Supply chain supply planning device and supply chain supply planning method

Abstract

Depending on the uncertainties surrounding the supply chain and the existence of countermeasures, it becomes possible to estimate the future distribution of historical information related to the supply chain. [Solution] The supply chain supply planning device 1 is characterized by comprising: a distribution calculation unit 11 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 123 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 the 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 123.

Description

Technical Field

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

Background Art

[0002] In recent years, against the backdrop of the 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 issues 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 strengthened regulations. 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 taking advantage of the looseness of the 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 trigger for this change is the disruption of the supply chain due to the pandemic, overtime regulations due to workstyle 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 is a problem that there are restrictions on long-hour constrained work, and competition for delivery capacity due to an increase in the delivery volume per individual is intensifying. Due to these problems of quantity, production, and quantity, there is a risk that the supply network will be disrupted if damage to the company 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 when deriving the total cost. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2022-034474 [Overview of the Initiative] [Problems that the invention aims to solve]

[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. [Means for solving the problem]

[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 based on past changes in the distribution of performance information for each process due to the influence of past events similar to the event when an event affecting the supply chain is input; 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 mode for carrying out the invention.

Advantages of the Invention

[0014] According to the present invention, it becomes possible to set parameters related to the supply chain to appropriate values according to the uncertainty surrounding the supply chain and the presence or absence of countermeasures.

Brief Description of the Drawings

[0015] [Figure 1] It is a configuration diagram of a supply chain supply planning device according to the present embodiment. [Figure 2] It is a diagram showing an example of parameter adjustment of the supply chain. [Figure 3] It is a diagram showing an example of parameter adjustment of the supply chain. [Figure 4] It is a diagram showing an example of parameter adjustment of the supply chain. [Figure 5] It is a diagram showing an example of parameter adjustment of the supply chain. [Figure 6A] It is a flowchart of supply chain supply planning processing. [Figure 6B] It is a flowchart of supply chain supply planning processing. [Figure 7] It is a sequence diagram of supply chain supply planning processing. [Figure 8] It is a diagram showing incident occurrence results. [Figure 9] It is a diagram showing constraint change performance information. [Figure 10] It is a diagram showing incident countermeasure performance information. [Figure 11] Among the supply chain supply performance information, it is a diagram showing incoming and outgoing shipments. [Figure 12] Among the supply chain supply performance information, it is a diagram showing production. [Figure 13] It is a diagram showing supply chain information between bases. [Figure 14] It is a diagram showing supply chain information of the BOM. [Figure 15] It is a diagram showing the estimated value of the parameter distribution. [Figure 16] It is a diagram showing the supply chain parameters. [Figure 17] It is a diagram showing the performance visualization screen. [Figure 18] It is a diagram showing the performance visualization screen. [Figure 19] It is a diagram showing the omen and countermeasure plan screen. [Figure 20] It is a diagram showing the parameter distribution prediction and setting recommendation value screen.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to each figure. In order to accurately formulate a supply chain plan, it is essential to adjust parameters according to the latest situation. Also, in the current uncertainty surrounding the supply chain, in order to formulate a plan with enhanced resilience, it is necessary to utilize the omen information detected in advance and adjust the parameters predicted in advance. The present invention adjusts the parameters of the supply chain using the omen information detected in advance.

[0017] FIG. 1 is a configuration diagram of a supply chain supply planning device 1 according to the present embodiment. The supply chain supply 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 influence degree calculation unit 123, a countermeasure plan reception unit 131, a similar countermeasure plan 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 the performance information regarding the processes of the supply chain 2 shown in FIG. 2 for a plurality of predetermined processes respectively.

[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 this 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 cases.

[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 on 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 influence of the actual information on 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 on each process calculated by the distribution improvement calculation unit 133. The distribution estimation unit 18 estimates the future distribution based on the degree of influence of the actual information on each process on the distribution calculated by the distribution influence calculation unit 123, the degree of improvement to the distribution of the actual information on 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 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 incoming and outgoing 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 precursors to 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 execution 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 constraint changes is also input, and the parameters are adjusted considering the degree of improvement in the parameter distribution due to the countermeasures. Based on information obtained from public sources regarding the occurrence of incidents and signs of changing constraints, the system calculates predicted improvements in the mean, distribution, and variance of parameters that are expected when preventative measures or countermeasures are planned, and then adjusts the future mean, distribution, and variance of the parameters.

[0039] Figure 5 shows an example of supply chain parameter adjustment. Figure 5 shows that the degree of improvement or deterioration of the future parameter distribution is estimated by taking into account that the variability in the distribution propagates to the 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, resulting in work-in-progress production and a longer production lead time. Taking this influence into account, we calculate predicted values ​​for the mean, distribution, and variance of the department's or company's parameters and adjust them accordingly for the future.

[0041] From the mean and variance distribution / variance of the parameters calculated above, the parameters to be set in the supply chain plan can be calculated. 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 planning 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 actual information such as 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 incidents and constraint changes, and calculates the impact on the distribution based on the latest risk information (step S104). The proposed countermeasures reception unit 131 accepts input of proposed countermeasures for risks based on the latest incident and constraint change risk information (step S105).

[0045] The similar countermeasure search unit 132 searches for past countermeasures similar to the input countermeasure (step S106). The distribution improvement calculation unit 133 calculates the degree of improvement in the distribution based on past improvement cases and the proposed countermeasures against the risks (step S107).

[0046] 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 (step S108). The distribution change 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 (step S109).

[0047] 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 (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 planning process. Initially, the supply chain planning and execution entity 40 carries out supply and demand activities (step S20) and transmits the actual information to the planning system 26 (step S21). The planning system 26 transmits the actual information to the supply chain supply planning device 1 (step S22). As a result, the supply chain supply planning device 1 performs distribution calculations using the distribution 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 uses the distribution impact calculation unit 123 to calculate the distribution impact of the incident information (step S32).

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

[0051] The supply chain planning and implementation entity 40 obtains improvement examples based on past countermeasures (step S40) and transmits the improvement examples to the planning system 26 (step S41). The planning system 26 transmits the improvement examples to the supply chain supply planning device 1 (step S42). Based on these improvement examples and countermeasures, 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 these changes before and after each process of the 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 this series of processes is repeated.

[0054] Figure 8 shows the incident occurrence record information 171. Incident occurrence record information 171 consists of a field for the location name, a field for the incident details, a field for the scale of the incident, and a field for the date of occurrence.

[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 field, a constraint details field, a change date field, and a constraint value field.

[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. Incident response performance information 173 consists of a field for the location name, a field for the content of the countermeasure, a field for the date the countermeasure was implemented, and a field for the value.

[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 from the 174 supply chain supply performance data points. The supply chain supply performance information 174 for incoming and outgoing shipments consists of the following columns: item name, supplier, destination, shipping date, arrival date, and quantity.

[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 data from the 174 supply chain performance data points. The supply chain 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. Supply chain information 175 consists of a Item 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 a bill of materials consists of a sub-item column, a parent-item column, and a quantity column. The child item column contains identifiers for the parts and materials necessary to manufacture the parent item described below. The parent item column contains identifiers for the parent item manufactured using the child item. The quantity column contains the quantity required to manufacture the parent item from the child item shown 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" 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 for which this parameter distribution is estimated. The "Period End Date" field stores the end date of the period for which this parameter distribution is estimated. The "Mean Value" field stores the mean value of the parameter distribution. The "Variance" field stores the variance value of the parameter distribution. The "Mode" field stores the mode of this parameter distribution. The "Worst Value" field stores the worst value of this parameter distribution.

[0069] Figure 16 shows the supply chain parameters 177. The supply chain parameter 177 consists 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 parameter 177 is the result of the processing of step S111 performed by the parameter adjustment unit 19. The supply chain parameter 177 shows 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 apply. The "Period End Date" field stores the end date of the period to which the supply chain parameters apply. The "Parameter Value" field stores the parameter value for supply chain 2.

[0070] Figure 17 shows the performance visualization screen 51. The performance visualization screen 51 contains 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 data will be aggregated. By selecting and entering these parameters, the user will transition to the actual data 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. This distribution graph 521 displays the parameter settings. 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 warning signs and countermeasures screen 53. The warning / countermeasures screen 53 displays the incident / constraint change warning import table 531, the countermeasures table, the "impact before and after supply chain" checkbox 533, and the parameter recalculation button 534.

[0074] The Incident / Constraint Change Prediction Import Table 531 displays import checkboxes for each row. By checking these 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 Date 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 was 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 occurred. The Information Source column stores the source from which this information was obtained.

[0076] The "Supply Chain Pre- and Post-Influence" checkbox 533 determines whether or not to reflect the pre- and post-influence between 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 / recommended setting screen 54 displays the parameter distribution prediction graph 541, the parameter distribution prediction table 542, and the recommended setting label 543.

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

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

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

[0081] [1] A distribution calculation unit (11) 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), When information on an incident affecting the supply chain (2) is input, a distribution impact calculation unit (123) calculates the degree of impact on 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 due to the impact of past incidents similar to the aforementioned incident, Based on the degree of influence on the distribution of the actual information for each of the processes calculated by the distribution influence calculation unit (123), the distribution estimation unit (18) estimates the future distribution, A supply chain planning device (1) characterized by comprising the following:

[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 system further includes 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. The supply chain supply planning device (1) according to feature 1.

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

[0085] [3] The system further includes a performance information collection unit (10) that aggregates performance information related to each of the aforementioned processes across multiple predetermined processes. The supply chain supply planning device (1) according to feature 1.

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

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

[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] When a countermeasure proposed by the user is input, the system further includes a distribution improvement calculation unit (133) that 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. The supply chain supply planning device (1) according to feature 1.

[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 system further includes a similar countermeasure search unit (132) that searches for past countermeasures similar to the input countermeasure. The supply chain supply planning device (1) according to feature 5.

[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 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), 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). The supply chain supply planning device (1) according to feature 5.

[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 system further includes a distribution change calculation unit (142) 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 propagate to the aforementioned processes. The supply chain supply planning device according to claim 5.

[0096] [9] The system further includes a propagation amount evaluation unit (141) 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. The supply chain supply planning apparatus according to claim 8.

[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 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 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). The supply chain supply planning device (1) according to feature 8.

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

[0100]

[11] The distribution calculation unit (11) 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), When an event affecting the supply chain (2) is input, the distribution impact calculation unit (123) calculates the degree of impact on 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 due to the influence of past events similar to the event, Based on the degree of influence on the distribution of the actual information for each of the processes calculated by the distribution influence calculation unit (123), the distribution estimation unit (18) estimates the future distribution in the following steps: A supply chain planning method characterized by having the following features.

[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] Variant form 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 parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments 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 implemented in part or in whole by 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. [Explanation of symbols]

[0105] 1. Supply chain planning device 10. Performance Information Collection Department 11 Distribution calculation section 121 Incident Information Collection Department 122 Similar Incident Search Department 123 Distribution influence calculation part 131 Countermeasure Proposal Reception Department 132 Similar Countermeasures Search Unit 133 Distribution improvement calculation part 141 Propagation Amount Evaluation Unit 142 Distribution Change Calculation Unit 18 Distribution estimation part 19 Parameter adjustment section 17 Data Storage Unit 2. Supply Chain 171 Incident Occurrence History Information 172 Constraint Change Performance Information 173 Incident Response Performance Information 174 Supply Chain Supply Performance Information 175 Supply Chain Information 176 Parameter distribution estimates 177 Supply Chain Parameters 21 Suppliers 22 Company-owned factories 31. Procurement Lead Time 32 Production capacity 33. Production lead time 34. Transportation lead time 40 Supply Chain Planning and Implementation Entity 41. Procurement Lead Time Distribution 42. Distribution of production capacity 43. Production Lead Time Distribution 44. Distribution of transport lead times 26 Planning Systems 27 User 143 Distribution Change Calculation Unit 51 Performance Visualization Screen 511 Base Combo Box 512-item combo box 513 Parameter Combo Box 514 Car 52 Performance Visualization Screen 521 Distribution graph 53 Warning Signs and Countermeasures Screen 531 Incident / Constraint Change Prediction Table 533 "Effects before and after supply chain" checkbox 534 Parameter Recalculation Button 54 Parameter Distribution Prediction / Recommended Settings Screen 541 Parameter Distribution Prediction Graph 542 Parameter Distribution Prediction Table 543 Recommended setting label

Claims

1. A distribution calculation unit 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, When information on an incident affecting the supply chain is input, a distribution impact calculation unit calculates the degree of impact on the distribution of performance information for each of the aforementioned processes based on the historical changes in the distribution of performance information for each of the aforementioned processes due to the impact of past incidents similar to the aforementioned incident. Based on the degree of influence on the distribution of the actual information for each of the processes calculated by the distribution influence calculation unit, a distribution estimation unit estimates the future distribution. A supply chain planning device characterized by comprising the following features.

2. The system further includes 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. The supply chain supply planning device according to feature 1.

3. The system further includes a performance information collection unit that aggregates performance information related to each of the aforementioned processes across multiple predetermined processes. The supply chain supply planning device according to feature 1.

4. It further includes a similar incident search unit that searches for past incidents similar to the entered incident. The supply chain supply planning device according to feature 1.

5. When a user inputs a proposed countermeasure, the system further includes a distribution improvement calculation unit that calculates the degree of improvement in the distribution of performance information for each of the aforementioned processes based on the historical changes in the distribution of performance information for each of the aforementioned processes when similar past countermeasures were implemented. The supply chain supply planning device according to feature 1.

6. It further includes a similar countermeasure search unit that searches for past countermeasures similar to the entered countermeasure. The supply chain supply planning device according to feature 5.

7. The distribution estimation unit estimates the future distribution based on the degree of influence of the actual information on 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 on each process calculated by the distribution improvement calculation unit. The supply chain supply planning device according to feature 5.

8. The system further includes a distribution change 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 propagate to the aforementioned processes. The supply chain supply planning device according to feature 5.

9. The system further includes 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. The supply chain supply planning device according to feature 8.

10. The distribution estimation unit estimates the future distribution based on the degree of influence of the actual information on each process on the distribution calculated by the distribution influence calculation unit, the degree of improvement to the distribution of the actual information on 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. The supply chain supply planning device according to feature 8.

11. The distribution calculation unit 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. When an event affecting the supply chain is input, the distribution impact calculation unit calculates the degree of impact on the distribution of performance information for each of the processes based on the historical changes in the distribution of performance information for each of the processes due to the influence of past events similar to the event, Based on the degree of influence on the distribution of the actual information for each of the processes calculated by the distribution influence calculation unit, the distribution estimation unit estimates the future distribution. A supply chain planning method characterized by having the following features.

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