Production planning logic analysis method and production planning logic analysis apparatus

Abstract

To provide a production plan logic analysis method and a production plan logic analysis device capable of predicting an aim of improving productivity of a current logic.SOLUTION: A production plan logic analysis method comprises: a production plan creation step of creating a production plan 24 using test production condition data 23 for each of a current logic 21 where an aim of productivity improvement is unknown and a test logic 22 where an aim of productivity improvement is known; and an evaluation step where similarity between the production plan 24 based on the current logic 21 and the production plan 24 based on the test logic 22 is evaluated to estimate the aim of productivity improvement in the current logic 21.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a production plan logic analysis method and a production plan logic analysis device.

Background Art

[0002] In recent years, production execution that can promptly respond to changes in the manufacturing environment has been demanded, and the importance of a scheduler for formulating production plans has been increasing.

[0003] However, although a scheduler has been introduced, the production plan logic (hereinafter sometimes simply referred to as logic) has not been sufficiently designed, and the logic cannot be improved. Therefore, in actual operation, there are many production lines that manually modify the production plan.

[0004] As technologies related to improving the logic of the scheduler, for example, there are Patent Document 1 and Patent Document 2.

[0005] In FIG. 1 and the summary of Patent Document 1, as a problem, "Providing a production plan improvement device for improving the quality of a production plan formulated manually and formulating a production plan that the user can accept in a short time." is described. As a solution, "An initial plan is taken in from an external device using the plan input means 1, the initial plan is used as a plan before improvement, and a plurality of improved plans obtained by changing a part of the plan before improvement are calculated using the plan improvement means 6. The difference display means 4 confirms the difference between the plan before improvement and the improved plan. If there is an improved plan that the device user can accept, that plan is specified again as the plan before improvement, and a plurality of improved plans are calculated again using the plan improvement means 6. When an acceptable improved plan cannot be calculated, the improvement logic of the plan improvement means 6 is corrected using the constraint change means 7 or the evaluation function change means 8. The above processing is repeated a plurality of times, and the best improved plan among the calculated improved plans is output to an external device using the plan output means 3." is described.

[0006] Figure 1 and abstract of Patent Document 2 state that "the purpose is to provide a technology that supports the identification of a scheduling method according to the characteristics of the manufacturing process. The production planning device comprises: a feature extraction unit that extracts feature information which is a characteristic of the manufacturing process from manufacturing information related to the manufacturing of a product; a storage unit that stores logic identification information which associates logic information indicating a scheduling method for the manufacturing of a product with feature information suitable for scheduling, and manufacturing information; a logic extraction unit that extracts logic candidates which are the logic information associated in the logic identification information which is the feature information extracted by the feature extraction unit; a logic identification unit which identifies the logic information to be used for scheduling from the logic candidates; and a scheduling unit which performs scheduling using software determined by the logic information identified by the logic identification unit." [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2008-262486 [Patent Document 2] International Publication No. 2017 / 103996 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] To improve the scheduler's logic, it's necessary to understand the current logic's objectives for productivity improvement—that is, the purpose behind its design—which allows for more efficient logic improvement.

[0009] However, if a third party other than the designer who created the current logic attempts to improve it, they must begin the improvement process without knowing the objective of increasing the productivity of the current logic.

[0010] Regarding the analysis of the objectives for improving the productivity of the current logic, Patent Documents 1 and 2 do not describe this, and no analytical method has been established.

[0011] For example, to analyze the goal of improving the productivity of the current logic, one might consider directly comparing the source code library of the logic with the current logic. However, since there are countless ways to implement the same goal, this method of analysis is not practical.

[0012] The problem that this invention aims to solve is to provide a production planning logic analysis method and a production planning logic analysis device that can predict the objective of improving the productivity of the current logic. [Means for solving the problem]

[0013] To solve the above problems, the production planning logic analysis method of the present invention, for example, A production planning logic analysis method performed by a production planning logic analysis device, The method is characterized by having a production planning step in which a production plan is formulated using test production condition data in both the current logic, where the objective of productivity improvement is unknown, and the test logic, where the objective of productivity improvement is known, and an evaluation step in which the objective of productivity improvement in the current logic is estimated by evaluating the similarity between the production plan based on the current logic and the production plan based on the test logic.

[0014] Furthermore, the production planning logic analysis device of the present invention includes, for example, a production planning unit that formulates a production plan using test production condition data in both the current logic, where the objective of productivity improvement is unknown, and the test logic, where the objective of productivity improvement is known, and an evaluation unit that estimates the objective of productivity improvement in the current logic by evaluating the similarity between the production plan based on the current logic and the production plan based on the test logic. [Effects of the Invention]

[0015] According to the present invention, it is possible to infer the aim of improving the productivity of the current logic.

[0016] Problems, configurations, and effects other than those described above will be clarified by the description of the embodiments for carrying out the following invention.

Brief Description of the Drawings

[0017] [Figure 1] Functional block diagram for explaining an example of the production plan logic analyzer of Example 1. [Figure 2] Diagram for explaining an example of the master data of the production plan logic analyzer of Example 1. [Figure 3] Diagram for explaining an example of the master data of the production plan logic analyzer of Example 1. [Figure 4] Diagram for explaining an example of the production condition data for testing of the production plan logic analyzer of Example 1. [Figure 5] Diagram for explaining an example of the production plan formulated by the production plan logic analyzer of Example 1. [Figure 6] Diagram for explaining an example of the Gantt chart in the production plan formulated by the production plan logic analyzer of Example 1. [Figure 7] Diagram for explaining an example of the display screen by the result display unit of Example 1. [Figure 8] Diagram for explaining an example of the evaluation of setup time reduction by the evaluation unit of Example 2. [Figure 9] Diagram for explaining an example of the evaluation of tact balancing by the evaluation unit of Example 2. [Figure 10] Diagram for explaining an example of the evaluation of delivery date compliance by the evaluation unit of Example 2. [Figure 11] Diagram for explaining an example of the display screen by the result display unit of Example 2.

Embodiments for Carrying out the Invention

[0018] Embodiments of the present invention will be described below with reference to the drawings. The embodiments are illustrative examples for explaining the present invention, and have been omitted and simplified as appropriate for clarity of explanation. The present invention can also be carried out in various other forms. Unless otherwise specified, each component may be singular or plural.

[0019] The positions, sizes, shapes, and ranges of the components shown in the drawings may not represent their actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in the drawings.

[0020] Examples of various types of information may be described using terms such as "table," "list," and "queue," but these types of information may also be represented by other data structures. For example, various types of information such as "XX table," "XX list," and "XX queue" may be referred to as "XX information." When describing identification information, terms such as "identification information," "identifier," "name," "ID," and "number" are used, and these terms are interchangeable.

[0021] When there are multiple components with the same or similar function, they may be described using the same symbol but with different subscripts. Furthermore, when it is not necessary to distinguish between these multiple components, the subscripts may be omitted in the description.

[0022] In the examples, the processes performed by executing a program may be described. Here, the computer executes the program using a processor (e.g., CPU, GPU) and performs the processing defined in the program using memory resources (e.g., memory) and interface devices (e.g., communication ports). Therefore, the main entity performing the processing by executing the program may be the processor. Similarly, the main entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The main entity performing the processing by executing the program may be an arithmetic unit, and may include dedicated circuits that perform specific processing. Here, dedicated circuits include, for example, FPGAs (Field Programmable Gate Arrays), ASICs (Application Specific Integrated Circuits), CPLDs (Complex Programmable Logic Devices), etc.

[0023] The program may be installed on the computer from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer. If the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. In addition, in the embodiment, two or more programs may be implemented as one program, or one program may be implemented as two or more programs. [Examples]

[0024] Figure 1 is a functional block diagram illustrating an example of a production planning logic analysis device in Example 1.

[0025] The production planning logic analysis device 100 of Example 1 includes a processing unit 10, a storage unit 20, an input unit 30, an output unit 40, and a communication unit 50. The production planning logic analysis device 100 can be implemented using a computer, for example.

[0026] The processing unit 10 includes, as functional blocks, a production planning unit 11, an evaluation unit 12, and a results display unit 13. Each functional block of the processing unit 10 can be implemented, for example, by executing a program in memory using a processor.

[0027] The memory unit 20 stores the current logic 21, test logic 22, test production condition data 23, production plan 24, and master data 25. The memory unit 20 can be implemented as a storage device such as a hard disk or memory.

[0028] The input unit 30 can be implemented by an input device such as a keyboard or mouse. The output unit 40 can be implemented by a display device such as a display. The communication unit 50 can be implemented by a network communication device, for example.

[0029] The production planning logic analysis method using the production planning logic analysis device 100 comprises a production planning step, an evaluation step, and a result display step.

[0030] In this embodiment, instead of directly comparing logics, the method used to infer the objective of productivity improvement in the current logic 21, where the objective of productivity improvement is unknown, is as follows.

[0031] First, in the production planning step, the production planning unit 11 formulates a production plan 24 using test production condition data 23 for both the current logic 21, where the goal of productivity improvement is unknown, and the test logic 22, where the goal of productivity improvement is known.

[0032] Next, in the evaluation step, the evaluation unit 12 evaluates the similarity between the production plan 24 based on the current logic 21 and the production plan 24 based on the test logic 22, thereby inferring the aim of productivity improvement in the current logic 21.

[0033] Finally, in the result display step, the result display unit 13 displays the estimated result on, for example, the display device of the output unit 40.

[0034] The goals of improving productivity include, but are not limited to, reducing the number of setups, achieving cycle balancing, and meeting deadlines. Other goals of improving productivity include workload leveling.

[0035] The following explains the specific process.

[0036] Figures 2 and 3 illustrate an example of master data for the production planning logic analysis device in Example 1.

[0037] Master data 25 is data describing information about production equipment, and the same information used for inputting into the scheduler can be used. Master data 25 includes, for example, item master 251, equipment master 252, process master 253, calendar information 254, shift information 255, and setup condition master 256. This information is described in tabular data format.

[0038] The item master 251 contains item IDs. Item A, Item B, and Item C are defined here.

[0039] Equipment master 252 contains equipment IDs. Equipment X, Equipment Y, and Equipment Z are defined here.

[0040] Process master 253 contains an item ID, a process ID, an equipment ID, and working time. Here, a process ID is defined for each combination of item and equipment, and working time is defined for each process ID.

[0041] Calendar information 254 contains an equipment ID, a date, and a shift ID. Here, the shifts that operate (e.g., fixed shifts) are defined for each combination of equipment and date.

[0042] Shift information 255 includes a shift ID, a start time, and an end time. Here, the start time and end time are defined for each shift.

[0043] The setup condition master 256 contains the equipment ID, the preceding item ID, the succeeding item ID, and the setup time. Here, the combinations of preceding and succeeding item IDs that require setup for each equipment ID are defined, and the setup time for each is defined. Setup time is the preparation time required when switching items. An example of setup is changing molds. Here, it is defined that a 5-minute preparation time is required when switching from item A to item B in equipment X.

[0044] Figure 4 illustrates an example of test production condition data for the production planning logic analyzer in Example 1.

[0045] The test production condition data 23 is test production condition data having the same structure as the production condition data used for input to the scheduler. The test production condition data 23 includes order information 231 and planned start date and time 232. This information is described in table format data. The test production condition data 23 can be pre-prepared, such as the production condition data actually used. If the test production condition data 23 is not prepared in advance, the production planning unit 11 may provide a test production condition data creation step before the production planning step, create the test production condition data 23 based on the master data 25, and store it in the storage unit 20.

[0046] Order information 231 contains an order ID, an item ID, a quantity, and a delivery date. Here, the items, quantities, and delivery dates to be produced are defined for each order.

[0047] Plan start date and time 232 is defined as the start date and time of the plan.

[0048] Figure 5 illustrates an example of a production plan formulated by the production planning logic analysis device of Example 1.

[0049] In the production planning step, the production planning unit 11 reads the current logic 21, the test logic 22, the test production condition data 23, and the master data 25. Using the test production condition data 23, it formulates a production plan 24 for both the current logic 21, where the goal of productivity improvement is unknown, and the test logic 22, where the goal of productivity improvement is known, and stores it in the storage unit 20.

[0050] The production plan 24 has the same structure as the one created by the scheduler. The production plan 24 is described using tabular data. For example, the production plan 24 includes equipment ID, order ID, item ID, task, start date and time, end date and time, and quantity. Here, a plan is output in which equipment X processes 10 units of item A, then sets up to switch to item B, and then equipment X processes 5 units of item B.

[0051] Figure 6 illustrates an example of a Gantt chart in a production plan generated by the production planning logic analysis device of Example 1.

[0052] When the production plan 24 is represented in a Gantt chart 240, it looks like Figure 6. Equipment X, Equipment Y, and Equipment Z are arranged vertically, and the horizontal axis represents time. However, Figure 6 is partially simplified to illustrate the concept of the Gantt chart 240. For example, the time on the horizontal axis is not strictly accurate, and the ratio of the time for process AX of item A of equipment X to the time for setup 241 differs from the actual time. Also, the color coding for each item is omitted in Figure 6.

[0053] In the evaluation step, the evaluation unit 12 reads the production plan 24 and evaluates the similarity between the production plan 24 based on the current logic 21 and the production plan 24 based on the test logic 22 to infer the objective of productivity improvement in the current logic 21. For example, if the similarity is above a predetermined threshold, it can be inferred that the objective of productivity improvement considered in the test logic 22 is also considered in the current logic 21.

[0054] In this embodiment, as an example of a method for evaluating similarity, we evaluate the similarity of Gantt chart 240. This allows for a simple method of evaluating similarity. Two methods are illustrated below, but the method is not limited to these, and other methods may be used.

[0055] One method for evaluating the similarity of Gantt charts 240 is to calculate the reciprocal of the mean squared error of the work time for each corresponding task in the production plan 24 using the current logic 21 and the production plan 24 using the test logic 22, and evaluate the similarity using this evaluation metric. In this case, it is not necessary to draw the chart in the format of Gantt chart 240. Since the production plan 24 and Gantt chart 240 correspond one-to-one, the similarity of Gantt chart 240 can be evaluated using this evaluation metric. The mean squared error is calculated by taking the square of the difference between the value from the current logic 21 and the value from the test logic 22 for each work time data (e.g., start date and time), summing these differences, and dividing by the total number of data points. The smaller the error, the smaller the value. Therefore, by using the reciprocal of the mean squared error as an evaluation metric, the value will be larger as the error is smaller, and it can be used as an evaluation metric for similarity.

[0056] A second method for evaluating the similarity of Gantt charts 240 is to plot the Gantt chart 240 of the production plan 24 using the current logic 21 and the Gantt chart 240 of the production plan 24 using the test logic 22, and evaluate them based on image similarity. Image similarity can be determined, for example, by image processing. In this case, the accuracy of the evaluation can be improved by color-coding the Gantt chart 240 by item.

[0057] Figure 7 illustrates an example of the display screen provided by the results display unit in Example 1.

[0058] In the results display step, the results display unit 13 receives information from the evaluation unit 12 that inferred results, such as the productivity improvement objective, its similarity to it, and whether it was considered or not, and displays the inferred results on the display device of the output unit 40, for example.

[0059] Figure 7 shows an example of the display screen 130, where the "Objective for Productivity Improvement" display field 131, the "Similarity" display field 132, and the "Considered / Not Considered" display field 133 show the estimated similarity and considered / not considered results for each of the multiple objectives for productivity improvement.

[0060] Here, it is inferred that the goal of productivity improvement is to reduce the number of setups, which has a similarity of 85%, to consider, while takt balancing is not considered, which has a similarity of 10%, and on-time delivery is not considered, which has a similarity of 20%. By displaying the results in this way, users of the production planning logic analysis device 100 can understand the goals of productivity improvement in the current logic 21.

[0061] While a single test logic 22 is sufficient, it is desirable to use multiple logics with different productivity improvement goals as test logic 22. By using multiple logics with different productivity improvement goals, it becomes possible to infer multiple productivity improvement goals and improve evaluation accuracy.

[0062] In this case, the production planning unit 11, in the production planning step, formulates a production plan 24 using multiple logics with different productivity improvement goals as test logic 22. Then, in the evaluation step, the evaluation unit 12 evaluates the similarity between the production plan 24 corresponding to each of the multiple logics with different productivity improvement goals and the production plan 24 based on the current logic 21, thereby inferring the productivity improvement goal of the current logic 21.

[0063] As explained above, this embodiment allows us to infer the objective of improving the productivity of the current logic 21. [Examples]

[0064] Example 2 is a modification of Example 1 and presents another example of a method for evaluating similarity. From Example 2 onward, the differences will be the main focus of the explanation, and redundant explanations will be omitted.

[0065] The basic configuration of the production planning logic analysis device 100 in Example 2 is the same as in Example 1.

[0066] In this embodiment, the production planning unit 11, in the production planning step, uses multiple data sets with different conditions that affect the items to be improved with the aim of improving productivity as test production condition data 23, and uses logic that includes the aim of improving productivity and logic that does not include it as test logic 22 to formulate a production plan 24.

[0067] Then, in the evaluation step of Example 2, the evaluation unit 12 aggregates the items that are to be improved in each production plan 24 formulated using the test production condition data 23, with the aim of improving productivity. By evaluating whether the aggregated result of the current logic 21 is similar to the aggregated result of the logic that includes the aim of improving productivity or the logic that does not, the evaluation unit 12 infers the aim of improving productivity in the current logic 21.

[0068] In Example 1, a method for evaluating the similarity of Gantt charts 240 was described as an example of a method for evaluating similarity. However, even if the goal of improving productivity is the same, the resulting production plans 24 may not necessarily be the same, so there are limitations to improving the accuracy of the evaluation.

[0069] Therefore, in Example 2, the accuracy of the similarity evaluation is improved by focusing on how the items to be improved in the production plan 24 are structured with the aim of increasing productivity.

[0070] The following explanation will use specific examples.

[0071] Figure 8 illustrates an example of the evaluation of the reduction in setup time by the evaluation unit in Example 2.

[0072] If the goal of improving productivity is to reduce the number of setups, the item to be improved in the production plan 24 is the number of setups, and the condition that influences this in the test production condition data 23 is the number of setup pairs.

[0073] Therefore, as test production condition data 23, we use data with different numbers of setup pairs: 5 sets, 10 sets, and 15 sets. As a method for counting the number of setup pairs, for example, we can extract all item ID pairs from the order information 231 shown in Figure 4, and if a set of preceding item ID and succeeding item ID exists for each pair in the setup condition master 256 shown in Figure 3, we can count it as a setup pair. For example, the pair of item A and item B is a setup pair.

[0074] The production planning unit 11 then formulates production plans 24 using data from 5, 10, and 15 cycles, each with different numbers of setup pairs, for the current logic 21, the considered logic (test logic 22 that takes setup reduction into account), and the unconsidered logic (test logic 22 that does not take setup reduction into account). Therefore, a total of nine production plans 24 are formulated.

[0075] Next, the evaluation unit 12 compiles the number of setups for each of the production plans 24. For example, in the production plan 24 of Figure 5, it can count the number of times a task is in the setup phase.

[0076] The aggregated results are plotted in a graph, as shown in Figure 8. In Figure 8, the horizontal axis represents the number of setup pairs (times), and the vertical axis represents the number of setups (times). It can be seen that the current logic graph 210 is more similar to the considered logic graph 221 than to the unconsidered logic graph 222.

[0077] Therefore, the evaluation unit 12 can infer whether the current logic 21 takes into account the reduction of setup time by evaluating whether the aggregation result of the current logic 21 is similar to the aggregation result of the considered logic or the unconsidered logic. A similar evaluation method would be to compare, for example, the reciprocal of the mean squared error between the aggregation result of the current logic 21 and the aggregation result of the considered logic with the reciprocal of the mean squared error between the aggregation result of the current logic 21 and the aggregation result of the unconsidered logic.

[0078] Figure 9 illustrates an example of tact balancing evaluation by the evaluation unit in Example 2.

[0079] If the goal of productivity improvement is takt balancing, the item to be improved in the production plan 24 is the makespan, and the influencing condition in the test production condition data 23 is the cycle time variability.

[0080] The method for counting cycle time variability is as follows: For example, for each item in the order information 231 in Figure 4, the maximum work time for the process in the process master 253 in Figure 2 is determined, all item pairs are extracted, and the sum of the differences in the maximum work times for each item pair is taken. Specifically, the maximum work time for item A is 20 seconds, the maximum work time for item B is 30 seconds, and the maximum work time for item C is 20 seconds. The difference in the maximum work times for item pairs is 10 seconds for item A and item B, 0 seconds for item A and item C, and 10 seconds for item B and item C. Therefore, the sum is 10 seconds + 0 seconds + 10 seconds = 20 seconds, and this is used as the cycle time variability.

[0081] The method for counting the makespan is, for example, the interval between the start date and time of the first process of the first workpiece and the end date and time of the final process of the last workpiece, as shown in production plan 24 in Figure 5.

[0082] The aggregated results are plotted in a graph, as shown in Figure 9. In Figure 9, the horizontal axis represents cycle time variability (seconds), and the vertical axis represents make span (hours). It can be seen that the current logic graph 210 is more similar to the unconsidered logic graph 222 than to the considered logic graph 221.

[0083] Therefore, we can infer whether or not the current logic 21 takes tact balancing into consideration.

[0084] Figure 10 illustrates an example of the evaluation of on-time delivery by the evaluation unit in Example 2.

[0085] If the goal of productivity improvement is on-time delivery, the item to be improved in the production plan 24 is the on-time delivery rate, and the condition that influences this in the test production condition data 23 is the delivery margin.

[0086] The method for counting the lead time margin is, for example, using the order information 231 and the planned start date and time 232 in Figure 4, and calculating it as the sum of Quantity × (Delivery Date - Planned Start Date and Time).

[0087] The method for counting the on-time delivery rate is as follows: For example, in production plan 24 of Figure 5, if the completion date and time of the final process of an ordered item is after the delivery date, it is considered a delayed work; if it is before the delivery date, it is considered a work on time. The number of works on time relative to the total number of works is used as the on-time delivery rate. For example, the total number of works is 10 for item A + 5 for item B = 15. Let's assume the number of delayed work is 2 for item B. The number of works on time is 13. Therefore, the on-time delivery rate can be calculated as 13 / 15 = 86.7%.

[0088] The aggregated results are plotted in a graph, as shown in Figure 10. In Figure 10, the horizontal axis represents the margin of error in delivery time (days), and the vertical axis represents the on-time delivery rate (%). It can be seen that the current logic graph 210 is more similar to the unconsidered logic graph 222 than to the considered logic graph 221.

[0089] Therefore, we can infer whether the current logic 21 takes deadlines into consideration or not.

[0090] Figure 11 illustrates an example of the display screen provided by the results display unit in Example 2.

[0091] The difference between this example and Figure 7 is the addition of a "Representation of Basis" check box 134. Furthermore, the same graph shown in Figure 8 is displayed as the basis for the estimated reduction in setup time, which is indicated by a check in the "Representation of Basis" check box 134.

[0092] As explained above, this embodiment provides the same effects as in Example 1, but also improves the accuracy of the similarity evaluation. [Examples]

[0093] Example 3 is a modification of Example 2, and is an example in which the similarity evaluation method is simplified compared to Example 2. In Example 3, the number of test logics 22 is reduced and the configuration does not use unconsidered logic. Although the evaluation accuracy is lower compared to Example 2, if it is expected that sufficient evaluation accuracy can be ensured by comparing the current logic 21 with the considered logic alone, the processing of the production planning unit 11 and the evaluation unit 12 can be reduced by using a configuration that does not use unconsidered logic.

[0094] Specifically, in the production planning step of Example 3, the production planning unit 11 uses multiple data sets with different conditions that affect the items to be improved with the aim of improving productivity as test production condition data 23, and uses logic that includes the aim of improving productivity as test logic 22 to formulate a production plan 24.

[0095] Then, in the evaluation step of Example 3, the evaluation unit 12 aggregates the items that are to be improved in each production plan 24 formulated using the test production condition data 23, with the aim of improving productivity, and evaluates whether the aggregated result of the current logic 21 is similar to the aggregated result of the logic that includes the aim of improving productivity, thereby inferring the aim of improving productivity in the current logic 21. Whether or not they are similar can be determined, for example, by comparing the evaluation index and the threshold.

[0096] Although embodiments of the present invention have been described above, the present invention is not limited to the configurations described in the embodiments, and various modifications are possible within the scope of the technical idea of ​​the present invention. Furthermore, some or all of the configurations described in each embodiment may be combined and applied. [Explanation of symbols]

[0097] 10 Processing Unit 11 Production Planning Department 12 Evaluation Department 13 Result display area 20 Memory section 21 Current Logic 22 Test Logic 23 Test production condition data 231 Order Information 232 Plan start date and time 24 Production Plan 240 Gantt chart 241 Preparation 25 Master Data 251 Item Master 252 Equipment Master 253 Process Master 254 Calendar Information 255 Shift Information 256 Setup Condition Master 30 Input section 40 Output section 50 Communications Department 100 Production Planning Logic Analysis Device 130 display screen 131 "Objectives for Productivity Improvement" display field 132 “Similarity” display field 133 “Considered / Not considered” display field 134 "Evidence Display" check box 210 Graph of the current logic 221 Graph of Consideration Logic 222 Graph of unconsidered logic

Claims

1. A production planning logic analysis method performed by a production planning logic analysis device, In both the current logic where the goal of productivity improvement is unknown and the test logic where the goal of productivity improvement is known, a production planning step is performed in which a production plan is formulated using test production condition data. A production plan logic analysis method characterized by comprising an evaluation step of inferring the aim of productivity improvement in the current logic by evaluating the similarity between the production plan based on the current logic and the production plan based on the test logic.

2. In claim 1, The production planning step involves formulating the production plan using multiple logics with different objectives for productivity improvement as the test logic, The production plan logic analysis method is characterized in that the evaluation step involves evaluating the similarity between the production plan corresponding to each of the multiple logics, each with a different objective for productivity improvement, and the production plan based on the current logic, thereby inferring the objective for productivity improvement in the current logic.

3. In claim 1, The production planning logic analysis method is characterized in that the evaluation step involves evaluating the similarity of the Gantt charts between the production plan based on the current logic and the production plan based on the test logic, thereby inferring the aim of productivity improvement in the current logic.

4. In claim 1, The production planning step involves formulating the production plan using multiple data sets with different conditions that affect the items to be improved according to the goal of productivity improvement as the test production condition data, and using logic that includes the goal of productivity improvement and logic that does not include it as the test logic. The production plan logic analysis method is characterized by the evaluation step of aggregating the items to be improved in each of the production plans formulated using the test production condition data, in order to determine the productivity improvement objective in the current logic by evaluating whether the aggregated result of the current logic is similar to the aggregated result of the logic that includes the productivity improvement objective or the logic that does not.

5. In claim 4, A production planning logic analysis method characterized in that the aim of productivity improvement is to reduce the number of setups, the item to be improved is the number of setups, and the influencing condition is the number of setup pairs.

6. In claim 4, A production planning logic analysis method characterized in that the objective of productivity improvement is takt balancing, the item to be improved is makespan, and the influencing condition is cycle time variability.

7. In claim 4, A production planning logic analysis method characterized in that the objective of productivity improvement is on-time delivery, the item to be improved is the on-time delivery rate, and the influencing condition is the delivery margin.

8. In claim 1, The production planning step involves formulating the production plan using multiple data sets with different conditions that affect the items to be improved according to the productivity improvement objective, as the test production condition data, and using logic that includes the productivity improvement objective as the test logic. The production plan logic analysis method is characterized by the evaluation step of aggregating the items to be improved in each of the production plans formulated using the test production condition data, in order to estimate the productivity improvement objective in the current logic, by evaluating whether the aggregated result of the current logic is similar to the aggregated result of the logic that includes the productivity improvement objective.

9. In claim 1, The system includes a result display step that displays the result of an estimation of whether the current logic takes into account each of the multiple productivity improvement objectives, The production planning logic analysis method is characterized in that the evaluation step involves inferring whether the current logic takes into account each of the multiple productivity improvement objectives.

10. In both the current logic where the goal of productivity improvement is unknown and the test logic where the goal of productivity improvement is known, the production planning unit formulates a production plan using test production condition data. A production planning logic analysis device characterized by having an evaluation unit that estimates the objective of productivity improvement in the current logic by evaluating the similarity between the production plan based on the current logic and the production plan based on the test logic.

Images