incident on the supply chain capacity from the occurrence of the incident to recovery using the recovery period information, the recovery pattern information, and the supply capacity information, and an evaluation value determination unit that determines an evaluation value for the countermeasure proposal according to the period for implementing the countermeasure.Furthermore, a supply chain risk assessment method using the supply chain risk assessment device, a supply chain risk assessment program that enables the device to function as a computer, and a storage medium storing the same are also aspects of the present embodiment.

[0014] In this embodiment, the impact scenario refers to the degree of impact on the supply chain's capabilities caused by an incident from the occurrence of the incident to recovery. More preferably, it refers to the degree of impact on the supply chain's operations and utilization capabilities from the occurrence of the incident to recovery. In addition, in this embodiment, the product (item) refers to an object handled in the supply chain, and includes raw materials, parts, assembly parts, modules, and units.

[0015] In the first embodiment, the information collection management unit 1100 collects various types of information in addition to the recovery period information 2060, the recovery pattern information 2120, and the supply capacity information. The incident impact scenario generation unit 1300 then uses these to generate impact scenarios.

[0016] In addition, in Example 1, an example will be described in which transportation lead time information 2070, production capacity information 2100, and inventory result information 2190 are used as supply capacity information. However, it is possible to generate incident impact scenario information using at least one of the transportation lead time information 2070, production capacity information 2100, and inventory result information 2190. Furthermore, in Example 1, transportation lead time impact scenario information 2130, inventory impact scenario information 2140, and production capacity impact scenario information 2150 are generated from the transportation lead time information 2070, inventory result information 2190, and production capacity information 2100, respectively. However, it is possible to use at least one of the transportation lead time impact scenario information 2130, inventory impact scenario information 2140, and production capacity impact scenario information 2150 as the impact scenario. Details of Example 1 will be described below with reference to the drawings.

[0017] 1 is a diagram illustrating an example of functional blocks of a supply chain risk assessment device 1000 according to Example 1. The supply chain risk assessment device 1000 includes an information collection and management unit 1100, a risk scenario generation unit 1200, an incident impact scenario generation unit 1300, a supply chain supply and demand optimization calculation unit 1400, a risk countermeasure proposal evaluation result display unit 1500, an input / output interface unit 1600, and a data storage unit 2000.

[0018] First, the information collection and management unit 1100 collects various information obtained from transaction data with suppliers and the like via the communication and input functions provided by the input / output interface unit 1600. In other words, this information is collected from other devices and systems connected using the communication function, or input by users and the like using the input function. The information collection and management unit 1100 then stores this information in the data storage unit 2000.

[0019] The collected information is listed below, along with the names of the information stored in the data storage unit 2000. Handling item data: handling item information 2010; Supplier location data obtained from map data or the like: supplier location information 2020; Risk data obtained from hazard maps or the like: incident occurrence risk information 2030; Incident occurrence probability data from hazard maps, etc.: risk occurrence probability information 2040; Period required for disaster recovery obtained from reports from the government, research institutions, etc.: recovery period information 2060; Supplier transportation lead time data obtained from supplier transaction data or the like: transportation lead time information 2070; Transportation lead time data at the time of incident occurrence obtained from reports from the government, research institutions, etc.: additional transportation lead time information 2080; Data on the ratio of inventory available at the time of incident occurrence obtained from reports from the government, research institutions, etc.: available inventory information 2090; Production capacity data of each company obtained from supplier transaction data or the like: production capacity information 2100; Data on the expected timing of incident occurrence: expected incident occurrence time information 2110; Product price data obtained from transaction data with suppliers or the like: item price information 2160 The following data are stored: BOM (Bill of Materials) data obtained from transaction data and design data with suppliers: BOM information 2170; demand information 2180, the company's sales plan data; and inventory performance information 2190, the company's and supplier's inventory performance obtained from the company's database, EDI (Electronic Data Interchange) system, etc. Note that the "company" refers to the buyer using the supply chain risk assessment device 1000. However, if the supply chain risk assessment device 1000 is implemented in a so-called cloud environment and shared by multiple companies, information on each company is collected and stored separately. Furthermore, a "company" is an example of an organization, and the subject of this embodiment is not limited to companies; it also includes corporations, public organizations, non-profit organizations, etc. Furthermore, this embodiment can be used not only by buyers, but also by suppliers, shipping companies, bases, and other parties involved in the supply chain.In this way, the supply chain risk assessment system shown in FIG. 2 may be realized as an in-house system for a company such as a buyer, or may be realized as a system that can be used by multiple companies (buyers and suppliers).

[0020] Furthermore, the information collection management unit 1100 receives input of recovery patterns and countermeasure proposals for incidents from the user using the input function provided by the input / output interface unit 1600. The information collection management unit 1100 then stores the recovery patterns as recovery pattern information 2120 in the data storage unit 2000. Furthermore, the information collection management unit 1100 stores the countermeasure proposals as countermeasure proposal information 2200 in the data storage unit 2000.

[0021] Furthermore, the risk scenario generation unit 1200 generates risk scenario information 2050 including incidents that a supplier may suffer and their probability data based on various information in the supply chain. Here, at least one of the following information is used: handled item information 2010, supplier location information 2020, incident occurrence risk information 2030, and risk occurrence probability information 2040. For this generation, the risk scenario generation unit 1200 uses at least one of the handled item information 2010, supplier location information 2020, incident occurrence risk information 2030, and risk occurrence probability information 2040. However, more preferably, all of these pieces of information are used. Here, it is desirable that this information be stored in the data storage unit 2000. Furthermore, it is desirable that the risk scenario generation unit 1200 store the generated risk scenario information 2050 in the data storage unit 2000.

[0022] The incident impact scenario generator 1300 also generates an impact scenario for each proposed countermeasure. Here, the impact scenario indicates the degree of impact on the supply chain's ability to respond to the incident from the occurrence of the incident to recovery. This ability includes transportation, inventory, and production capacity. An example of the generation of this impact scenario is shown below. The incident impact scenario generator 1300 uses various information to generate transportation lead time impact scenario information 2130, which indicates an impact scenario of the incident on the supplier's transportation lead time. For this generation, at least one of risk scenario information 2050, recovery period information 2060, transportation lead time information 2070, additional transportation lead time information 2080, and estimated incident occurrence time information 2110 is used. However, more preferably, all of this information is used. The incident impact scenario generator 1300 preferably stores the generated transportation lead time impact scenario information 2130 in the data storage unit 2000.

[0023] The incident impact scenario generator 1300 also uses various types of information to generate inventory impact scenario information 2140 caused by an incident on supplier inventory. The various types of information used include at least one of risk scenario information 2050, available inventory information 2090, estimated incident occurrence time information 2110, and actual inventory information 2190. However, it is more preferable to use all of this information. The incident impact scenario generator 1300 preferably stores the generated inventory impact scenario information 2140 in the data storage unit 2000.

[0024] The incident impact scenario generator 1300 also uses various types of information to generate production capacity impact scenario information 2150 on a supplier's production capacity due to an incident. The various types of information used include at least one of risk scenario information 2050, recovery period information 2060, production capacity information 2100, assumed incident occurrence time information 2110, and recovery pattern information 2120. However, it is more preferable to use all of this information. The incident impact scenario generator 1300 stores the generated production capacity impact scenario information 2150 in the data storage unit 2000.

[0025] As a result of the above, the incident impact scenario generation unit 1300 creates an impact scenario including transportation lead time impact scenario information 2130 , inventory impact scenario information 2140 , and production capacity impact scenario information 2150 .

[0026] The generation of impact scenarios in this embodiment can be performed as follows. The incident impact scenario generation unit 1300 creates impact scenarios using recovery period information 2060, recovery pattern information 2120, and supply capacity information. The supply capacity information is information related to supply time in the supply chain, and includes transportation lead time information 2070, production capacity information 2100, and actual inventory information 2190. In this case, the incident impact scenario generation unit 1300 generates transportation lead time impact scenario information 2130 from the recovery period information 2060 and the transportation lead time information 2070. The incident impact scenario generation unit 1300 also generates inventory impact scenario information 2140 from the actual inventory information 2190. The incident impact scenario generation unit 1300 also generates production capacity impact scenario information 2150 from the recovery pattern information 2120 and production capacity information 2100.

[0027] The supply chain supply and demand optimization calculation unit 1400 is an example of an evaluation value determination unit that determines the evaluation value of a countermeasure proposal, and calculates estimated opportunity loss information 2210 that indicates the estimated opportunity loss due to the incident as an example of the evaluation value. This opportunity loss amount includes the estimated opportunity loss amount of the relevant company. The supply chain supply and demand optimization calculation unit 1400 will be described below using these examples.

[0028] First, the supply chain supply and demand optimization calculation unit 1400 generates a supply chain model. To do this, the supply chain supply and demand optimization calculation unit 1400 uses at least one of the following: handling item information 2010, transportation lead time information 2070, production capacity information 2100, transportation lead time impact scenario information 2130, inventory impact scenario information 2140, production capacity impact scenario information 2150, item price information 2160, BOM information 2170, demand information 2180, actual inventory information 2190, countermeasure plan information 2200, and countermeasure plan information 2200. However, more preferably, all of this information is used. The details of generating the supply chain model will be explained later.

[0029] Next, the supply chain supply and demand optimization calculation unit 1400 calculates estimated opportunity loss information 2210 indicating the estimated opportunity loss amount of the company through a supply chain supply and demand optimization calculation that calculates the PSI (Production, Sales, Inventory) of the company and its suppliers. Then, the supply chain supply and demand optimization calculation unit 1400 preferably stores the calculated estimated opportunity loss information 2210 in the data storage unit 2000.

[0030] The risk countermeasure proposal evaluation result display unit 1500 is an example of an output unit that outputs various information. In this embodiment, the risk countermeasure proposal evaluation result display unit 1500 displays risk scenario information 2050, countermeasure proposal information 2200, countermeasure proposal information 2200, and estimated opportunity loss information 2210. Furthermore, the risk countermeasure proposal evaluation result display unit 1500 displays the expected value and total amount of the countermeasure cost and estimated opportunity loss amount for each risk scenario and countermeasure proposal.

[0031] 2 is a diagram showing an example of the hardware configuration of a supply chain risk assessment system including a supply chain risk assessment device 1000 in this embodiment. In the supply chain risk assessment system, the supply chain risk assessment device 1000 is connected to a terminal device 40 and an information provider device 50 via a network 30. First, the supply chain risk assessment device 1000 can be realized by a computer. Therefore, the supply chain risk assessment device 1000 has a CPU 11, RAM 12, ROM 13, an auxiliary storage device 14, a display device 15, an input device 16, a media reader 17, and an information receiver / transmitter 18. Each component of this device will be described below.

[0032] The CPU 11 is an example of a processor that executes various calculations. To execute the calculations, the CPU 11 executes a predetermined supply chain risk assessment program loaded from the auxiliary storage device 14 to the RAM 12, thereby performing various processes.

[0033] The supply chain risk assessment device 1000 is an application program that can be executed on, for example, an OS (Operating System) program. This supply chain risk assessment program may be installed into the auxiliary storage device 14 from a portable storage medium via the media reader 17. In this manner, the supply chain risk assessment program is stored on the storage medium. The CPU 11 then executes the functions of the information collection and management unit 1100, risk scenario generation unit 1200, incident impact scenario generation unit 1300, and supply chain supply and demand optimization calculation unit 1400 in accordance with the supply chain risk assessment program.

[0034] The RAM 12 is a memory that stores the supply chain risk assessment program executed by the CPU 11, data necessary for executing this supply chain risk assessment program, etc. The ROM 13 is a memory that stores the program necessary for starting up the supply chain risk assessment device 1000, etc.

[0035] The auxiliary storage device 14 is, for example, a device such as an HDD (Hard Disk Drive). It may also be an SSD (Solid State Drive) that uses flash memory or the like. The auxiliary storage device 14 may be implemented as a device separate from the supply chain risk assessment device 1000. In this case, the auxiliary storage device 14 may be implemented as a file server connected to the network 30. Furthermore, the auxiliary storage device 14 may be provided both within the supply chain risk assessment device 1000 and externally, and may share the responsibility of storing information, etc.

[0036] The display device 15 is, for example, a CRT display, an LCD (Liquid Crystal Display), or an organic EL (Electro-Luminescence) display. The display device 15 performs the function of the risk countermeasure proposal evaluation result display unit 1500 shown in FIG. 1. The input device 16 is, for example, a keyboard, a mouse, a microphone, or the like. The input device 16 performs the function of the input / output interface unit 1600 shown in FIG. 1. If the supply chain risk assessment device 1000 is implemented as a server, the display device 15 and the input device 16 can be omitted. In this case, the functions of the display device 15 and the input device 16 are provided in the terminal device 40. The display device 15 and the input device 16 may be integrated into one device, such as a touch panel.

[0037] The media reading device 17 is a device that reads information from a portable storage medium such as a CD-ROM. The information receiving / transmitting device 18 is a device that transmits and receives data to and from an external device such as a terminal device 40 via the network 30. For example, the information receiving / transmitting device 18 can be realized by a communication device that communicates with the network 30 such as a wired LAN or wireless LAN, a dial-up router, an infrared communication device, or the like. The information receiving / transmitting device 18 executes the function of the input / output interface unit 1600 in FIG. 1.

[0038] In this embodiment, the supply chain risk assessment device 1000 is implemented by a server, and therefore a network 30, terminal device 40, and information provider device 50 are also used, and these will be described below. The network 30 may be of any type, such as a LAN or WAN, as long as it enables communication between devices. The terminal device 40 can be implemented by a computer such as a PC or tablet terminal, and has the functions of a display device 15 and an input device 16, and receives operations from a user and displays processing results from the supply chain risk assessment device 1000. Although only one terminal device 40 is shown in FIG. 2, multiple terminal devices may be used.

[0039] The information providing device 50 includes an EDI system and a system for providing corporate information, and can be realized by a computer such as a server that provides various types of information. The supply chain risk assessment device 1000 acquires various types of information from the information providing device 50, as will be described later. This concludes the explanation of the configuration of this embodiment, and next we will explain the processing flow of this embodiment.

[0040] 3 is a flowchart showing the overall processing flow of the supply chain risk assessment device 1000 in Example 1. First, the risk scenario generation unit 1200 of the supply chain risk assessment device 1000 generates risk scenario information 2050 indicating a supplier's risk scenario (Step S10). Furthermore, the incident impact scenario generation unit 1300 generates an incident impact scenario based on the generated risk scenario information 2050, etc. (Step S20).

[0041] The supply chain supply and demand optimization calculation unit 1400 then executes a supply chain supply and demand optimization calculation based on the generated incident impact scenario (step S30). The risk countermeasure proposal evaluation result display unit 1500 then displays the expected value of the estimated opportunity loss amount and the countermeasure cost (step S40). The processing illustrated by this flowchart will be described in detail below. Specifically, the details of step S10 will be described using FIGS. 4 and 7 to 11.

[0042] Details of step S20 will be explained using Figures 5 and 12 to 21, and details of the simulation of step S30 will be explained using Figures 6 and 22 to 28. Furthermore, the detailed screen displayed in step S40 will be described later using Figure 28.

[0043] 4 is a flowchart showing the flow of the risk scenario generation process (step S10) in this embodiment. The process in FIG. 4 will be described in detail below.

[0044] First, the risk scenario generation unit 1200 acquires various information via the input / output interface unit 1600 (step S110). The acquired information includes at least one of information on items handled by suppliers, location information on each supplier, incident occurrence risk information related to each supplier's location information, and risk occurrence probability. However, more preferably, all of this information is acquired. The risk scenario generation unit 1200 stores the acquired data in the data storage unit 2000 as handled item information 2010, supplier location information 2020, incident occurrence risk information 2030, and risk occurrence probability information 2040. Here, the processing of step S110 is repeatedly executed for each supplier. Here, the data acquired in the processing of step S110 and the processing thereof will be described in detail.

[0045] First, Fig. 7 is a diagram showing an example of handling item information 2010 used in the first embodiment. In Fig. 7, the handling item information 2010 has fields for company name (2011) and item (2012). As such, the handling item information 2010 shows data on suppliers and the items handled by the suppliers. Data for the handling item information 2010 is acquired, for example, by manual input or data upload via the input / output interface unit 1600, or by connecting to an EDI system or the like.

[0046] 8 is a diagram showing an example of supplier location information 2020 used in Example 1. In FIG. 8, the supplier location information 2020 has fields for company name (2021) and location information (2022). The supplier location information 2020 indicates the location information of the location of each supplier stored in the handling item information 2010 of FIG. 7. The supplier location information 2020 obtains the latitude and longitude information of a company from EDI or public company information by comparing address information present in the EDI or public company information with public map information.

[0047] 9A and 9B are diagrams showing examples of incident occurrence risk information 2030 used in Example 1. In the present application, the incident occurrence risk information 2030 is described as being divided into two parts, FIG. 9A and FIG. 9B, but it may also be configured as an integrated piece.

[0048] 9A shows that incident occurrence risk information 2030 has fields for risk (2031a), incident (2032a), and polygon ID (2033a). In this way, incident occurrence risk information 2030 shows data on risks that may occur at suppliers, incidents that may occur at suppliers due to risks, and polygon ID data related to the extent of damage.

[0049] FIG. 9B also shows that the incident occurrence risk information 2030 has fields for polygon ID (2031b) and damage range (2032b). Here, FIG. 9B shows location information for identifying the damage range for the polygon ID in FIG. 9A. For example, the damage range for polygon ID "1" of the risk "flood" and incident "0.0 to 0.5 m inundation" in FIG. 9A is as follows: That is, the area within the polygon consisting of polygon ID "1" and damage range "35.36469700, 139.36724700, 35.35327094, 139.36715574, 35.35034200, 139.36867800" in FIG. 9B.

[0050] The incident occurrence risk information 2030 can be acquired from public information such as hazard maps provided by the Ministry of Land, Infrastructure, Transport and Tourism or the Geospatial Information Authority of Japan. It is also possible to purchase hazard map information provided by insurance companies, rating agencies, etc., and register the data via the input / output interface unit 1600. Therefore, the information providing device 50 in FIG. 2 can provide hazard maps (information).

[0051] FIG. 10 is a diagram showing an example of risk occurrence probability information 2040. This risk occurrence probability information 2040 has fields for risk (2041) and occurrence probability (2042). The risk occurrence probability information 2040 indicates the occurrence probability of a risk corresponding to the incident occurrence risk information 2030. Here, the risk occurrence probability is obtained from public information such as hazard maps provided by the Ministry of Land, Infrastructure, Transport and Tourism or the Geospatial Information Authority of Japan. It is also possible to purchase hazard map information provided by insurance companies, rating agencies, etc., and register the data via the input / output interface unit 1600.

[0052] Returning to the flowchart of Figure 4, the risk scenario generation process will be described. Next, the risk scenario generation unit 1200 uses various information to generate risk scenario information 2050 for each supplier and for each incident occurrence risk (step S120). Here, at least one of the above-mentioned supplier location information 2020, incident occurrence risk information 2030, and risk occurrence probability information 2040 is used as the various information. However, in a more preferred embodiment, all information is used. Note that step S120 is repeatedly executed for each incident occurrence risk.

[0053] The information and data generated in this step and their processing will be described in detail below. FIG. 11 is a diagram showing an example of risk scenario information 2050 used in Example 1. This risk scenario information 2050 has fields for company name (2051), incident (2052), and occurrence probability (2053). The risk scenario information 2050 indicates the anticipated incidents for each company and their occurrence probability. A method for generating the risk scenario information 2050 by the risk scenario generation unit 1200 will be described below.

[0054] For example, the supplier location information for company "Company A" in Figure 8 is "35.5510000, 139.6820000," and for each polygon ID in Figure 9, a determination is made as to whether the supplier location information is inside or outside the polygon defined by the damage range. In the example of Figures 9A and 9B, the result is as follows: The polygon for risk "flood," incident "0.5 to 3.0 m flooding," and damage range "35.552857, 139.679958, 35.557557, 139.682905, 35.547137, 139.684566, 35.544282, 139.691100, 35.553149, 139.694955" for polygon ID "1000" is targeted. Within this polygon exists the supplier location information "35.5510000, 139.6820000" of the company name "Company A" in the supplier location information 2020 shown in Fig. 8. Furthermore, since the occurrence probability of the risk "flood" in the risk occurrence probability information 2040 shown in Fig. 10 is "0.00001", the incident of the company name "Company A" in Fig. 11 is "flooding 0.5 to 3.0 m" and the occurrence probability is "0.00001".

[0055] This concludes the description of step S120, i.e., the processing of step S10. Next, the details of the processing of step S20 in Fig. 3 will be described. Fig. 5 is a flowchart showing the flow of the incident impact scenario generation processing (step S20) in the first embodiment.

[0056] First, the incident impact scenario generator 1300 acquires various pieces of information via the input / output interface unit 1600 (step S210). The incident impact scenario generator 1300 stores the acquired information in the data storage unit 2000. The acquired and stored information includes at least one of the following: Information regarding the recovery period of an incident: recovery period information 2060; Information regarding the transportation lead time: transportation lead time information 2070; Information regarding the lead time added due to the impact of an incident when it occurs: additional transportation lead time information 2080; Information regarding inventory loss when an incident occurs: available inventory information 2090; Information regarding production capacity: production capacity information 2100; Information regarding the expected time when an incident occurs: expected incident occurrence time information 2110; Information regarding the pattern of the recovery period from an incident: recovery pattern information 2120. The following describes the details of this information and the processing from step S220 onward. FIG. 12 is a diagram showing an example of the recovery period information 2060 used in the first embodiment. The recovery period information 2060 has fields for incident (2061) and recovery period (2062). In this way, the recovery period information 2060 indicates the recovery period, which is the period from the occurrence of an incident to the recovery to a normal state. The recovery period information 2060 is obtained, for example, by web crawling or scraping from public information such as disaster reports issued by the government or research institutions. Note that private information obtained separately may also be registered by manual input or data upload via the input / output interface unit 1600.

[0057] FIG. 13 is a diagram showing an example of transportation lead time information 2070 used in the first embodiment. The transportation lead time information 2070 has fields for transportation source (2071), transportation destination (2072), and lead time (2073). In this manner, the transportation lead time information 2070 indicates the transportation lead time from the transportation source company to the transportation destination company. The transportation lead time information 2070 is obtained, for example, by manual input or data upload via the input / output interface unit 1600, or by connecting to an ERP system, EDI system, or the like.

[0058] FIG. 14 is an example of additional transportation lead time information 2080 used in the first embodiment. The additional transportation lead time information 2080 has fields for incident (2081), additional transportation lead time (2082), and impact period (2083). As such, the additional transportation lead time information 2080 indicates the additional transportation lead time for an incident and its impact period. For example, the additional transportation lead time information 2080 is linked to past supplier transaction data, whether or not past incidents have occurred, and the details of the incidents from an EDI system or the like connected via the input / output interface unit 1600, and is calculated and acquired using a method such as machine learning. Note that the additional transportation lead time information 2080 may also be acquired by manual input or data upload.

[0059] FIG. 15 is a diagram showing an example of available inventory information 2090 used in the first embodiment. The available inventory information 2090 includes an incident (2091) and an available inventory coefficient (2092). Therefore, the available inventory information 2090 indicates the coefficient of the amount of available inventory at the time of the incident. A specific example of the available inventory information 2090 will be described below with reference to FIG. 15. If a company has 100 units in stock before the incident occurs, and an incident of "flooding of 0.0 to 0.5 m" occurs, the amount of available inventory is "100 x 1.0 = 100." Furthermore, when an incident of "flooding of 0.5 to 3.0 m" occurs, the amount of available inventory is "100 x 0.5 = 50."

[0060] The available inventory information 2090 is obtained, for example, by manual input or data upload via the input / output interface unit 1600. For example, if the incident is flooding, the available inventory information 2090 is calculated mechanically based on the inventory management status and building information, and the flood depth, to determine what percentage of inventory is available. If the incident is an earthquake, the available inventory information 2090 can estimate the building condition at the time of the earthquake from the structural seismic resistance index.

[0061] FIG. 16 is a diagram showing an example of production capacity information 2100 used in the first embodiment. The production capacity information 2100 has fields for company name (2101), time (2102), and production capacity (2103). Therefore, the production capacity information 2100 indicates information related to a company's production capacity at a certain time, such as the number of units that can be produced. A specific example of the production capacity information 2100 will now be described with reference to FIG. 16 . At the time "June 23, 2023" for company name "Company A," the production capacity is "140." For example, the production capacity information 2100 is acquired from a company's own ERP system or a supplier's production management system via the input / output interface unit 1600. The production capacity information 2100 may also be acquired by manual input or data upload.

[0062] FIG. 17 is a diagram illustrating an example of the estimated incident occurrence time information 2110 used in the first embodiment. The estimated incident occurrence time information 2110 has fields for a company name (2111), an incident (2112), and an estimated incident occurrence time (2113). Therefore, the estimated incident occurrence time information 2110 indicates the estimated occurrence time of an incident that will occur at a company. A specific example of the estimated incident occurrence time information 2110 will be described below with reference to FIG. 17 . The estimated incident occurrence time for a company named "Company A" and an incident "flooding of 0.5 to 3.0 meters" is "June 26, 2023." For example, the estimated incident occurrence time information 2110 is acquired by manual input or data upload via the input / output interface unit 1600. For example, the estimated incident occurrence time information 2110 can be set arbitrarily by the user, or a time can be generated using random numbers within a specific period.

[0063] 18 is a diagram showing an example of an input screen for recovery pattern information 2120 in the first embodiment. In FIG. 18, a pattern input screen 181 and a pattern input screen 182 are example screens for inputting a recovery pattern of production capacity. Here, the unit time t is defined as "the number of days elapsed since the occurrence of the incident / recovery period". However, it is assumed that the number of days elapsed since the occurrence of the incident does not exceed the recovery period, and the possible values ​​of the unit time t are 0≦t≦1. Production capacity correction coefficient α tis defined as the degree of recovery of production capacity from the incident at time t, and the production capacity at time t is calculated by multiplying the production capacity 2103 in the production capacity information 2100 of FIG. 16 by α t At the time of recovery, that is, at t=1, α t By setting this, we express that production capacity is returning to normal.

[0064] The specific contents of FIG. 18 will be described below. The pattern input screen 181 in FIG. 18 shows an example of inputting a recovery pattern of a square wave. This is an example that simulates a case where recovery to normal times is achieved as quickly as possible. For example, the data example on the pattern input screen 181 shows that the production capacity does not return at all from the time of incident occurrence until recovery, remaining at zero, and then recovers all at once to the original production capacity upon recovery. Assume that the production capacity of the company name "Company A" in the production capacity information 2100 shown in FIG. 16 is constant at "140", an incident occurs, and the recovery period is four days. In this case, "Day 1: t = (1 / 4), α ０．２５ = 0.0, Day 2: t = (2 / 4), α ０．５ = 0.0, Day 3: t = (3 / 4), α ０．７５ = 0.0, 4th day: t = (4 / 4), α １．０ = 1.0". And the production capacity is "Day 1: 140 x α ０．２５ = 0, Day 2: 140 x α ０．５ = 0, 3rd day: 140 x α ０．７５ = 0.0, 4th day: 140 × α １．０ = 100".

[0065] The pattern input screen 182 also shows an example of inputting a linear recovery pattern. This is an example that simulates a gradual recovery to normal times after the occurrence of an incident. The data example on the pattern input screen 182 shows that production capacity is restored linearly from the time an incident occurs until recovery. Assume that the production capacity of the company "Company A" in the production capacity information 2100 shown in FIG. 16 is constant at "140", an incident occurs, and the recovery period is four days. In this case, "Day 1: t = (1 / 4), α ０．２５ =0.25, t=(2 / 4), α ０．５ = 0.05, Day 3: t = (3 / 4), α０．７５ = 0.75, Day 4: Day 4: t = (4 / 4), α １．０ = 1.0". And the production capacity is "Day 1: 140 x α ０．２５ = 35, Day 2: 140 x α ０．５ = 70, 3rd day: 140 x α ０．７５ = 105, 4th day: 140 x α １．０ =140".

[0066] Furthermore, a recovery pattern setting 183 shows an example of a setting screen for a recovery pattern for an incident. The recovery pattern setting 183 accepts settings for which recovery pattern to adopt for each incident.

[0067] Returning to the flowchart of Figure 5, the processing from step S220 onwards will be explained. The incident impact scenario generation unit 1300 uses various information to generate transport lead time impact scenario information 2130 (step S220). This various information includes at least one of risk scenario information 2050, transport lead time information 2070, additional transport lead time information 2080, and assumed incident occurrence time information 2110. However, more preferably, all of this information is used. Note that steps S220 to S240 are repeatedly executed for each risk scenario. Next, all of the information used in step S220 and its processing will be explained in detail.

[0068] 19 is a diagram showing an example of transportation lead time impact scenario information 2130 in Example 1. The transportation lead time impact scenario information 2130 has fields for transportation source (2131), transportation destination (2132), time point (2133), and incident consideration lead time (2134). In this way, the transportation lead time impact scenario information 2130 indicates the lead time from the transportation source to the transportation destination when an incident occurs.

[0069] Next, a specific example of a method for generating the transport lead time impact scenario information 2130 will be described. For example, for the company name "Company A" in the risk scenario information 2050 shown in FIG. 11, the scenario is as follows. First, the additional lead time when the incident "flooding to a depth of 0.5 to 3.0 m" occurs is "2 days" from the transport source "Company A" to the transport destination "Company H" in the transport lead time information 2070 shown in FIG. 13. Then, for the incident "flooding to a depth of 0.5 to 3.0 m" in the additional transport lead time information 2080 shown in FIG. 14, the additional transport lead time is "5 days" and the impact period is "7 days." Furthermore, the incident occurrence time for the company name "Company A" in the assumed incident occurrence time information 2110 shown in FIG. 17 is "6 / 26 / 2023." From the above, the incident-considered transport lead time from the transport source "Company A" to the transport destination "Company H" in the transport lead time impact scenario information 2130 shown in Figure 19 from "2023 / 6 / 26" to "2023 / 7 / 3" is "2 days + 7 days = 9 days."

[0070] Returning to the flowchart of Figure 5, step S230 will now be described. The incident impact scenario generator 1300 uses various information to generate inventory impact scenario information 2140 (step S230). Here, the various information includes at least one of risk scenario information 2050, available inventory information 2090, and assumed incident occurrence time information 2110. However, it is more preferable to use all of this information. Next, the information used in step S230 and its processing will be described in detail.

[0071] 20 is a diagram showing an example of inventory impact scenario information 2140 in Example 1. The inventory impact scenario information 2140 has fields for company name (2141), time point (2142), and available inventory coefficient (2143). Therefore, the inventory impact scenario information 2140 indicates the available inventory coefficient of the company at the time of the incident occurrence.

[0072] Next, a specific example of a method for generating inventory impact scenario information 2140 by the incident impact scenario generator 1300 will be described below. For example, assume that an incident of "flooding to a depth of 0.5 to 3.0 m" occurs at company "Company A" in FIG. 11. In this case, the available inventory coefficient for incident "flooding to a depth of 0.5 to 3.0 m" in FIG. 15 is "0.5." The incident occurrence time for company "Company A" in the estimated incident occurrence time information 2110 shown in FIG. 17 is "June 26, 2023." Based on these factors, the available inventory coefficient for company "Company A" at the time "June 26, 2023" in the inventory impact scenario information 2140 shown in FIG. 20 is "0.5."

[0073] Returning to the flowchart of Figure 5, step S240 will now be described. The incident impact scenario generator 1300 uses various information to generate production capacity impact scenario information 2150 (step S240). This information includes at least one of risk scenario information 2050, recovery period information 2060, production capacity information 2100, assumed incident occurrence time information 2110, and recovery pattern information 2120. However, it is more preferable to use all of this information. Next, the information used in step S240 and its processing will be described in detail.

[0074] 21 is a diagram showing an example of production capacity impact scenario information 2150 in Example 1. The production capacity impact scenario information 2150 has fields for company name (2151), time point (2152), and incident-considered production capacity (2153). Therefore, the production capacity impact scenario information 2150 indicates the production capacity that takes into account the company's recovery state from the incident at the time the incident occurred.

[0075] Next, a specific example of a method for generating the production capacity impact scenario information 2150 by the incident impact scenario generator 1300 will be described below with reference to FIGS.

[0076] According to the risk scenario information 2050 shown in Figure 11, Company A will experience flooding of 0.5 to 3.0 meters on June 26, 2023. In this case, the recovery period due to the flooding is seven days, as shown in Figure 12, and it can be seen that Company A's production capacity will be affected from June 26, 2023 to July 3, 2023. Furthermore, the recovery pattern for the production capacity for that period (production capacity information 2100 in Figure 16) is set to "linear with a correction coefficient of 0.0 when an incident occurs and 1.0 when recovery occurs" according to the pattern input screen 182 in Figure 18. Therefore, Company A's daily production capacity from June 26, 2023 to July 3, 2023 will be 0, 20, 40, 60, 80, 100, 120, and 140.

[0077] This concludes the description of step S240, that is, the processing of step S20.

[0078] Next, details of step S30 in Fig. 3 will be described. Fig. 6 is a flowchart showing the flow of the supply chain supply and demand optimization calculation process (step S30) in the first embodiment.

[0079] First, the supply chain supply and demand optimization calculation unit 1400 acquires various information via the input / output interface unit 1600 (step S310). The supply chain supply and demand optimization calculation unit 1400 stores the acquired information in the data storage unit 2000. The acquired and stored information includes at least one of the following: information on the unit price of products handled by the supplier: item price information 2160; BOM information on the products handled: BOM information 2170; demand information on the supplier's own company: demand information 2180; information on the initial inventory of supply chain constituent companies: inventory performance information 2190; and information on countermeasures proposed for incidents: countermeasures proposal information 2200. The following describes this information and the processing from step S320 onward in detail. Figure 22 is a diagram showing an example of item price information 2160 in the first embodiment. The item price information 2160 has fields for company name (2161), item (2162), quantity (2163), and unit price (2164). Therefore, the item price information 2160 indicates the unit price of each product and quantity handled by the company. The item price information 2160 is obtained, for example, by manual input or data upload via the input / output interface unit 1600, or by linking with an EDI system or the like.

[0080] 23 is a diagram showing an example of BOM information 2170 in the first embodiment. The BOM information 2170 has fields for parent parts (2171) and child parts (2172). Therefore, the BOM information 2170 indicates child parts required to manufacture the parent parts. The BOM information 2170 is acquired, for example, from an ERP system or a trading company such as a supplier via the input / output interface unit 1600.

[0081] FIG. 24 is an example of demand information 2180 in the first embodiment. This data table has fields for time (2181), company name (2182), item (2183), and demand (2184). Therefore, the demand information 2180 is information regarding the demand for products (items) handled by a company at each time. The demand information 2180 is obtained, for example, by manually inputting or uploading information generated by the company's sales plan, demand forecast, etc., via the input / output interface unit 1600, or by connecting to the company's own database. The company's own database can be realized by the above-mentioned file server or information providing device 50.

[0082] 25 is a diagram showing an example of inventory performance information 2190 in Example 1. The inventory performance information 2190 has fields for time (2191), company name (2192), item (2193), and inventory quantity (2194). Therefore, the inventory performance information 2190 is information regarding the inventory quantity of products (items) handled by the company at the initial point in time of the simulation. The inventory performance information 2190 is obtained, for example, by manual input or data upload via the input / output interface unit 1600, or by connecting to an ERP system, EDI system, or the like.

[0083] Returning to the flowchart of FIG. 6 , the explanation of the supply chain supply and demand optimization calculation process will continue. The supply chain supply and demand optimization calculation unit 1400 sets up a supply chain model (step S320). To set up this supply chain model, the supply chain supply and demand optimization calculation unit 1400 performs MRP calculations based on the company's demand information. As a result, the supply chain supply and demand optimization calculation unit 1400 sets the company's demand data, BOM data, transportation lead time data, inventory data, and production capacity data necessary to calculate the company's and its suppliers' PSIs. For example, this information uses handling item information 2010, transportation lead time information 2070, production capacity information 2100, item price information 2160, BOM information 2170, demand information 2180, and inventory performance information 2190.

[0084] Furthermore, the supply chain demand and supply optimization calculation unit 1400 acquires the countermeasure proposal information 2200 (step S330). Next, the information used in step S330 will be described in detail.

[0085] FIG. 26 is a diagram showing an example of a countermeasure proposal information input screen, which is an input screen for countermeasure proposal information 2200 in Example 1. FIG. 26 shows three screens, countermeasure proposal information input screens 261 to 263. First, the countermeasure proposal information input screen 261 shows an example of an input screen when setting a countermeasure proposal for inventory increase. In the example of FIG. 26, the countermeasure proposal information input screen 261 shows an input example in which the inventory addition amount for item "ProdA" of company "Company H" is set to 500, the countermeasure implementation period required to increase inventory is set to 3 days, and the countermeasure cost is set to 1 million yen. In this case, it means that "Company H" will implement a countermeasure to increase the inventory of "ProdA" by a uniform amount of 500 units from the time (2191) of the inventory actual result information 2190 in FIG. 25 to the time when the supply and demand optimization calculation is completed, at an investment of 1 million yen.

[0086] The countermeasure proposal information input screen 262 shows an example of an input screen for setting countermeasure proposals for multi-company purchasing. In the example of FIG. 26, the countermeasure proposal information input screen 262 indicates that when "Company H" purchases item "Item A," the supplier companies are "Company A" and "Company D," the purchasing ratio is "0.7, 0.3," and the countermeasure cost is 3 million yen. The countermeasure implementation period for multi-company purchasing refers to the period required to change the purchasing ratio of the supplier if an incident occurs at either of the supplier companies. The screen also shows that if an incident occurs at Company A, the implementation period for changing Company D's purchasing ratio from 0.3 to 1.0 is 7 days.

[0087] 26, the countermeasure information input screen 263 shows an example of an input screen for setting countermeasures for supply chain switching. In the example of Fig. 26, the countermeasure information input screen 263 shows that when switching from the switching target "Company B" that handles item "Parts A" to the switching destination "Company E," the countermeasure implementation period is "14 days" and the countermeasure cost is "0.5 million yen."

[0088] Returning to the flowchart of Figure 6, the explanation of the supply chain supply and demand optimization calculation process will continue. The supply chain supply and demand optimization calculation unit 1400 sets an incident countermeasure supply chain model for each risk scenario and each proposed countermeasure (step S340). To set this incident countermeasure supply chain model, the supply chain supply and demand optimization calculation unit 1400 uses information on the supply chain model set in step S320, for example. Settings related to the supply chain, transportation lead time, inventory, and production capacity are updated. Here, the information used for this update is at least one of risk scenario information 2050, transportation lead time impact scenario information 2130, inventory impact scenario information 2140, production capacity impact scenario information 2150, and countermeasure proposal information 2200.

[0089] As an example of this update, a case will be described where, according to the risk scenario information 2050 in Figure 11, Company A is expected to experience flooding of 0.5 to 3.0 m on June 26, 2023. In the production capacity information 2100 in Figure 16, Company A's production capacity from June 26, 2023 to July 3, 2023 is updated to 140. Furthermore, according to the production capacity impact scenario information 2150 in Figure 21, Company A's production capacity from June 26, 2023 to July 3, 2023 is updated to 0, 20, 40, 60, 80, 100, 120, and 140.

[0090] Returning to the flowchart of FIG. 6 , the explanation of the supply chain supply and demand optimization calculation process continues. The supply chain supply and demand optimization calculation unit 1400 performs a supply chain supply and demand optimization calculation (step S350). As the supply chain supply and demand optimization calculation, the supply chain supply and demand optimization calculation unit 1400 performs a supply and demand optimization calculation based on a PSI receipt and payment calculation, for example, using the incident response supply chain model set in step S340 as input. As a result, the supply chain supply and demand optimization calculation unit 1400 identifies and outputs the estimated opportunity loss amount for the companies constituting the supply chain. The estimated opportunity loss amount is defined, for example, as "(total delivery request - number of products delivered on the delivery request date)." Here, the supply and demand optimization calculation is performed using a simulation method, a mathematical optimization method, or the like.

[0091] Furthermore, the supply chain supply and demand optimization calculation unit 1400 calculates the estimated opportunity loss amount (step S360). Next, the information used in step S360 and the details of the processing thereof will be described.

[0092] First, FIG. 27 is a diagram showing an example of estimated opportunity loss information 2210 in Example 1. The estimated opportunity loss information 2210 has fields for target company (2211), occurrence probability (2212), incident (2213), countermeasure (2214), and estimated opportunity loss (2215). Therefore, the estimated opportunity loss information 2210 is information regarding the estimated opportunity loss amount for each countermeasure when an incident is assumed to occur at the target company. The supply chain supply and demand optimization calculation unit 1400 calculates the estimated opportunity loss information 2210 based on the supply and demand optimization calculation in step S350 by multiplying the calculated estimated opportunity loss amount by the unit price (2164) of the item price information 2160 in FIG. 22. This concludes the description of step S360, i.e., step S30.

[0093] Next, an example of the output contents of the processing results in Example 1 will be described. Fig. 28 is a diagram showing an example of an output screen of the risk countermeasure proposal evaluation result display unit 1500 in Example 1. Fig. 28 shows an example of a screen that outputs the expected value of the estimated opportunity loss amount and the countermeasure cost for each countermeasure proposal for each risk scenario by the supply chain risk assessment device 1000.

[0094] Here, the expected value of the estimated opportunity loss is calculated by the supply chain supply and demand optimization calculation unit 1400 according to "occurrence probability of risk scenario x estimated opportunity loss amount." Here, according to the estimated opportunity loss information 2210 in Figure 27, if Company A experiences flooding of 0.5 to 3.0 meters, the estimated opportunity loss amount when the countermeasure plan of increasing inventory is implemented is 800,000 million yen, and the occurrence probability is 0.00001. Therefore, the expected value of the estimated opportunity loss amount for inventory increase in Figure 28 is "800,000 million yen x 0.00001 = 8 million yen."

[0095] According to the above-described first embodiment, it is possible to check the countermeasure costs, the expected value of the estimated opportunity loss, and the total value of the expected value of the estimated opportunity loss and the countermeasure costs for each risk scenario. Therefore, it is possible to check the average value of the expected value of the estimated opportunity loss and the total value of the countermeasure costs for each countermeasure plan in ascending order. This makes it possible to easily select a countermeasure plan with higher profitability. Note that, as a modification of the first embodiment, it is also possible to check the average value of the expected value of the estimated opportunity loss and the total value of the countermeasure costs in descending order. This concludes the explanation of the first embodiment.

[0096] The present invention is not limited to the above-described embodiment and Example 1, and includes various modifications. For example, the above-described embodiment and Example 1 have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of these embodiment and Example 1 with other configurations, and it is also possible to add other configurations to the configuration of the above-described embodiment and Example 1. Furthermore, it is possible to add, delete, or replace part of the configuration of the embodiment and Example 1 with other configurations.

[0097] Furthermore, some or all of the above-described configurations, functions, processing units, processing means, etc. may be realized in hardware, for example, by designing them as integrated circuits. Furthermore, the above-described configurations, functions, etc. may be realized in software by a processor interpreting and executing a supply chain risk assessment program that realizes each function. Information such as the supply chain risk assessment program, tables, and files that realize 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.

[0098] 1000 Supply chain risk assessment device 1100 Information collection and management unit 1200 Risk scenario generation unit 1300 Incident impact scenario generation unit 1400 Supply chain supply and demand optimization calculation unit 1500 Risk countermeasure proposal evaluation result display unit 1600 Input / output interface unit 2000 Data storage unit

Claims

1. A supply chain risk assessment device for assessing a supply chain in transactions between organizations, the supply chain risk assessment device having: an information collection and management unit that collects recovery period information indicating a recovery period for an incident occurring in the supply chain, recovery pattern information indicating a pattern of recovery for the incident, and supply capacity information indicating the supply capacity of products that are the target of the supply chain; an input / output interface unit that accepts countermeasure proposals for the incident; an incident impact scenario generation unit that generates an impact scenario indicating the degree of impact caused by the incident on the capacity of the supply chain from the occurrence of the incident to recovery, using the recovery period information, recovery pattern information, and supply capacity information; and an evaluation value identification unit that identifies an evaluation value of the countermeasure proposal according to the period for implementing the countermeasure proposal.

2. A supply chain risk assessment device according to claim 1, wherein the assessment value determination unit is a supply chain demand and supply optimization calculation unit that calculates the amount of opportunity loss due to the incident.

3. A supply chain risk assessment device according to claim 2, wherein the supply chain demand and supply optimization calculation unit calculates the expected value of the opportunity loss amount and the total value of the countermeasure costs.

4. A supply chain risk assessment device according to claim 1, wherein the supply capacity information includes transportation lead time information, production capacity information and inventory performance information.

5. A supply chain risk assessment device as described in claim 4, wherein the incident impact scenario generation unit generates transportation lead time impact scenario information, inventory impact scenario information and production capacity impact scenario information as the impact scenarios from the transportation lead time information, the production capacity information and the inventory performance information.

6. A supply chain risk assessment method for evaluating a supply chain in transactions between organizations using a supply chain risk assessment device, wherein an information collection and management unit collects recovery period information indicating a recovery period for an incident occurring in the supply chain, recovery pattern information indicating a pattern of recovery for the incident, and supply capacity information indicating the supply capacity of the products that are the target of the supply chain, an input / output interface unit accepts countermeasure proposals for the incident, an incident impact scenario generation unit generates an impact scenario indicating the degree of impact of the incident on the capacity of the supply chain from the occurrence of the incident to recovery, using the recovery period information, recovery pattern information, and supply capacity information, and an evaluation value determination unit determines an evaluation value of the countermeasure proposal according to the period for implementing the countermeasure proposal.

7. A supply chain risk assessment method according to claim 6, wherein the evaluation value determination unit functions as a supply chain demand and supply optimization calculation unit that calculates the amount of opportunity loss due to the incident.

8. A supply chain risk assessment method according to claim 7, wherein the supply chain demand and supply optimization calculation unit calculates the expected value of the opportunity loss amount and the total value of the countermeasure costs.

9. A supply chain risk assessment method according to claim 6, wherein the supply capacity information includes transportation lead time information, production capacity information and inventory performance information.

10. A supply chain risk assessment method as described in claim 9, wherein the incident impact scenario generation unit generates transportation lead time impact scenario information, inventory impact scenario information and production capacity impact scenario information as the impact scenarios from the transportation lead time information, the production capacity information and the inventory performance information.

Images