Planning logic evaluation support system and method

Abstract

To provide a tool that facilitates the introduction of a scheduler.SOLUTION: According to an aspect of the present invention, a scheduling logic evaluation assisting system includes a control unit, a storing unit, an input unit, and an output unit. The scheduling logic evaluation assisting system also includes: a feature analyzing unit for a process of extracting the feature of the process from master data; a productivity reduction factor extracting unit that extracts, based on the feature of the process, a focused characteristic parameter that affects a productivity; and a test production data generating unit that generates test production data which has the focused characteristic parameter distributed within a predetermined range.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technology for introducing logic into a system for creating plans such as production plans. [Background technology]

[0002] In recent years, there has been a demand for production execution that can quickly respond to changes in the manufacturing environment, and schedulers have become increasingly important. A production scheduler, one example of a scheduler, is a system or software that optimally allocates raw materials and production resources to meet demand.

[0003] For example, in the case of industrial product production, a production scheduler inputs the amount of resources such as materials, processing machines, and workers, as well as the timing of receipt and delivery, and the specifications and delivery dates of the products to be produced, and determines the optimal production schedule (production plan).

[0004] The operation of a production scheduler involves the steps of modeling the input, designing and implementing logic to determine the rules the plan will follow, and then creating a production plan using the production scheduler. After creating the plan, work instructions are issued and progress is managed according to the plan.

[0005] The production scheduler itself has already been commercialized and is available on the market. For example, Patent Document 1 describes the logic design and implementation.

[0006] Patent Document 2 aims to provide a technology that supports the identification of a scheduling method according to the characteristics of a manufacturing process, and describes a feature extraction unit that extracts feature information that is a feature of the manufacturing process from manufacturing information related to the manufacture of a product.

[0007] Patent Document 3 discloses a technique for determining whether the number of test cases for testing logic is excessive or insufficient. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Patent Publication No. 2021-11193 [Patent Document 2] WO 2017 / 103996 A1 [Patent Document 3] Japanese Patent Application Laid-Open No. 2007-26360 Summary of the Invention [Problem to be solved by the invention]

[0009] When introducing a production scheduler, just like designing a general system, workers first organize requirements such as the KPIs (key performance indicators) they want to improve and the characteristics of the production line. To create a production scheduler that meets these requirements, they model the production line and design logic for formulating production plans for it. The production scheduler is configured according to these models and logic, and then implemented in the production planning system for operation.

[0010] However, the tasks of modeling the manufacturing line and designing the planning logic depend on know-how, which hinders the introduction of schedulers. For example, when trying to introduce a scheduler, there are so many settings for modeling and planning logic that only experienced personnel can set them up.

[0011] The setting items that need to be configured include, for example, setting dispatching rules for equipment, selection criteria and allocation direction for the order in which resources are selected, setting lot size, whether or not to split lots, and resource occupancy conditions.

[0012] These settings are hurdles that hinder the adoption of schedulers and require long-term work to implement.

[0013] Therefore, an object of the present invention is to provide a tool that makes it easy to introduce a scheduler. [Means for solving the problem]

[0014] One aspect of the present invention is a planning logic evaluation support system including a control unit, a memory unit, an input unit, and an output unit, and including a process feature analysis unit that extracts process features from master data, a productivity reduction cause extraction unit that extracts characteristic parameters of interest that affect productivity based on the process features, and a test production data generation unit that generates test production data in which the characteristic parameters of interest are dispersed within a predetermined range.

[0015] Another aspect of the present invention is a planning logic evaluation support method executed by an information processing device having a control unit, a storage unit, an input unit, and an output unit, and including: a process feature analysis step of extracting process features from master data; a productivity reduction factor extraction step of extracting characteristic parameters of interest that affect productivity based on the process features; and a test production data generation step of generating test production data in which the characteristic parameters of interest are dispersed within a predetermined range. [Effects of the Invention]

[0016] According to the present invention, a tool that makes it easy to introduce a scheduler can be provided. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a block diagram showing the overall configuration of a plan logic evaluation support device according to an embodiment; [Figure 2] FIG. 10 is a table showing an example of a process master. [Figure 3] FIG. 10 is a table illustrating an example of facility master data. [Figure 4] FIG. 10 is a table showing an example of a setup master. [Figure 5] FIG. 3 is a flowchart showing the processing of the plan logic evaluation support device. [Figure 6] FIG. 10 is a flow chart showing an example of details of process feature analysis. [Figure 7] FIG. 1 is a graph diagram showing an example of a graph structure of a process flow. [Figure 8] FIG. 10 is a table showing an example of characteristic data of a process. [Figure 9] FIG. 10 is a table showing an example of data on factors behind decline in productivity. [Figure 10] FIG. 10 is a flow diagram showing details of the generation of production test data. [Figure 11] Conceptual diagram of test production data generation. [Figure 12] An image of the screen used to generate production data for testing the planning logic. DETAILED DESCRIPTION OF THE INVENTION

[0018] The following describes the embodiments in detail with reference to the drawings. However, the present invention should not be construed as being limited to the description of the embodiments shown below. Those skilled in the art will readily understand that the specific configuration can be modified within the scope of the concept and spirit of the present invention.

[0019] In the configuration of the invention described below, the same parts or parts having similar functions are denoted by the same reference numerals in different drawings, and redundant explanations may be omitted.

[0020] When there are multiple elements having the same or similar functions, they may be described using the same reference numeral with different subscripts. However, when there is no need to distinguish between multiple elements, the subscripts may be omitted.

[0021] The designations "first," "second," "third," etc. in this specification are used to identify components and do not necessarily limit the number, order, or content thereof. Furthermore, numbers used to identify components are used in different contexts, and numbers used in one context do not necessarily indicate the same configuration in another context. Furthermore, this does not prevent a component identified by a certain number from also serving the function of a component identified by another number.

[0022] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings etc. may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings etc.

[0023] All publications, patents, and patent applications cited herein are incorporated by reference in their entirety.

[0024] As mentioned earlier, when introducing a production scheduler, the production line is modeled based on master data that represents the specifications of the products, equipment, and processes, and logic is designed to create an effective production plan for that. When designing the logic, it is necessary to set various configuration items (dispatching rules, resource selection criteria and allocation direction, lot size settings, whether or not to split lots, or resource occupancy conditions, etc.). Once the logic is designed, it is implemented in the production scheduler and test production data is entered to perform scheduling. The schedule results are evaluated based on desired KPIs (operating rate, delivery date adherence rate, etc.) to evaluate (test) whether the logic is actually effective. If the evaluation results are favorable, it is implemented and put into actual operation. If the logic is not effective, the configuration items are revised and the logic is redesigned. Test (or actual) production data includes, for example, the production items, quantities, and delivery dates.

[0025] In the planning logic evaluation process when introducing the scheduler described above, it is necessary to create test production data that covers situations where productivity is likely to decline, and to verify the planning results against that data. However, if test production data is created and evaluated using past performance, it may not necessarily include data on situations where productivity is likely to decline, making verification impossible. For example, even if the utilization rate is evaluated based on last year's performance data, when takt time balance between equipment was achieved, it is not possible to verify if the balance worsens this year.

[0026] To avoid insufficient verification, it is necessary to prepare test production data that covers all situations where productivity is likely to decrease, but this requires know-how.To make verification easier, it is necessary to create test production data without know-how.

[0027] At this time, it is possible to generate test production data by assigning parameters that will be input to the scheduler. However, if there are an infinite number of parameters, the number of test cases will be enormous, making it difficult to evaluate the results.

[0028] In the following embodiment, the situations to be tested are narrowed down and test production data is created so as not to omit situations that are likely to reduce productivity, and the planning results for that data are evaluated.The inventors have made it possible to narrow down the situations to be tested by focusing on the fact that, from a productivity perspective, there is a correspondence between process flow characteristics and factors that reduce productivity.This embodiment proposes to simplify the implementation of schedulers by providing a technology to support the evaluation of planning logic when implementing an implementation scheduler.

[0029] To give a specific example, the system of the embodiment identifies, based on the characteristics of a production process, a parameter (referred to as a target characteristic parameter) that has a large effect on productivity (e.g., expressed as a KPI) in the production process having those characteristics. Test points are set so that the target characteristic parameter varies within a predetermined range, and production data having the target characteristic parameter of the test point is generated as test production data. [Example]

[0030] 1 is a block diagram showing the overall configuration of a plan logic evaluation support device according to an embodiment. The plan logic evaluation support device 100 can generally be configured as an information processing device such as a server. As with a typical server, the hardware configuration includes a storage unit 110, a control unit 120, an input unit 130, an output unit 140, a communication unit 150, and the like.

[0031] In this embodiment, the storage unit 110 can be configured from various combinations depending on the application, such as a volatile semiconductor memory such as a DRAM (Dynamic Random Access Memory), a magnetic disk device, a non-volatile semiconductor memory, and the like.

[0032] The control unit 120 is a unit that performs various processes, and is realized, for example, by a central processing unit executing a program stored in the memory unit 110. In Fig. 1, the functions implemented in software are expressed as functional blocks: a process characteristic analysis unit 121, a productivity decline factor analysis unit 122, a test production data generation unit 123, a productivity decline factor display unit 124, and a test case list display unit 125. The functions of each unit will be explained later together with the processing flow.

[0033] The input unit 130 may use commonly known components such as a keyboard or mouse. The output unit 140 may use commonly known components such as a display device or printer. The communication unit 150 is an interface for communicating with external resources of the plan logic evaluation support device 100.

[0034] The plan logic evaluation support device 100 can communicate with a production planning system 300 via an external network 200 using a communication unit 150. The network 200 may be configured as a wired or wireless network. Note that each element of the plan logic evaluation support device 100 in FIG. 1 can also be configured as an external information processing device connected via the network 200, and does not necessarily have to be configured as a single information processing device. For example, it can also be configured as a cloud.

[0035] The production planning system 300 can be configured with an information processing device such as a server, and the production scheduler described above is implemented as software. The production planning system 300 stores in advance master data 310, for example, information on production resources such as manufacturing machines and the number of workers, information on the products to be produced, information on the parts used in the products, information on the order of processes, etc. The production environment is then reproduced using the master data 310.

[0036] The production planning system 300 outputs a production plan that determines when, how much, what, how many people, and which production equipment to use to produce, in order to minimize manufacturing costs or maximize throughput, for example. The production scheduler generates planning logic for outputting the production plan based on the master data 310 and the settings set by the user.

[0037] Production schedulers themselves are already commercially available, and their basic configuration is publicly known, so a detailed explanation will be omitted. A production scheduler generally sets the product items, quantities, and delivery dates to be produced based on the above master data, and allocates multiple manufacturing orders to production resources in an allocation sequence that follows pre-set settings. The allocation results are then visually output, for example, as a Gantt chart.

[0038] When generating planning logic, operators must set constraints, such as the priority of allocation order when multiple production resources are available, the priority of order when multiple tasks are involved, production lot size, setup time, and travel time between processes.These settings often reflect the know-how of experienced workers who understand the situation on-site.

[0039] The performance of the planning logic depends on the content of the setting items, but generally, once the planning logic is created, it is tested and evaluated, and the generation of planning logic is repeated until a satisfactory evaluation is obtained. In this embodiment, a method for appropriately evaluating the planning logic is proposed.

[0040] The configuration and operation of the plan logic evaluation support device 100 in Fig. 1 will be described in order. The master information storage unit 111 stores, for example, a copy of all or part of the master data 310 of the production planning system 300. In this example, the master information storage unit 111 includes a process master, an equipment master, and a setup master.

[0041] FIG. 2 is a table diagram showing an example of a process master. The process master 1111 stores data that represents the production process of the item to be produced. In the example of the process master 1111 in FIG. 2, the item ID of the item to be produced is associated with the process ID of the process required for production and the equipment ID of the equipment required for that process. The process master 1111 is assumed to be digitized in advance by the business operator that performs production.

[0042] FIG. 3 is a table diagram showing an example of an equipment master. The equipment master 1112 stores data related to the equipment used in the production process. In the example of the equipment master 1112 in FIG. 2, the specifications of each piece of equipment are stored for the equipment ID of that piece of equipment. In the example of FIG. 3, the capacity of a buffer set up upstream in the process of each piece of equipment is stored as an example. A specific example of a buffer is a work-in-progress storage area placed in front of the equipment. The equipment master 1112 is assumed to be digitized in advance by the production company.

[0043] FIG. 4 is a table diagram showing an example of a setup master. The setup master 1113 stores data related to setup in the production process. Here, setup refers to the work of changing the installation and settings of processing machines, jigs, devices, etc. to suit the product to be manufactured. In the example of the setup master 1113 in FIG. 4, the previous item ID of the previous item, the next item ID of the next item, and the setup time required for the setup are stored for the equipment ID of the equipment that performs the setup. The setup master 1113 is assumed to be digitized in advance by the production company.

[0044] The data examples in Figures 2 to 4 are simplified, and other information may be included in actual use. The plan logic evaluation support device 100 of this embodiment analyzes the characteristics of the production process based on the above master data and extracts factors that cause productivity decline from the analyzed production process characteristics. Then, test production data that is sensitive to the productivity decline is generated. Note that in this embodiment, an example has been shown in which the process master 1111, equipment master 1112, and setup master 1113 are used to analyze the characteristics of the production process, but other data related to the production process may be used instead of or in addition to these data.

[0045] 5 shows an example of a processing flow of the plan logic evaluation support device 100. The process feature analysis unit 121 performs process feature analysis S400 based on the master information in the master information storage unit 111. The productivity decline factor analysis unit 122 performs productivity decline factor analysis S500 based on the results of the feature analysis S400. The test production data generation unit 123 generates test production data S600 based on the results of productivity decline factor analysis S500.

[0046] An example of a detailed flow of the process characteristic analysis S400 is shown in Fig. 6. First, the process characteristic analysis unit 121 reads out the process master 1111, the equipment master 1112, and the setup master 1113 from the master information storage unit 111 (S401).

[0047] The process feature analysis unit 121 generates a graph structure of the connection relationships between equipment, buffers, and setups from the information of the read process master 1111, equipment master 1112, and setup master 1113 (S402).

[0048] Fig. 7A is an example of a graph structure of a process flow generated by the process feature analysis unit 121 based on the data in Fig. 2, Fig. 3, and Fig. 4. To generate the graph structure, it is sufficient to connect the equipment in the order of the process IDs. In the graph structure of Fig. 7, symbols indicated by white circles indicate equipment (without setup), symbols indicated by black circles indicate buffers, and symbols indicated by gray circles indicate equipment (with setup).

[0049] The equipment master 1112 in Fig. 3 indicates the buffers placed before the equipment. From the equipment master 1112 in Fig. 3, it can be seen that the capacity of each buffer placed before the equipment "M1", "M2", and "M3" is 10 (arbitrary units), and that there is no buffer before the equipment "M4".

[0050] From the process master 1111 in Figure 2, it can be seen that both equipment IDs "M1" and "M2" can be used in the process with process ID "Proc1." Therefore, the graph structure of the process flow in A in Figure 7 branches from the first buffer (capacity 10) to equipment "M1" and "M2."

[0051] From the process master 1111 in Figure 2, we can see that only equipment ID "M3" can be used in the next process with process ID "Proc2." Therefore, in the graph structure of the process flow, the flow that branches into "M1" and "M2" merges with "M3." From the equipment master in Figure 3, we can see that a buffer is placed before "M3."

[0052] 4, it can be seen that "M3" is equipment with setup, and that setup is required when changing the production item. Therefore, "M3" is shown with a gray circle, which indicates equipment with setup.

[0053] From the process master 1111 in Figure 2, we can see that the next process after "M3" is "M4" and that "M4" does not have a buffer. Therefore, there is no buffer between "M3" and "M4". It is a rule that a buffer must be provided at the end of each process.

[0054] In the above example, buffer information is stored in the equipment master 1112, but versatility can also be improved by providing a separate buffer master that associates equipment IDs with buffer IDs. Also, in the above example, a separate setup master 1113 is provided, but setup information may be added to the equipment master 1112. In other words, the data format is free as long as it reflects the characteristics of the process.

[0055] Next, returning to FIG. 6, the process feature analysis unit 121 divides the graph structure of the process flow by buffers and decomposes it into modules (S403).

[0056] FIG. 7B shows the graph structure of FIG. 7A cut at the buffer indicated by the black circle, and two modules extracted.

[0057] Next, the process feature analysis unit 121 compares the extracted module with the process feature data stored in the process feature library storage unit 112, and extracts the features of the process flow (S404).

[0058] 8 shows an example of process feature data 800 stored in the process feature library storage unit 112. Features are stored corresponding to the graph structure of a partial process. The process feature data 800 is created in advance by an expert based on on-site know-how and stored as library data.

[0059] In the example of Figure 8, a graph in which equipment is in a parallel structure is associated with a job shop, which is a process characterized by grouping and organizing machinery and equipment with similar functions and performance (#1). Also, a graph in which equipment is in a serial structure is associated with a flow shop, which is a process characterized by organizing equipment according to this flow, if the processing route is the same (#2). Furthermore, if setup equipment exists, the setup equipment is associated (#3).

[0060] In this embodiment, as a result of the feature extraction by the process feature analysis unit 121, it is determined that the target process includes the features of a job shop, a flow shop, and a setup facility (S405).

[0061] In the above embodiment, as shown in Fig. 7, a process is divided into modules, and the modules are compared with process feature data 700 to extract features. As another example, features can be extracted by using a neural network or the like that has undergone machine learning, with the process master, equipment master, and setup master data as input to the neural network and the process features as output. Well-known supervised learning can be used as machine learning, but details will be omitted here.

[0062] Returning to FIG. 5, the productivity decline factor analysis unit 122 performs a productivity decline factor analysis based on the result of feature extraction received from the process feature analysis unit 121, ie, the process flow features (S500).

[0063] The productivity decline factor analysis unit 122 refers to the productivity decline factor library storage unit 113 and identifies the factors of productivity decline corresponding to the characteristics of the process flow.

[0064] 9 shows an example of productivity reduction factor data 900 stored in the productivity reduction factor library storage unit 113. Factors of productivity reduction are stored in accordance with the characteristics of the process flow. The productivity reduction factor data 900 is created in advance by an expert with knowledge of productivity reduction factors based on on-site know-how, and is stored as library data.

[0065] For example, "variation in the number of workpieces that can be assigned to equipment" is stored in response to the "job shop" process flow feature. This data was set by an expert based on on-site know-how that, when a process flow includes a job shop, variation in the number of workpieces (objects to be processed) that can be assigned to equipment can be a factor in reducing productivity. Therefore, the characteristic parameter of interest required to select production test data that will reveal this factor in reducing productivity is "the difference between the maximum and minimum number of workpieces that can be assigned." A specific example of test production data that reduces productivity is data that shows a large variation in the number of workpieces that can be assigned, and in which workpieces that can be assigned only to specific equipment are concentrated.

[0066] Furthermore, when a process flow includes setup equipment, there is on-site know-how that a large number of workpiece setup conditions can be a factor in reducing productivity. Here, the "number of workpiece setup conditions" is the characteristic parameter of interest.

[0067] In this embodiment, characteristic parameters of interest that have a large impact on productivity are identified in accordance with the characteristics of the process flow, and test production data is generated that varies the characteristic parameters of interest. Because it is possible to identify parameters that are meaningful for productivity (input items, input quantities, shifts, etc.), it is possible to narrow down the situations that should be tested, and the planning logic can be efficiently evaluated.

[0068] Returning to FIG. 5, the test production data generation unit 123 generates test production data based on the factors of the productivity decline received from the productivity decline factor analysis unit 122 (S600).

[0069] Fig. 10 shows an example of the detailed flow of the test production data generation S600. First, the test production data generation unit 123 sets the range of the characteristic parameters of interest extracted as a result of the productivity decline factor analysis S500. Here, the "maximum-minimum number of allocatable workpieces" and "number of workpiece setup conditions" are used as examples of the characteristic parameters of interest. Here, the best and worst values ​​are set as the ranges covered by the related parameters.

[0070] A conceptual diagram of the test production data generation S600 is shown in Fig. 11. A in Fig. 11 shows the concept of setting the ranges for "maximum value - minimum value of the number of workpieces that can be assigned" and "number of workpiece setup conditions."

[0071] The greater the variation in the number of workpieces that can be assigned, the lower the productivity, so the "maximum number of workpieces that can be assigned - minimum value" is varied between the maximum value (worst) and the minimum value (best).

[0072] The larger the "number of work setup conditions," the lower the productivity, so the "number of work setup conditions" is varied between the maximum value (worst) and the minimum value (best).

[0073] Generally, the test production data includes data such as the product item, production quantity, and delivery date, but may not directly include the target characteristic parameter. In such cases, the test production data and the target characteristic parameter are associated with each other based on the master information stored in the master information storage unit 111.

[0074] For the "maximum number of assignable workpieces minus minimum number," for example, the equipment used by the production item specified by the test production data is identified from the process master 1111 (Figure 2), and the difference between the maximum and minimum buffer capacity of the equipment is calculated from the equipment master 1112 (Figure 3) and used.

[0075] For the "number of setup conditions for workpieces," the number of setup conditions is searched for in the setup master 1113 (Fig. 4) based on the production items specified by the test production data. For example, if "ProdA," "ProdB," and "ProdC" are the production items, the number of setup conditions is 3, meaning that at least two setups are required, which means that the number of setups is large (worst case). Also, if the production item is only "ProdA," the number of setup conditions is 1, meaning that the number of setups is 0, which means that the number of setups is small (best case). In the case of a more complex master, the number of setup conditions can be determined using the attributes of the items.

[0076] Next, the test production data generation unit 123 sets grid points within the range of the set target characteristic parameter to determine test points (S602). The concept of setting test points is shown in B of Fig. 11. The grid points can be set in any manner, but for example, the range of the set target characteristic parameter can be divided into a predetermined number of equal intervals (for example, four divisions).

[0077] Next, the test production data generation unit 123 generates test production data with the same or similar conditions for each test point. Fig. 11C shows a conceptual diagram of test production data generation. Test production data is generated from master information, with data having a combination close to the values ​​of the characteristic parameters of interest for each test point. For example, at a point where the number of setup conditions is two, a combination of "ProdA" and "ProdB" is generated as production items from the setup master 1113. If there are multiple production data with the same conditions, they can be selected randomly or based on a predetermined rule.

[0078] As mentioned above, if the test production data does not include the characteristic parameter of interest, the test production data and the characteristic parameter of interest may be associated in advance by referring to the master information in the master information storage unit 111. Alternatively, production data that is the same as or close to the characteristic parameter of interest of the test point may be generated from the master information each time. A simple method for selecting the value closest to the test point value is to perform a brute force search on all combinations. Other known search methods may also be used.

[0079] Next, the man-machine interface of the embodiment will be described. The productivity decline cause display unit 124 and test case list display unit 125 in Fig. 1 support the user in generating production data for testing. The productivity decline cause display unit 124 and test case list display unit 125 display information to be shown to the user on a display or the like of the output unit 140. In addition, the productivity decline cause display unit 124 and test case list display unit 125 accept user input from a keyboard or the like of the input unit 130.

[0080] FIG. 12 is an image diagram of a screen for generating test production data for the planning logic, which is displayed on the output unit 140.

[0081] The productivity reduction factor display unit 124 displays the characteristic parameters of interest of the factors of productivity reduction extracted as a result of the productivity reduction factor analysis (S500) on the productivity reduction factor selection screen 1201. The user can select the characteristic parameters of interest as the axes of the test points. In the above embodiment, two axes are used, but one axis or three or more axes may be selected.

[0082] The test case list display unit 125 displays an image corresponding to C in FIG. 11 on the test case list display screen 1202.

[0083] When the user specifies a test point, the test case list display unit 125 displays the contents of the test production data on the test production data display screen 1203 .

[0084] The generated test production data is stored in the test production data storage unit 114. The test production data is input to the production scheduler of the production planning system 300, and the output production plan is evaluated, thereby enabling efficient evaluation of the planning logic.

[0085] As described above, the embodiment proposes a plan logic evaluation support device that includes a process feature extraction unit that extracts process features from master data, a productivity decline factor extraction unit that extracts productivity decline factors based on the process features, and an automatic test production data generation unit that generates test production data with factors assigned to the productivity decline factors. Conventionally, know-how was required to test situations where productivity decline is likely to occur. The technology of the embodiment focuses on the correspondence between process flow features and productivity decline factors, extracts productivity decline factors from the process flow, and generates test production data with a range of factors, enabling know-how-less testing. This enables even non-expert workers to evaluate plan logic, facilitating the introduction of schedulers.

[0086] According to the above embodiment, an efficient production plan can be realized, which reduces energy consumption, reduces carbon emissions, prevents global warming, and contributes to the realization of a sustainable society. [Explanation of symbols]

[0087] Process feature analysis unit 121, productivity decline factor analysis unit 122, test production data generation unit 123, productivity decline factor display unit 124, test case list display unit 125, master information storage unit 111, process feature library storage unit 112, productivity decline factor library storage unit 113, test production data storage unit 114

Claims

1. A control unit, a memory unit, an input unit, and an output unit, a process feature analysis unit that extracts process features from the master data; a productivity reduction factor extraction unit that extracts characteristic parameters of interest that affect productivity based on the characteristics of the process; a test production data generation unit that generates test production data in which the characteristic parameters of interest are dispersed within a predetermined range; Equipped with the storage unit includes a productivity reduction factor library storage unit that stores productivity reduction factor data in which the process features and the target characteristic parameters are associated with each other; the productivity decline factor extraction unit extracts a characteristic parameter of interest by referring to the productivity decline factor data; Planning logic evaluation support system.

2. It also has a display section for the causes of productivity decline, the productivity reduction factor display unit has a function of displaying the extracted characteristic parameters of interest and allowing a user to select one or more of them. The planning logic evaluation support system according to claim 1.

3. It also has a test case list display section, the test case list display unit maps the test production data using the characteristic parameter of interest selected by the user as an axis and displays the data to the user. The planning logic evaluation support system according to claim 2.

4. The test production data generation unit generating test cases in which the values ​​of the characteristic parameters of interest are dispersed for the characteristic parameters of interest within a predetermined range; The planning logic evaluation support system according to claim 3.

5. The test production data generation unit generating production test data having the same value as or the closest value to the characteristic parameter of interest of the test case; The planning logic evaluation support system according to claim 4.

6. A control unit, a memory unit, an input unit, and an output unit, a process feature analysis unit that extracts process features from the master data; a productivity reduction factor extraction unit that extracts characteristic parameters of interest that affect productivity based on the characteristics of the process; a test production data generation unit that generates test production data in which the characteristic parameters of interest are dispersed within a predetermined range; Equipped with the storage unit includes a process feature library that stores the process features as element patterns; the feature analysis unit generates a patterned process from the master data and compares it with the element pattern to extract the process features; Planning logic evaluation support system.

7. An information processing device including a control unit, a storage unit, an input unit, and an output unit, a process feature analysis step of extracting process features from the master data; a productivity reduction factor extraction step of extracting characteristic parameters of interest that affect productivity based on the characteristics of the process; a test production data generating step for generating test production data in which the characteristic parameter of interest is dispersed within a predetermined range; Equipped with the storage unit includes a productivity reduction factor library storage unit that stores productivity reduction factor data in which the process features and the target characteristic parameters are associated with each other; the productivity reduction factor extraction step extracts a characteristic parameter of interest by referring to the productivity reduction factor data; A method to support planning logic evaluation.

8. It also has a step to display the causes of productivity decline, the productivity reduction factor display step displays the extracted characteristic parameters of interest and allows a user to select one or more of them; The planning logic evaluation support method according to claim 7.

9. It also has a step to display a list of test cases. the test case list display step maps the test production data using the characteristic parameters of interest selected by the user as axes and displays the data to the user; The method for supporting evaluation of planning logic according to claim 8.

10. The test production data generating step includes: generating test cases in which the values ​​of the characteristic parameters of interest are dispersed for the characteristic parameters of interest within a predetermined range; The planning logic evaluation support method according to claim 9.

11. The test production data generating step includes: generating production test data having the same value as or the closest value to the characteristic parameter of interest of the test case; The planning logic evaluation support method according to claim 10.

12. the storage unit includes a process feature library that stores the process features as element patterns; the feature analysis step generates a patterned process from the master data and compares it with the element pattern to extract process features; The method for supporting planning logic evaluation according to claim 11.

13. The test production data generating step includes: generating test production data having the same value as or the closest value to the characteristic parameter of interest of the test case based on the master data; The method for supporting planning logic evaluation according to claim 11.

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