WO2016113844A1 - Supply chain design device, supply chain design method, and program - Google Patents

Supply chain design device, supply chain design method, and program Download PDF

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Publication number
WO2016113844A1
WO2016113844A1 PCT/JP2015/050666 JP2015050666W WO2016113844A1 WO 2016113844 A1 WO2016113844 A1 WO 2016113844A1 JP 2015050666 W JP2015050666 W JP 2015050666W WO 2016113844 A1 WO2016113844 A1 WO 2016113844A1
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Prior art keywords
value
supply chain
data
master
chain design
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PCT/JP2015/050666
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French (fr)
Japanese (ja)
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崇文 智田
野本　多津
順子 細田
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株式会社日立製作所
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Priority to PCT/JP2015/050666 priority Critical patent/WO2016113844A1/en
Publication of WO2016113844A1 publication Critical patent/WO2016113844A1/en

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to a supply chain design device, a supply chain design method, and a program.
  • Patent Document 1 has a logistics design unit that performs processing for calculating costs including tariffs on logistics routes between bases using input data for logistics design, and the logistics design unit is based on the input data.
  • Logistics that configures a combination of logistics routes, constructs a combination of agreements, adds costs including tariffs according to the agreement for each logistics route and path, and thereby calculates total costs including tariffs for each logistics route A design device is described.
  • Patent Document 1 discloses a technology related to supply chain design for minimizing the cost for given design conditions.
  • the master data value used for supply chain design may change depending on the time and circumstances, such as an increase in the shipping lead time of the shipping route by avoiding conflict areas. Need to update. However, when there are many site candidates and logistics route candidates to be examined, there are a large number of master data values to be prepared in advance, and there are no man-hours for appropriate review.
  • the supply chain may be designed using. As a result, there is a problem that a supply chain designed using master data values that do not match the actual situation has an adverse effect such as an increase in inventory and cost more than expected rather than an assumption at the time of design.
  • the present invention has been made in view of the above problems, and an object of the present invention is to provide a supply chain design apparatus capable of always providing a supply chain design plan based on an appropriate master data value.
  • a supply chain design device is a supply chain design device that calculates a supply chain design plan, and includes a storage unit and a control unit, and the storage unit distributes the supply chain.
  • Business scenario that is data related to the component structure of the item to be processed and the estimated demand in each market, master data values that are data related to logistics and money flow in the supply chain, and master values that are data related to the update history of the master data values Historical data, supply amount actual data that is data related to the supply amount of items that circulate in the supply chain, transport amount actual data that is data related to the transport amount that circulates in the supply chain, and evaluation index results related to evaluation indicators for supply chain design
  • the control unit is stored in the storage unit
  • Each model data is used as an input value, a model error between the design value and the actual value is obtained using a predetermined supply chain mathematical model, and the ease of change of the master data value to be estimated is calculated using the master value history data Then, a combination of the master data values having a small model error and
  • the supply chain design apparatus can always provide a supply chain design plan based on an appropriate master data value.
  • FIG. 1 is a diagram illustrating an example of a functional configuration of a supply chain design apparatus 100 and a supply chain design system 1000 according to the present embodiment.
  • the supply chain design system 1000 includes a supply chain design apparatus 100 that performs supply chain design, and supply chain operation result information (for example, the supply amount of each base and the transportation of each route) at each base on the supply chain. And a performance collection device 150 that collects quantity and supply chain evaluation indices).
  • the supply chain design apparatus 100 is communicably connected to the result collection apparatus 150 so as to obtain operation result information via a network N such as the Internet or a LAN (Local Area Network).
  • a network N such as the Internet or a LAN (Local Area Network).
  • the behavior of the supply chain may differ from the behavior assumed in the initial supply chain design.
  • actual values of supply chain evaluation indexes for example, total inventory amount and total cost
  • design values master data values and Deviations (errors) from design variables (calculated from supply chain mathematical models using business scenarios as input values).
  • the main cause of the discrepancy between the actual value of the supply chain evaluation index and the design value is, for example, “If the operation according to the design variable is not performed for some reason (for example, parts were procured more than the design) Etc. ”or“ when the business scenario is different from the assumption (for example, the actual demand is below the assumption) ”or“ the master data value is significantly different from the actual situation (for example, the conflict area Due to the avoidance of transportation, the transportation lead time of the shipping route has become longer, etc.) ”.
  • the user must identify the reason and respond to the operation as designed, If necessary, it is sufficient to redesign the supply chain using data from various business scenarios.
  • the cause is that the master data value is significantly different from the actual condition, the user identifies the master data value that is set to an inappropriate value, updates it to an appropriate value, The chain needs to be redesigned.
  • the master data value is constantly measured for all transportation sections and all bases, the master data value having a large deviation may be specified using the corresponding actual value.
  • the measurement granularity is generally coarser than the granularity of the master data value, and there may be a master data value such as a section that is not directly measured. For example, the transport lead time for a certain section (AD) is measured, but the transport lead time for each route (AB, BC, CD) in the section may not be measured. . Further, the above three main causes may occur at the same time.
  • the supply chain design apparatus 100 is configured so that when there is a discrepancy between the actual value of the supply chain evaluation index and the design value because the master data value is significantly different from the actual value, The master data value that is not appropriate is identified using the operation result value, and an estimated value that is an appropriate value of the master data value is calculated.
  • the supply chain design device 100 includes a control unit 101, a storage unit 102, and a communication unit 103.
  • the storage unit 102 includes demand data 200, product configuration data 210, supply source data 220, site candidate data 230, logistics candidate data 240, master value changeability data 250, master value history data 260, Supply amount record data 270, transport amount record data 280, and evaluation index record data 290 are included.
  • master data value may be simply expressed as “master value”.
  • Each of the demand data 200 and the product configuration data 210 constitutes a business scenario representing a situation in which the user uses the supply chain. For example, information on the component configuration of items distributed in the supply chain and the estimated demand in each market. Is included.
  • the product configuration data 210, the supply source data 220, the site candidate data 230, and the logistics candidate data 240 each constitute a master data value that defines logistics and money flow on the supply chain. Information on the allowable amount, lead time, usage cost, and basic unit for each route is included.
  • business scenarios and master data values are data representing supply chain design constraints.
  • FIG. 2 is a diagram illustrating an example of a table configuration of the demand data 200.
  • the demand data 200 is data representing the product demand in each market.
  • the demand data 200 includes a record in which a demand ID 201, an item 202, a market 203, and a demand amount 204 are associated with each other.
  • the demand ID 201 is an ID that uniquely identifies a record of the demand data 200.
  • the item 202 is information for specifying a product.
  • the market 203 is information for specifying a market.
  • the demand amount 204 is information representing the demand amount in the market for the associated product.
  • the assumed demand amount in the market “M1” of the item “car” is 100 units (for example, “1000”) per unit time (for example, “day”). Represents.
  • FIG. 3 is a diagram showing an example of a table configuration of the product configuration data 210.
  • the product configuration data 210 is data representing the component configuration of each product item.
  • the product configuration data 210 includes a record in which a configuration ID 211, a parent item 212, a child item 213, and a number 214 are associated with each other.
  • the configuration ID 211 is information for uniquely identifying the product configuration record.
  • the parent item 212 is information for specifying a parent item for the child item 213.
  • the child item 213 is information for specifying a child item that is a component of the parent item 212.
  • the number 214 is information representing the required amount of the child item 213 in the parent item 212.
  • the record of the configuration ID “ST1” has a required amount of “tire” that is one of the component parts (child items) for one unit of the parent item “car” (for example, “1000”). ”)".
  • FIG. 4 is a diagram showing an example of a table configuration of the supply source data 220.
  • the supply source data 220 is data representing the supply source of each product component.
  • the supply source data 220 is a record in which a supply source ID 221, an item 222, a site candidate ID 223, a supply capability 224, a supply LT (LT: Lead Time) 225, and a unit price 226 are associated with each other. have.
  • LT Lead Time
  • the supplier ID 221 is information for uniquely identifying the supplier record.
  • the item 222 is information for specifying a supply item.
  • the base candidate ID 223 is information for specifying a supply source base.
  • the supply capability 224 is information representing the supplyable number of supply sources for the associated item 222.
  • the supply LT 225 is information indicating the lead time when the associated item 222 is procured at the supply source.
  • the unit price 226 is information representing the cost when the associated item 222 is procured from the supplier.
  • one of the supplier bases of the item “tire” is the supplier of the base code “V1”
  • the supply capacity of the supplier base is “1000” units per unit time.
  • the supply LT is “3” units, and the supply unit price is “50” units (for example, “100,000 yen”).
  • FIG. 5 is a diagram showing an example of the table configuration of the base candidate data 230.
  • the base candidate data 230 is data representing base candidates of supply chain components.
  • the site candidate data 230 includes a record in which a site candidate ID 231, a name 232, a type 233, and a usage cost 234 are associated with each other.
  • the site candidate ID 231 is information for uniquely identifying a site candidate record.
  • the name 232 is information for specifying the name of the base.
  • the type 233 is information for specifying the base type.
  • the types of bases include, for example, a supplier, a factory, and a warehouse.
  • the use cost 234 is information for specifying a cost (for example, a cost of a base on a route where the transport amount takes a positive value in any of the distribution routes) that occurs when the base is used.
  • the record of the base candidate ID “V1” has the name “base A”, the base type “supplier”, and a cost of “1000” is incurred when using the base. Represents.
  • FIG. 6 is a diagram showing an example of the table configuration of the distribution candidate data 240.
  • the distribution candidate data 240 is data representing distribution route candidates specified by a product shipping source and arrival destination.
  • the logistics candidate data 240 includes a record in which a logistics candidate ID 241, a shipping source 242, a destination 243, a transportation item 244, a transportation LT 245, and a transportation unit price 246 are associated with each other. .
  • the logistics candidate ID 241 is information for uniquely identifying a logistics candidate record.
  • the shipping source 242 is information specifying the shipping source base of the physical distribution route.
  • the arrival destination 243 is information for specifying the arrival destination base.
  • the transport item 244 is information for specifying a transport item.
  • the transport LT 245 is information for specifying the transport lead time of the item on the physical distribution route.
  • the transportation unit price 246 is information for specifying the transportation unit price of the item on the physical distribution route.
  • the route of the distribution candidate ID “R1” is “P1”
  • the arrival destination is “W1”
  • the transport lead time when the item “car” is transported through the route is “5”. It is a unit and represents that the unit price of transportation is “10” unit.
  • FIG. 7 is a diagram showing an example of the table configuration of the master value changeability data 250.
  • the master value changeability data 250 is data representing the changeability of each master data value.
  • the master value changeability data 250 includes a changeability ID 251, a table name 252, a record ID 253, a master value name 254, an estimation flag 255, a changeability 256, and an upper limit value 257.
  • the lower limit value 258 is associated with the record.
  • the changeability ID 251 is information for uniquely identifying the master value changeability record.
  • the table name 252 is information that specifies the name of the data table in which the associated master value is stored.
  • the record ID 253 is information for specifying the record of the data table in which the associated master value is stored.
  • the master value name 254 is information that identifies the name of the master value associated with the changeability.
  • the estimation flag 255 is information indicating whether or not the master value is a target of a master data value estimated value calculation process to be described later (target or non-target).
  • the changeability 256 is information for specifying the changeability of the associated master value.
  • the upper limit value 257 and the lower limit value 258 are information for specifying an upper limit value and a lower limit value of values that can be taken by the associated master value.
  • the changeability 256 is information calculated by a master data value estimated value calculation process to be described later, and stores the latest changeability. Further, the values stored in the estimation flag 255, the upper limit value 257, and the lower limit value 258 are information set in advance by the user.
  • the changeability record “E1” “transport LT” stored in the record identified by “R1” of “distribution candidate data 240” is an object of the master data value estimated value calculation process, This indicates that the ease of change used for estimation is “10” units, the upper limit value that the master value can take is “10”, and the lower limit value is “ ⁇ 10% of the current value”.
  • FIG. 8 is a diagram showing an example of the table configuration of the master value history data 260.
  • the master value history data 260 is data representing a history of updated (corrected) master data values.
  • the master value history data 260 is a record in which a history ID 261, a table name 262, a record ID 263, a master value name 264, a record data acquisition time 265, and a record master value 266 are associated with each other. Have.
  • the history ID 261 is information for uniquely identifying the master value history data record.
  • the table name 262 is information for specifying the name of the data table in which the associated master value is stored.
  • the record ID 263 is information for specifying the record of the data table in which the associated master value is stored.
  • the master value name 264 is information that identifies the name of the master value associated with the actual master value 266.
  • the actual data acquisition time 265 is information indicating the date when the actual master value was acquired, that is, the actual master value was calculated and the record was generated.
  • the actual master value 266 is information for specifying the estimated value of the master data value calculated by the estimated value calculation process of the associated master data value.
  • the history record “H1” is a value calculated as “9/30” with respect to the master data value of “transport LT” stored in the record identified by “R1” of “distribution candidate data 240”. Represents “5”.
  • the record of the master value history data 260 is added each time the estimated value calculation process of the master data value is executed and a new value (estimated value) of the master data value is calculated.
  • FIG. 9 is a diagram showing an example of the table configuration of the actual supply amount data 270.
  • the supply amount result data 270 is data representing an operation result in the supply amount of the supply chain.
  • the supply amount record data 270 includes a record in which a record ID 270, a base ID 272, an item 273, and a supply amount 274 are associated with each other.
  • the record ID 271 is information for uniquely identifying the record data record of the supply amount.
  • the base ID 272 is information for specifying a supply source base.
  • the item 273 is information for specifying a supply item.
  • the supply amount 274 is information for specifying the supply amount of the item at the base.
  • the record “A1-1” indicates that the item “automobile” is supplied in units of “1000” at the base with the base candidate ID “P1”.
  • FIG. 10 is a diagram showing an example of a table configuration of the actual transportation volume data 280.
  • the actual transportation volume data 280 is data representing operational performance in the transportation volume of the supply chain.
  • the actual transportation amount data 280 includes a record in which the actual ID 281, the route ID 282, the item 283, and the transportation amount 284 are associated with each other.
  • the record ID 281 is information for uniquely identifying the record data record of the transport amount.
  • the route ID 282 is information for specifying a transportation route.
  • the item 283 is information for specifying the transportation item.
  • the transport amount 284 is information for specifying the transport amount of the item on the route.
  • the record “A1-2” indicates that the item “car” is transported by “100” units on the route with the route candidate ID “R1”.
  • FIG. 11 is a diagram showing an example of a table configuration of the performance data 290 of the evaluation index.
  • the performance data 290 of the evaluation index is data representing the evaluation index at the time of supply chain design.
  • the performance data of the evaluation index has a record in which a performance ID 291, a total cost 292, and a total inventory amount 293 are associated with each other.
  • the result ID 291 is information for uniquely identifying the result data record of the evaluation index.
  • the total cost 292 is information for specifying the total cost that is one of the evaluation indexes.
  • the total inventory quantity 293 is information for specifying the total inventory quantity that is one of the evaluation indexes.
  • the record “A1-3” indicates that the total cost is “20000” units and the total inventory amount is “2000” units.
  • the control unit 101 includes an overall control unit 111, an input unit 112, an output unit 113, a supply chain design unit 114, a changeability evaluation unit 115, and a master value estimation unit 116.
  • the overall control unit 111 is a central functional unit that performs various processes of the supply chain design apparatus 100. For example, the overall control unit 111 outputs information and instructions acquired via the input unit 112 to a predetermined functional unit according to the type and content of the information and instructions, thereby supplying the supply chain design device 100. Control the whole process.
  • the input unit 112 is a functional unit that receives an instruction to execute a predetermined process and input of information from a user via an input device (for example, a hardware device such as a mouse, a keyboard, and a touch panel) provided in the supply chain design apparatus 100.
  • an input device for example, a hardware device such as a mouse, a keyboard, and a touch panel
  • the output unit 113 is a functional unit that outputs information (such as information display) to the user via an output device (for example, a display) included in the supply chain design apparatus 100.
  • the supply chain design unit 114 is a functional unit that calculates a supply chain design plan. Specifically, the supply chain design unit 114 satisfies physical and time constraints such as demand in each market, supply capacity of each base, lead time, transport capacity of each physical distribution route, and supply chain evaluation. Calculate the supply chain design plan that optimizes the index.
  • the supply chain design unit 114 uses a predetermined mathematical model stored in advance in the storage unit 102, for example, by a branch and bound method, which is one of the mathematical optimization techniques, to give a given constraint.
  • a supply chain design plan that satisfies the conditions and optimizes the supply chain evaluation index is calculated.
  • the total cost calculated from the product supply cost (production cost), the transport cost, the base use cost (for example, the base establishment cost, etc.), the inventory amount of each base, and the transport of each transport route is handled as a supply chain evaluation index, the present invention can be similarly applied even when other evaluation index is used.
  • the mathematical model of the supply chain is the flow restriction (mass conservation law) and lead time restriction (restriction that parts entering the base can be removed from the base after the lead time of the base) and
  • a cost generation model for example, the cost generated by each route is calculated by adding the transport unit price ⁇ transport amount for all items
  • a supply chain evaluation index for example, total inventory amount and total cost
  • the supply chain mathematical model is a simulation model that calculates a supply chain evaluation index using a business scenario and a supply chain design plan as inputs, and the master data value defines the system specification of the supply chain to be simulated. It can also be regarded as a value.
  • the presence / absence of a base candidate is usually determined based on whether or not the transport amount on the distribution route including the base is greater than zero.
  • the supply amount of parts and products at each base is equal to the minimum necessary amount corresponding to the transportation amount in order to prevent unnecessary stagnation of products. Therefore, the supply chain design can be expressed by the transport amount for each distribution route, but in this embodiment, as described later, from the viewpoint of using information indicating the operation results of the supply chain, The supply chain design is expressed by the transport amount of each transport route.
  • design variables Supply chain design is the calculation of design variables (supplied amount of each base and transport amount of each transportation route) that optimizes the supply chain evaluation index using given master data values and business scenarios. Shall mean.
  • the changeability evaluation unit 115 is a functional unit that evaluates changeability indicating whether or not the master data value is easy to change with respect to the master data value used for supply chain design. Specifically, the changeability evaluation unit 115 calculates the changeability of the master data value set as a target by the user in the master data value estimated value calculation process, as will be described later. Note that there are master data values whose values are likely to change and those that are difficult to change depending on their characteristics. For example, the transport lead time of an international transport route by a shipping service is likely to change compared to the transport lead time of a domestic transport route by a truck service. Therefore, the ease of change is set for the transport lead time of the international transport route for shipping services compared to domestic truck services.
  • the master value estimation unit 116 is a functional unit that calculates an estimated value of the master data value. Specifically, the master value estimation unit 116 calculates an estimated value of a master data value that has a small error in the mathematical model of the supply chain and a high changeability with respect to the operation results of the supply chain design. Details of the estimated value calculation processing of the master data value will be described later.
  • the communication unit 103 is a functional unit that performs information communication with an external device (for example, a performance collection device 150 or a database system (not shown)).
  • an external device for example, a performance collection device 150 or a database system (not shown)
  • FIG. 12 is a diagram illustrating an example of a hardware configuration of the supply chain design apparatus 100.
  • the supply chain design apparatus 100 is realized by a computer such as a PC (personal computer) or a workstation, for example.
  • the supply chain design device 100 includes a CPU (Central Processing Unit) 302, a RAM (Random Access Memory) 303, a ROM (Read Only Memory) 304, an arithmetic processing device 301, an external storage device 305, and a communication device 306. And an input device 307, an output device 308, and a bus 309 for connecting the devices to each other.
  • a CPU Central Processing Unit
  • RAM Random Access Memory
  • ROM Read Only Memory
  • the external storage device 305 is, for example, a nonvolatile storage device such as a hard disk device or a flash memory.
  • the communication device 306 is a device such as a communication module that performs information communication with an external device (for example, a performance collection device 150 or a database system (not shown)).
  • an external device for example, a performance collection device 150 or a database system (not shown)
  • the input device 307 is a device that receives input from the user of the supply chain design device 100 such as a touch panel, a keyboard, and a mouse.
  • the output device 308 is a device that displays image information such as a display.
  • the control unit 101 of the supply chain design apparatus 100 shown in FIG. 1 is realized by a program that causes the CPU 302 to perform processing. This program is stored in the ROM 304 or the external storage device 305, loaded onto the RAM 303 for execution, and executed by the CPU 302.
  • the storage unit 102 of the supply chain design apparatus 100 shown in FIG. 1 is realized by any one of the RAM 303, the ROM 304, the external storage device 305, or a combination thereof.
  • the communication unit 103 is realized by the communication device 306.
  • FIG. 13 is a diagram showing an example of the flow of such processing by PAD (Problem Analysis Diagram).
  • PAD Processed Analysis Diagram
  • the input unit 112 reads the supply chain design conditions (step S101). Specifically, the input unit 112 acquires the demand data 200 and product configuration data 210 that are business scenarios, the supply source data 220, the site candidate data 230, and the logistics candidate data 240 that are master data values from the storage unit 102. To do.
  • the input unit 112 reads performance data (step S102). Specifically, the input unit 112 acquires the actual supply amount data 270, the transport amount actual data 280, and the evaluation index actual data 290 from the storage unit 102.
  • FIG. 14 is a diagram showing an example of a detailed flow of the model error evaluation process by PAD.
  • the supply chain design unit 114 executes a supply chain evaluation index simulation (step S201). Specifically, the supply chain design unit 114 uses the design conditions acquired from the storage unit 102, that is, the business scenario and the master data value, the actual supply amount data 270, and the actual transport amount data 280 as input values, and supplies the supply chain.
  • the simulation value of the supply chain evaluation index is calculated by using the mathematical model as a simulation model.
  • Such model error evaluation processing is performed when the supply amount and the transport amount of actual data are applied to the supply chain network constructed by the master data value, that is, an evaluation index based on the actual data of the supply chain, that is, This is a process for calculating the total cost and the total inventory quantity based on the actual data.
  • the supply chain design unit 114 calculates a model error (step S202). Specifically, the supply chain design unit 114 calculates an error between the performance data of the evaluation index acquired from the storage unit 102 and the simulation value of the supply chain evaluation index calculated in step S201. More specifically, the supply chain design unit 114 adds a value obtained by adding a product obtained by multiplying each of the total cost and total inventory quantity of the performance data of the evaluation index by a predetermined coefficient (for example, ⁇ , ⁇ ). Then, the model error is calculated based on the difference from the value obtained by adding each of the total cost calculated as the simulation value and the total inventory amount multiplied by the coefficients ( ⁇ , ⁇ ).
  • a predetermined coefficient for example, ⁇ , ⁇
  • the supply chain design unit 114 determines whether or not the model error calculated in step S202 is larger than a predetermined threshold (step S104). If the model error is less than the predetermined threshold value, the supply chain design unit 114 ends the master data value estimated value calculation process. On the other hand, when the model error is larger than the predetermined threshold value, the changeability evaluation unit 115 performs a master value changeability evaluation (step S105).
  • FIG. 15 is a diagram showing an example of a detailed flow of the master value changeability evaluation process by PAD.
  • the changeability evaluation unit 115 reads the master value history data 260 from the storage unit 102 (step S301).
  • the changeability evaluation unit 115 determines whether or not the number of master value history data 260 is smaller than a predetermined threshold (step S302). That is, the changeability evaluation unit 115 determines whether or not the number of records stored in the master value history data 260 is less than a predetermined threshold.
  • the changeability evaluation unit 115 sets all the master data specified using the table name 262 and the master value name 264 of the master value history data 260 as keys. Among the values, “1” is uniformly set to the ease of change which is the estimation target of the master value (step S303).
  • the changeability evaluation unit 115 specifies all master data values from the table name 262 and the master value name 264 of the master value history data 260 when the master value history data 260 is less than a predetermined threshold. Also, the changeability evaluation unit 115, when “target” is set in the estimation flag 255 of the master value changeability data 250 in which the identified master data value is stored, the changeability of the master data value 256 is set to “1”. The changeability evaluation unit 115 performs the same process on all the specified master data values.
  • the number of master value history data 260 is small, a highly accurate changeability calculation and a master data value cannot be estimated, so a uniform value is set for the changeability.
  • the user may set the changeability value.
  • a message recommending that the master data value be corrected to an appropriate value may be presented to the user, and the master data value estimated value calculation process may be terminated.
  • FIG. 16 is a diagram showing an example of the master value changeability setting screen 400.
  • the user Prior to the execution of the master data value estimated value calculation process, the user makes settings in advance on the master value changeability setting screen 400 for predetermined items such as whether the master value is to be estimated or not. Can do.
  • the master value changeability setting screen 400 has a master value selection display area 401, a changeability display area 402, and a master value history display area 403.
  • the master value selection display area 401 is provided with an input field for selecting and inputting a table 411, a record 412, and a master value 413.
  • the changeability display area 402 includes an input field 421 for selectively inputting whether or not to set the estimation target, and an input field for inputting an upper limit value, a lower limit value and changeability (an input field 422 and an input field, respectively). 423, an input field 424).
  • the user uniquely specifies the master data value to be set for the estimation target of the master value from the table 411, the record 412 and the master value 413, and selects “target” from the estimation target selection input field 421. Further, the user inputs an upper limit value and a lower limit value of the master data value to be estimated. Further, when the user presses an “OK” button 431 after inputting these pieces of information, these input values become the estimation flag 255, the changeability 256, the upper limit value 257, and the lower limit value of the master value changeability data 250. 258.
  • the changeability input field 424 is an input field when the user himself / herself sets a desired value as changeability when the number of master value history data 260 is smaller than the threshold value, as described above.
  • the master value history display area 403 includes a master value history graph 441 that displays the transition of master values updated in the past in time series, a maximum value 442 of master values that have been updated in the past, and a minimum value 443.
  • the average value 444 and the change coefficient 445 are displayed.
  • the changeability evaluation part 115 is specified.
  • the actual master value 266 of the master data value is extracted from the master value history data 260, and when there are a plurality of master data values, the maximum value and the minimum value, the average value, and the coefficient of variation are calculated.
  • the changeability evaluation unit 115 stores the calculated variation coefficient in the changeability 256 of the master value changeability data 250 as a changeability value.
  • the variation coefficient may be calculated by dividing the standard deviation of the actual master value extracted from the master value history data 260 by the average value, or may be calculated using the average change rate of the actual master value. .
  • step S302 when the master value history data 260 is greater than the predetermined threshold, the changeability evaluation unit 115 sets the master data value specified using the table name 262 and the master value name 264 of the master value history data 260 as keys. Among them, the master value change easiness is calculated for all master data values that are subject to estimation of the master value (step S304).
  • the changeability evaluation unit 115 stores the calculated changeability in the changeability 256 of the master value changeability data 250 (step S305).
  • the master value estimation unit 116 estimates a master data value to be estimated (step S106). Specifically, the master value estimation unit 116 calculates a combination of master data values with a small model error and a high changeability.
  • the master value estimation unit 116 handles the master data value set as the estimation target in the master value changeability data 250 as a variable determined by the branch and bound method, and handles it as a variable at the time of supply chain design.
  • the design variable is set to be handled as a constant set in the actual data. That is, at the time of supply chain design, the master data value that is a constant input value is set as a variable input value, and the actual data that is the variable input value is set as a constant.
  • the master value estimation unit 116 is improved when the model error is small, and the result value is improved if the changeability of the master data value for changing the value to the estimated value is large (the increase / decrease direction of the value is not limited and is optimal)
  • the objective function shown by the following mathematical formula (1) is set as a function to mean approaching the value.
  • the “changeability of the master data value for changing the value to the estimated value” is expressed by the following formula (2).
  • Master data value changeability to change the value to the estimated value ((1 / max (master data value changeability or ⁇ )) ⁇ (master data value estimated value ⁇ master data value current value) 2 ) Total (2)
  • the first term is a term for minimizing the violating quantity of the constraint condition
  • the second term is for reducing the model error (deviation from the actual value of the supply chain evaluation index).
  • the third term is a term for increasing the ease of changing the master data value for changing the value to the estimated value.
  • the weighting coefficient of each term is set in advance so that the first term> the second term> the third term.
  • the said Numerical formula (2) is a type
  • ⁇ is a value larger than 0 and a sufficiently small value (for example, 10 ⁇ 6, etc.).
  • the model error is small and the estimated value is calculated by calculating the variable that optimizes the objective function using the branch and bound method. It is possible to calculate a combination of master data values for which the master data value to be changed is large and a master data value in such a case.
  • the branch and bound method can obtain a plurality of solutions whose objective function values are close to the best value, a plurality of solutions may be stored in the storage unit 102 as an alternative presented to the user.
  • the objective function is not limited to the above mathematical formulas (1) and (2), and when the model error is small and when the master data value for changing the value to the estimated value is easy to change. Any function may be used as long as the result value is improved (increase / decrease direction of the value is not limited, meaning that the value approaches an optimum value).
  • FIG. 17 is a diagram illustrating an example of a display screen 500 for a master data value estimation result.
  • the master data value estimation result screen 500 has a master data value estimation result display area 501, a model error display area 502, and a changeability display area 503.
  • the output unit 113 uses the predetermined information such as the master data value and the network configuration information (not shown) stored in advance in the storage unit 102 to supply the supply chain plan information indicating the network configuration of the supply chain (showing the base and route) Is displayed in the master data value estimation result display area 501. Further, the output unit 113 uses the master value specified and estimated in step S106 and the value thereof, and the amount of change in the master value, that is, the amount of deviation between the current master value and the estimated value is greater than a predetermined threshold. Identify routes and locations. Further, the output unit 113 highlights the identified physical distribution route or base (for example, draws a bold line or fills a frame indicating the base).
  • the output unit 113 displays detailed information on the selected distribution route or base. For example, when the logistics route from the base V3 to P2 in FIG. 17 is selected by the user, the output unit 113 displays the current value (current master value) and the estimated value for the transport LT on the route. In such a display 511, the output unit 113 identifies the record storing the information related to the selected distribution route from the distribution candidate data 240, and acquires the value (current value) stored in the transport LT 245 as the master value. To do. Further, the output unit 113 compares and displays the estimated value calculated in step S106 together with the acquired current value.
  • the output unit 113 displays the current value and the estimated value of the usage cost of the base.
  • the output unit 113 identifies a record storing information on the selected site W2 from the site candidate data 230, and uses the value (current value) stored in the usage cost 234 as the master value. get. Further, the output unit 113 compares and displays the estimated value calculated in step S107 together with the acquired current value.
  • the display method of the master data value estimation result display area 501 is not limited to the network configuration diagram as described above, and for example, the bases and distribution routes may be displayed in a list form.
  • the output unit 113 displays the total cost, the total inventory amount, and the error in the model error display area 502. Specifically, the output unit 113, via the supply chain design unit 114, in the process of step S106 (master value estimation process), a solution (model error is small) and the value of the objective function is close to the best value.
  • the simulation value and the model error are obtained in the same manner as in step S201 and step S202, with the combination of the master data values that increase the ease of change and its value) as fixed input values. Further, the output unit 113 displays the obtained simulation values (total cost and total inventory amount) and the model error in the model error display area 502.
  • the output unit 113 displays the changeability in the changeability display area 503. Specifically, the output unit 113 displays the changeability of the changed master data value calculated in step S ⁇ b> 304 in the changeability display area 503. In addition, when there are a plurality of master data values to be changed, the output unit 113 displays a list of their ease of change.
  • the display format of changeability is not limited to list display. For example, when the user selects a highlighted base or distribution route, only the changeability of the base or distribution route is displayed. May be displayed.
  • the user can determine whether or not the change of the master data value is appropriate.
  • the output unit 113 displays an apply button 541 and a cancel button 542, and accepts a user's press via the input unit 112.
  • the output unit 113 reflects the master value correction proposal presented to the user (step S108). Specifically, the output unit 113 updates the value of the master data value to the estimated value calculated in step S106.
  • the output unit 113 switches and displays each result so that the user can select and specify each result. Further, in this example, an example is shown in which the validity of all the master data values is uniformly confirmed. However, the estimation result may be partially rejected, or the user may explicitly input an update value. .
  • the function blocks of the supply chain design device 100 are classified according to main processing contents in order to facilitate understanding of the functions of the supply chain design device 100 realized in the present embodiment.
  • the present invention is not limited by the way of classification and its name.
  • each configuration of the supply chain design apparatus 100 can be classified into more components according to the processing content.
  • it can also classify
  • the present invention is not limited to the above-described embodiments, and includes various modifications.
  • the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described.
  • a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • control lines and information lines indicate what is considered necessary for the description, and not all control lines and information lines on the product are necessarily shown. In practice, it can be considered that almost all the components are connected to each other.

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Abstract

The present invention can consistently provide a supply chain design plan based on an appropriate master data value. In the present invention, a storage unit has a business scenario, a master data value, master value history data, supply amount performance record data, transport amount performance record data, and evaluation index performance record data. Taking the individual items of data stored in the storage unit as input values, a control unit finds model error between a design value and a performance record value using a prescribed supply chain mathematical model, calculates the ease of altering the to-be-estimated master data value using the master value history data, and calculates a combination of master data values in which the model error is low and the ease of alternation is high and the estimated values of the master data values.

Description

サプライチェーン設計装置、サプライチェーン設計方法およびプログラムSupply chain design apparatus, supply chain design method and program
　本発明は、サプライチェーン設計装置、サプライチェーン設計方法およびプログラムに関する。 The present invention relates to a supply chain design device, a supply chain design method, and a program.
　特許文献１には、物流設計のための入力データを用いて、拠点間の物流経路における関税を含めたコストを計算する処理を行う物流設計部を有し、物流設計部は、入力データに基づき、物流経路の組合せを構成し、協定の組合せ構成し、物流経路及びパスごとに、協定に応じた関税額を含むコストを加算し、これにより物流経路ごとの関税を含む総コストを計算する物流設計装置が記載されている。 Patent Document 1 has a logistics design unit that performs processing for calculating costs including tariffs on logistics routes between bases using input data for logistics design, and the logistics design unit is based on the input data. Logistics that configures a combination of logistics routes, constructs a combination of agreements, adds costs including tariffs according to the agreement for each logistics route and path, and thereby calculates total costs including tariffs for each logistics route A design device is described.
特開２０１３－８９１０１号公報JP2013-89101A
　一般に、サプライチェーン設計では、利用可能な拠点候補や物流経路候補の特性を表す情報（例えば、許容量、利用コスト、リードタイムなど以下、「マスタデータ値」という）およびビジネスシナリオ（各市場の想定需要量、対象品目の部品構成など）をはじめとするサプライチェーン設計条件に対して、サプライチェーン評価指標（例えば、在庫量やコストなど）を最適とするサプライチェーンが設計される。特許文献１には、与えられた設計条件に対してコストを最小とするためのサプライチェーン設計に関する技術が開示されている。 In general, in supply chain design, information indicating the characteristics of available base candidates and logistics route candidates (for example, allowable data, usage cost, lead time, etc., hereinafter referred to as “master data values”) and business scenarios (assumptions for each market) A supply chain that optimizes a supply chain evaluation index (for example, inventory amount, cost, etc.) is designed with respect to supply chain design conditions such as demand amount and component configuration of target items. Patent Document 1 discloses a technology related to supply chain design for minimizing the cost for given design conditions.
　サプライチェーン設計に用いられるマスタデータ値は、例えば、紛争地域回避による船便輸送経路の輸送リードタイムの長期化など、時期や状況に応じて変化することがあるため、マスタデータ値などを適宜見直し、更新する必要がある。しかしながら、検討すべき拠点候補、物流経路候補が多い場合には、予め準備するマスタデータ値の数が膨大に存在し、適切な見直しを行う工数がないため、実態と合っていないマスタデータ値を用いてサプライチェーンが設計されることがある。その結果、実態と合っていないマスタデータ値を用いて設計されたサプライチェーンは、設計時の想定よりも在庫量やコストが想定以上に増加するなどの悪影響を及ぼすという問題が生じる。 The master data value used for supply chain design may change depending on the time and circumstances, such as an increase in the shipping lead time of the shipping route by avoiding conflict areas. Need to update. However, when there are many site candidates and logistics route candidates to be examined, there are a large number of master data values to be prepared in advance, and there are no man-hours for appropriate review. The supply chain may be designed using. As a result, there is a problem that a supply chain designed using master data values that do not match the actual situation has an adverse effect such as an increase in inventory and cost more than expected rather than an assumption at the time of design.
　本発明は、上記問題に鑑みてなされたものであり、常時、適正なマスタデータ値に基づくサプライチェーン設計案を提供可能なサプライチェーン設計装置の提供を目的とする。 The present invention has been made in view of the above problems, and an object of the present invention is to provide a supply chain design apparatus capable of always providing a supply chain design plan based on an appropriate master data value.
　上記課題を解決するため、本発明に係るサプライチェーン設計装置は、サプライチェーン設計案を算出するサプライチェーン設計装置であって、記憶部と制御部とを備え、前記記憶部は、サプライチェーンを流通する品目の部品構成および各市場の想定需要量に関するデータであるビジネスシナリオと、サプライチェーン上の物流および金流に関するデータであるマスタデータ値と、該マスタデータ値の更新履歴に関するデータであるマスタ値履歴データと、サプライチェーンを流通する品目の供給量に関するデータである供給量実績データと、サプライチェーンを流通する輸送量に関するデータである輸送量実績データと、サプライチェーン設計の評価指標に関する評価指標実績データとを有し、前記制御部は、前記記憶部に格納されている各データを入力値とし、所定のサプライチェーン数理モデルを用いて設計値と実績値とのモデル誤差を求め、前記マスタ値履歴データを用いて推定対象となるマスタデータ値の変更容易性を算出し、前記モデル誤差が小さく、かつ、前記変更容易性が大きい前記マスタデータ値の組合せおよびその推定値を算出する。 In order to solve the above problems, a supply chain design device according to the present invention is a supply chain design device that calculates a supply chain design plan, and includes a storage unit and a control unit, and the storage unit distributes the supply chain. Business scenario that is data related to the component structure of the item to be processed and the estimated demand in each market, master data values that are data related to logistics and money flow in the supply chain, and master values that are data related to the update history of the master data values Historical data, supply amount actual data that is data related to the supply amount of items that circulate in the supply chain, transport amount actual data that is data related to the transport amount that circulates in the supply chain, and evaluation index results related to evaluation indicators for supply chain design And the control unit is stored in the storage unit Each model data is used as an input value, a model error between the design value and the actual value is obtained using a predetermined supply chain mathematical model, and the ease of change of the master data value to be estimated is calculated using the master value history data Then, a combination of the master data values having a small model error and a large changeability and an estimated value thereof are calculated.
　本発明に係るサプライチェーン設計装置によれば、常時、適正なマスタデータ値に基づくサプライチェーン設計案を提供することができる。 The supply chain design apparatus according to the present invention can always provide a supply chain design plan based on an appropriate master data value.
本発明の一実施形態に係るサプライチェーン設計装置の機能構成およびサプライチェーン設計システムの一例を示した図である。It is a figure showing an example of functional composition of a supply chain design device and a supply chain design system concerning one embodiment of the present invention. 本発明の一実施形態に係る需要データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the demand data which concerns on one Embodiment of this invention. 本発明の一実施形態に係る製品構成データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the product structure data which concerns on one Embodiment of this invention. 本発明の一実施形態に係る供給元データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the supply source data which concerns on one Embodiment of this invention. 本発明の一実施形態に係る拠点候補データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the base candidate data which concerns on one Embodiment of this invention. 本発明の一実施形態に係る物流候補データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the physical distribution candidate data which concerns on one Embodiment of this invention. 本発明の一実施形態に係るマスタ値変更容易性データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the master value change ease data which concerns on one Embodiment of this invention. 本発明の一実施形態に係るマスタ値履歴データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the master value historical data which concerns on one Embodiment of this invention. 本発明の一実施形態に係る供給量の実績データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the performance data of the supply amount which concerns on one Embodiment of this invention. 本発明の一実施形態に係る輸送量の実績データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the performance data of the transportation amount which concerns on one Embodiment of this invention. 本発明の一実施形態に係る評価指標の実績データのテーブル構成の一例を示した図である。It is the figure which showed an example of the table structure of the performance data of the evaluation index which concerns on one Embodiment of this invention. 本発明の一実施形態に係るサプライチェーン設計装置のハードウェア構成の一例を示した図である。It is the figure which showed an example of the hardware constitutions of the supply chain design apparatus which concerns on one Embodiment of this invention. 本発明の一実施形態に係るマスタデータ値の推定値算出処理の流れの一例をＰＡＤ（Ｐｒｏｂｌｅｍ　Ａｎａｌｙｓｉｓ　Ｄｉａｇｒａｍ）により示した図である。It is the figure which showed an example of the flow of the estimated value calculation process of the master data value which concerns on one Embodiment of this invention by PAD (Problem Analysis Diagram). 本発明の一実施形態に係るモデル誤差評価処理の詳細な流れの一例をＰＡＤにより示した図である。It is the figure which showed an example of the detailed flow of the model error evaluation process which concerns on one Embodiment of this invention by PAD. 本発明の一実施形態に係るマスタ値変更容易性評価処理の詳細な流れの一例をＰＡＤにより示した図である。It is the figure which showed an example of the detailed flow of the master value change ease evaluation processing which concerns on one Embodiment of this invention by PAD. 本発明の一実施形態に係るマスタ値変更容易性設定画面の一例を示した図である。It is the figure which showed an example of the master value change ease setting screen which concerns on one Embodiment of this invention. 本発明の一実施形態に係るマスタデータ値推定結果の表示画面の一例を示す図である。It is a figure which shows an example of the display screen of the master data value estimation result which concerns on one Embodiment of this invention.
　以下、本発明の一実施形態に係るサプライチェーン設計装置について図面を用いて説明する。 Hereinafter, a supply chain design apparatus according to an embodiment of the present invention will be described with reference to the drawings.
　＜第一実施形態＞
図１は、本実施形態に係るサプライチェーン設計装置１００の機能構成およびサプライチェーン設計システム１０００の一例を示した図である。図示するように、サプライチェーン設計システム１０００は、サプライチェーン設計を行うサプライチェーン設計装置１００と、サプライチェーン上の各拠点におけるサプライチェーンの運用実績情報（例えば、各拠点の供給量、各経路の輸送量およびサプライチェーン評価指標など）を収集する実績収集装置１５０とを有する。なお、サプライチェーン設計装置１００は、インターネットやＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）などのネットワーク網Ｎを介して、運用実績情報を取得できるよう実績収集装置１５０と通信可能に接続されている。 <First embodiment>
FIG. 1 is a diagram illustrating an example of a functional configuration of a supply chain design apparatus 100 and a supply chain design system 1000 according to the present embodiment. As shown in the figure, the supply chain design system 1000 includes a supply chain design apparatus 100 that performs supply chain design, and supply chain operation result information (for example, the supply amount of each base and the transportation of each route) at each base on the supply chain. And a performance collection device 150 that collects quantity and supply chain evaluation indices). The supply chain design apparatus 100 is communicably connected to the result collection apparatus 150 so as to obtain operation result information via a network N such as the Internet or a LAN (Local Area Network).
　サプライチェーンの運用においては、サプライチェーンの挙動が、当初のサプライチェーン設計で想定した挙動と異なる場合がある。このような挙動すなわち実際の運用と設計との乖離を表す現象の１つとして、サプライチェーン評価指標（例えば、総在庫量や総コストなど）の実績値と、設計値（後述するマスタデータ値およびビジネスシナリオを入力値として用い、サプライチェーン数理モデルにより算出される設計変数の値）との乖離（誤差）が挙げられる。 In supply chain operation, the behavior of the supply chain may differ from the behavior assumed in the initial supply chain design. As one of the phenomena representing such behavior, that is, a divergence between actual operation and design, actual values of supply chain evaluation indexes (for example, total inventory amount and total cost) and design values (master data values and Deviations (errors) from design variables (calculated from supply chain mathematical models using business scenarios as input values).
　ここで、サプライチェーン評価指標の実績値と設計値との乖離の主な原因は、例えば、「何かしらの理由により設計変数通りの運用が行われなかった場合（例えば、設計以上に部品を調達したなど）」または、「ビジネスシナリオが想定と異なっていた場合（例えば、実際の需要量が想定を下回っていた）」または、「マスタデータ値が実態と大きく異なっていた場合（例えば、紛争地域を回避した輸送のために、船便輸送経路の輸送リードタイムが長くなっているなど）」が考えられる。 Here, the main cause of the discrepancy between the actual value of the supply chain evaluation index and the design value is, for example, “If the operation according to the design variable is not performed for some reason (for example, parts were procured more than the design) Etc. ”or“ when the business scenario is different from the assumption (for example, the actual demand is below the assumption) ”or“ the master data value is significantly different from the actual situation (for example, the conflict area Due to the avoidance of transportation, the transportation lead time of the shipping route has become longer, etc.) ”.
　乖離の主な原因が、設計変数通りの運用が行われなかった場合やビジネスシナリオが想定と異なっていた場合、ユーザは、その理由を見極めて、設計通りの運用を行うよう対応したり、新たなビジネスシナリオのデータを用いてサプライチェーンを再設計するなど、必要に応じて対応すれば良い。一方で、「マスタデータ値が実態と大きく異なっていたこと」が原因の場合、ユーザは、適正ではない値が設定されているマスタデータ値を特定し、適切な値に更新した上で、サプライチェーンを再度設計する必要がある。 If the main cause of the divergence is not operating as designed variables, or if the business scenario is different from what is assumed, the user must identify the reason and respond to the operation as designed, If necessary, it is sufficient to redesign the supply chain using data from various business scenarios. On the other hand, if the cause is that the master data value is significantly different from the actual condition, the user identifies the master data value that is set to an inappropriate value, updates it to an appropriate value, The chain needs to be redesigned.
　マスタデータ値が全ての輸送区間や全拠点について常時計測されたものであれば、対応する実績値を用いて乖離が大きいマスタデータ値を特定すれば良い。しかしながら、計測のコストおよび工数が大きいことから、計測粒度はマスタデータ値の粒度より粗いのが一般的であり、直接計測されていない区間などのマスタデータ値が存在する場合がある。例えば、ある区間（Ａ－Ｄ）の輸送リードタイムは計測されているが、区間内の経路ごと（Ａ－Ｂ、Ｂ－Ｃ、Ｃ－Ｄ）の輸送リードタイムは計測されていないことがある。さらに、上記３つの主な原因が同時に発生している場合もある。 If the master data value is constantly measured for all transportation sections and all bases, the master data value having a large deviation may be specified using the corresponding actual value. However, since the measurement cost and man-hours are large, the measurement granularity is generally coarser than the granularity of the master data value, and there may be a master data value such as a section that is not directly measured. For example, the transport lead time for a certain section (AD) is measured, but the transport lead time for each route (AB, BC, CD) in the section may not be measured. . Further, the above three main causes may occur at the same time.
　本実施形態に係るサプライチェーン設計装置１００は、マスタデータ値が実態と大きく異なっていたことを原因としてサプライチェーンの評価指標の実績値と設計値との乖離が生じている場合に、サプライチェーンの運用実績値を用いて、適正ではないマスタデータ値を特定し、かかるマスタデータ値の適正値である推定値を算出するものである。 The supply chain design apparatus 100 according to the present embodiment is configured so that when there is a discrepancy between the actual value of the supply chain evaluation index and the design value because the master data value is significantly different from the actual value, The master data value that is not appropriate is identified using the operation result value, and an estimated value that is an appropriate value of the master data value is calculated.
　図１に示すように、本実施形態に係るサプライチェーン設計装置１００は、制御部１０１と、記憶部１０２と、通信部１０３とを有している。 As shown in FIG. 1, the supply chain design device 100 according to this embodiment includes a control unit 101, a storage unit 102, and a communication unit 103.
　記憶部１０２は、需要データ２００と、製品構成データ２１０と、供給元データ２２０と、拠点候補データ２３０と、物流候補データ２４０と、マスタ値変更容易性データ２５０と、マスタ値履歴データ２６０と、供給量の実績データ２７０と、輸送量の実績データ２８０と、評価指標の実績データ２９０とを有している。なお、以下では、「マスタデータ値」を単に「マスタ値」と表現することがある。 The storage unit 102 includes demand data 200, product configuration data 210, supply source data 220, site candidate data 230, logistics candidate data 240, master value changeability data 250, master value history data 260, Supply amount record data 270, transport amount record data 280, and evaluation index record data 290 are included. Hereinafter, the “master data value” may be simply expressed as “master value”.
　また、需要データ２００および製品構成データ２１０は各々、ユーザがサプライチェーンを利用する状況を表すビジネスシナリオを構成し、例えば、サプライチェーンを流通する品目の部品構成や、各市場の想定需要量に関する情報が含まれる。また、製品構成データ２１０と、供給元データ２２０と、拠点候補データ２３０と、物流候補データ２４０とは各々、サプライチェーン上の物流、金流を規定するマスタデータ値を構成し、例えば、拠点、経路ごとの許容量、リードタイム、利用コストおよび原単位に関する情報が含まれる。 Each of the demand data 200 and the product configuration data 210 constitutes a business scenario representing a situation in which the user uses the supply chain. For example, information on the component configuration of items distributed in the supply chain and the estimated demand in each market. Is included. In addition, the product configuration data 210, the supply source data 220, the site candidate data 230, and the logistics candidate data 240 each constitute a master data value that defines logistics and money flow on the supply chain. Information on the allowable amount, lead time, usage cost, and basic unit for each route is included.
　また、これらのビジネスシナリオおよびマスタデータ値は、サプライチェーン設計の制約条件を表すデータである。 Also, these business scenarios and master data values are data representing supply chain design constraints.
　図２は、需要データ２００のテーブル構成の一例を示した図である。需要データ２００は、各市場における製品需要量を表すデータである。具体的には、需要データ２００は、需要ＩＤ２０１と、品目２０２と、市場２０３と、需要量２０４とが対応付けられたレコードを有している。 FIG. 2 is a diagram illustrating an example of a table configuration of the demand data 200. The demand data 200 is data representing the product demand in each market. Specifically, the demand data 200 includes a record in which a demand ID 201, an item 202, a market 203, and a demand amount 204 are associated with each other.
　需要ＩＤ２０１は、需要データ２００のレコードを一意に識別するＩＤである。品目２０２は、製品を特定する情報である。市場２０３は、市場を特定する情報である。需要量２０４は、対応付けられた製品の市場における需要量を表す情報である。本例では、需要ＩＤ「Ｄ１」のレコードは、品目「自動車」の市場「Ｍ１」における想定需要量が単位時間（例えば、「日」）当たり１００単位（例えば、「１０００台」）であることを表している。 The demand ID 201 is an ID that uniquely identifies a record of the demand data 200. The item 202 is information for specifying a product. The market 203 is information for specifying a market. The demand amount 204 is information representing the demand amount in the market for the associated product. In this example, in the record with the demand ID “D1”, the assumed demand amount in the market “M1” of the item “car” is 100 units (for example, “1000”) per unit time (for example, “day”). Represents.
　図３は、製品構成データ２１０のテーブル構成の一例を示した図である。製品構成データ２１０は、各製品品目の部品構成を表すデータである。具体的には、製品構成データ２１０は、構成ＩＤ２１１と、親品目２１２と、子品目２１３と、員数２１４とが対応付けられたレコードを有している。 FIG. 3 is a diagram showing an example of a table configuration of the product configuration data 210. The product configuration data 210 is data representing the component configuration of each product item. Specifically, the product configuration data 210 includes a record in which a configuration ID 211, a parent item 212, a child item 213, and a number 214 are associated with each other.
　構成ＩＤ２１１は、製品構成レコードを一意に識別する情報である。親品目２１２は、子品目２１３に対する親品目を特定する情報である。子品目２１３は、親品目２１２の構成要素である子品目を特定する情報である。員数２１４は、親品目２１２における子品目２１３の所要量を表す情報である。本例では、構成ＩＤ「ＳＴ１」のレコードは、親品目「自動車」の１単位について、構成部品（子品目）の１つである「タイヤ」の所要量が「４」単位（例えば、「１０００個」）であることを表している。 The configuration ID 211 is information for uniquely identifying the product configuration record. The parent item 212 is information for specifying a parent item for the child item 213. The child item 213 is information for specifying a child item that is a component of the parent item 212. The number 214 is information representing the required amount of the child item 213 in the parent item 212. In this example, the record of the configuration ID “ST1” has a required amount of “tire” that is one of the component parts (child items) for one unit of the parent item “car” (for example, “1000”). ")".
　図４は、供給元データ２２０のテーブル構成の一例を示した図である。供給元データ２２０は、各製品構成要素の供給元を表すデータである。具体的には、供給元データ２２０は、供給元ＩＤ２２１と、品目２２２と、拠点候補ＩＤ２２３と、供給能力２２４と、供給ＬＴ（ＬＴ：Ｌｅａｄ　Ｔｉｍｅ）２２５と、単価２２６とが対応付けられたレコードを有している。 FIG. 4 is a diagram showing an example of a table configuration of the supply source data 220. The supply source data 220 is data representing the supply source of each product component. Specifically, the supply source data 220 is a record in which a supply source ID 221, an item 222, a site candidate ID 223, a supply capability 224, a supply LT (LT: Lead Time) 225, and a unit price 226 are associated with each other. have.
　供給元ＩＤ２２１は、供給元レコードを一意に識別する情報である。品目２２２は、供給品目を特定する情報である。拠点候補ＩＤ２２３は、供給元拠点を特定する情報である。供給能力２２４は、対応付けられた品目２２２について供給元の供給可能数を表す情報である。供給ＬＴ２２５は、対応付けられた品目２２２について供給元で調達する際のリードタイムを表す情報である。単価２２６は、対応付けられた品目２２２について供給元から調達する際のコストを表す情報である。本例では、供給元ＩＤ「Ｓ１」のレコードは、品目「タイヤ」の供給元拠点の１つが拠点コード「Ｖ１」の供給元であり、供給元拠点の供給能力が単位時間当たり「１０００」単位であり、供給ＬＴが「３」単位であり、供給単価が「５０」単位（例えば、「100,000円」）であることを表している。 The supplier ID 221 is information for uniquely identifying the supplier record. The item 222 is information for specifying a supply item. The base candidate ID 223 is information for specifying a supply source base. The supply capability 224 is information representing the supplyable number of supply sources for the associated item 222. The supply LT 225 is information indicating the lead time when the associated item 222 is procured at the supply source. The unit price 226 is information representing the cost when the associated item 222 is procured from the supplier. In this example, in the record of the supplier ID “S1”, one of the supplier bases of the item “tire” is the supplier of the base code “V1”, and the supply capacity of the supplier base is “1000” units per unit time. The supply LT is “3” units, and the supply unit price is “50” units (for example, “100,000 yen”).
　図５は、拠点候補データ２３０のテーブル構成の一例を示した図である。拠点候補データ２３０は、サプライチェーン構成要素の拠点候補を表すデータである。具体的には、拠点候補データ２３０は、拠点候補ＩＤ２３１と、名称２３２と、種類２３３と、利用コスト２３４とが対応付けられたレコードを有している。 FIG. 5 is a diagram showing an example of the table configuration of the base candidate data 230. As shown in FIG. The base candidate data 230 is data representing base candidates of supply chain components. Specifically, the site candidate data 230 includes a record in which a site candidate ID 231, a name 232, a type 233, and a usage cost 234 are associated with each other.
　拠点候補ＩＤ２３１は、拠点候補レコードを一意に識別する情報である。名称２３２は、拠点の名称を特定する情報である。種類２３３は、拠点種類を特定する情報である。なお、拠点の種類には、例えば、供給元、工場および倉庫などがある。利用コスト２３４は、拠点を利用する場合に発生するコスト（例えば、物流ルートのいずれかいおいて輸送量が正値をとるルート上の拠点のコスト）を特定する情報である。本例では、拠点候補ＩＤ「Ｖ１」のレコードは、名称が「拠点Ａ」であり、拠点種類は「供給元」であり、拠点を利用する際には「１０００」単位のコストが発生することを表している。 The site candidate ID 231 is information for uniquely identifying a site candidate record. The name 232 is information for specifying the name of the base. The type 233 is information for specifying the base type. The types of bases include, for example, a supplier, a factory, and a warehouse. The use cost 234 is information for specifying a cost (for example, a cost of a base on a route where the transport amount takes a positive value in any of the distribution routes) that occurs when the base is used. In this example, the record of the base candidate ID “V1” has the name “base A”, the base type “supplier”, and a cost of “1000” is incurred when using the base. Represents.
　図６は、物流候補データ２４０のテーブル構成の一例を示した図である。物流候補データ２４０は、製品の発送元および着荷先で特定される物流ルートの候補を表すデータである。具体的には、物流候補データ２４０は、物流候補ＩＤ２４１と、発送元２４２と、着荷先２４３と、輸送品目２４４と、輸送ＬＴ２４５と、輸送単価２４６とが対応付けられたレコードを有している。 FIG. 6 is a diagram showing an example of the table configuration of the distribution candidate data 240. The distribution candidate data 240 is data representing distribution route candidates specified by a product shipping source and arrival destination. Specifically, the logistics candidate data 240 includes a record in which a logistics candidate ID 241, a shipping source 242, a destination 243, a transportation item 244, a transportation LT 245, and a transportation unit price 246 are associated with each other. .
　物流候補ＩＤ２４１は、物流候補レコードを一意に識別する情報である。発送元２４２は、物流経路の発送元拠点を特定する情報である。着荷先２４３は、着荷先拠点を特定する情報である。輸送品目２４４は、輸送品目を特定する情報である。輸送ＬＴ２４５は、当該物流経路における品目の輸送リードタイムを特定する情報である。輸送単価２４６は、当該物流経路における品目の輸送単価を特定する情報である。本例では、物流候補ＩＤ「Ｒ１」の経路の発送元が「Ｐ１」であり、着荷先が「Ｗ１」であり、当該経路を通じて品目「自動車」を輸送する際の輸送リードタイムが「５」単位であり、輸送単価が「１０」単位であることを表している。 The logistics candidate ID 241 is information for uniquely identifying a logistics candidate record. The shipping source 242 is information specifying the shipping source base of the physical distribution route. The arrival destination 243 is information for specifying the arrival destination base. The transport item 244 is information for specifying a transport item. The transport LT 245 is information for specifying the transport lead time of the item on the physical distribution route. The transportation unit price 246 is information for specifying the transportation unit price of the item on the physical distribution route. In this example, the route of the distribution candidate ID “R1” is “P1”, the arrival destination is “W1”, and the transport lead time when the item “car” is transported through the route is “5”. It is a unit and represents that the unit price of transportation is “10” unit.
　図７は、マスタ値変更容易性データ２５０のテーブル構成の一例を示した図である。マスタ値変更容易性データ２５０は、各マスタデータ値の変更容易性を表すデータである。具体的には、マスタ値変更容易性データ２５０は、変更容易性ＩＤ２５１と、テーブル名称２５２と、レコードＩＤ２５３と、マスタ値名称２５４と、推定フラグ２５５と、変更容易性２５６と、上限値２５７と、下限値２５８とが対応付けられてレコードを有している。 FIG. 7 is a diagram showing an example of the table configuration of the master value changeability data 250. The master value changeability data 250 is data representing the changeability of each master data value. Specifically, the master value changeability data 250 includes a changeability ID 251, a table name 252, a record ID 253, a master value name 254, an estimation flag 255, a changeability 256, and an upper limit value 257. , The lower limit value 258 is associated with the record.
　変更容易性ＩＤ２５１は、マスタ値変更容易性レコードを一意に識別する情報である。テーブル名称２５２は、対応付けられたマスタ値が格納されているデータテーブルの名称を特定する情報である。レコードＩＤ２５３は、対応付けられたマスタ値が格納されている前記データテーブルのレコードを特定する情報である。マスタ値名称２５４は、変更容易性が対応付けられたマスタ値の名称を特定する情報である。推定フラグ２５５は、マスタ値が後述するマスタデータ値の推定値算出処理の対象であるか否か（対象または非対象）を示す情報である。変更容易性２５６は、対応付けられたマスタ値の変更容易性を特定する情報である。上限値２５７および下限値２５８は、対応付けられたマスタ値が取り得る値の上限値および下限値を特定する情報である。なお、変更容易性２５６は、後述するマスタデータ値の推定値算出処理によって算出される情報であり、最新の変更容易性が格納される。また、推定フラグ２５５、上限値２５７および下限値２５８に格納される値は各々、ユーザによって予め設定される情報である。本例では、変更容易性レコード「Ｅ１」は、「物流候補データ２４０」の「Ｒ１」で特定されるレコードに格納された「輸送ＬＴ」がマスタデータ値の推定値算出処理の対象であり、推定に用いる変更容易性が「１０」単位であり、マスタ値が取り得る値の上限値が「１０」、下限値が「現在値の－１０％」であることを表している。 The changeability ID 251 is information for uniquely identifying the master value changeability record. The table name 252 is information that specifies the name of the data table in which the associated master value is stored. The record ID 253 is information for specifying the record of the data table in which the associated master value is stored. The master value name 254 is information that identifies the name of the master value associated with the changeability. The estimation flag 255 is information indicating whether or not the master value is a target of a master data value estimated value calculation process to be described later (target or non-target). The changeability 256 is information for specifying the changeability of the associated master value. The upper limit value 257 and the lower limit value 258 are information for specifying an upper limit value and a lower limit value of values that can be taken by the associated master value. The changeability 256 is information calculated by a master data value estimated value calculation process to be described later, and stores the latest changeability. Further, the values stored in the estimation flag 255, the upper limit value 257, and the lower limit value 258 are information set in advance by the user. In this example, in the changeability record “E1”, “transport LT” stored in the record identified by “R1” of “distribution candidate data 240” is an object of the master data value estimated value calculation process, This indicates that the ease of change used for estimation is “10” units, the upper limit value that the master value can take is “10”, and the lower limit value is “−10% of the current value”.
　図８は、マスタ値履歴データ２６０のテーブル構成の一例を示した図である。マスタ値履歴データ２６０は、更新（修正）されたマスタデータ値の履歴を表すデータである。具体的には、マスタ値履歴データ２６０は、履歴ＩＤ２６１と、テーブル名称２６２と、レコードＩＤ２６３と、マスタ値名称２６４と、実績データ取得時刻２６５と、実績マスタ値２６６とが対応付けられたレコードを有している。 FIG. 8 is a diagram showing an example of the table configuration of the master value history data 260. The master value history data 260 is data representing a history of updated (corrected) master data values. Specifically, the master value history data 260 is a record in which a history ID 261, a table name 262, a record ID 263, a master value name 264, a record data acquisition time 265, and a record master value 266 are associated with each other. Have.
　履歴ＩＤ２６１は、マスタ値履歴データレコードを一意に識別する情報である。テーブル名称２６２は、対応付けられたマスタ値が格納されているデータテーブルの名称を特定する情報である。レコードＩＤ２６３は、対応付けられたマスタ値が格納されている前記データテーブルのレコードを特定する情報である。マスタ値名称２６４は、実績マスタ値２６６が対応付けられたマスタ値の名称を特定する情報である。実績データ取得時刻２６５は、前回、実績マスタ値を取得した日付すなわち実績マスタ値が算出され、当該レコードが生成された日付を示す情報である。実績マスタ値２６６は、対応付けられたマスタデータ値の推定値算出処理によって算出されたマスタデータ値の推定値を特定する情報である。本例では、履歴レコード「Ｈ１」は、「物流候補データ２４０」の「Ｒ１」で特定されるレコードに格納された「輸送ＬＴ」がマスタデータ値について、「９／３０」に算出された値が「５」であることを表している。なお、マスタ値履歴データ２６０のレコードは、マスタデータ値の推定値算出処理が実行され、マスタデータ値の新たな値（推定値）が算出される度に追加される。 The history ID 261 is information for uniquely identifying the master value history data record. The table name 262 is information for specifying the name of the data table in which the associated master value is stored. The record ID 263 is information for specifying the record of the data table in which the associated master value is stored. The master value name 264 is information that identifies the name of the master value associated with the actual master value 266. The actual data acquisition time 265 is information indicating the date when the actual master value was acquired, that is, the actual master value was calculated and the record was generated. The actual master value 266 is information for specifying the estimated value of the master data value calculated by the estimated value calculation process of the associated master data value. In this example, the history record “H1” is a value calculated as “9/30” with respect to the master data value of “transport LT” stored in the record identified by “R1” of “distribution candidate data 240”. Represents “5”. The record of the master value history data 260 is added each time the estimated value calculation process of the master data value is executed and a new value (estimated value) of the master data value is calculated.
　図９は、供給量の実績データ２７０のテーブル構成の一例を示した図である。供給量の実績データ２７０は、サプライチェーンの供給量における運用実績を表すデータである。具体的には、供給量の実績データ２７０は、実績ＩＤ２７０と、拠点ＩＤ２７２と、品目２７３と、供給量２７４とが対応付けられたレコードを有している。 FIG. 9 is a diagram showing an example of the table configuration of the actual supply amount data 270. The supply amount result data 270 is data representing an operation result in the supply amount of the supply chain. Specifically, the supply amount record data 270 includes a record in which a record ID 270, a base ID 272, an item 273, and a supply amount 274 are associated with each other.
　実績ＩＤ２７１は、供給量の実績データレコードを一意に識別する情報である。拠点ＩＤ２７２は、供給元拠点を特定する情報である。品目２７３は、供給品目を特定する情報である。供給量２７４は、当該拠点における品目の供給量を特定する情報である。本例では、レコード「Ａ１－１」は、拠点候補ＩＤが「Ｐ１」の拠点において、品目「自動車」を「１０００」単位供給したことを表している。 The record ID 271 is information for uniquely identifying the record data record of the supply amount. The base ID 272 is information for specifying a supply source base. The item 273 is information for specifying a supply item. The supply amount 274 is information for specifying the supply amount of the item at the base. In this example, the record “A1-1” indicates that the item “automobile” is supplied in units of “1000” at the base with the base candidate ID “P1”.
　図１０は、輸送量の実績データ２８０のテーブル構成の一例を示した図である。輸送量の実績データ２８０は、サプライチェーンの輸送量における運用実績を表すデータである。具体的には、輸送量の実績データ２８０は、実績ＩＤ２８１と、経路ＩＤ２８２と、品目２８３と、輸送量２８４とが対応付けられたレコードを有している。 FIG. 10 is a diagram showing an example of a table configuration of the actual transportation volume data 280. The actual transportation volume data 280 is data representing operational performance in the transportation volume of the supply chain. Specifically, the actual transportation amount data 280 includes a record in which the actual ID 281, the route ID 282, the item 283, and the transportation amount 284 are associated with each other.
　実績ＩＤ２８１は、輸送量の実績データレコードを一意に識別する情報である。経路ＩＤ２８２は、輸送経路を特定する情報である。品目２８３は、輸送品目を特定する情報である。輸送量２８４は、当該経路における品目の輸送量を特定する情報である。本例では、レコード「Ａ１－２」は、経路候補ＩＤが「Ｒ１」の経路において、品目「自動車」を「１００」単位輸送したことを表している。 The record ID 281 is information for uniquely identifying the record data record of the transport amount. The route ID 282 is information for specifying a transportation route. The item 283 is information for specifying the transportation item. The transport amount 284 is information for specifying the transport amount of the item on the route. In this example, the record “A1-2” indicates that the item “car” is transported by “100” units on the route with the route candidate ID “R1”.
　図１１は、評価指標の実績データ２９０のテーブル構成の一例を示した図である。評価指標の実績データ２９０は、サプライチェーン設計時の評価指標を表したデータである。具体的には、評価指標の実績データは、実績ＩＤ２９１と、総コスト２９２と、総在庫量２９３とが対応付けられたレコードを有している。 FIG. 11 is a diagram showing an example of a table configuration of the performance data 290 of the evaluation index. The performance data 290 of the evaluation index is data representing the evaluation index at the time of supply chain design. Specifically, the performance data of the evaluation index has a record in which a performance ID 291, a total cost 292, and a total inventory amount 293 are associated with each other.
　実績ＩＤ２９１は、評価指標の実績データレコードを一意に識別する情報である。総コスト２９２は、評価指標の１つである総コストを特定する情報である。総在庫量２９３は、評価指標の１つである総在庫量を特定する情報である。本例では、レコード「Ａ１－３」は、総コストが「２００００」単位、総在庫量が「２０００」単位であったことを表している。 The result ID 291 is information for uniquely identifying the result data record of the evaluation index. The total cost 292 is information for specifying the total cost that is one of the evaluation indexes. The total inventory quantity 293 is information for specifying the total inventory quantity that is one of the evaluation indexes. In this example, the record “A1-3” indicates that the total cost is “20000” units and the total inventory amount is “2000” units.
　図１に戻って説明する。制御部１０１は、全体制御部１１１と、入力部１１２と、出力部１１３と、サプライチェーン設計部１１４と、変更容易性評価部１１５と、マスタ値推定部１１６とを有している。 Referring back to FIG. The control unit 101 includes an overall control unit 111, an input unit 112, an output unit 113, a supply chain design unit 114, a changeability evaluation unit 115, and a master value estimation unit 116.
　全体制御部１１１は、サプライチェーン設計装置１００の様々な処理を行う中心的な機能部である。例えば、全体制御部１１１は、入力部１１２を介して取得した情報や指示を、これらの情報や指示の種類および内容に応じて、所定の機能部などに出力することで、サプライチェーン設計装置１００全体の処理を制御する。 The overall control unit 111 is a central functional unit that performs various processes of the supply chain design apparatus 100. For example, the overall control unit 111 outputs information and instructions acquired via the input unit 112 to a predetermined functional unit according to the type and content of the information and instructions, thereby supplying the supply chain design device 100. Control the whole process.
　入力部１１２は、サプライチェーン設計装置１００が備える入力装置（例えば、マウス、キーボードおよびタッチパネルなどのハードウェア装置）を介して、ユーザから所定処理の実行指示や情報の入力を受け付ける機能部である。 The input unit 112 is a functional unit that receives an instruction to execute a predetermined process and input of information from a user via an input device (for example, a hardware device such as a mouse, a keyboard, and a touch panel) provided in the supply chain design apparatus 100.
　出力部１１３は、サプライチェーン設計装置１００が備える出力装置（例えば、ディスプレイなど）を介して、ユーザに情報を出力（情報の表示など）する機能部である。 The output unit 113 is a functional unit that outputs information (such as information display) to the user via an output device (for example, a display) included in the supply chain design apparatus 100.
　サプライチェーン設計部１１４は、サプライチェーン設計案を算出する機能部である。具体的には、サプライチェーン設計部１１４は、各市場の需要や各拠点の供給能力、リードタイム、各物流経路の輸送能力などの物理的、時間的な制約条件を満たし、かつ、サプライチェーン評価指標を最適とするサプライチェーン設計案を算出する。 The supply chain design unit 114 is a functional unit that calculates a supply chain design plan. Specifically, the supply chain design unit 114 satisfies physical and time constraints such as demand in each market, supply capacity of each base, lead time, transport capacity of each physical distribution route, and supply chain evaluation. Calculate the supply chain design plan that optimizes the index.
　より具体的には、サプライチェーン設計部１１４は、例えば、予め記憶部１０２に格納された所定の数理モデルを用いて、数理最適化技術の１つである分枝限定法により、所与の制約条件を満たし、かつ、サプライチェーン評価指標が最適となるサプライチェーン設計案を算出する。なお、本実施形態では、製品供給コスト（生産コスト）、輸送コスト、拠点利用コスト（例えば、拠点新設費など）から算出される総コスト、および、各拠点の在庫量、各輸送経路の輸送中在庫量から算出される総在庫量をサプライチェーン評価指標として取り扱うものとするが、本発明は、他の評価指標を用いた場合でも同様に適用することができる。 More specifically, the supply chain design unit 114 uses a predetermined mathematical model stored in advance in the storage unit 102, for example, by a branch and bound method, which is one of the mathematical optimization techniques, to give a given constraint. A supply chain design plan that satisfies the conditions and optimizes the supply chain evaluation index is calculated. In the present embodiment, the total cost calculated from the product supply cost (production cost), the transport cost, the base use cost (for example, the base establishment cost, etc.), the inventory amount of each base, and the transport of each transport route Although the total inventory quantity calculated from the inventory quantity is handled as a supply chain evaluation index, the present invention can be similarly applied even when other evaluation index is used.
　ここで、サプライチェーンの数理モデルとは、各拠点におけるフロー制約（質量保存則）ならびにリードタイム制約（拠点に入った部品は、当該拠点のリードタイム経過後に拠点から出すことができるという制約）および供給能力制約（例えば、輸送経路の輸送量上限など）といった物理的・時間的な制約のほか、コスト発生モデル（例えば、各経路で発生するコストは、輸送単価×輸送量を全品目について足し合わせた値であるなど）を数式で表現した制約式と、サプライチェーンの評価指標（例えば、総在庫量および総コスト）を数式で表現したものである。 Here, the mathematical model of the supply chain is the flow restriction (mass conservation law) and lead time restriction (restriction that parts entering the base can be removed from the base after the lead time of the base) and In addition to physical and time constraints such as supply capacity constraints (for example, the upper limit of the transport amount of the transport route), a cost generation model (for example, the cost generated by each route is calculated by adding the transport unit price × transport amount for all items) And a supply chain evaluation index (for example, total inventory amount and total cost) expressed in mathematical formulas.
　なお、サプライチェーンの数理モデルは、ビジネスシナリオおよびサプライチェーン設計案を入力として、サプライチェーンの評価指標を算出するシミュレーションモデルであり、マスタデータ値は、シミュレーション対象であるサプライチェーンのシステム特定を定義する値であると見なすこともできる。 The supply chain mathematical model is a simulation model that calculates a supply chain evaluation index using a business scenario and a supply chain design plan as inputs, and the master data value defines the system specification of the supply chain to be simulated. It can also be regarded as a value.
　また、通常、拠点候補の利用の有無は、当該拠点を含む物流ルートにおける輸送量が０より大きいか否かで判断される。また、最適なサプライチェーンでは、製品の不要な滞留が生じないようにするため、各拠点の部品および製品の供給量は、輸送量に応じた必要最小限の量に等しくなる。そのため、物流ルートごとの輸送量によりサプライチェーン設計を表現することができるが、本実施形態では、後述するように、サプライチェーンの運用実績を示す情報を利用する観点から、各拠点の供給量と、各輸送経路の輸送量とによりサプライチェーン設計が表現されるものとする。以下、これらの総コストおよび総在庫量を設計変数と呼ぶ。また、サプライチェーン設計とは、所与のマスタデータ値およびビジネスシナリオを用いて、サプライチェーン評価指標を最適とする設計変数（各拠点の供給量および各輸送経路の輸送量）を算出することを意味するものとする。 In addition, the presence / absence of a base candidate is usually determined based on whether or not the transport amount on the distribution route including the base is greater than zero. In addition, in an optimal supply chain, the supply amount of parts and products at each base is equal to the minimum necessary amount corresponding to the transportation amount in order to prevent unnecessary stagnation of products. Therefore, the supply chain design can be expressed by the transport amount for each distribution route, but in this embodiment, as described later, from the viewpoint of using information indicating the operation results of the supply chain, The supply chain design is expressed by the transport amount of each transport route. Hereinafter, these total costs and total inventory quantities are referred to as design variables. Supply chain design is the calculation of design variables (supplied amount of each base and transport amount of each transportation route) that optimizes the supply chain evaluation index using given master data values and business scenarios. Shall mean.
　変更容易性評価部１１５は、サプライチェーン設計に用いるマスタデータ値に対して、マスタデータ値が変更しやすいデータであるかを表す変更容易性を評価する機能部である。具体的には、変更容易性評価部１１５は、後述するように、マスタデータ値の推定値算出処理において、ユーザが対象として設定したマスタデータ値の変更容易性を算出する。なお、マスタデータ値には、その特性により、値が変化しやすいものと、変化し難いものとが存在する。例えば、船便による国際輸送経路の輸送リードタイムは、トラック便による国内輸送経路の輸送リードタイムと比較して値が変化し易い。そのため、船便の国際輸送経路の輸送リードタイムには、国内トラック便と比較して大きな変更容易性が設定される。 The changeability evaluation unit 115 is a functional unit that evaluates changeability indicating whether or not the master data value is easy to change with respect to the master data value used for supply chain design. Specifically, the changeability evaluation unit 115 calculates the changeability of the master data value set as a target by the user in the master data value estimated value calculation process, as will be described later. Note that there are master data values whose values are likely to change and those that are difficult to change depending on their characteristics. For example, the transport lead time of an international transport route by a shipping service is likely to change compared to the transport lead time of a domestic transport route by a truck service. Therefore, the ease of change is set for the transport lead time of the international transport route for shipping services compared to domestic truck services.
　マスタ値推定部１１６は、マスタデータ値の推定値を算出する機能部である。具体的には、マスタ値推定部１１６は、サプライチェーン設計の運用実績に対して、サプライチェーンの数理モデルの誤差が小さく、かつ、変更容易性が大きいマスタデータ値の推定値を算出する。マスタデータ値の推定値算出処理の詳細は後述する。 The master value estimation unit 116 is a functional unit that calculates an estimated value of the master data value. Specifically, the master value estimation unit 116 calculates an estimated value of a master data value that has a small error in the mathematical model of the supply chain and a high changeability with respect to the operation results of the supply chain design. Details of the estimated value calculation processing of the master data value will be described later.
　通信部１０３は、外部装置（例えば、実績収集装置１５０や図示しないデータベースシステムなど）との間で情報通信を行う機能部である。 The communication unit 103 is a functional unit that performs information communication with an external device (for example, a performance collection device 150 or a database system (not shown)).
　以上、サプライチェーン設計装置１００の機能構成について説明した。 The functional configuration of the supply chain design apparatus 100 has been described above.
　図１２は、サプライチェーン設計装置１００のハードウェア構成の一例を示した図である。サプライチェーン設計装置１００は、例えば、ＰＣ（パーソナルコンピュータ）やワークステーションなどの計算機により実現される。 FIG. 12 is a diagram illustrating an example of a hardware configuration of the supply chain design apparatus 100. The supply chain design apparatus 100 is realized by a computer such as a PC (personal computer) or a workstation, for example.
　サプライチェーン設計装置１００は、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）３０２と、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）３０３と、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）３０４とを有する演算処理装置３０１と、外部記憶装置３０５と、通信装置３０６と、入力装置３０７と、出力装置３０８と、各々の装置を相互に接続するバス３０９と、を有している。 The supply chain design device 100 includes a CPU (Central Processing Unit) 302, a RAM (Random Access Memory) 303, a ROM (Read Only Memory) 304, an arithmetic processing device 301, an external storage device 305, and a communication device 306. And an input device 307, an output device 308, and a bus 309 for connecting the devices to each other.
　外部記憶装置３０５は、例えば、ハードディスク装置やフラッシュメモリなどの不揮発性記憶装置である。 The external storage device 305 is, for example, a nonvolatile storage device such as a hard disk device or a flash memory.
　通信装置３０６は、外部装置（例えば、実績収集装置１５０や図示しないデータベースシステムなど）との間で情報通信を行う通信モジュールなどの装置である。 The communication device 306 is a device such as a communication module that performs information communication with an external device (for example, a performance collection device 150 or a database system (not shown)).
　入力装置３０７は、例えば、タッチパネル、キーボードおよびマウスなどサプライチェーン設計装置１００のユーザからの入力を受け付ける装置である。 The input device 307 is a device that receives input from the user of the supply chain design device 100 such as a touch panel, a keyboard, and a mouse.
　出力装置３０８は、例えば、ディスプレイなど画像情報の表示を行う装置である。 The output device 308 is a device that displays image information such as a display.
　なお、図１に示したサプライチェーン設計装置１００の制御部１０１は、ＣＰＵ３０２に処理を行わせるプログラムによって実現される。このプログラムは、ＲＯＭ３０４または外部記憶装置３０５内に格納され、実行にあたってＲＡＭ３０３上にロードされ、ＣＰＵ３０２により実行される。なお、図１に示すサプライチェーン設計装置１００の記憶部１０２は、ＲＡＭ３０３、ＲＯＭ３０４および外部記憶装置３０５のいずれか又はこれらの組合せにより実現される。また、通信部１０３は、通信装置３０６により実現される。 The control unit 101 of the supply chain design apparatus 100 shown in FIG. 1 is realized by a program that causes the CPU 302 to perform processing. This program is stored in the ROM 304 or the external storage device 305, loaded onto the RAM 303 for execution, and executed by the CPU 302. The storage unit 102 of the supply chain design apparatus 100 shown in FIG. 1 is realized by any one of the RAM 303, the ROM 304, the external storage device 305, or a combination thereof. The communication unit 103 is realized by the communication device 306.
　以上、サプライチェーン設計装置１００のハードウェア構成について説明した。 The hardware configuration of the supply chain design apparatus 100 has been described above.
　［動作の説明］
次に、サプライチェーン設計装置１００で実行されるマスタデータ値の推定値算出処理について説明する。図１３は、かかる処理の流れの一例をＰＡＤ（Ｐｒｏｂｌｅｍ　Ａｎａｌｙｓｉｓ　Ｄｉａｇｒａｍ）により示した図である。マスタデータ値の推定値算出処理は、例えば、サプライチェーン設計装置１００のユーザからの実行指示に基づいて開始される。 [Description of operation]
Next, the master data value estimated value calculation process executed by the supply chain design apparatus 100 will be described. FIG. 13 is a diagram showing an example of the flow of such processing by PAD (Problem Analysis Diagram). The estimated value calculation process of the master data value is started based on an execution instruction from the user of the supply chain design apparatus 100, for example.
　処理が開始されると、入力部１１２は、サプライチェーンの設計条件を読み込む（ステップＳ１０１）。具体的には、入力部１１２は、ビジネスシナリオである需要データ２００および製品構成データ２１０と、マスタデータ値である供給元データ２２０、拠点候補データ２３０および物流候補データ２４０とを記憶部１０２から取得する。 When the processing is started, the input unit 112 reads the supply chain design conditions (step S101). Specifically, the input unit 112 acquires the demand data 200 and product configuration data 210 that are business scenarios, the supply source data 220, the site candidate data 230, and the logistics candidate data 240 that are master data values from the storage unit 102. To do.
　次に、入力部１１２は、実績データを読み込む（ステップＳ１０２）。具体的には、入力部１１２は、供給量の実績データ２７０と、輸送量の実績データ２８０と、評価指標の実績データ２９０とを記憶部１０２から取得する。 Next, the input unit 112 reads performance data (step S102). Specifically, the input unit 112 acquires the actual supply amount data 270, the transport amount actual data 280, and the evaluation index actual data 290 from the storage unit 102.
　次に、サプライチェーン設計部１１４は、モデル誤差評価を行う（ステップＳ１０３）。図１４は、モデル誤差評価処理の詳細な流れの一例をＰＡＤにより示した図である。モデル誤差評価処理が開始されると、サプライチェーン設計部１１４は、サプライチェーン評価指標シミュレーションを実行する（ステップＳ２０１）。具体的には、サプライチェーン設計部１１４は、記憶部１０２から取得した設計条件すなわちビジネスシナリオおよびマスタデータ値と、供給量の実績データ２７０および輸送量の実績データ２８０とを入力値とし、サプライチェーンの数理モデルをシミュレーションモデルとして用いることにより、サプライチェーン評価指標のシミュレーション値を算出する。 Next, the supply chain design unit 114 performs model error evaluation (step S103). FIG. 14 is a diagram showing an example of a detailed flow of the model error evaluation process by PAD. When the model error evaluation process is started, the supply chain design unit 114 executes a supply chain evaluation index simulation (step S201). Specifically, the supply chain design unit 114 uses the design conditions acquired from the storage unit 102, that is, the business scenario and the master data value, the actual supply amount data 270, and the actual transport amount data 280 as input values, and supplies the supply chain. The simulation value of the supply chain evaluation index is calculated by using the mathematical model as a simulation model.
　このようなモデル誤差の評価処理は、マスタデータ値によって構築されたサプライチェーンネットワークに対して実績データの供給量および輸送量を適用した場合に、当該サプライチェーンの実績データに基づく評価指標、すなわち、実績データに基づく総コストおよび総在庫量を算出する処理である。 Such model error evaluation processing is performed when the supply amount and the transport amount of actual data are applied to the supply chain network constructed by the master data value, that is, an evaluation index based on the actual data of the supply chain, that is, This is a process for calculating the total cost and the total inventory quantity based on the actual data.
　次に、サプライチェーン設計部１１４は、モデル誤差を算出する（ステップＳ２０２）。具体的には、サプライチェーン設計部１１４は、記憶部１０２から取得した評価指標の実績データと、ステップＳ２０１により算出されたサプライチェーン評価指標のシミュレーション値との誤差を算出する。より具体的には、サプライチェーン設計部１１４は、評価指標の実績データの総コストおよび総在庫量の各々に所定の係数（例えば、α、β）を掛けたものを足し合わせて得た値と、シミュレーション値として算出された総コストおよび総在庫量の各々に前記係数（α、β）を掛けたものを足し合わせて得た値との差分によりモデル誤差を算出する。 Next, the supply chain design unit 114 calculates a model error (step S202). Specifically, the supply chain design unit 114 calculates an error between the performance data of the evaluation index acquired from the storage unit 102 and the simulation value of the supply chain evaluation index calculated in step S201. More specifically, the supply chain design unit 114 adds a value obtained by adding a product obtained by multiplying each of the total cost and total inventory quantity of the performance data of the evaluation index by a predetermined coefficient (for example, α, β). Then, the model error is calculated based on the difference from the value obtained by adding each of the total cost calculated as the simulation value and the total inventory amount multiplied by the coefficients (α, β).
　図１３に戻って説明する。サプライチェーン設計部１１４は、ステップＳ２０２で算出したモデル誤差が所定の閾値よりも大きいか否かを判定する（ステップＳ１０４）。そして、モデル誤差が所定の閾値未満である場合、サプライチェーン設計部１１４は、マスタデータ値の推定値算出処理を終了する。一方で、モデル誤差が所定の閾値よりも大きい場合、変更容易性評価部１１５は、マスタ値変更容易性評価を実行する（ステップＳ１０５）。 Referring back to FIG. The supply chain design unit 114 determines whether or not the model error calculated in step S202 is larger than a predetermined threshold (step S104). If the model error is less than the predetermined threshold value, the supply chain design unit 114 ends the master data value estimated value calculation process. On the other hand, when the model error is larger than the predetermined threshold value, the changeability evaluation unit 115 performs a master value changeability evaluation (step S105).
　図１５は、マスタ値変更容易性評価処理の詳細な流れの一例をＰＡＤにより示した図である。マスタ値変更容易性評価処理が開始されると、変更容易性評価部１１５は、マスタ値履歴データ２６０を記憶部１０２から読み込む（ステップＳ３０１）。 FIG. 15 is a diagram showing an example of a detailed flow of the master value changeability evaluation process by PAD. When the master value changeability evaluation process is started, the changeability evaluation unit 115 reads the master value history data 260 from the storage unit 102 (step S301).
　次に、変更容易性評価部１１５は、マスタ値履歴データ２６０の数が所定の閾値よりも少ないか否かを判定する（ステップＳ３０２）。すなわち、変更容易性評価部１１５は、マスタ値履歴データ２６０に格納されているレコード数が所定の閾値よりも少ないか否かを判定する。そして、マスタ値履歴データ２６０が閾値よりも少ない（閾値未満）場合、変更容易性評価部１１５は、マスタ値履歴データ２６０のテーブル名称２６２およびマスタ値名称２６４をキーとして特定される全てのマスタデータ値のうち、マスタ値の推定対象となっている変更容易性に「１」を一律設定する（ステップＳ３０３）。具体的には、変更容易性評価部１１５は、マスタ値履歴データ２６０が所定の閾値よりも少ない場合、マスタ値履歴データ２６０のテーブル名称２６２およびマスタ値名称２６４から全てのマスタデータ値を特定する。また、変更容易性評価部１１５は、特定したマスタデータ値が格納されているマスタ値変更容易性データ２５０の推定フラグ２５５に「対象」が設定されている場合、かかるマスタデータ値の変更容易性２５６に「１」を設定する。変更容易性評価部１１５は、特定した全てのマスタデータ値に対して同様の処理を行う。 Next, the changeability evaluation unit 115 determines whether or not the number of master value history data 260 is smaller than a predetermined threshold (step S302). That is, the changeability evaluation unit 115 determines whether or not the number of records stored in the master value history data 260 is less than a predetermined threshold. When the master value history data 260 is less than the threshold value (less than the threshold value), the changeability evaluation unit 115 sets all the master data specified using the table name 262 and the master value name 264 of the master value history data 260 as keys. Among the values, “1” is uniformly set to the ease of change which is the estimation target of the master value (step S303). Specifically, the changeability evaluation unit 115 specifies all master data values from the table name 262 and the master value name 264 of the master value history data 260 when the master value history data 260 is less than a predetermined threshold. . Also, the changeability evaluation unit 115, when “target” is set in the estimation flag 255 of the master value changeability data 250 in which the identified master data value is stored, the changeability of the master data value 256 is set to “1”. The changeability evaluation unit 115 performs the same process on all the specified master data values.
　また、本例では、マスタ値履歴データ２６０の数が少ない場合に、精度の高い変更容易性の算出およびマスタデータ値の推定ができないため、変更容易性に一律の値を設定したが、例えば、変更容易性の値をユーザが設定しても良い。また、ユーザに対して、マスタデータ値を適切な値に修正することを推奨するメッセージを提示して、マスタデータ値の推定値算出処理を終了するようにしても良い。 Further, in this example, when the number of master value history data 260 is small, a highly accurate changeability calculation and a master data value cannot be estimated, so a uniform value is set for the changeability. The user may set the changeability value. Also, a message recommending that the master data value be corrected to an appropriate value may be presented to the user, and the master data value estimated value calculation process may be terminated.
　ここで、マスタ値の推定対象に関する設定について説明する。図１６は、マスタ値変更容易性設定画面４００の一例を示した図である。ユーザは、マスタデータ値の推定値算出処理の実行に先立ち、予め、マスタ値変更容易性設定画面４００からマスタ値を推定対象とするか又は非対象とするかなど所定の項目について設定を行うことができる。 Here, the setting related to the master value estimation target will be described. FIG. 16 is a diagram showing an example of the master value changeability setting screen 400. Prior to the execution of the master data value estimated value calculation process, the user makes settings in advance on the master value changeability setting screen 400 for predetermined items such as whether the master value is to be estimated or not. Can do.
　具体的には、マスタ値変更容易性設定画面４００は、マスタ値選択表示領域４０１と、変更容易性表示領域４０２と、マスタ値履歴表示領域４０３とを有している。マスタ値選択表示領域４０１は、テーブル４１１と、レコード４１２と、マスタ値４１３とを選択入力する入力欄が設けられている。また、変更容易性表示領域４０２は、推定対象に設定するか否かを選択入力する入力欄４２１と、上限値、下限値および変更容易性を入力する入力欄（各々、入力欄４２２、入力欄４２３、入力欄４２４）とが設けられている。ユーザは、マスタ値の推定対象について設定したいマスタデータ値をテーブル４１１、レコード４１２およびマスタ値４１３から一意に特定し、推定対象の選択入力欄４２１から「対象」を選択する。また、ユーザは、推定対象としたマスタデータ値の上限値および下限値を入力する。また、ユーザは、これらの情報の入力後に「ＯＫ」ボタン４３１を押下することにより、これらの入力値がマスタ値変更容易性データ２５０の推定フラグ２５５、変更容易性２５６、上限値２５７および下限値２５８に設定される。 Specifically, the master value changeability setting screen 400 has a master value selection display area 401, a changeability display area 402, and a master value history display area 403. The master value selection display area 401 is provided with an input field for selecting and inputting a table 411, a record 412, and a master value 413. The changeability display area 402 includes an input field 421 for selectively inputting whether or not to set the estimation target, and an input field for inputting an upper limit value, a lower limit value and changeability (an input field 422 and an input field, respectively). 423, an input field 424). The user uniquely specifies the master data value to be set for the estimation target of the master value from the table 411, the record 412 and the master value 413, and selects “target” from the estimation target selection input field 421. Further, the user inputs an upper limit value and a lower limit value of the master data value to be estimated. Further, when the user presses an “OK” button 431 after inputting these pieces of information, these input values become the estimation flag 255, the changeability 256, the upper limit value 257, and the lower limit value of the master value changeability data 250. 258.
　なお、変更容易性の入力欄４２４は、前述の通り、マスタ値履歴データ２６０の数が閾値よりも少ない場合にユーザ自らが所望の値を変更容易性に設定する場合の入力欄である。 The changeability input field 424 is an input field when the user himself / herself sets a desired value as changeability when the number of master value history data 260 is smaller than the threshold value, as described above.
　また、マスタ値履歴表示領域４０３には、過去に更新されたマスタ値の推移を時系列に表示したマスタ値履歴グラフ４４１と、過去に更新されたマスタ値の最大値４４２と、最小値４４３と、平均値４４４と、変更係数４４５とが表示される。入力部１１２を介して、マスタ値選択表示領域４０１および変更容易性表示領域４０２への入力と、「ＯＫ」ボタン４３１の押下による指示をユーザから受け付けると、変更容易性評価部１１５は、特定されたマスタデータ値の実績マスタ値２６６をマスタ値履歴データ２６０から抽出し、複数ある場合には、それらの最大値および最小値と、平均値と、変動係数とを算出する。また、変更容易性評価部１１５は、算出した変動係数を変更容易性の値としてマスタ値変更容易性データ２５０の変更容易性２５６に格納する。なお、変動係数は、マスタ値履歴データ２６０から抽出した実績マスタ値の標準偏差をその平均値で除することにより算出されても良く、実績マスタ値の平均変化速度を用いて算出されても良い。 The master value history display area 403 includes a master value history graph 441 that displays the transition of master values updated in the past in time series, a maximum value 442 of master values that have been updated in the past, and a minimum value 443. The average value 444 and the change coefficient 445 are displayed. When the input to the master value selection display area 401 and the changeability display area 402 and the instruction by pressing the “OK” button 431 are received from the user via the input unit 112, the changeability evaluation part 115 is specified. The actual master value 266 of the master data value is extracted from the master value history data 260, and when there are a plurality of master data values, the maximum value and the minimum value, the average value, and the coefficient of variation are calculated. Further, the changeability evaluation unit 115 stores the calculated variation coefficient in the changeability 256 of the master value changeability data 250 as a changeability value. The variation coefficient may be calculated by dividing the standard deviation of the actual master value extracted from the master value history data 260 by the average value, or may be calculated using the average change rate of the actual master value. .
　図１５に戻って説明する。ステップＳ３０２において、マスタ値履歴データ２６０が所定の閾値よりも多い場合、変更容易性評価部１１５は、マスタ値履歴データ２６０のテーブル名称２６２およびマスタ値名称２６４をキーとして特定されるマスタデータ値のうち、マスタ値の推定対象となっている全てのマスタデータ値に対してマスタ値変更容易性を算出する（ステップＳ３０４）。 Referring back to FIG. In step S302, when the master value history data 260 is greater than the predetermined threshold, the changeability evaluation unit 115 sets the master data value specified using the table name 262 and the master value name 264 of the master value history data 260 as keys. Among them, the master value change easiness is calculated for all master data values that are subject to estimation of the master value (step S304).
　具体的には、マスタ値履歴データ２６０のテーブル名称２６２およびマスタ値名称２６４をキーとして特定したマスタデータ値のうち、推定対象となっているマスタデータ値毎に、実績マスタ値の標準偏差を平均値で除算した変動係数を変更容易性として算出する。また、変更容易性評価部１１５は、算出した変更容易性を、マスタ値変更容易性データ２５０の変更容易性２５６に格納する（ステップＳ３０５）。 Specifically, among the master data values specified using the table name 262 and the master value name 264 of the master value history data 260 as keys, the standard deviation of the actual master values is averaged for each master data value to be estimated. The coefficient of variation divided by the value is calculated as changeability. In addition, the changeability evaluation unit 115 stores the calculated changeability in the changeability 256 of the master value changeability data 250 (step S305).
　図１３に戻って説明する。マスタ値変更容易性評価処理が終了すると、マスタ値推定部１１６は、推定対象のマスタデータ値を推定する（ステップＳ１０６）。具体的には、マスタ値推定部１１６は、モデル誤差が小さく、かつ、変更容易性が大きいマスタデータ値の組合せを算出する。 Referring back to FIG. When the master value changeability evaluation process ends, the master value estimation unit 116 estimates a master data value to be estimated (step S106). Specifically, the master value estimation unit 116 calculates a combination of master data values with a small model error and a high changeability.
　より具体的には、マスタ値推定部１１６は、マスタ値変更容易性データ２５０で推定対象として設定されたマスタデータ値を分枝限定法で決定する変数として取り扱い、サプライチェーン設計時には変数として取り扱っていた設計変数を実績データで設定した定数として取り扱うよう設定する。すなわち、サプライチェーン設計時には、定数の入力値であったマスタデータ値を変数の入力値とし、変数の入力値であった実績データを定数として取り扱うよう設定される。 More specifically, the master value estimation unit 116 handles the master data value set as the estimation target in the master value changeability data 250 as a variable determined by the branch and bound method, and handles it as a variable at the time of supply chain design. The design variable is set to be handled as a constant set in the actual data. That is, at the time of supply chain design, the master data value that is a constant input value is set as a variable input value, and the actual data that is the variable input value is set as a constant.
　また、マスタ値推定部１１６は、推定対象として設定されたマスタデータ値が取り得る範囲の値をマスタ値変更容易性データ２５０の上限値２５７および下限値２５８から特定し、制約条件として設定する。なお、粒度が粗い計測データであれば、それらを制約条件として追加しても良い（例えば、経路「Ｒ１」の輸送ＬＴ＋経路「Ｒ２」の輸送ＬＴ＋経路「Ｒ３」の輸送ＬＴ＝３０など）。また、マスタ値推定部１１６は、モデル誤差が小さくなれば向上し、推定値に値を変更するマスタデータ値の変更容易性が大きければ結果値が向上（値の増減方向は限定されず、最適値に近づくの意味）する関数として下記数式（１）で示される目的関数を設定する。 Also, the master value estimation unit 116 specifies the range of values that can be taken by the master data value set as the estimation target from the upper limit value 257 and the lower limit value 258 of the master value changeability data 250, and sets it as a constraint condition. If the measurement data has a coarse granularity, they may be added as constraints (for example, transport LT of route “R1” + transport LT of route “R2” + transport LT of route “R3” = 30). Further, the master value estimation unit 116 is improved when the model error is small, and the result value is improved if the changeability of the master data value for changing the value to the estimated value is large (the increase / decrease direction of the value is not limited and is optimal) The objective function shown by the following mathematical formula (1) is set as a function to mean approaching the value.
<数１>
　目的関数＝重み係数１×（制約条件違反量の合計）＋重み係数２×（モデル誤差：サプライチェーン評価指標の実績値との乖離量）＋重み係数３×（推定値に値を変更するマスタデータ値の変更容易性）の最小化　　　　　　　　　　　　　　　　　・・・（１） <Equation 1>
Objective function = weighting factor 1 × (total amount of constraint violation) + weighting factor 2 × (model error: deviation from actual value of supply chain evaluation index) + weighting factor 3 × (master that changes the value to an estimated value) Minimization of data value changeability (1)
　なお、数式（１）の第３項に係る「推定値に値を変更するマスタデータ値の変更容易性」は、下記の数式（２）で表現される。
推定値に値を変更するマスタデータ値の変更容易性＝（（１／max（マスタデータ値の変更容易性又はε））×（マスタデータ値の推定値－マスタデータ値の現在値）２）の合計　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　・・・（２） The “changeability of the master data value for changing the value to the estimated value” according to the third term of the formula (1) is expressed by the following formula (2).
Master data value changeability to change the value to the estimated value = ((1 / max (master data value changeability or ε)) × (master data value estimated value−master data value current value) 2 ) Total (2)
　上記数式（１）において、第１項は、制約条件の違反数量を最小化するための項であり、第２項は、モデル誤差（サプライチェーン評価指標の実績値との乖離）を小さくするための項であり、第３項は、推定値に値を変更するマスタデータ値の変更容易性を大きくするための項である。また、各項の重み係数は、第１項＞第２項＞第３項となるように予め設定される。また、上記数式（２）は、推定値に値を変更するマスタデータ値の変更容易性を表す式である。マスタデータ値の推定値（変数）と、マスタデータ値の現在値との乖離量にマスタデータ値の変更容易性の逆数を乗算した値を用いる。数式（２）では、εは０より大きい値であり、かつ、充分小さい値（例えば、１０－６など）である。 In the above formula (1), the first term is a term for minimizing the violating quantity of the constraint condition, and the second term is for reducing the model error (deviation from the actual value of the supply chain evaluation index). The third term is a term for increasing the ease of changing the master data value for changing the value to the estimated value. Further, the weighting coefficient of each term is set in advance so that the first term> the second term> the third term. Moreover, the said Numerical formula (2) is a type | formula showing the change ease of the master data value which changes a value to an estimated value. A value obtained by multiplying the amount of deviation between the estimated value (variable) of the master data value and the current value of the master data value by the reciprocal of the changeability of the master data value is used. In Equation (2), ε is a value larger than 0 and a sufficiently small value (for example, 10 −6, etc.).
　以上の制約条件と目的関数で定義される最適化問題に対して、分枝限定法を用いて、目的関数が最適となる変数を算出することにより、モデル誤差が小さく、かつ、推定値に値を変更するマスタデータ値の変更容易性が大きいマスタデータ値の組合せと、かかる場合のマスタデータ値の値とを算出することができる。 For the optimization problem defined by the above constraints and the objective function, the model error is small and the estimated value is calculated by calculating the variable that optimizes the objective function using the branch and bound method. It is possible to calculate a combination of master data values for which the master data value to be changed is large and a master data value in such a case.
　なお、分枝限定法は、目的関数の値が最良値に近い複数の解を求めることができるため、ユーザに提示する代替案として複数の解を記憶部１０２に格納しても良い。また、目的関数は、上記の数式（１）、（２）に限定されるものではなく、モデル誤差が小さくなった場合および推定値に値を変更するマスタデータ値の変更容易性が大きい場合に結果値が向上（値の増減方向は限定されず、最適値に近づくの意味）する関数であれば良い。 In addition, since the branch and bound method can obtain a plurality of solutions whose objective function values are close to the best value, a plurality of solutions may be stored in the storage unit 102 as an alternative presented to the user. Further, the objective function is not limited to the above mathematical formulas (1) and (2), and when the model error is small and when the master data value for changing the value to the estimated value is easy to change. Any function may be used as long as the result value is improved (increase / decrease direction of the value is not limited, meaning that the value approaches an optimum value).
　次に、出力部１１３は、マスタ値修正案をユーザに提示する（ステップＳ１０７）。具体的には、出力部１１３は、マスタデータ値推定結果の表示画面を表示する。図１７は、マスタデータ値推定結果の表示画面５００の一例を示す図である。マスタデータ値推定結果画面５００は、マスタデータ値推定結果表示領域５０１と、モデル誤差表示領域５０２と、変更容易性表示領域５０３とを有している。 Next, the output unit 113 presents the master value correction plan to the user (step S107). Specifically, the output unit 113 displays a display screen for the master data value estimation result. FIG. 17 is a diagram illustrating an example of a display screen 500 for a master data value estimation result. The master data value estimation result screen 500 has a master data value estimation result display area 501, a model error display area 502, and a changeability display area 503.
　出力部１１３は、マスタデータ値や予め記憶部１０２に格納されている図示しないネットワーク構成情報などの所定情報を用いて、サプライチェーンのネットワーク構成を示したサプライチェーン計画情報（拠点や経路を示した図）をマスタデータ値推定結果表示領域５０１に表示する。また、出力部１１３は、ステップＳ１０６において特定および推定されたマスタ値およびその値を用いて、マスタ値の変更量すなわち現在のマスタ値の値と推定値との乖離量が所定の閾値より大きい物流経路や拠点を特定する。また、出力部１１３は、特定した物流経路や拠点を強調表示（例えば、太線で描画したり、拠点を示す枠を塗り潰すなど）する。 The output unit 113 uses the predetermined information such as the master data value and the network configuration information (not shown) stored in advance in the storage unit 102 to supply the supply chain plan information indicating the network configuration of the supply chain (showing the base and route) Is displayed in the master data value estimation result display area 501. Further, the output unit 113 uses the master value specified and estimated in step S106 and the value thereof, and the amount of change in the master value, that is, the amount of deviation between the current master value and the estimated value is greater than a predetermined threshold. Identify routes and locations. Further, the output unit 113 highlights the identified physical distribution route or base (for example, draws a bold line or fills a frame indicating the base).
　また、強調表示された物流経路や拠点をユーザが選択したことを通知する情報を入力部１１２から取得すると、出力部１１３は、選択された物流経路や拠点の詳細な情報を表示する。例えば、図１７の拠点Ｖ３からＰ２への物流経路がユーザによって選択されると、出力部１１３は、かかる経路における輸送ＬＴについて現在値（現在のマスタ値の値）と推定値とを表示する。このような表示５１１にあたり、出力部１１３は、選択された物流経路に関する情報を格納したレコードを物流候補データ２４０から特定し、そのマスタ値として輸送ＬＴ２４５に格納されている値（現在値）を取得する。また、出力部１１３は、取得した現在値と共に、ステップＳ１０６において算出された推定値を比較表示する。 Further, when information notifying that the user has selected the highlighted distribution route or base is acquired from the input unit 112, the output unit 113 displays detailed information on the selected distribution route or base. For example, when the logistics route from the base V3 to P2 in FIG. 17 is selected by the user, the output unit 113 displays the current value (current master value) and the estimated value for the transport LT on the route. In such a display 511, the output unit 113 identifies the record storing the information related to the selected distribution route from the distribution candidate data 240, and acquires the value (current value) stored in the transport LT 245 as the master value. To do. Further, the output unit 113 compares and displays the estimated value calculated in step S106 together with the acquired current value.
　また、例えば、図１７の拠点Ｗ２がユーザによって選択されると、出力部１１３は、かかる拠点の利用コストの現在値と推定値とを表示する。このような表示５１２にあたり、出力部１１３は、選択された拠点Ｗ２に関する情報を格納したレコードを拠点候補データ２３０から特定し、そのマスタ値として利用コスト２３４に格納されている値（現在値）を取得する。また、出力部１１３は、取得した現在値と共に、ステップＳ１０７において算出された推定値を比較表示する。 For example, when the base W2 in FIG. 17 is selected by the user, the output unit 113 displays the current value and the estimated value of the usage cost of the base. In such a display 512, the output unit 113 identifies a record storing information on the selected site W2 from the site candidate data 230, and uses the value (current value) stored in the usage cost 234 as the master value. get. Further, the output unit 113 compares and displays the estimated value calculated in step S107 together with the acquired current value.
　このように、マスタ値の現在値とステップＳ１０６で算出された推定値との乖離量が大きい物流経路や拠点を強調表示したり、マスタ値の現在値および推定値を比較表示することにより、マスタ値と推定値との乖離が大きいサプライチェーン上の箇所をユーザが容易に把握することができる。 In this way, by highlighting a physical distribution route or base having a large discrepancy between the current value of the master value and the estimated value calculated in step S106, or by comparing and displaying the current value and the estimated value of the master value, The user can easily grasp the location on the supply chain where the difference between the value and the estimated value is large.
　なお、マスタデータ値推定結果表示領域５０１の表示方法は、上記のようなネットワーク構成図であることに限定されず、例えば、拠点および物流経路を一覧表形式で表示するようにしても良い。 Note that the display method of the master data value estimation result display area 501 is not limited to the network configuration diagram as described above, and for example, the bases and distribution routes may be displayed in a list form.
　また、出力部１１３は、総コストと、総在庫量と、誤差とをモデル誤差表示領域５０２に表示する。具体的には、出力部１１３は、サプライチェーン設計部１１４を介して、ステップＳ１０６の処理（マスタ値の推定処理）において、目的関数の値が最良値に近くなる解（モデル誤差が小さく、かつ、変更容易性が大きくなるマスタデータ値の組合せとその値）を固定の入力値として、ステップＳ２０１およびステップＳ２０２と同様にシミュレーション値およびモデル誤差を求める。また、出力部１１３は、求めたシミュレーション値（総コストおよび総在庫量）と、モデル誤差とをモデル誤差表示領域５０２に表示する。 Further, the output unit 113 displays the total cost, the total inventory amount, and the error in the model error display area 502. Specifically, the output unit 113, via the supply chain design unit 114, in the process of step S106 (master value estimation process), a solution (model error is small) and the value of the objective function is close to the best value. The simulation value and the model error are obtained in the same manner as in step S201 and step S202, with the combination of the master data values that increase the ease of change and its value) as fixed input values. Further, the output unit 113 displays the obtained simulation values (total cost and total inventory amount) and the model error in the model error display area 502.
　また、出力部１１３は、変更容易性を変更容易性表示領域５０３に表示する。具体的には、出力部１１３は、ステップＳ３０４で算出された変更されるマスタデータ値の変更容易性を変更容易性表示領域５０３に表示する。なお、変更されるマスタデータ値が複数ある場合には、出力部１１３は、それらの変更容易性をリスト表示する。なお、変更容易性の表示形式はリスト表示に限定されず、例えば、強調表示された拠点や物流経路をユーザが選択した場合、かかる拠点や物流経路の変更容易性のみを変更容易性表示領域５０３に表示するようにしても良い。 Also, the output unit 113 displays the changeability in the changeability display area 503. Specifically, the output unit 113 displays the changeability of the changed master data value calculated in step S <b> 304 in the changeability display area 503. In addition, when there are a plurality of master data values to be changed, the output unit 113 displays a list of their ease of change. The display format of changeability is not limited to list display. For example, when the user selects a highlighted base or distribution route, only the changeability of the base or distribution route is displayed. May be displayed.
　このように、マスタデータ値が変更されるマスタデータ値の変更容易性を表示することにより、ユーザは、かかるマスタデータ値の変更が妥当であるか否かを判断することができる。 Thus, by displaying the changeability of the master data value in which the master data value is changed, the user can determine whether or not the change of the master data value is appropriate.
　また、出力部１１３は、適用ボタン５４１およびキャンセルボタン５４２を表示し、入力部１１２を介してユーザの押下を受け付ける。また、適用ボタン５４１の押下を受け付けた場合、出力部１１３は、ユーザに提示したマスタ値修正案を反映する（ステップＳ１０８）。具体的には、出力部１１３は、マスタデータ値の値をステップＳ１０６で算出された推定値に更新する。 Also, the output unit 113 displays an apply button 541 and a cancel button 542, and accepts a user's press via the input unit 112. When the pressing of the apply button 541 is received, the output unit 113 reflects the master value correction proposal presented to the user (step S108). Specifically, the output unit 113 updates the value of the master data value to the estimated value calculated in step S106.
　また、ステップＳ１０６で得られた推定結果が複数ある場合には、出力部１１３は、各結果についてユーザが選択して指定できるように切り替えて表示する。また、本例では、全てのマスタデータ値の妥当性を一律に確認する例を示しているが、部分的に推定結果を棄却したり、ユーザが明示的に更新値を入力する形式としても良い。 Also, when there are a plurality of estimation results obtained in step S106, the output unit 113 switches and displays each result so that the user can select and specify each result. Further, in this example, an example is shown in which the validity of all the master data values is uniformly confirmed. However, the estimation result may be partially rejected, or the user may explicitly input an update value. .
　なお、サプライチェーン設計装置１００の機能ブロックは、本実施形態において実現されるサプライチェーン設計装置１００の機能を理解容易にするために、主な処理内容に応じて分類したものであり、各機能の分類の仕方やその名称によって、本発明が制限されることはない。また、サプライチェーン設計装置１００の各構成は、処理内容に応じて、さらに多くの構成要素に分類することもできる。また、一つの構成要素がさらに多くの処理を実行するように分類することもできる。 The function blocks of the supply chain design device 100 are classified according to main processing contents in order to facilitate understanding of the functions of the supply chain design device 100 realized in the present embodiment. The present invention is not limited by the way of classification and its name. In addition, each configuration of the supply chain design apparatus 100 can be classified into more components according to the processing content. Moreover, it can also classify | categorize so that one component may perform more processes.
　また、本発明は上記した実施例に限定されるものではなく、様々な変形例が含まれる。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加、削除、置換をすることが可能である。 Further, the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.
　また、上記説明では、制御線や情報線は、説明上必要と考えられるものを示しており、製品上必ずしも全ての制御線や情報線を示しているとは限らない。実際には殆ど全ての構成が相互に接続されていると考えて良い。 In the above description, control lines and information lines indicate what is considered necessary for the description, and not all control lines and information lines on the product are necessarily shown. In practice, it can be considered that almost all the components are connected to each other.
１０００・・・サプライチェーン設計システム、１００・・・サプライチェーン設計装置、１５０・・・実績収集装置、１０１・・・制御部、１０２・・・記憶部、
１０３・・・通信部、１１１・・・全体制御部、１１２・・・入力部、
１１３・・・出力部、１１４・・・サプライチェーン設計部、
１１５・・・変更容易性評価部、１１６・・・マスタ値推定部、
２００・・・需要データ、２１０・・・製品構成データ、２２０・・・供給元データ、
２３０・・・拠点候補データ、２４０・・・物流候補データ、
２５０・・・マスタ値変更容易性データ、２６０・・・マスタ値履歴データ、
２７０・・・実績（供給量）データ、２８０・・・実績（輸送量）データ、
２９０・・・実績（評価指標）データ 1000 ... supply chain design system, 100 ... supply chain design device, 150 ... results collection device, 101 ... control unit, 102 ... storage unit,
103 ... Communication unit, 111 ... Overall control unit, 112 ... Input unit,
113 ... Output unit, 114 ... Supply chain design unit,
115 ... changeability evaluation unit, 116 ... master value estimation unit,
200 ... Demand data, 210 ... Product configuration data, 220 ... Supplier data,
230 ... site candidate data, 240 ... logistics candidate data,
250 ... Master value changeability data, 260 ... Master value history data,
270 ... actual (supply) data, 280 ... actual (transportation) data,
290 ... Result (evaluation index) data

Claims (14)

  1. 　サプライチェーン設計案を算出するサプライチェーン設計装置であって、
    　記憶部と制御部とを備え、
    　前記記憶部は、
    　サプライチェーンを流通する品目の部品構成および各市場の想定需要量に関するデータであるビジネスシナリオと、サプライチェーン上の物流および金流に関するデータであるマスタデータ値と、該マスタデータ値の更新履歴に関するデータであるマスタ値履歴データと、サプライチェーンを流通する品目の供給量に関するデータである供給量実績データと、サプライチェーンを流通する輸送量に関するデータである輸送量実績データと、サプライチェーン設計の評価指標に関する評価指標実績データとを有し、
    　前記制御部は、
    　前記記憶部に格納されている各データを入力値とし、所定のサプライチェーン数理モデルを用いて設計値と実績値とのモデル誤差を求め、
    　前記マスタ値履歴データを用いて推定対象となるマスタデータ値の変更容易性を算出し、
    　前記モデル誤差が小さく、かつ、前記変更容易性が大きい前記マスタデータ値の組合せおよびその推定値を算出する
    ことを特徴とするサプライチェーン設計装置。     A supply chain design device for calculating a supply chain design plan,
    A storage unit and a control unit;
    The storage unit
    Business scenario, which is data related to the parts configuration of items distributed in the supply chain and the estimated demand in each market, master data values which are data related to logistics and gold flow on the supply chain, and data related to the update history of the master data values Master value history data, supply amount actual data that is related to the supply amount of items that circulate in the supply chain, transport amount actual data that is related to the transport amount that circulates in the supply chain, and evaluation indicators for supply chain design Evaluation index performance data on
    The controller is
    Using each data stored in the storage unit as an input value, using a predetermined supply chain mathematical model, obtain a model error between the design value and the actual value,
    Calculate the changeability of the master data value to be estimated using the master value history data,
    A supply chain design apparatus characterized by calculating a combination of master data values with a small model error and a large changeability and an estimated value thereof.
  2. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記ビジネスシナリオと、前記マスタデータ値と、前記供給量実績データと、前記輸送量実績データとを入力値として前記サプライチェーン数理モデルに入力し、
    　前記サプライチェーン数理モデルにより算出された実績値による評価指標と、前記評価指標実績データとの誤差である前記モデル誤差を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    Input the business scenario, the master data value, the actual supply amount data, and the actual transport amount data as input values to the supply chain mathematical model,
    A supply chain design device for calculating the model error, which is an error between an evaluation index based on an actual value calculated by the supply chain mathematical model and the evaluation index actual data.
  3. 　請求項２に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　総コストおよび総在庫量によって示される前記実績値による評価指標と、前記評価指標実績データのモデル誤差を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 2,
    The controller is
    A supply chain design device for calculating an evaluation index based on the actual value indicated by a total cost and a total inventory amount, and a model error of the evaluation index actual data.
  4. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記マスタ値履歴データに含まれる過去のマスタデータ値の標準偏差をその平均値で除算した変動係数である前記変更容易性を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    The supply chain design apparatus, wherein the changeability is calculated as a coefficient of variation obtained by dividing a standard deviation of past master data values included in the master value history data by an average value thereof.
  5. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　マスタデータ値の平均変化速度により前記変更容易性を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    A supply chain design apparatus, wherein the changeability is calculated based on an average change speed of a master data value.
  6. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記モデル誤差が小さくなる場合、および、前記推定値に変更される前記マスタデータ値の前記変更容易性が大きい場合に結果値が向上する所定の目的関数を用いて、前記マスタデータ値の組合せおよびその推定値を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    A combination of the master data values using a predetermined objective function that improves a result value when the model error is small, and when the changeability of the master data value changed to the estimated value is large, and A supply chain design device characterized by calculating the estimated value.
  7. 　請求項６に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記ビジネスシナリオおよび前記マスタデータ値による制約条件の違反数量を最小化するための第１項と、前記モデル誤差を小さくするための第２項と、前記推定値に変更するマスタデータ値の変更容易性を大きくするための第３項とを有する前記目的関数を用いて、前記マスタデータ値の組合せおよびその推定値を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 6,
    The controller is
    The first term for minimizing the quantity of violations of the constraints due to the business scenario and the master data value, the second term for reducing the model error, and easy change of the master data value to be changed to the estimated value A supply chain design apparatus for calculating a combination of the master data values and an estimated value thereof using the objective function having a third term for increasing performance.
  8. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　所定の上限値および下限値の範囲内となる前記マスタデータ値の推定値を算出する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    A supply chain design apparatus for calculating an estimated value of the master data value within a range between a predetermined upper limit value and a lower limit value.
  9. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記マスタデータ値を一意に特定するための入力欄と、当該マスタデータ値を前記推定値の算出対象とするか否かを設定する入力欄と、前記上限値および下限値を設定する入力欄とを備えた設定画面を表示する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    An input field for uniquely identifying the master data value; an input field for setting whether or not the master data value is to be calculated for the estimated value; and an input field for setting the upper limit value and the lower limit value A supply chain design device characterized by displaying a setting screen with
  10. 　請求項１に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　サプライチェーンのネットワーク構成図と、前記マスタデータ値の現在値と、前記マスタデータ値の推定値とを表示し、該現在値および推定値の乖離が大きいサプライチェーンネットワーク構成上の拠点および物流経路を強調して表示する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 1,
    The controller is
    A network configuration diagram of the supply chain, a current value of the master data value, and an estimated value of the master data value are displayed. Supply chain design device characterized by highlighting.
  11. 　請求項１０に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記モデル誤差を示す総コストと、総在庫量と、誤差値とを更に表示する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 10,
    The controller is
    A supply chain design apparatus, further displaying a total cost indicating the model error, a total inventory amount, and an error value.
  12. 　請求項１０に記載のサプライチェーン設計装置であって、
    　前記制御部は、
    　前記マスタデータ値の前記変更容易性を更に表示する
    ことを特徴とするサプライチェーン設計装置。     The supply chain design device according to claim 10,
    The controller is
    A supply chain design device further displaying the changeability of the master data value.
  13. 　サプライチェーン設計装置で実行されるサプライチェーン設計方法であって、
    　サプライチェーンを流通する品目の部品構成および各市場の想定需要量に関するデータであるビジネスシナリオと、サプライチェーン上の物流および金流に関するデータであるマスタデータ値と、該マスタデータ値の更新履歴に関するデータであるマスタ値履歴データと、サプライチェーンを流通する品目の供給量に関するデータである供給量実績データと、サプライチェーンを流通する輸送量に関するデータである輸送量実績データと、サプライチェーン設計の評価指標に関する評価指標実績データとを有する記憶ステップと、
    　前記記憶部に格納されている各データを入力値とし、所定のサプライチェーン数理モデルを用いて設計値と実績値とのモデル誤差を求め、
    　前記マスタ値履歴データを用いて推定対象となるマスタデータ値の変更容易性を算出し、
    　前記モデル誤差が小さく、かつ、前記変更容易性が大きい前記マスタデータ値の組合せおよびその推定値を算出する制御ステップとを実行する
    ことを特徴とするサプライチェーン設計方法。     A supply chain design method executed by a supply chain design device,
    Business scenario, which is data related to the parts configuration of items distributed in the supply chain and the estimated demand in each market, master data values which are data related to logistics and gold flow on the supply chain, and data related to the update history of the master data values Master value history data, supply amount actual data that is related to the supply amount of items that circulate in the supply chain, transport amount actual data that is related to the transport amount that circulates in the supply chain, and evaluation indicators for supply chain design A storage step having evaluation index result data for
    Using each data stored in the storage unit as an input value, using a predetermined supply chain mathematical model, obtain a model error between the design value and the actual value,
    Calculate the changeability of the master data value to be estimated using the master value history data,
    A supply chain design method comprising: executing a combination of the master data values with a small model error and a high changeability and a control step for calculating an estimated value thereof.
  14. 　コンピュータを、サプライチェーン設計装置として機能させるプログラムであって、
    　前記コンピュータを、
    　サプライチェーンを流通する品目の部品構成および各市場の想定需要量に関するデータであるビジネスシナリオと、サプライチェーン上の物流および金流に関するデータであるマスタデータ値と、該マスタデータ値の更新履歴に関するデータであるマスタ値履歴データと、サプライチェーンを流通する品目の供給量に関するデータである供給量実績データと、サプライチェーンを流通する輸送量に関するデータである輸送量実績データと、サプライチェーン設計の評価指標に関する評価指標実績データとを有する記憶部と、
    　前記記憶部に格納されている各データを入力値とし、所定のサプライチェーン数理モデルを用いて設計値と実績値とのモデル誤差を求め、
    　前記マスタ値履歴データを用いて推定対象となるマスタデータ値の変更容易性を算出し、
    　前記モデル誤差が小さく、かつ、前記変更容易性が大きい前記マスタデータ値の組合せおよびその推定値を算出する、制御部として機能させる
    ことを特徴とするプログラム。     A program that causes a computer to function as a supply chain design device,
    The computer,
    Business scenario, which is data related to the parts configuration of items distributed in the supply chain and the estimated demand in each market, master data values which are data related to logistics and gold flow on the supply chain, and data related to the update history of the master data values Master value history data, supply amount actual data that is related to the supply amount of items that circulate in the supply chain, transport amount actual data that is related to the transport amount that circulates in the supply chain, and evaluation indicators for supply chain design A storage unit having evaluation index result data regarding,
    Using each data stored in the storage unit as an input value, using a predetermined supply chain mathematical model, obtain a model error between the design value and the actual value,
    Calculate the changeability of the master data value to be estimated using the master value history data,
    A program that functions as a control unit that calculates a combination of master data values with a small model error and a large changeability and an estimated value thereof.
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