Supply chain supply planning device and supply chain supply planning method

Abstract

A supply chain supply planning device (1) is characterized by comprising: a distribution calculation unit (11) that calculates, on the basis of record information pertaining to supply and demand activities of a supply chain, a distribution pertaining to record information pertaining to each process constituting the supply chain; a distribution influence degree calculation unit (123) that, when information pertaining to an incident that influences the supply chain is input, calculates, on the basis of the record of a change in a distribution of record information pertaining to each past process due to the influence of a past incident similar to the incident, the degree of influence on the distribution of the record information pertaining to each process; and a distribution estimation unit that estimates a future distribution on the basis of the degree of influence on the distribution of the record information pertaining to each process calculated by the distribution influence degree calculation unit (123).

Description

Supply Chain Supply Planning Device and Supply Chain Supply Planning Method

[0001] The present invention relates to a supply chain supply planning device and a supply chain supply planning method.

[0002] In recent years, against the backdrop of increasing geopolitical risks, etc., risk prediction of supply chain disruptions and supply chain management that connects product and parts supplies between companies have become increasingly important. Therefore, it is necessary to quickly confirm where and what kind of impact will occur when a risk occurs and take countermeasures.

[0003] The main challenges in supply chain management have come to require not only following customer needs but also optimizing plans in the face of supply uncertainties and strong constraints due to pandemics and regulatory enhancements. Conventional supply chain management is to follow demand fluctuations in line with customer needs and optimize the overall supply chain plan through information sharing. That is, even in situations with supply risks, it was possible to move forward with demand following by making use of the looseness of constraints.

[0004] However, supply chain management required in recent years is to respond to demand fluctuations while supply uncertainty and strong constraints coexist, and to optimize the overall plan through information sharing. The triggers for this change are supply chain disruptions due to pandemics, overtime regulations due to work style reforms, and logistics constraints due to the 2024 problem.

[0005] For example, in procurement, it is required to suppress investment due to future uncertainty in order to suppress the destabilization of management due to intensified competition. In production, there is a problem that it is difficult to adjust capacity due to overtime restrictions, and it is difficult to adjust the employment of direct employees. In logistics, there are restrictions on long-hour restraint work, and there is a problem that competition for delivery capacity due to an increase in individual delivery volumes is intensifying. Due to these problems of quantity, production, and quantity, there is a risk that the supply network will be disrupted if self-company damage occurs due to an incident.

[0006] To properly set each parameter within the supply chain and develop and execute an optimal supply chain plan, a parameter setting method based on accurate risk assessments is necessary. Based on the signs of incidents and changes in constraints, as well as proposed countermeasures for incidents, parameters can be adjusted to be more reliable, enabling the development of a highly reliable supply chain plan.

[0007] Patent Document 1 describes an invention in which a CPU obtains a total cost derived in advance by summing the costs of each department in the supply chain, including the inventory management department and the production management department, determines whether or not any of the items related to multiple indicators managed by each department has been adjusted, and updates the display to reflect the total cost and the impact on other items when the adjustment amount is adjusted for the items used in deriving the total cost.

[0008] Japanese Patent Publication No. 2022-034474

[0009] Patent Document 1 describes how to determine production volume variability and inventory volume variability, and how to display the effects of adjusting for production volume variability and average inventory volume. Furthermore, Patent Document 1 describes how to determine changes in variability due to production leveling rate and safety stock achievement rate by taking measures such as leveling the judgment production volume and keeping inventory levels within a predetermined range. However, while Patent Document 1 determines the changes in the distribution of parameters before and after from a supply chain perspective, it does not determine the degree of their impact.

[0010] In recent years, prediction technologies for risk events such as disasters have advanced. Risk events create variability in trading performance, which in turn leads to variability, improvement, or deterioration of parameters. However, even if risk events such as disasters can be detected in advance, there is no consideration given to how to estimate the risks within the supply chain or how to develop optimal plans to address those risks.

[0011] Therefore, the present invention aims to set parameters related to the supply chain to appropriate values ​​depending on the uncertainties surrounding the supply chain and the availability of countermeasures.

[0012] To solve the aforementioned problems, the supply chain supply planning device of the present invention is characterized by comprising: a distribution calculation unit that calculates the distribution of performance information for each process constituting the supply chain based on performance information of supply and demand activities of the supply chain; a distribution impact calculation unit that, when information of an incident affecting the supply chain is input, calculates the degree of impact on the distribution of performance information for each process based on past changes in the distribution of performance information for each process due to the impact of past incidents similar to the incident; and a distribution estimation unit that estimates the future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit.

[0013] The supply chain supply planning method of the present invention is characterized by comprising the steps of: a distribution calculation unit calculating the distribution of performance information for each process constituting the supply chain based on performance information of supply and demand activities of the supply chain; a distribution impact calculation unit calculating the degree of impact on the distribution of performance information for each process when an event affecting the supply chain is input, based on past changes in the distribution of performance information for each process due to the influence of past events similar to the event; and a distribution estimation unit estimating a future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit. Other means will be described in the embodiments for carrying out the invention.

[0014] According to the present invention, it is possible to set parameters related to the supply chain to appropriate values ​​depending on the uncertainties surrounding the supply chain and whether or not countermeasures are in place.

[0015] This is a diagram of the configuration of the supply chain supply planning device according to this embodiment. This is a diagram showing an example of supply chain parameter adjustment. This is a diagram showing an example of supply chain parameter adjustment. This is a diagram showing an example of supply chain parameter adjustment. This is a diagram showing an example of supply chain parameter adjustment. This is a flowchart of the supply chain supply planning process. This is a flowchart of the supply chain supply planning process. This is a sequence diagram of the supply chain supply planning process. This is a diagram showing the incident occurrence record. This is a diagram showing the constraint change record information. This is a diagram showing the incident countermeasure record information. This is a diagram showing the inbound and outbound shipments from the supply chain supply performance information. This is a diagram showing the production from the supply chain supply performance information. This is a diagram showing supply chain information between sites. This is a diagram showing the BOM supply chain information. This is a diagram showing the parameter distribution estimate. This is a diagram showing the supply chain parameters. This is a diagram showing the performance visualization screen. This is a diagram showing the performance visualization screen. This is a diagram showing the warning and countermeasure proposal screen. This is a diagram showing the parameter distribution prediction and recommended setting value screen.

[0016] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the figures. In order to accurately plan a supply chain, it is essential to adjust parameters in accordance with the latest situation. Furthermore, in order to plan a resilient plan amidst the current uncertainty surrounding supply chains, it is necessary to utilize predictive information detected in advance and adjust parameters in advance. The present invention adjusts supply chain parameters using predictive information detected in advance.

[0017] Figure 1 is a diagram showing the configuration of the supply chain planning device 1 according to this embodiment. The supply chain planning device 1 includes a performance information collection unit 10, a distribution calculation unit 11, an incident information collection unit 121, a similar incident search unit 122, a distribution impact calculation unit 123, a countermeasure proposal reception unit 131, a similar countermeasure proposal search unit 132, a distribution improvement degree calculation unit 133, a propagation amount evaluation unit 141, a distribution change amount calculation unit 142, a distribution estimation unit 18, a parameter adjustment unit 19, and a data storage unit 17.

[0018] The performance information collection unit 10 collects supply chain demand and supply performance information. The performance information collection unit 10 aggregates performance information related to the processes of the supply chain 2 shown in Figure 2 across multiple predetermined processes.

[0019] The distribution calculation unit 11 calculates the distribution of performance information for each process constituting the supply chain based on performance information of supply and demand activities in the supply chain. In other words, the distribution calculation unit 11 calculates the distribution of performance information such as lead time and capacity, which are the parameter values ​​to be calculated.

[0020] The incident information collection unit 121 collects the latest incident and constraint change risk information. The similar incident search unit 122 searches for past incidents similar to the incidents collected by the incident information collection unit 121. The distribution impact calculation unit 123 refers to the distribution of parameters at the time of past incidents and constraint change occurrences and calculates the impact on the distribution based on the latest risk information. In other words, when information on an incident affecting the supply chain is input to the distribution impact calculation unit 123, it calculates the impact on the distribution of performance information for each process based on the past changes in the distribution of performance information for each process due to the impact of past incidents similar to the current incident.

[0021] The proposed countermeasures reception unit 131 accepts input of proposed countermeasures for risks based on the latest incident and constraint change risk information. The similar countermeasures search unit 132 searches for past countermeasures similar to the ones received by the proposed countermeasures reception unit 131. The distribution improvement calculation unit 133 calculates the degree of improvement in the distribution due to the proposed countermeasures for risks, based on past improvement examples.

[0022] In other words, the proposed countermeasures reception unit 131 accepts input of countermeasures considered by the user. The similar countermeasures search unit 132 searches for past countermeasures similar to the proposed countermeasures. The distribution improvement calculation unit 133 calculates the degree of improvement in the distribution of performance information for each process based on the actual changes in the distribution of performance information for each process in the past when similar past countermeasures were implemented, as received by the proposed countermeasures reception unit 131.

[0023] The propagation amount evaluation unit 141 evaluates the propagation of the distribution of actual information related to the processes before and after the process to be calculated. The distribution change amount calculation unit 142 calculates the amount of change in the distribution based on the propagation of the distribution of actual information related to the processes before and after the process to be calculated.

[0024] The distribution estimation unit 18 estimates the future distribution of performance information by adding the amount of change in the distribution of performance information related to the preceding and succeeding processes, based on the degree of impact of risk and the degree of improvement due to risk response, to the distribution of performance information to be calculated.

[0025] The distribution estimation unit 18 estimates the future distribution based on the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit 123. The distribution estimation unit 18 estimates the future distribution based on the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit 133, in addition to the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit 123. The distribution estimation unit 18 estimates the future distribution based on the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit 133, in addition to the degree of influence of the actual information for each process calculated by the distribution influence calculation unit 123, and the degree of change in the distribution of the actual information related to each process calculated by the distribution change amount calculation unit 143.

[0026] The parameter adjustment unit 19 adjusts the parameters of the supply and demand activities of the supply chain 2 based on the actual distribution and the future distribution estimated by the distribution estimation unit 18.

[0027] The data storage unit 17 is composed of a large-capacity storage device such as a hard disk or an SSD (Solid State Drive). The data storage unit 17 stores incident occurrence history information 171, constraint change history information 172, incident countermeasure history information 173, supply chain supply history information 174, supply chain information 175, parameter distribution estimates 176, and supply chain parameters 177.

[0028] Incident occurrence history information 171 stores information about incidents that have occurred in the past. Constraint change history information 172 stores information about constraint changes that have occurred in the past. Incident countermeasure history information 173 stores information about countermeasures proposed for past incidents.

[0029] Supply chain supply performance information 174 stores information on past inbound and outbound shipments in the supply chain, as well as production information. Supply chain information 175 stores supply information between locations and bills of materials for supplied items.

[0030] The parameter distribution estimate 176 stores the estimated parameter distribution of supply chain 2. The supply chain parameters 177 are the parameters of supply chain 2, such as procurement lead time, production capacity, production lead time, and transportation lead time shown in Figures 2 to 5.

[0031] Figure 2 shows an example of parameter adjustment for supply chain 2. Supply chain 2 consists of a supplier 21, the company's own factory 22, and a destination 23. Supplier 21 is not limited to one, but may be multiple. Destination 23 is not limited to one, but may be multiple. The parameter for supplier 21 is procurement lead time 31. The parameters for the company's own factory 22 are production capacity 32 and production lead time 33. The parameter for destination 23 is transportation lead time 34.

[0032] The supply chain planning and execution entity 40 calculates the procurement lead time 31 from the procurement lead time distribution 41, which is the distribution of actual information. The supply chain planning and execution entity 40 calculates the production capacity 32 from the production capacity distribution 42, which is the distribution of actual information. The supply chain planning and execution entity 40 calculates the production lead time 33 from the production lead time distribution 43, which is the distribution of actual information. The supply chain planning and execution entity 40 calculates the transportation lead time 34 from the transportation lead time distribution 44, which is the distribution of actual information. The supply chain planning and execution entity 40 adjusts each parameter considering the actual distribution (step S71).

[0033] To properly set each parameter of Supply Chain 2 and formulate and execute an optimal supply chain plan, a parameter setting method based on a plausible risk estimate is necessary.

[0034] When adjustments are based on past performance data, the parameter values ​​will be based on past risk outcomes and will not adequately prepare for future risks. Furthermore, even if there is a prospect of improvement in the situation due to incidents or changes in constraints, the parameter settings will be based on high risk assumptions, resulting in excessive risk response.

[0035] Therefore, by proactively identifying events that cause variability, improvement, or deterioration of parameters based on transaction history, we can achieve optimal planning and execution through parameter settings based on risk estimates. Furthermore, by adjusting parameters to be more reliable based on signs of incidents and changes in constraints, as well as proposed countermeasures for incidents, it becomes possible to formulate a highly reliable supply chain plan.

[0036] Figure 3 shows an example of parameter adjustment for supply chain 2. In Figure 3, the supply chain planning and execution entity 40 uses information that captures signs of incidents and changes in constraints to estimate the degree of improvement or deterioration of the future parameter distribution (step S72). Then, the supply chain planning and execution entity 40 adjusts the parameters used in planning based on the results (step S73).

[0037] The supply chain planning and implementation entity 40 will revise the mean, distribution, and variance of future parameters based on information obtained from public sources, such as warning signs of incidents and changes in constraints, and information on normalization after incidents and changes in constraints. Warning signs of incidents and changes in constraints contribute to the deterioration of the mean, distribution, and variance of parameters. Normalization after incidents and changes in constraints contributes to the improvement of the mean, distribution, and variance of parameters.

[0038] Figure 4 shows an example of supply chain parameter adjustment. Information on countermeasures taken in response to signs of incidents and changes in constraints is also input, and the parameters are adjusted considering the degree of improvement in the parameter distribution due to the countermeasures. Based on signs of incidents and changes in constraints obtained from public information, the expected improvement in the mean and distribution / variance of parameters when these events are prevented or countermeasures are taken is calculated, and the mean and distribution / variance of future parameters are corrected.

[0039] Figure 5 shows an example of supply chain parameter adjustment. Figure 5 illustrates how the degree of improvement or deterioration of the future parameter distribution is estimated by taking into account that variability in the distribution propagates to preceding and succeeding processes.

[0040] The parameters of a department or company are influenced by the parameter performance of preceding and succeeding departments or companies. For example, if the procurement lead time is long, parts may not be available, production may be in a work-in-progress state, and the production lead time may be extended. Taking this influence into account, the mean, distribution, and variance of the department's or company's parameters are predicted, and these future mean, distribution, and variance of parameters are adjusted accordingly.

[0041] From the mean, variability distribution, and variance of the parameters calculated above, the parameters to be set in the supply chain plan can be determined. Therefore, more reliable parameters can be set that reflect the signs of incident occurrence, proposed countermeasures, and the latest status of preceding and succeeding processes.

[0042] Figures 6A and 6B are flowcharts of the supply chain supply plan process. The performance information collection unit 10 collects supply chain demand and supply performance information (step S100). The distribution calculation unit 11 calculates the distribution of parameter values to be calculated, such as the performance information of lead time and capacity (step S101).

[0043] The incident information collection unit 121 collects the latest incident and constraint change risk information (step S102). The similar incident search unit 122 searches for past incidents similar to the incident collected by the incident information collection unit 121 (step S103).

[0044] The distribution impact calculation unit 123 refers to the distribution of parameters at the time of past incident and constraint change, and calculates the impact on the distribution based on the latest risk information (step S104). The countermeasure proposal reception unit 131 receives the input of countermeasure proposals for risks based on the latest incident and constraint change risk information (step S105).

[0045] The similar countermeasure proposal search unit 132 searches for past countermeasure proposals similar to the input countermeasure proposal (step S106). The distribution improvement calculation unit 133 calculates the improvement degree of the distribution by the countermeasure proposal for the risk based on past improvement cases (step S107).

[0046] The propagation amount evaluation unit 141 evaluates the propagation of the distribution of performance information related to the processes before and after the process to be calculated (step S108). The distribution change amount calculation unit 142 calculates the change amount of the distribution based on the propagation of the distribution of performance information related to the processes before and after the process to be calculated (step S109).

[0047] The distribution estimation unit 18 adds the change amount of the distribution of performance information related to the processes before and after to the distribution of the performance information to be calculated, based on the influence degree by the risk and the improvement degree by the risk response, and estimates the distribution of future performance information (step S110). The parameter adjustment unit 19 adjusts the parameters of the supply chain 2 based on the actual distribution and the estimated distribution (step S111).

[0048] Figure 7 is a sequence diagram of the supply chain supply plan process. Initially, the supply chain planning and execution entity 40 conducts supply and demand activities (step S20) and transmits performance information to the planning system 26 (step S21). The planning system 26 transmits the performance information to the supply chain supply planning device 1 (step S22). Thereby, the supply chain supply planning device 1 performs distributed calculation by the distributed calculation unit 11 (step S23).

[0049] Next, the supply chain planning and execution entity 40 acquires an incident (step S30) and transmits the incident information to the supply chain supply planning device 1 (step S31). The supply chain supply planning device 1 calculates the distribution influence degree of the incident information by the distribution influence degree calculation unit 123 (step S32).

[0050] The supply chain supply planning device 1 displays the incident information to the user 27 (step S33). The user 27 considers a countermeasure plan for the incident information (step S34) and inputs the countermeasure plan (step S35).

[0051] The supply chain planning and execution entity 40 acquires improvement cases based on past countermeasure plans (step S40) and transmits the improvement cases to the planning system 26 (step S41). The planning system 26 transmits the improvement cases to the supply chain supply planning device 1 (step S42). Based on this improvement case and the countermeasure plan, the distribution improvement degree calculation unit 133 calculates the distribution improvement degree (step S43).

[0052] The supply chain planning and execution entity 40 acquires the changes before and after each process of the supply chain 2 (step S50) and transmits the changes before and after each process of the supply chain 2 to the planning system 26 (step S51). The planning system 26 transmits the changes before and after each process of the supply chain 2 to the supply chain supply planning device 1 (step S52). Based on the changes before and after each process of this supply chain 2, the distribution change amount calculation unit 143 of the supply chain supply planning device 1 calculates the distribution change amount (step S53).

[0053] The distribution estimation unit 18 of the supply chain supply planning device 1 estimates the future distribution based on the distribution of actual information, the degree of distribution impact, the degree of distribution improvement, and the amount of distribution change (step S60). Then, the parameter adjustment unit 19 adjusts the parameters (step S61) and transmits the adjusted parameters to the planning system 26. The planning system 26 formulates a supply and demand plan based on the adjusted parameters (step S63) and displays the proposed supply and demand plan to the user 27 (step S64). As a result, the user 27 confirms the proposed countermeasures (step S65) and instructs the supply chain planning and execution entity 40 to implement them (step S66). The supply chain planning and execution entity 40 carries out supply and demand activities based on the proposed countermeasures (step S67), and repeats this series of processes.

[0054] Figure 8 shows the incident occurrence record information 171. The incident occurrence record information 171 consists of a location name column, an incident details column, an incident scale column, and an occurrence date column.

[0055] The "Location Name" field stores the name of the location where the incident occurred. The "Incident Details" field stores information describing the nature of the incident. The "Incident Scale" field stores information describing the scale of the incident. The "Date of Occurrence" field stores the date the incident occurred.

[0056] Figure 9 shows the constraint change performance information 172. The constraint change performance information 172 consists of a location name column, a constraint details column, a change date column, and a constraint value column.

[0057] The "Location Name" field stores the name of the location where the constraint change occurred. The "Constraint Details" field stores the content of the constraint. The "Change Date" field stores the date and time the change occurred due to the constraint. The "Constraint Value" field stores the value that is constrained.

[0058] Figure 10 shows the incident response performance information 173. The incident response performance information 173 consists of a location name column, a countermeasure details column, a countermeasure implementation date column, and a value column.

[0059] The "Location Name" field stores the name of the location where the incident countermeasures were implemented. The "Countermeasure Details" field stores the details of the countermeasures taken for the incident. The "Countermeasure Implementation Date" field stores the date and time the countermeasures were implemented for the incident. The "Value" field stores the value of the incident countermeasures.

[0060] Figure 11 shows the inbound and outbound shipments section of the supply chain supply performance information 174. The supply chain supply performance information 174 related to inbound and outbound shipments consists of a Item Name column, a Supplier column, a Destination column, a Shipment Date column, a Arrival Date column, and a Quantity column.

[0061] The Item Name column stores the name of the item being received or shipped. The Supplier column stores the name of the supplier of the item being received or shipped. The Destination column stores the name of the destination of the item being received or shipped. The Shipment Date column stores the shipment date of the item being received or shipped. The Arrival Date column stores the shipment date of the item being received or shipped. The Quantity column stores the quantity of the item being received or shipped.

[0062] Figure 12 shows the production portion of the supply chain supply performance information 174. The supply chain supply performance information 174 related to production consists of a column for item name, a column for location name, a column for production date, and a column for quantity.

[0063] The Item Name column stores the name of the produced item. The Location Name column stores the name of the production location. The Production Date column stores the date and time of production. The Quantity column stores the quantity of the produced item.

[0064] Figure 13 shows the supply chain information 175 between locations. The supply chain information 175 consists of a product name column, a supplier column, and a destination column.

[0065] The "Item Name" column stores the name of the item supplied across multiple locations. The "Supplier" column stores the name of the location from which the item was supplied. The "Destination" column stores the name of the location to which the item was supplied.

[0066] Figure 14 shows supply chain information from a Bill of Materials (BOM). A BOM is a list of parts and materials necessary to manufacture a product in the manufacturing industry. BOMs play a crucial role in processes such as product design, manufacturing, purchasing, and after-sales service.

[0067] The supply chain information in the bill of materials consists of a child item column, a parent item column, and a quantity column. The child item column stores identifiers of the parts and materials necessary to manufacture the parent item described below. The parent item column stores identifiers of the parent item manufactured using the child item. The quantity column stores the quantity required to manufacture the parent item from the child item indicated in the child item column.

[0068] Figure 15 shows the parameter distribution estimate 176. The parameter distribution estimate 176 consists of a location name column, an item name column, a parameter type column, a period start date column, a period end date column, a mean value column, a variance column, a mode value column, and a worst value column. The parameter distribution estimate 176 is the result of the processing of step S110 performed by the distribution estimation unit 18. The parameter distribution estimate 176 shows statistical information of the distribution of future performance information, such as the mean value, variance, mode value, and worst value. The location name column stores the name of the location. The item name column stores the name of the item. The parameter type column stores information about the type of parameter. The period start date column stores the start date of the period for which this parameter distribution is estimated. The period end date column stores the end date of the period for which this parameter distribution is estimated. The mean value column stores the mean value of the parameter distribution. The variance column stores the variance value of the parameter distribution. The Mode column stores the mode of this parameter distribution. The Worst Value column stores the worst value of this parameter distribution.

[0069] Figure 16 shows the supply chain parameters 177. The supply chain parameters 177 consist of a location name field, an item name field, a parameter type field, a period start date field, a period end date field, and a parameter value field. The supply chain parameters 177 are the result of the processing of step S111 performed by the parameter adjustment unit 19. The supply chain parameters 177 show the result of adjusting the parameters of supply chain 2 based on the actual distribution and the estimated distribution. The location name field stores the name of the location. The item name field stores the name of the item. The parameter type field stores information about the type of parameter. The period start date field stores the start date of the period to which the supply chain parameters are applied. The period end date field stores the end date of the period to which the supply chain parameters are applied. The parameter value field stores the parameter value of supply chain 2.

[0070] Figure 17 shows the performance visualization screen 51. The performance visualization screen 51 has a location combo box 511, an item combo box 512, a parameter combo box 513, and a period picker 514.

[0071] The location combo box 511 is a combo box for selecting a location. The item combo box 512 is a combo box for selecting an item. The parameter combo box 513 is a combo box for selecting parameters related to supply chain 2. The period picker 514 is a picker for multiple date and time inputs for entering the period for which actual results will be aggregated. By selecting and entering these parameters, the screen switches to the actual results visualization screen 52, which will be described later.

[0072] Figure 18 shows the performance visualization screen 52. The performance visualization screen 52 displays a distribution graph 521 calculated based on the parameters selected and entered on the performance visualization screen 51. The parameter settings are displayed on this distribution graph 521. By referring to this distribution graph 521 and the parameter settings, it is possible to visualize the degree to which the desired performance has been achieved.

[0073] Figure 19 shows the indicator / countermeasures screen 53. The indicator / countermeasures screen 53 includes an incident / constraint change indicator import table 531, a countermeasures table, a "supply chain impact before and after" checkbox 533, and a parameter recalculation button 534.

[0074] The Incident / Constraint Change Prediction Import Table 531 displays import checkboxes for each row. By checking these import checkboxes, you can specify which incidents or constraint changes to import.

[0075] The Incident / Constraint Change Prediction Import Table 531 further includes an Area column, an Incident column, a Scale column, an Occurrence Time column, and an Information Source column. The Area column stores the area where the incident or constraint change occurs. The Incident column stores information about the incident or constraint change that is anticipated as a precursor. The Scale column stores information about the scale of the incident or constraint change. The Occurrence Time column stores the time when the incident or constraint change occurs. The Information Source column stores the source from which this information was obtained.

[0076] The "Influences Before and After the Supply Chain" checkbox 533 determines whether or not to reflect the influences before and after each location in the supply chain. The parameter recalculation button 534 recalculates these parameters and transitions to the parameter distribution prediction / recommended setting screen 54, which will be described later.

[0077] Figure 20 shows the parameter distribution prediction and recommended setting screen 54. The parameter distribution prediction and recommended setting screen 54 displays the parameter distribution prediction graph 541, the parameter distribution prediction table 542, and the recommended setting label 543.

[0078] The parameter distribution forecast graph 541 displays the actual distribution and forecast distribution of the parameters. The parameter distribution forecast table 542 includes actual distribution columns and forecast distribution columns, and contains rows for mean, variance, mode, and worst value. The recommended setting value label 543 displays the current and updated parameters calculated from the parameter distribution forecast table 542. Here, it is shown that the inventory basis parameter is currently 16 days, but should be updated to 18 days.

[0079] [Effects of the Embodiment] Depending on the uncertainties surrounding the supply chain and the availability of countermeasures, parameters related to the supply chain can be set to appropriate values.

[0080] The configuration and effects of the present invention will be described below.

[0081] [1] A supply chain supply planning device (1) comprising: a distribution calculation unit (11) that calculates the distribution of performance information for each process constituting the supply chain (2) based on performance information of supply and demand activities of the supply chain (2); a distribution impact calculation unit (123) that, when information of an incident affecting the supply chain (2) is input, calculates the degree of impact on the distribution of performance information for each process based on past changes in the distribution of performance information for each process due to the impact of past incidents similar to the incident; and a distribution estimation unit (18) that estimates a future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit (123).

[0082] This makes it possible to estimate the future distribution of historical data related to the supply chain, depending on the uncertainties surrounding the supply chain.

[0083] [2] The supply chain supply planning device (1) according to claim 1, further comprising a parameter adjustment unit (19) that adjusts the parameters of the supply and demand activities of the supply chain (2) based on the future distribution estimated by the distribution estimation unit (18) and the actual distribution.

[0084] This makes it possible to set supply chain parameters to appropriate values ​​in response to uncertainties surrounding the supply chain.

[0085] [3] The supply chain supply planning device (1) according to claim 1, further comprising a performance information collection unit (10) that aggregates performance information related to each of the above processes over a plurality of predetermined processes.

[0086] This makes it possible to accurately collect performance information for each process that makes up the supply chain.

[0087] [4] The supply chain supply planning device (1) according to claim 1, further comprising a similar incident search unit (122) that searches for past incidents similar to the input incident.

[0088] This allows for accurate assessment of the impact of an incident and estimation of the future distribution of historical data related to the supply chain.

[0089] [5] The supply chain supply planning device (1) according to claim 1, further comprising a distribution improvement calculation unit (133) that, when a countermeasure proposed by the user is input, calculates the degree of improvement in the distribution of performance information for each of the processes based on the past changes in the distribution of performance information for each of the processes when similar past countermeasures were implemented.

[0090] This allows for accurate assessment of the impact of an incident and estimation of the future distribution of historical data related to the supply chain.

[0091] [6] The supply chain supply planning device (1) according to claim 5, further comprising a similar countermeasure search unit (132) for searching past countermeasures similar to the input countermeasure.

[0092] This allows for accurate estimation of the impact of proposed countermeasures and makes it possible to predict the future distribution of historical data related to the supply chain.

[0093] [7] The supply chain supply planning device (1) according to claim 5, wherein the distribution estimation unit (18) estimates a future distribution based on the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit (123), as well as the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit (133).

[0094] This will allow us to more accurately estimate the impact of proposed countermeasures and set appropriate parameters for the supply chain.

[0095] [8] The supply chain supply planning device according to claim 5, further comprising a distribution change amount calculation unit (142) that calculates the degree of change in performance information related to each process, taking into account that changes in the distribution of performance information of other processes related to each of the processes propagate to the processes.

[0096] [9] The supply chain supply planning device according to claim 8, further comprising a propagation amount evaluation unit (141) that evaluates the amount of propagation of changes in the distribution of performance information of other processes related to each of the processes to the processes.

[0097] This allows for accurate estimation of the impact of changes in the distribution of performance information for each process and other related processes on the amount of propagation to each process, making it possible to estimate the future distribution of performance information related to the supply chain.

[0098]

[10] The supply chain supply planning device (1) according to 8, wherein the distribution estimation unit (18) estimates a future distribution based on the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit (123), the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit (133), and the degree of change in the distribution of the actual information related to each process calculated by the distribution change calculation unit (142).

[0099] This allows us to set appropriate parameters for the supply chain by further considering the impact of propagation rates.

[0100]

[11] A supply chain supply planning method comprising: a distribution calculation unit (11) calculating the distribution of performance information for each process constituting the supply chain (2) based on performance information of supply and demand activities of the supply chain (2); a distribution impact calculation unit (123) calculating the degree of impact on the distribution of performance information for each process when an event affecting the supply chain (2) is input, based on past changes in the distribution of performance information for each process due to the influence of past events similar to the event; and a distribution estimation unit (18) estimating a future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit (123).

[0101] This makes it possible to estimate the future distribution of historical data related to the supply chain, depending on the uncertainties surrounding the supply chain.

[0102] 《Modifications》 The present invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. It is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0103] Each of the above configurations, functions, processing units, and processing means may be partially or entirely implemented in hardware, such as an integrated circuit. Each of the above configurations and functions may also be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function can be stored in a recording device such as memory, a hard disk, or an SSD (Solid State Drive), or on a recording medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0104] In each embodiment, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected.

[0105] 1. Supply Chain Planning System 10. Performance Information Collection Unit 11. Distribution Calculation Unit 121. Incident Information Collection Unit 122. Similar Incident Search Unit 123. Distribution Impact Calculation Unit 131. Countermeasure Proposal Reception Unit 132. Similar Countermeasure Proposal Search Unit 133. Distribution Improvement Calculation Unit 141. Propagation Amount Evaluation Unit 142. Distribution Change Amount Calculation Unit 18. Distribution Estimation Unit 19. Parameter Adjustment Unit 17. Data Storage Unit 2. Supply Chain 171. Incident Occurrence Performance Information 172. Constraint Change Performance Information 173. Incident Countermeasure Performance Information 174. Supply Chain Supply Performance Information 175. Supply Chain Information 176. Parameter Distribution Estimate 177. Supply Chain Parameters 21. Supplier 22. Company Factory 31. Procurement Lead Time 32. Production Capacity 33. Production Lead Time 34. Transportation Lead Time 40. Supply Chain Planning and Execution Entity 41. Procurement Lead Time Distribution 42. Production Capacity Distribution 43 Production Lead Time Distribution 44 Transportation Lead Time Distribution 26 Planning System 27 Users 143 Distribution Change Calculation Unit 51 Performance Visualization Screen 511 Site Combo Box 512 Item Combo Box 513 Parameter Combo Box 514 Car 52 Performance Visualization Screen 521 Distribution Graph 53 Prediction / Countermeasure Proposal Screen 531 Incident / Constraint Change Prediction Import Table 533 "Supply Chain Pre- and Post-Impact" Checkbox 534 Parameter Recalculation Button 54 Parameter Distribution Prediction / Recommended Setting Value Screen 541 Parameter Distribution Prediction Graph 542 Parameter Distribution Prediction Table 543 Recommended Setting Value Label

Claims

1. A supply chain supply planning device comprising: a distribution calculation unit that calculates the distribution of performance information for each process constituting the supply chain based on performance information of supply and demand activities of the supply chain; a distribution impact calculation unit that, when information of an incident affecting the supply chain is input, calculates the degree of impact on the distribution of performance information for each process based on past changes in the distribution of performance information for each process due to the impact of past incidents similar to the incident; and a distribution estimation unit that estimates the future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit.

2. The supply chain supply planning device according to claim 1, further comprising a parameter adjustment unit that adjusts the parameters of the supply and demand activities of the supply chain based on the future distribution estimated by the distribution estimation unit and the actual distribution.

3. The supply chain supply planning apparatus according to claim 1, further comprising a performance information collection unit that aggregates performance information related to each of the aforementioned processes across a plurality of predetermined processes.

4. The supply chain supply planning device according to claim 1, further comprising a similar incident search unit that searches for past incidents similar to the input incident.

5. The supply chain supply planning device according to claim 1, further comprising a distribution improvement calculation unit that, when a countermeasure proposed by the user is input, calculates the degree of improvement in the distribution of performance information for each of the aforementioned processes based on the past changes in the distribution of performance information for each of the aforementioned processes when similar past countermeasures were implemented.

6. The supply chain planning device according to claim 5, further comprising a similar countermeasure search unit for searching for past countermeasures similar to the input countermeasure.

7. The supply chain supply planning device according to claim 5, wherein the distribution estimation unit estimates a future distribution based on the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit, as well as the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit.

8. The supply chain supply planning device according to claim 5, further comprising a distribution change amount calculation unit that calculates the degree of change in performance information related to each of the aforementioned processes, taking into account that changes in the distribution of performance information of other processes related to each of the aforementioned processes are propagated to the aforementioned processes.

9. The supply chain supply planning device according to claim 8, further comprising a propagation amount evaluation unit that evaluates the amount at which changes in the distribution of performance information of other processes related to each of the aforementioned processes propagate to the aforementioned processes.

10. The supply chain supply planning device according to claim 8, wherein the distribution estimation unit estimates a future distribution based on the degree of influence of the actual information for each process on the distribution calculated by the distribution influence calculation unit, the degree of improvement to the distribution of the actual information for each process calculated by the distribution improvement calculation unit, and the degree of change in the distribution of the actual information related to each process calculated by the distribution change calculation unit.

11. A supply chain supply planning method characterized by comprising: a step of a distribution calculation unit calculating the distribution of performance information for each process constituting the supply chain based on performance information of supply and demand activities of the supply chain; a step of a distribution impact calculation unit calculating the degree of impact on the distribution of performance information for each process when an event affecting the supply chain is input, based on past changes in the distribution of performance information for each process due to the influence of past events similar to the event; and a step of a distribution estimation unit estimating a future distribution based on the degree of impact on the distribution of performance information for each process calculated by the distribution impact calculation unit.

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