JP2007265127A - Supply chain optimization support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a supply chain optimization support method capable of establishing a supply chain that can even cope with a case in which a production plan changes with fewer false factors due to time and which occurs together with demand fluctuation in the future by performing simulation in which separation as time goes by does not occur to an optimization result of the simulation from components supply to distribution. <P>SOLUTION: Simulation of the supply chain is performed on the basis of each information on a human factor, a component factor, a production plan, and production and demand fluctuation by grasping circulation of objects in all work in real time by using an ERP package and considering comparison of unit formats of the components. The supply chain having fewer time false factors as time goes by and including prevention of production delay due to a shortage of components when the demand fluctuates in the future is established. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to the optimal construction of a supply chain including production processes from parts procurement to logistics, and in particular, the construction of a supply chain that does not hinder production even when demand fluctuations occur in the future. It relates to a supply chain optimization support method that can be used.

  There are multiple business activities such as procurement, production, distribution, and sales of materials and parts from procurement of materials to delivery to consumers before the product reaches the consumer. Is done. Optimization of such a chain of business activities, the so-called supply chain, has been studied as an important issue.

  When constructing a supply chain that determines the optimal form of a supply chain in which such multiple business activities exist, production information such as production volume and delivery date, forms of procurement / production / logistics, and information about their bases It is necessary to consider from a plurality of viewpoints.

  For this reason, in order to optimize the supply chain, a system for creating a supply chain model, simulating with a simulator, evaluating the supply chain, and optimizing it has been developed.

  In addition, in the supply chain, if there is a weak part in the chain, such as the flow of information or information, such as an unclear stock quantity, the supply chain may be broken. Therefore, in order to optimize the flow of materials and information on business activities such as procurement, production, distribution, and sales of materials and parts from procurement of materials to delivery to consumers, all operations in the supply chain must be performed in real time. The flow of things needs to be captured. For this reason, information system packages called ERP (Enterprise Resource Planning) packages (integrated business packages) have been introduced for the core business of companies.

The product production plan in the supply chain is created based on the initial demand of the product, that is, demand information. Here, the demand indicates the demand amount and time of the product, and the production amount and delivery date of the product are set based on these demands.
However, the demand does not always stay constant and may change in the future. Therefore, in the construction of the supply chain, it is desirable to prevent production delays due to missing parts even when the fluctuation occurs.

  Further, in the procurement of materials such as parts, conventionally, in order to produce a product, a part to be used for the product is ordered, and production is performed after the delivery of the part. However, not only parts are delivered, but they are also increasingly delivered as assembly parts, that is, units combining parts. In the construction of the supply chain, it is desired to consider the degree of optimal unitization when delivering the unit.

  For the optimization of the supply chain based on the result of the simulation, each work of logistics and commercial flow is simulated according to the supply chain plan (model), and multiple viewpoints such as inventory, cost, profit, working capital, cash flow, etc. Discloses that a supply chain proposal is quantitatively evaluated (see, for example, Patent Document 1).

  In the process including manual work of the supply chain plan (model), there are human factors of worker fatigue, proficiency, and stress that change over time. If this is not taken into consideration, there is a concern that the simulation optimization results may vary. Patent Document 1 simulates each work and quantitatively evaluates a supply chain plan from a plurality of viewpoints, thereby providing a supply chain configuration suitable for business resources such as funds, corporate resources, markets, product life cycles, etc. Although it is a method of supporting decision, since the change of the human factor with the passage of time is not taken into consideration, there is a possibility that the optimization result of the simulation may vary with the passage of time. In addition, the comparison of unitization in the procurement of parts that affect the efficiency of production processes and costs is not taken into consideration.

  In addition, in order to grasp the distribution cost related to the distribution center, it is combined with a simulator that builds a virtual distribution center, and by making on-site improvements that cannot be done in reality, it is possible to grasp the current distribution cost and improve measures from a cost standpoint. The realization of verification is disclosed (for example, see Patent Document 2).

  Patent Document 2 is a method of performing a simulation based on the inventory and the movement instruction information of a worker (hereinafter also referred to as a person) to verify improvement measures. The level of proficiency for each worker can be calculated because it can be implemented and measured at the center, but there is no viewpoint on the degree of fatigue for each worker over time, and there is a risk that the simulation results will vary with time. is there. In addition, the comparison of unitization in the procurement of parts that affect the efficiency of production processes and costs is not taken into consideration.

  In addition, when producing products at multiple production bases, the production plan can be referred to from the unit manufacturer, the production plan from the unit manufacturer is received, and the production plan and the product configuration information are used. In addition, a production management system is disclosed in which a calculation for obtaining a demand amount of a component is performed and the obtained demand amount is in a state that can be referred to from a component manufacturer (for example, see Patent Document 3).

  Patent Document 3 is a method for the purpose of efficiently managing from ordering to delivery of parts. However, the unit manufacturer and the parts manufacturer only refer to prior information on ordering and ordering, and the simulation of the supply chain is as follows. There is a concern that it cannot respond promptly to fluctuations in demand.

  For the fluctuations in demand, scenario information is created from the predicted values of the product, market, and daily demand calculated by the demand forecasting system, the accuracy of past demand forecasts, and the order results at the time of planning. A method is disclosed that reads the target value of the index and the characteristic information of the base, calculates the value of the management index from the stockout quantity and inventory quantity calculated for each scenario, and determines the production quantity within the range of material supply quantity and production capacity (For example, see Patent Document 4).

Patent Document 4 is a method for determining the production amount so as to minimize adverse effects on management indicators such as shortage and inventory even if demand fluctuations occur in advance, even if demand fluctuations occur.
Since changes in the human factors with the passage of time are not taken into account, there is a possibility that a deviation may occur in the material supply amount and the production capacity optimization result with the passage of time. Further, it does not take into account the procurement of parts that affect the efficiency and cost of the production process, for example, comparison of unitization.
JP 2002-145421 A JP 2004-118321 A JP 2003-15727 A JP 2005-316932 A

  The present invention has been made in view of the above situation, and in order to solve the above-described problem, a supply chain including a production process from product parts procurement to physical distribution based in at least one of a plurality of countries and regions. Create a model, perform simulation with a simulator, and build a supply chain that is based on the simulation results so that the simulation optimization results from parts procurement to logistics do not cause any deviation over time This provides a supply chain optimization support method that enables the construction of a supply chain that has fewer error factors due to time and can respond to production plan fluctuations due to future fluctuations in demand. The purpose is to do.

The above object is achieved by the following method.
1. Create a supply chain model that models the supply chain, including the production process from product parts procurement to logistics, based in at least one of multiple countries and regions, and reproduce and simulate it on the simulator. A supply chain optimization support method for optimizing the supply chain,
ERP (Enterprise Resource Planning) package,
Means for storing design information data including parts used in the product and the quantity of the parts, product process chart and configuration data;
Means for storing part factor data representing specifications for making parts into assembly units;
A means of storing performance data during production of existing products;
A data estimation means for estimating and creating the operation status data of the operation rate, cycle time, work time and non-defective product rate in the process of the supply chain with reference to the actual data, and means for storing the operation status data;
Human factor information data of the fatigue level, proficiency level, and stress level of the production process and logistics workers on the supply chain estimated and created by the data estimation means with reference to the actual data and the operation status data Means for storing
Based on demand information at the time of product production planning,
Demand fluctuation prediction means for generating demand fluctuation prediction information when future demand fluctuates, and means for storing the demand fluctuation prediction information;
Means for storing the presence or absence of parts shortage when demand changes as an index when evaluating the supply chain model with a simulator, and an evaluation index of the minimum appropriate inventory amount,
Based on the design information data and the production plan information of the product stored in the integrated database of the ERP package, a master supply chain model that models the form of the supply chain is created by the master model creating means,
Based on the part factor data, the human factor information, the product production plan information, the production information, and the demand fluctuation prediction information stored in the integrated database of the ERP package, the master supply chain model is Perform simulation, create one or more secondary supply chain models with secondary model creation means,
Select the optimal secondary supply chain model by performing a simulation based on the evaluation index and evaluating the secondary supply chain model with the evaluation selection means,
A supply chain optimization support method, characterized in that a supply chain is constructed based on the selected secondary supply chain model.
2. 2. The supply chain optimization support method according to 1, wherein a simulation result when the secondary supply chain model is created by the secondary model creation means is transmitted to the ERP package and stored in the integrated database.
3. 3. The supply chain optimization support method according to 1 or 2, wherein the ERP package is SAP R / 3 (registered trademark) of SAP.
4). The component factor data includes unit assembly shape data representing the assembly shape of the unit created and calculated by the unit setting means based on the design information data and a predetermined unit creation standard, the number of assembly steps of the unit, and the assembly. 4. The supply chain optimization support method according to any one of claims 1 to 3, comprising assembly characteristic data of a unit representing a difficulty level, a required proficiency level, and a skill level.
5). The actual data includes the existing operation status data of the operation rate, cycle time, work time and non-defective product rate in the existing supply chain process of the existing product, worker fatigue, worker proficiency, and worker stress. 5. The supply chain optimization support method according to any one of 1 to 4, characterized in that the supply chain optimization support method includes:
6). The demand fluctuation forecasting information is a demand fluctuation forecasting means that refers to demand information and sales performance information in a past simulation of an existing product based on demand information at the time of product production planning, and calculates a demand fluctuation forecast result. 6. The supply chain optimization support method according to any one of 1 to 5, wherein the supply chain optimization support method is generated by setting a department in charge and a person in charge who calculate and predict demand based on the prediction result.
7). The production plan information includes at least a product production amount and a delivery date, and the production information includes at least inventory information, order information, delivery date information, price information, and product inventory information of parts and units, and the demand fluctuation prediction information. 7. The supply chain optimization support method according to any one of 1 to 6, characterized in that it includes at least a variable production amount that is a production amount variation accompanying a demand variation and a delivery date.
8). The human factor information is included in the supply chain that is estimated and created by referring to the performance data, with transition data creating means for creating data representing the transition of the worker's fatigue level, work proficiency level, and stress level. Includes worker fatigue transition data, work proficiency transition data, and stress degree transition data used as data for determining and predicting the fatigue level, proficiency level, and stress level of workers on the production process and logistics supply chain 8. The supply chain optimization support method according to any one of 1 to 7, wherein:
9. Means for storing information on the supply chain base, information on communication environment status, communication environment information, and distribution information on distribution status, and the information / communication environment information is created when the secondary supply chain model is created. 9. The supply chain optimization support method according to any one of 1 to 8, wherein simulation is performed in consideration of distribution information.
10. When evaluating with the simulator in selecting the optimal secondary supply chain model, a modified secondary supply chain model in which at least one of product delivery time and part lead time is used as a parameter and the initial planned value of the parameter is changed is created separately 10. The supply chain optimization support method according to any one of 1 to 9, wherein simulation is performed.
11. The operating supply chain configuration is set as a master supply chain model, and the supply chain is optimized using the supply chain optimization support method described in any one of 1 to 10 during operation. Supply chain optimization support method.

A supply chain model that models the form of the supply chain including the production process from parts procurement to logistics is created, reproduced on the simulator and simulated, and when the optimal supply chain is built based on the results,
Using the ERP package, the simulation captures the flow of goods in all operations in real time, and considers comparison of parts procurement / delivery forms, that is, unitization forms, and the human factor information, the part factor data Based on the production plan information, the production information, and the demand fluctuation prediction information, and further taking into account the information / communication environment information and logistics information, there are few temporal error factors over time, and It can be a simulation that incorporates prevention of production delays due to missing parts in the event that demand changes in the future.

  Furthermore, the supply chain model based on the simulation result is evaluated by a simulator based on the evaluation index, and the optimal supply chain model is selected, so that the procurement of materials with less error factors with the passage of time can be reduced. It is possible to support the construction of a supply chain that optimizes the flow of materials and information on business activities such as procurement, production, distribution, and sales of materials and parts before they are delivered.

  In addition, the simulation makes it possible to minimize the stock amount of parts that can be handled without any shortage even if demand changes in the future. In addition, the process cost and delivery time are optimized. Thus, it becomes possible to easily select a unit form of parts at the time of procurement and delivery of parts.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings, but the present invention is not limited thereto.

  FIG. 1 is a diagram of a supply chain for optimally constructing a supply chain including a production process having manual operations from product parts procurement to logistics based in at least one of a plurality of countries and regions according to the present invention. It is a flowchart which shows one Embodiment of the optimization assistance method.

  In FIG. 1, a database 1 is means for storing the various data and information described above.

  The ERP package 2 is an integrated business package that has an integrated database and stores product production volume and delivery schedule production plan information, parts and unit inventory information, order information, delivery date information, price information, product inventory information, and other production information. Stored. In the present embodiment, SAP (R / 3) (registered trademark) manufactured by SAP is used.

  Hereinafter, the ERP package 2 is also referred to as SAP (R / 3) (registered trademark).

  The data estimation creation means 3 is the existing operation status data of the operation rate, cycle time, work time, and non-defective product rate in the existing supply chain of the existing product stored in the database 1, the worker's fatigue level, the worker's proficiency Data on the operating rate, cycle time, working time, and non-defective rate operating rate that estimates the operating status in the process of the supply chain with reference to the actual data including the existing human factor information on the degree and the stress level of the worker 4 Create Furthermore, with reference to the actual data and the created operational status data, the human factors of the fatigue level, proficiency level, and stress level of workers on the production process and logistics supply chain included in the supply chain were estimated. Human factor information is created and stored in the database 1. In FIG. 1, the operation status data 4 is illustrated independently of the database 1, but may be stored in the database 1.

  The transition data creation means 5 determines the fatigue level, proficiency level, and stress level of workers on the production process and logistics supply chain included in the supply chain with reference to the actual data and the human factor information. The worker's fatigue level transition data, work proficiency level transition data, and stress level transition data, which are data for prediction, are estimated, created, and stored in the database 1.

  The function of the transition data creation means 5 may be provided in the simulator.

  Here, the worker's degree of fatigue represents the degree to which fatigue changes with time when engaged in a certain work. FIG. 3 is a schematic diagram showing an example of fatigue level transition data showing the fatigue level and the passage of time. The fatigue level transition data is a fatigue curve in FIG. 3, but is not limited thereto, and may be a data table, for example.

  The proficiency level of the worker represents the proficiency level in which a preset proficiency level changes with time when a certain worker engages in a certain task. FIG. 4 is a schematic diagram showing an example of proficiency level transition data showing the proficiency level and the passage of time. The proficiency level transition data is a proficiency curve in FIG. 4, but is not limited thereto, and may be a data table, for example.

  The worker's stress level indicates the level of stress that changes when the worker is engaged in a certain task with the passage of time. FIG. 5 is a schematic diagram showing an example of stress level transition data showing the stress level and the passage of time. The stress degree transition data is a stress curve in FIG. 5, but is not limited thereto, and may be a data table, for example.

  The unit setting means 6 sets one or more unit assembly shapes based on design information data stored in the database 1 and predetermined unit creation criteria, and creates unit assembly shape data for each unit. The unitization creation standard is a reference index for the degree (unit) of parts assembly when parts are assembled into units in product assembly. Further, the unit setting means 6 calculates unit assembly characteristic data of the assembly man-hours, assembly difficulty level, required proficiency level and skill level for each unit. The unit assembly shape data and the unit assembly characteristic data are stored in the database 1 as part factor data.

  The demand fluctuation prediction means 7 generates the demand fluctuation prediction information. The generation of the demand fluctuation prediction information is based on demand information at the time of production planning, referring to demand information and sales performance information in the past simulation of existing products, and calculating the demand fluctuation prediction result. With reference to this, the department in charge and the person in charge who perform the demand prediction make corrections and settings as necessary. The correction and setting are performed when the responsible department and the person in charge input information to the demand fluctuation prediction means 7 by an input means (not shown).

  The master model creation means 9 is based on the product production amount and delivery schedule production plan information stored in the SAP (R / 3) (registered trademark) 2 integrated database and the design information data stored in the database 1. Develop a master supply chain model (hereinafter also referred to as a master SC model) that examines the form including supply chain bases including production processes from parts procurement to logistics on the simulator and models the form of the supply chain To do. Information and data stored in the integrated database of SAP (R / 3) (registered trademark) 2 are taken in via the relay program 8.

  Hereinafter, transmission and reception of information and data with the integrated database of SAP (R / 3) (registered trademark) 2 is performed via the relay program 8.

  At the time of creating the master supply chain model, the product or similar product is selected from the actual data of existing products produced in the past stored in the database 1, and data at the time of production such as work standard time, work standard man-hour, It is preferable to refer to the data of the work difficulty level and the member procurement time in order to improve the accuracy of the master supply chain model.

  The master simulation means 10 is a simulator based on the human factor information, the part factor data, the production plan information, and the demand fluctuation prediction information. The master supply chain model created by the master model creation means 9 is a simulator. A simulation is performed for each unit shape.

  In the simulation, it is preferable to add the worker's fatigue level transition data, work skill level transition data, and stress level transition data as human factor information. Thereby, the error of the human factor accompanying progress of time can be reduced.

  The assembly shape of the unit can be changed according to the result of the simulation.

  The secondary model creation means 11 creates one or more secondary supply chain models (hereinafter also referred to as secondary SC models) based on the simulation result of the master simulation means 10.

  The secondary simulation means 12 is a simulator in which the secondary supply chain model is stored in the database 1 in advance, information on bases in the supply chain, information on the communication environment status, communication environment information, logistics information on the logistics status, and SAP (R / 3) (registered trademark) 2 based on product and component inventory information, order information, delivery date information, price information, and product inventory information production information stored in the integrated database A secondary simulation is performed for each form of the secondary supply chain model.

  Based on the simulation result of the secondary simulation means 12, the evaluation selection means 13 determines whether or not there is a missing part and a missing part when the demand as an index for performing the simulation stored in the database 1 fluctuates. The secondary supply chain model is evaluated by a simulator based on the evaluation index of the minimum appropriate inventory quantity that can be handled without producing goods, and the optimum secondary supply chain model is selected.

  When evaluating with a simulator in selecting the secondary supply chain model, a modified secondary supply chain model in which at least one of the production amount and delivery date of the product of the production plan information is used as a parameter, and the initial planned value of the parameter is changed, It is preferable to make a separate analysis and perform a comparative evaluation by simulation. As a result, the range of comparison is widened, and a comparison in accordance with an actual system becomes possible.

  Based on the secondary supply chain model including the selected modified secondary supply chain model, the supply chain is designed by the supply chain construction means 14.

  The secondary simulation results, evaluation and selection, and the supply chain design results are transmitted and stored via the relay program 8 in the integrated database of SAP (R / 3) (registered trademark) 2. Thereby, information such as a delivery date change instruction and a delivery quantity change instruction can be reflected in the information of the integrated database in real time.

  FIG. 2 shows an example of a flowchart for optimizing the construction of the supply chain using the supply chain optimization support method shown in FIG.

  First, in step S101, the existing operation status data of the operation rate, cycle time, work time, and non-defective product rate in the existing supply chain of the existing product stored in the database 1 by the data estimation creation means 3 and the fatigue degree of the worker , Operation rate, cycle time, work time, and non-defective rate that estimated the operation status in the process of the supply chain with reference to performance data consisting of existing human factor information on worker proficiency and worker stress level Operation status data 4 is created.

  In step S102, with reference to the actual data and the operation status data created in step S101, a worker having a fatigue level, proficiency level, and stress level of workers in the supply chain of the production process and physical distribution included in the supply chain Human factor information that estimates the physical factor is created and stored in the database 1.

  In step S103, the transition data creation means 5 refers to the actual data and the human factor information created in step S102, and the fatigue level and proficiency of workers on the production chain and logistics supply chain included in the supply chain are determined. The worker's fatigue level transition data, work proficiency level transition data, and stress level transition data, which are data for determining and predicting the degree and the stress level, are estimated, created, and stored in the database 1.

In step S104, the unit setting means 6 sets one or more unit assembly shapes based on design information data stored in the database 1 and predetermined unit creation criteria, and sets unit assembly shape data for each unit. At the same time, the unit assembly characteristic data of the assembly man-hours, the assembly difficulty level, the required proficiency level, and the skill level for each unit is calculated. The unit assembly shape data and the unit assembly characteristic data are stored in the database 1 as part factor data.
In step S105, the demand fluctuation prediction means 7 generates the demand fluctuation prediction information. Responsible for calculating demand fluctuation forecast results by referring to demand information and sales performance information in past simulations of existing products based on demand information at the time of production planning and referring to the forecast results Department and person in charge modify and set as necessary.

  In step S <b> 106, the master model creation means 7 produces the product production amount and delivery schedule information stored in the SAP (R / 3) (registered trademark) 2 integrated database and the design information stored in the database 1. Based on the data, we will examine on the simulator the form including the supply chain base including the production process from parts procurement to logistics, and create one or more master supply chain models that model the supply chain form.

  In step S107, the master simulation means 10 uses the human factor information estimated and created in step S102 as the master supply chain model created in step 106, and the worker fatigue level transition data estimated and created in step S103. Based on the work proficiency level transition data and stress level transition data, the part factor data created in step S104, the production plan information, and the demand fluctuation prediction information created in step S105, a simulator is used for each unit form. Perform a simulation.

  In step S108, the secondary model creating means 11 creates one or more secondary supply chain models based on the result of the master simulation.

  In step S109, the secondary simulation means 12 uses the simulator to store the secondary supply chain model in advance in the database, information on the supply chain bases, information on the communication environment status, communication environment information, and logistics on the logistics status. Information and one of the products stored in the integrated database of SAP (R / 3) (registered trademark) 2 on the basis of parts and unit inventory information, order information, delivery date information, price information, and product inventory information. A secondary simulation is performed for each form of the secondary supply chain model.

  The result of the secondary simulation is transmitted and stored in the integrated database of SAP (R / 3) (registered trademark) 2 via the relay program 8.

  In step S110, for the secondary supply chain model based on the secondary simulation of step S109, at least one of the product production amount and delivery date of the production plan information is used as a parameter, and the initial plan value of the parameter is changed. Create a secondary supply chain model separately.

  In step S111, the evaluation selection unit 13 changes the secondary supply chain model based on the secondary simulation in step S109 and the modified secondary supply chain model in step S110 when the demand that serves as an index for performing the simulation fluctuates. Based on the evaluation of the presence or absence of missing parts and the evaluation index of the minimum appropriate inventory amount, the simulator evaluates and selects the secondary supply chain model including the optimal modified secondary supply chain model.

  The result of the selection is transmitted and stored in the integrated database of SAP (R / 3) (registered trademark) 2 via the relay program 8.

  In step S112, the supply chain is designed by the supply chain construction means 12 based on the secondary supply chain model selected in step S111. The specifications, configuration, etc. of the supply chain by the design are transmitted and stored via the relay program 8 in the integrated database of SAP (R / 3) (registered trademark) 2.

  In addition, in the flowchart shown in FIG. 2, by setting the operating supply chain form as a master supply chain model, the operating supply chain can be simulated using the aforementioned supply chain optimization support method. It is possible to optimize the operating supply chain.

  Based on the above, the supply chain simulation using the supply chain model incorporates prevention of production delays due to missing parts when there are few time error factors over time and demand changes in the future. In addition, simulation is possible, and the supply chain model based on the result of the simulation is evaluated by a simulator based on the evaluation index, and an optimum supply chain model is selected, so that there are few error factors with time. It can support the construction of a supply chain that optimizes the flow of materials and information on business activities such as procurement, production, logistics, and sales of materials and parts from procurement of materials to delivery to consumers.

  In addition, the simulation makes it possible to set the minimum appropriate inventory amount that can be handled without any missing parts even if the demand for the parts inventory changes in the future, and the process cost and delivery date are optimized. Therefore, it becomes possible to easily select a unit form for parts procurement and delivery.

It is a flow figure showing an example of one embodiment of a supply chain optimization support method concerning the present invention. 5 is a flowchart of an embodiment of a supply chain optimization support method according to the present invention. It is a fatigue curve showing the degree of fatigue and the passage of time. This is a learning curve that shows the proficiency level and the passage of time. It is a stress curve showing the degree of stress and time.

Explanation of symbols

1 Database 2 ERP Package SAP R / 3 (SAP) (Registered Trademark)
3 Data estimation creation means 4 Operation status data 5 Transition data creation means 6 Unit setting means 7 Demand fluctuation prediction means 8 Relay program 9 Master model creation means 10 Master simulation means 11 Secondary model creation means 12 Secondary simulation means 13 Evaluation selection means 14 Supply chain construction means

Claims (11)

Create a supply chain model that models the supply chain, including the production process from product parts procurement to logistics, based in at least one of multiple countries and regions, and reproduce and simulate it on the simulator. A supply chain optimization support method for optimizing the supply chain,
ERP (Enterprise Resource Planning) package,
Means for storing design information data including parts used in the product and the quantity of the parts, product process chart and configuration data;
Means for storing part factor data representing specifications for making parts into assembly units;
A means of storing performance data during production of existing products;
A data estimation means for estimating and creating the operation status data of the operation rate, cycle time, work time and non-defective product rate in the process of the supply chain with reference to the actual data, and means for storing the operation status data;
Human factor information data of the fatigue level, proficiency level, and stress level of the production process and logistics workers on the supply chain estimated and created by the data estimation means with reference to the actual data and the operation status data Means for storing
Based on demand information at the time of product production planning,
Demand fluctuation prediction means for generating demand fluctuation prediction information when future demand fluctuates, and means for storing the demand fluctuation prediction information;
Means for storing the presence or absence of parts shortage when demand changes as an index when evaluating the supply chain model with a simulator, and an evaluation index of the minimum appropriate inventory amount,
Based on the design information data and the production plan information of the product stored in the integrated database of the ERP package, a master supply chain model that models the form of the supply chain is created by the master model creating means,
Based on the part factor data, the human factor information, the product production plan information, the production information, and the demand fluctuation prediction information stored in the integrated database of the ERP package, the master supply chain model is Perform simulation, create one or more secondary supply chain models with secondary model creation means,
Select the optimal secondary supply chain model by performing a simulation based on the evaluation index and evaluating the secondary supply chain model with the evaluation selection means,
A supply chain optimization support method, characterized in that a supply chain is constructed based on the selected secondary supply chain model. 2. The supply chain optimization support according to claim 1, wherein a simulation result at the time of creating the secondary supply chain model by the secondary model creating means is transmitted to the ERP package and stored in the integrated database. Method. 3. The supply chain optimization support method according to claim 1, wherein the ERP package is SAP R / 3 (registered trademark) of SAP. The component factor data includes unit assembly shape data representing the assembly shape of the unit created and calculated by the unit setting means based on the design information data and a predetermined unit creation standard, the number of assembly steps of the unit, and the assembly. 4. The supply chain optimization support method according to any one of claims 1 to 3, comprising assembly characteristic data of a unit representing a difficulty level, a required proficiency level, and a skill level. The actual data includes the existing operation status data of the operation rate, cycle time, work time and non-defective product rate in the existing supply chain process of the existing product, worker fatigue, worker proficiency, and worker stress. 5. The supply chain optimization support method according to claim 1, wherein the supply chain optimization support method is based on existing human factor information. The demand fluctuation forecasting information is a demand fluctuation forecasting means for referring to demand information and sales performance information in a past simulation of an existing product based on demand information at the time of product production planning, and to obtain a demand fluctuation forecasting result. 6. The supply chain optimization support according to any one of claims 1 to 5, wherein the supply chain optimization support is generated by setting a department and a person in charge who calculate and predict demand based on the prediction result. Method. The production plan information includes at least a product production amount and a delivery date, and the production information includes at least inventory information, order information, delivery date information, price information, and product inventory information of parts and units, and the demand fluctuation prediction information. 7. The supply chain optimization support method according to claim 1, comprising: a variable production amount that is a change in production amount due to a change in demand and a delivery date. The human factor information is included in the supply chain that is estimated and created by referring to the performance data, with transition data creating means for creating data representing the transition of the worker's fatigue level, work proficiency level, and stress level. Includes worker fatigue transition data, work proficiency transition data, and stress degree transition data used as data for determining and predicting the fatigue level, proficiency level, and stress level of workers on the production process and logistics supply chain 8. The supply chain optimization support method according to claim 1, wherein the supply chain is optimized. Means for storing information on the supply chain bases, information on communication environment status, communication environment information, and logistics information on logistics status,
The supply chain optimization support according to any one of claims 1 to 8, wherein a simulation is performed in consideration of the information / communication environment information and logistics information when creating the secondary supply chain model. Method. When evaluating with the simulator in selecting the optimal secondary supply chain model, a modified secondary supply chain model in which at least one of the product delivery date and the part lead time is used as a parameter and the initial planned value of the parameter is changed is created separately. 10. The supply chain optimization support method according to claim 1, wherein simulation is performed. A supply chain configuration is set as a master supply chain model, and the supply chain is optimized using the supply chain optimization support method according to any one of claims 1 to 10. Supply chain optimization support method during operation.

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