Production simulation equipment

Abstract

A production simulation kit consisting of one or more processors and a set of storage equipment. One or more records where one or more recording devices will store the recorded information. Production, which includes information about the start time as recorded and the end time as recorded. Each stage of the production process and its simulation models include information about the time. Each step involves a group of production resources that can be allocated to that step. Operating time of each production resource within each group of production resources, and the rules for... To control the production line, one or more processors will use production record information. And the simulation model is used to perform the simulation, and to make a comparison between the recorded information and the results. Production and simulation results are used to calculate simulation errors;

Description

production simulation device Incorporation by Reference

[0001] This application claims priority from Japanese Patent Application No. 2019-209009, filed on November 19, 2019, the contents of which are incorporated herein by reference.

[0002] The present invention relates to production simulation.

[0003] Production simulation is a method for estimating future production progress in factories, etc., and is useful for formulating production plans and developing countermeasures when production problems occur. Production simulation requires process information that defines the processing time and required production resources (equipment, workers, etc.) for each process of each item, production resource information that defines the number of production resources and their future operating times, and production control rule information that determines the start order of items in each process and the production resources to be used.

[0004] Here, in order to effectively utilize production simulation, it is important to increase the accuracy of the production simulation, and to increase the accuracy of the production simulation, it is important to increase the accuracy of each of the above-mentioned information. For example, if there is a discrepancy between the process time used in the simulation and the actual process time, the error of the simulation relative to the production results will be large.

[0005] However, it is difficult to accurately define all the information manually, especially in the case of multi-product production. To address this issue, there are methods for defining various information from past production performance data. For example, as described in Reference 1, there is a method for creating reference data for the number of equipment units and process times from past production performance data.

[0006] Japanese Patent Application Laid-Open No. 2008-234526

[0007] Literature 1 describes a method for creating information such as the number of equipment units and process times from production performance data and using this information to perform a production simulation, but if the production simulation is to be used for production planning, it is not enough to simply create each piece of information; it is necessary to evaluate the error of the simulation itself using that information. If the error is large, it is necessary to identify the cause of the error and take measures to resolve it.

[0008] A production simulation is characterized by the complex intertwining of the process time information, production resource information, production control rule information, and other information. For example, if the process time for a certain process group deviates from the actual situation, the arrival time of items at the next process in that process group will also deviate from the actual situation. If the start order of the next process is determined by the arrival order of the items, then the deviation in the arrival time of the items will lead to a deviation in the start order of the process.

[0009] As such, production simulations have the tendency for errors in one process to propagate to other processes, making it difficult to identify the causes of errors in production simulations. For these reasons, it is important to identify the main causes of simulation errors in order to improve the accuracy of production simulations.

[0010] In order to solve the above-described problems, one aspect of the present disclosure is a production simulation device that estimates the progress of processes on a production line, including one or more processors and one or more storage devices, wherein the one or more storage devices store production performance information including information on the actual start time and actual completion time of each process of a production job, a simulation model including information on the process time for each process, a group of production resources that can be assigned to each of the processes, the operating time of each production resource in the group of production resources, and a production control rule for the production line, and the one or more processors execute a simulation using the production performance information and the simulation model, and calculate a simulation error by comparing the production performance information with the result of the simulation.

[0011] According to one aspect of the present disclosure, highly accurate production simulation can be realized.

[0012] FIG. 1 is a functional block diagram of a production simulation device. FIG. 2 is a hardware and software configuration diagram of a production simulation device. FIG. 3 is a schematic diagram of a production performance data table. FIG. 4 is a schematic diagram of a production process data table. FIG. 5 is a schematic diagram of an equipment data table. FIG. 6 is a schematic diagram of a worker data table. FIG. 7 is a schematic diagram of a production start sequence rule data table. FIG. 8 is a schematic diagram of an equipment allocation rule data table. FIG. 9 is a schematic diagram of a worker allocation rule data table. FIG. 10 is a processing flowchart of a control unit of a production simulation device. FIG. 11 is a schematic diagram showing an example of a display screen. FIG. 12 is a schematic diagram showing an example of a display screen. FIG. 13 is a schematic diagram showing an example of an embodiment of a production simulation system.

[0013] Hereinafter, embodiments will be described with reference to the accompanying drawings. It should be noted that the embodiments are merely examples for realizing the present invention and do not limit the technical scope of the present invention.

[0014] When using production simulations for production planning, etc., it is important to improve the accuracy of the production simulation. To address this, there is a method of deriving the information required for simulation, such as the processing time of each process, from actual production data, but if the simulation using the derived information has large errors, it is necessary to identify the cause of the error and take measures to resolve the cause.

[0015] Production simulations involve a complex intertwining of process information, production resource information, production control rule information, and other information, and errors in one process tend to propagate to other processes. In production simulations with this nature, it is necessary to identify the main causes of errors. The system described below calculates simulation errors by comparing actual production performance with simulation results. This makes it possible to identify error factors and achieve highly accurate production simulations. This makes it possible to improve the feasibility and optimality of production plans formulated using production simulations.

[0016] 1A is a functional block diagram of a production simulation device 100. As shown in the figure, the production simulation device 100 includes an input unit 110, a storage unit 120, a control unit 130, and a display unit 140.

[0017] The input unit 110 accepts input of various information from outside the production simulation device 100. The display unit 140 displays information from the storage unit on a screen. The storage unit 120 includes a production performance data storage area 121, a production process data storage area 122, a production resource data storage area 123, a production control rule data storage area 124, and a simulation result data storage area 125.

[0018] The production performance data storage area 121 stores information specifying past processing performance in a production process. The production process data storage area 122 stores information specifying information such as the process time for each process. The production resource data storage area 123 stores information specifying the operating times of production resources such as equipment and workers. The production control rule data storage area 124 stores information specifying production control rules such as production start order rules. The simulation result data storage area 125 stores information specifying simulation results.

[0019] The control unit 130 includes a performance data extraction unit 131 , a simulation model division unit 132 , a performance reflection unit 133 , a simulation execution unit 134 , and a simulation error calculation unit 135 .

[0020] FIG. 1B shows an example of the hardware and software configuration of the production simulation device 100. In the example of FIG. 1B, the production simulation device 100 is configured as a single computer. The production simulation device 100 includes a processor 310, a memory 320, an auxiliary storage device 330, a network (NW) interface 340, an I / O interface 345, an input device 351, and an output device 352. The above components are connected to each other by a bus. The memory 320, the auxiliary storage device 330, or a combination thereof is a storage device including a non-transitory storage medium, and may correspond to the storage unit 120.

[0021] The memory 320 is configured, for example, by a semiconductor memory, and is used mainly to hold programs and data. The programs stored in the memory 320 include an operating system (not shown), a performance data extraction program 321, a simulation model division program 322, a performance reflection program 323, a simulation execution program 324, a simulation error calculation program 325, and a user interface program 326.

[0022] The processor 310 executes various processes in accordance with the programs stored in the memory 320. The processor 310 operates in accordance with the programs to realize various functional units. For example, the processor 310 functions as the control unit 130, specifically, the performance data extraction unit 131, the simulation model division unit 132, the performance reflection unit 133, the simulation execution unit 134, and the simulation error calculation unit 135, in accordance with the respective programs. The processor 310 operates in accordance with a user interface program 326 to function as the input unit 110 and the display unit 140.

[0023] The auxiliary storage device 330 is configured with a large capacity storage device such as a hard disk drive or solid state drive, and is used to retain programs and data for a long period of time. The auxiliary storage device 330 stores the production performance data table 210, the production process data table 220, the equipment data table 230, the worker data table 240, the production start sequence rule model data table 250, the equipment allocation rule data table 260, the worker allocation rule data table 270, and the simulation result data table 280.

[0024] A production performance data table 210 is an example of information stored in the production performance data storage area 121. A production process data table 220 is an example of information stored in the production process data storage area 122. An equipment data table 230 and a worker data table 240 are examples of information stored in the production resource data storage area 123.

[0025] The production start sequence rule model data table 250, the equipment allocation rule data table 260, and the worker allocation rule data table 270 are examples of information stored in the production control rule data storage area 124. The simulation result data table 280 is an example of information stored in the simulation result data storage area 125.

[0026] For convenience of explanation, the programs 321 to 326 are stored in the memory 320, and the tables 210, 220, 230, 240, 250, 260, 270, and 280 are stored in the auxiliary storage device 330, but the storage location of the data in the production simulation device 100 is not limited. For example, the programs and data stored in the auxiliary storage device 330 are loaded into the memory 320 at startup or when needed, and the processor 310 executes the programs, thereby performing various processes in the production simulation device 100. Therefore, in the following, the terms functional unit, program, processor 310, and subject of the process by the production simulation device 100 are interchangeable.

[0027] The network interface 340 is an interface for connecting to a network. The production simulation device 100 communicates with other devices in the system via the network interface 340. The input device 351 is a hardware device that allows a user to input instructions, information, etc., and includes, for example, a keyboard and a pointing device. The output device 352 is a hardware device that displays various images for input and output, such as a display device.

[0028] The production simulation device 100 includes one or more processors and one or more memory devices. Each processor may include a single or multiple arithmetic units or processing cores. A processor may be implemented as, for example, a central processing unit, a microprocessor, a microcomputer, a microcontroller, a digital signal processor, a state machine, a logic circuit, a graphics processing unit, a system on a chip, and / or any device that manipulates signals based on control instructions.

[0029] The functions of the production simulation device 100 may be implemented by distributed processing using a computer system including a plurality of computers. The plurality of computers communicate with each other via a network to execute processing in a cooperative manner.

[0030] 2 shows an example of the configuration of a production performance data table 210. The production performance data table 210 has a job ID column 211, an item ID column 212, a process number column 213, a process ID column 214, a start time column 215, a completion time column 216, an equipment ID column 217, a worker ID column 218, and an attribute information column 219. Each row of the production performance data table 210 is identified by a job ID and a process number.

[0031] The job ID column 211 stores information that identifies each production job (also simply called a job). A job represents an object to be processed in a production process. The item ID column 212 stores information that specifies the item of the job. The process number column 213 stores information that specifies the order of the process in which the item should be processed. The process ID column 214 stores information that specifies the process of the process number of the item.

[0032] In this embodiment, the process ID is unique for each combination of an item ID and a process number, and the combination of an item ID and a process number is unique for each process ID. Each process in each job is called a task. In other words, one row in the production performance data table 210 corresponds to one task.

[0033] The start time column 215 and completion time column 216 store information on the actual start time and actual completion time of the process, respectively. The equipment ID column 217 and worker ID column 218 store information identifying the equipment and worker that processed the process of the job, respectively. The attribute information column 219 stores attribute information related to the job and the process, such as the product name, size, and delivery date of the job, and the required completion time of the process of the job. Figure 3 shows an example of the configuration of a production process data table 220. The production process data table 220 has a process ID column 221, a process time column 222, one or more assignable equipment ID columns 223, and one or more assignable worker ID columns 224.

[0034] Each row in the production process data table 220 is identified by a process ID. The process ID column 221 stores information identifying the process. The process time column 222 stores information indicating the time required to process the process. The assignable equipment ID column 223 and the assignable worker ID column 224 store information identifying the equipment and worker that can process the process, respectively.

[0035] 4 shows an example of the configuration of the equipment data table 230. The equipment data table 230 has an equipment ID column 231, an operation start time column 232, and an operation end time column 233. The equipment ID column 231 stores information for identifying equipment. The operation start time column 232 and the operation end time column 233 store the times when the equipment starts and ends operation, respectively.

[0036] 5 shows an example of the configuration of the worker data table 240. The worker data table 240 has a worker ID column 241, an operation start time column 242, and an operation end time column 243. The worker ID column 241 stores information that identifies the worker. The operation start time column 242 and the operation end time column 243 store the times when the worker starts and ends work, respectively.

[0037] The production start sequence rule data table, the equipment allocation rule data table, and the worker allocation rule data table shown in FIGS. 6, 7, and 8 are stored.

[0038] 6 shows an example of the configuration of the production start sequence rule model data table 250. The production start sequence rule model data table 250 has an equipment ID column 251 and a production start sequence rule ID column 252. The equipment ID column 251 stores information identifying equipment. The production start sequence rule column 252 stores information identifying the production start sequence rule for that equipment. The production start sequence rule is a rule used when determining the next job to be processed from among jobs waiting to be processed in a certain equipment, and typical rules include first-in, first-out, and due date order.

[0039] 7 shows an example of the configuration of the equipment allocation rule data table 260. The equipment allocation rule data table 260 has a process ID column 261 and an equipment allocation rule ID column 262. The process ID column 261 stores information that identifies a process. The equipment allocation rule column 262 stores information that identifies the equipment allocation rule for that process. When multiple pieces of allocatable equipment are defined for one process in the production process data table 220, the equipment allocation rule is a rule that determines which piece of equipment is to be assigned to each task that corresponds to that process.

[0040] 8 shows an example of the configuration of the worker assignment rule data table 270. The worker assignment rule data table 270 has a process ID column 271 and a worker assignment rule ID column 272. The process ID column 271 stores information that identifies a process. The worker assignment rule ID column 272 stores information that identifies the worker assignment rule for that process. A worker assignment rule is a rule that determines which worker is to be assigned to each task that corresponds to a process when multiple assignable workers are defined for that process in the production process data table 220.

[0041] 9 shows an example of the configuration of the simulation result data table 280. The simulation result data table 280 has a simulation model ID column 281 and a simulation error column 282. The simulation model ID column 281 stores information that identifies a simulation model. The simulation error column 282 stores information that indicates the error of the simulation using the simulation model.

[0042] 10 is a flowchart showing a series of processes in the control unit 130. The processes of this embodiment will be described below with reference to this flowchart.

[0043] Steps S100 to S200 are processes performed by the performance data extraction unit 131. First, in step S100, the performance data extraction unit 131 acquires the start time and end time of the simulation period input by the user through the input unit 110. The start time and end time of the simulation period are respectively defined as ts and t f Let's say.

[0044] Next, in step S200, the performance data extraction unit 131 extracts production performance data of the job group processed during the simulation period from the production performance data table 210. Hereinafter, the production performance data extracted by this process will be referred to as target performance data.

[0045] Step S300 is a process performed by the simulation execution unit 134 and the simulation error calculation unit 135. The simulation execution unit 134 calculates the simulation error for the simulation period t s ~t f The simulation error calculation unit 135 calculates the simulation error by comparing the simulation result with the target performance data. s ~t f The simulation model for the simulation of whole It is called.

[0046] When executing a simulation, the simulation start time t s It is necessary to identify the state of the production line in the simulation period t (hereinafter referred to as the initial state). Here, the state of the production line represents information on job groups waiting to be processed in a process, job groups in process, and information on assigned equipment and workers. This information can be identified from the target performance data mentioned above. In addition, when executing a simulation, it is necessary to identify the state of the production line in the simulation period t s ~t f This information can also be identified from the target performance data mentioned above.

[0047] In this embodiment, the simulation error E is calculated using the following equation 1.

[0048]

[0049] Here, N taskrepresents the total number of tasks in the simulation. act k and t sim k represent the actual and simulated completion times of the kth task, respectively. Hereafter, the error of the overall simulation is referred to as E whole It is called.

[0050] Steps S400 to S500 are processes performed by the simulation model dividing unit 132. In this embodiment, the simulation model dividing unit 132 divides the overall simulation model in two stages, from the time perspective and the production resource perspective, to obtain a plurality of submodels.

[0051] The time-perspective model division process will be described below. First, in step S400, the simulation model division unit 132 divides the simulation model into time-perspective divisions N T Next, in step S500, the simulation model dividing unit 132 obtains the simulation period t s ~t f N T Divide into equal pieces.

[0052] The division method is not limited to this. Here, the start time and the end time of each divided period are respectively set as t s i and t f i (i=1, 2,..., N T ) and period t s i ~t f i The model for simulating the above is the submodel M. i This division allows each submodel to handle only a portion of the tasks in the overall simulation.

[0053] Specifically, submodel M i is the period t on the target actual data. s i ~t f i The simulation starts at time ts i Information on the initial state of the production line in period t s i ~t f i The job to be input to the production line and the information on the input time can be identified from the target performance data. Therefore, the simulation of each sub-model can be performed independently, and sub-models with large simulation errors can be identified.

[0054] Next, the division process from the viewpoint of production resources will be described. In this process, each sub-model obtained by the time-perspective model division described above is further divided into multiple sub-models from the viewpoint of production resources. i The simulation model obtained by dividing the above from the viewpoint of resources is called the submodel M. i,j (j=1, 2, ..., N R i , N R i is the number of divisions).

[0055] Here, when dividing the simulation model, the simulation model dividing unit 132 divides the model so that production resources are not shared among multiple sub-models. In this embodiment, two division methods will be described: process data-based division and production performance data-based division.

[0056] In the process data based division, the simulation model division unit 132 first divides the sub-model M i The process group targeted by this sub-model is obtained from the task group targeted by the simulation model division unit 132. Next, the simulation model division unit 132 divides the process group into a plurality of sub-process groups. At this time, the sub-process groups are defined so that an arbitrary process X and an arbitrary process Y that belongs to a sub-process group different from process X do not share assignable equipment or workers. Then, the simulation model targeted by the j-th sub-process group is defined as the sub-model M. i,j Let's say.

[0057] In the production performance data-based division, the simulation model division unit 132 divides the sub-model M iThe task group to be handled by is divided into multiple subtask groups. At this time, the subtask groups are defined so that the equipment and workers in the production performance data of any task X are different from the equipment and workers in the production performance data of any task Y that belongs to a subtask group different from task X. Then, the simulation model for the j-th subtask group is called the submodel M. i,j Let's say.

[0058] By the above division, each submodel M i,j The simulations can be performed independently, and sub-models with large simulation errors can be identified. The two methods of process data-based division and production performance data-based division may be switched by user input, or the two methods may be executed automatically, and there are no particular limitations on how they are used.

[0059] Step S600 is a process performed by the simulation execution unit 134 and the simulation error calculation unit 135. In step S600, the simulation execution unit 134 calculates each sub-model M i and each sub-model M by dividing the production resource viewpoint i,j Run the simulation.

[0060] For a sub-model with no preceding process or task, a job is submitted according to the performance data. The allocation rules for production resources (equipment and workers) are adjusted as necessary to prevent the sharing of production resources (equipment and workers) between sub-models based on production performance data division.

[0061] The simulation error calculation unit 135 calculates the error E of each sub-model using Equation 1. i , E i,j The calculation results are stored in the simulation result data table 280.

[0062] Step S700 is a process performed by the performance reflecting unit 133. This process reflects information extracted from the production performance data table 210 for elements such as the process time and production control rules of each sub-model, thereby generating a new group of sub-models. In this embodiment, an example of a method for reflecting performance information will be described for the process time, the production start sequence rule, the equipment allocation rule, and the worker allocation rule. The elements for reflecting performance information may be determined, for example, by design or according to user specification.

[0063] Regarding the process time, the result reflection unit 133 calculates the time from the process start time to the process completion time in the production result data as the process time of each task and reflects it in the simulation model. In other words, in a newly generated sub-model, the result reflection unit 133 does not use the process time information defined in the production process data table 220, but uses the process time of each task calculated by the above method.

[0064] Regarding the production start order rule, the performance reflecting unit 133 acquires the processing order of each task in each facility from the production performance data table 210 and reflects this in the simulation model. In other words, in a newly generated sub-model, when selecting the next task to be processed from a group of tasks waiting to be processed for a certain facility, the performance reflecting unit 133 does not use the rules defined in the production start order rule model data table 250, but instead selects the task with the earliest performance processing order from the group of tasks waiting to be processed.

[0065] Regarding the equipment allocation rules, the performance reflecting unit 133 obtains the equipment allocated to each task from the production performance data table 210 and reflects this in the simulation model. In other words, when selecting equipment to be allocated to a certain task in a newly generated sub-model, the performance reflecting unit 133 selects the equipment allocated based on the performance of the task without using the rules defined in the equipment allocation rule data table 260. The worker allocation rules are the same as the equipment allocation rules.

[0066] In step S700, a new group of sub-models is created by switching between reflecting and not reflecting actual results for each of the process time, start sequence rule, equipment allocation rule, and worker allocation rule of each sub-model. i,j The newly created sub-model is called the sub-model M a,b,c,d i,j Here, a, b, c, and d are 0 or 1, respectively, which indicate whether or not actual results are reflected in the process time, start sequence rule, equipment allocation rule, and worker allocation rule, with 1 meaning that actual results are reflected.

[0067] For example, M 1,0,0,0 i,j is submodel M i,j represents a model that reflects actual results only in process time, and M 0,0,0,0 i,j is M i,j The multiple sub-models M obtained by the above are equivalent to a,b,c,d i,j By comparing the errors of M, it is possible to identify the large factors that contribute to the error. 1,1,1,1 i,j For the error of M 0,1,1,1 i,j If the error of is large, M i,j It can be interpreted that one of the main causes of error in the calculation is the process time.

[0068] Step S8000 is a process performed by the simulation execution unit 134 and the simulation error calculation unit 135. In step S800, the simulation execution unit 134 executes the simulation for each of the sub-models M a,b,c,d i,j The simulation error calculation unit 135 calculates the error of each sub-model M a,b,c,d i,j Error E a,b,c,d i,j The calculation results are stored in the simulation result data table 280.

[0069] It is possible to omit dividing the overall simulation model from the time perspective and / or dividing the overall simulation model from the production resource perspective. A new overall simulation model may be generated by reflecting the performance information in the overall simulation model. i A new sub-model may be generated by reflecting the results in the above.

[0070] The generation of a new overall simulation model or a new sub-model by reflecting performance information on the overall simulation model or the sub-model may be omitted. The process of S600 or S700 may be omitted for a specific type of sub-model. For example, the process of S600 may be omitted for a sub-model based on process data division, and the processes of S700 and S8000 may be executed instead.

[0071] As described above, by calculating the error between actual production results and simulation results, it is possible to identify the cause of the error and achieve a highly accurate production simulation. This makes it possible to improve the feasibility and optimality of production plans formulated using production simulation. Furthermore, by using actual production results instead of some of the information that can be estimated in simulation during the simulation period, such as time perspective division, production resource perspective division, and reflection of actual production results, it becomes possible to more easily identify factors that have a large impact on the error.

[0072] In addition, by dividing a production simulation model into multiple sub-models that can be simulated independently of each other, such as with time-perspective division and production resource-perspective division, and evaluating the simulation error for each sub-model, it is possible to identify sub-models with large errors. New models are generated by reflecting information extracted from production performance data on model elements such as process time and production control rules in the overall simulation model or sub-models, and model elements that have a large impact on errors can be identified by comparing the errors when the production performance information is reflected and when it is not.

[0073] 11A and 11B show examples of a display screen of information from the storage unit 120 by the display unit 140. Each of Fig. 11A and Fig. 11B shows a portion of one display screen. As shown in Fig. 11A, the screen displayed by the display unit 140 includes, for example, an overall simulation result display area 141, a time-perspective divided sub-model simulation result display area 142, a pre-division model selection area 143, and a production resource-perspective divided sub-model simulation result display area 144. As shown in Fig. 11B, the screen further includes, for example, a pre-performance model selection area 145, a performance-reflected sub-model simulation result display area 146, and a model element-specific evaluation result display area 147.

[0074] The overall simulation result display area 141 displays the overall simulation model M whole The time-perspective divided sub-model simulation result display area 142 displays the simulation results of each sub-model M divided by time perspective. i The production resource perspective divided sub-model simulation result display area 144 displays the sub-model M selected in the pre-division model selection area 143. i Sub-model M divided from the viewpoint of production resources i,j Display the simulation results.

[0075] The performance reflected sub-model simulation result display area 146 displays the sub-model M selected in the performance not reflected model selection area 145. i,j Sub-model M reflecting the actual data a,b,c,d i,j The model element evaluation result display area displays information indicating the degree of influence of each model element, such as process time, on the simulation error.

[0076] For example, in the example shown in FIG. i,j The comparison results of errors when the actual results are reflected and when they are not reflected in each model element are displayed. Here, for example, the "average error with reflected actual results" and "average error without reflected actual results" in the "process time" row of the model element evaluation result display area 147 in FIG. 11B are values ​​calculated by the following formulas 2 and 3, respectively.

[0077]

[0078]

[0079] In other words, the average error with (without) performance reflection represents the average value of the errors of all sub-models that reflect (or do not reflect) performance information for the target model element. i,j This is useful for identifying factors that have a large impact on the error of a measurement.

[0080] 12 is a schematic diagram of a production simulation system according to this embodiment. As shown in the figure, the production simulation system includes a production simulation device 100, a production performance information management device 200, and a production condition information management device 300, which can transmit and receive information via a network 400. The production performance information management device 200 transmits production performance data to the production simulation device 100. In addition, the production condition information management device 300 transmits process data, production resource data, production control rule data, etc. to the production simulation device 100.

[0081] 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 to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0082] Furthermore, some or all of the above-mentioned configurations, functions, processing units, etc. may be implemented in hardware, for example, by designing them as integrated circuits. Furthermore, the above-mentioned configurations, functions, etc. may be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function can be stored in memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card or SD card. Furthermore, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines in the product. In reality, it can be assumed that almost all configurations are interconnected.

Claims

1. A production simulation system is used to estimate the progress of a production line stage. This system consists of one or more processors and one or more log systems. These log systems store production log information, which includes information on the recorded start and end times of each production task stage, and a simulation model containing information on the step time of each stage, the production resource groups that can be allocated to each stage, the operating time of each production resource for each group of production resources, and the production control rules for the production line. The one or more processors use this production log information and the simulation model to perform a simulation and compare the production log information with the simulation results to calculate the simulation error.The production simulation suite specified in Claim 1 is a production simulation suite in which one or more processors use information extracted from production log information instead of a portion of the information that can be estimated in such simulation.

3. The production simulation suite specified in Claim 1 is a production simulation suite in which one or more processors subdivide such simulation model into numerous independent submodels, perform simulations with each of these numerous submodels, and compare the production log information with the results of each of these numerous submodels to calculate the simulation error. 4.The production simulation set specified in Claim 3 is a production simulation set in which, the production process consists of tasks, numerous submodels, and targets each of these numerous subtasks, the production resources in the production record information of any task among these numerous subtasks differ from the production resources in the production record information of any task among these numerous subtasks.

5. The production simulation set specified in Claim 3 is a production simulation set in which, numerous submodels, and targets each of these numerous sub-processes, the production resources that can be allocated in the simulation model of any step among these numerous sub-processes differ from the production resources that can be allocated in the simulation model of any step among these numerous sub-processes.

6. The production simulation set specified in Claim 3 is a production simulation set in which, numerous submodels, and each of these numerous submodels, and targets the time period from which the simulation time of the simulation model is subdivided. 7.The production simulation suite specified in Claim 1 is a production simulation suite in which one or more such processors construct numerous new simulation models by reflecting information extracted from production log information to at least some of, among other things, the step times in such simulation models, the allocateable production resource groups, the execution times of such allocateable production resources, and the production control rules, and then compare the results between such production log information and the results of each of these numerous new simulation models to calculate the simulation error.

8. The production simulation suite specified in Claim 1 is a production simulation suite in which one or more such processors display such simulation error.9.Production simulation methods use simulation software to estimate the progress of production line steps. This software stores production logs, including record start and end times for each production task, and a simulation model containing information on the step times of each step, the groups of production resources available for each step, the operating time of each production resource and its group, and the production control rules for the line. The software uses these production logs and the simulation model to perform a simulation, comparing the production logs with the simulation results to calculate simulation errors.