Manufacturing management support methods, manufacturing management support programs, and manufacturing management support systems

Abstract

To efficiently set priorities for investigating data locations that may lead to business improvements or new service creation.SOLUTION: In a manufacturing management support method of supporting manufacturing management of products that involve multiple processes, actual data that associate time, product ID, and process ID that identifies the process where the product with the product ID was staying at the time is arranged in a coordinate system with a first axis and a second axis as coordinate axes. Then, from a production planning line for each product ID, which connects the actual data for each product ID with a straight line, specific data location candidates that includes turbulent flow locations where the production planning line adjacent to the first axis direction is not parallel to the second axis direction are extracted, and the production planning lines that include at least part of the same specific data location candidate are extracted and grouped. Based on a time width indicating width for each process of a specific data location area that defines area for setting a new production planning line for laminarizing the production planning line based on the grouped production planning lines, investigation priority for the specific data location area is set.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to a manufacturing management support method, a manufacturing management support program, and a manufacturing management support system.

Background Art

[0002] Conventionally, for example, a technique has been provided for visualizing the manufacturing situation in which each product flows through a manufacturing line having a plurality of processes. Visualization of such a manufacturing situation is expected to provide suggestions not only for manufacturing management such as grasping abnormalities in the manufacturing line, but also for improving business issues and creating new services.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are various types of suggestions for improving business issues and creating new services. If data locations give the same type of suggestion, the priority of investigation can be set by comparing them in the same column. However, in the case of data locations that give different types of suggestions, even if the priority of investigation is set, the priorities are set based on different criteria and do not accurately indicate the priority. For this reason, there has been a possibility of inconvenience that the investigation of the data location to be prioritized is postponed. That is, it has been difficult to set the priority of which data location should be investigated preferentially.

[0005] Also, even if there are data locations that can be grouped into the same group to be investigated simultaneously as giving the same type of suggestion, it has been difficult to group such data locations.

[0006] These problems are not limited to manufacturing; they can occur in any business involving multiple processes.

[0007] This invention was made with the above points in mind, and aims to efficiently set priorities for investigating data locations that have the potential to lead to business improvements or the creation of new services. [Means for solving the problem]

[0008] To solve the above-mentioned problems, in one aspect of the present invention, a manufacturing management support system that assists in the management of product manufacturing in a manufacturing line that performs multiple processes provides a manufacturing management support method, wherein actual data, which associates time, a product ID that identifies the product, and a process ID that identifies the process in which the product with the product ID was located at that time, is arranged in a coordinate system with a first axis and a second axis as coordinate axes, with the time taken in the first axis direction and the process taken in the second axis direction, and specific data including turbulence points where adjacent production plan lines in the first axis direction are not parallel in the second axis direction is obtained from the production plan line for each product ID, which is formed by connecting the actual data with straight lines for each product ID The method is characterized by comprising: a specific data location candidate extraction step for extracting location candidates; a group creation step for extracting and grouping production plan lines that include at least a portion of the same specific data location candidate extracted by the specific data location candidate extraction step; and a priority setting step for setting the investigation priority of the specific data location area based on a time width indicating the width of each process in the specific data location area that defines the range for setting a new production plan line to laminarize the production plan line based on the production plan line grouped by the group creation step, and a time interval indicating the interval between each process in the production plan line that is not grouped. [Effects of the Invention]

[0009] According to the present invention, for example, it is possible to efficiently set priorities for investigating data locations that may lead to business improvements or the creation of new services. [Brief explanation of the drawing]

[0010] [Figure 1A] Figure showing an example of laminar flow in a production plan. [Figure 1B] Figure showing an example of turbulent flow in a production plan. [Figure 2] Figure showing the configuration of the production management support system according to Embodiment 1. [Figure 3A] Figure showing customer flow data. [Figure 3B] Figure showing visualized customer flow data. [Figure 4] Flowchart showing the investigation priority setting process according to Embodiment 1. [Figure 5] Figure for explaining the extraction process of candidate specific data locations. [Figure 6A] Figure for explaining the production plan line extraction process. [Figure 6B] Figure showing the grouped production plan lines in the laminarized customer flow data. [Figure 7A] Figure for explaining the time difference calculation process by process. [Figure 7B] Figure showing the result of the time difference calculation process by process. [Figure 8] Figure showing the specific data location area. [Figure 9] Figure for explaining the investigation priority calculation process of the specific data location area. [Figure 10] Figure showing the GUI showing the result of the investigation priority setting process according to Embodiment 1. [Figure 11] Figure showing the types of suggestions according to Embodiment 2. [Figure 12] Figure showing the GUI showing the result of the investigation priority setting process according to Embodiment 2. [Figure 13] Figure showing the hardware of a computer.

Modes for Carrying Out the Invention

[0011] Hereinafter, embodiments according to the present application will be described with reference to the drawings. The embodiments are examples for explaining the present application including the drawings. In the embodiments, for clarity of explanation, appropriate omissions and simplifications are made. Unless otherwise particularly limited, the components of the embodiments may be singular or plural. Also, a form combining one embodiment and another embodiment is also included in the embodiments according to the present application.

[0012] The same or similar components are given the same reference numerals, and in subsequent embodiments of those already presented, the description may be omitted or only the description centered on the differences may be given. Also, when there are a plurality of the same or similar components, they may be described with different subscripts attached to the same reference numeral. Also, when it is not necessary to distinguish these plural components, the subscripts may be omitted in the description.

[0013] In the following embodiments, various information is described in a table format, but the various information may be in a data format other than the table format. Also, for example, various designations such as "XX information", "XX table", "XX list", "XX queue", etc. are interchangeable. For example, "XX table" may be called "XX list". Also, when explaining identification information, expressions such as "identification information", "identifier", "name", "ID", "number", etc. are used, but these are interchangeable.

[0014] In the following embodiments, a processor such as an MPU (Micro Processing Unit) and a memory that realize the "YY processing" of step Sn (n is a natural number) by executing a program can be called a "YY unit".

[0015] (Laminar flow and turbulent flow of a production plan having a plurality of processes) Prior to the description of the embodiments, the laminar flow and turbulent flow of a production plan in a production line having a plurality of processes will be described. FIG. 1A is a diagram showing an example of the laminar flow of a production plan. FIG. 1B is a diagram showing an example of the turbulent flow of a production plan.

[0016] If no problems arise during the execution of the production plan, all products to be manufactured will be processed with the same lead time throughout all processes from the upstream to the downstream. In this case, as shown in Figure 1A, in a production plan table with time on the horizontal axis (first axis) and processes on the vertical axis (second axis), the production plan line 51 will be parallel at regular intervals throughout all processes, for example, processes 1 to 6, from the upstream to the downstream. The production plan line 51 is created by plotting the time each product spends in each process from the upstream to the downstream on the production plan table and connecting the points plotted for each product ID with a single straight line. The production plan line is also called a flow line.

[0017] The state in which the production plan line 51 is parallel is called "laminar flow." The production plan line 51 is fundamentally designed to always be laminar in order to improve productivity, such as reducing manufacturing costs. However, in the actual execution of the production plan, laminar flow can break down, resulting in "turbulence" in the production plan line 51.

[0018] "Turbulence" refers to disturbances in the production plan line 51 caused by the following factors. For example, a product may remain in a certain process for longer than the predetermined time. This is called "stagnation." In "stagnation," other products that were introduced into the production line after a particular product may complete all processes on the production line before that product.

[0019] Furthermore, a product may be processed at some stage of the production line, overtaking other products that were introduced earlier, and thus completing its entire production line process before the other products. This is called "overtaking."

[0020] Furthermore, a product may be discontinued at some point in the production line, and the subsequent processes may not be carried out. This is called "discontinuation." Discontinuation can occur, for example, if the product in question is defective.

[0021] These "stagnation," "overtaking," and "production halts" create turbulence in the production plan line 51. This turbulence can sometimes lead to the inconvenience of extending the production lead time beyond the specified time.

[0022] Therefore, in the manufacturing industry, there is a domain characteristic that manufacturing lead times can be shortened by identifying data points corresponding to turbulence in the production schedule, resolving the turbulence, and converting it to laminar flow.

[0023] Furthermore, the aforementioned "stagnation," "overtaking," and "production halt" are merely examples of factors contributing to turbulence on the production plan line 51. Turbulence on the production plan line 51 is not limited to those caused by "stagnation," "overtaking," and "production halt," but broadly includes areas that are not "laminar flow" as they appear in the production plan due to various factors.

[0024] In the following embodiment, we focus on these domain characteristics and group "candidate data locations that will lead to improvement of turbulent areas (problems)" when laminarizing (improving) them. Then, we set the "degree of improvement through laminarization," defined by a common criterion that is independent of the "type of suggestion for problem improvement" corresponding to the "candidate data locations that will lead to problem improvement," as the priority for investigation.

[0025] This makes it possible to compare the priorities of which data points that could lead to problem improvement should be considered first among different types of insights for problem improvement and potential data points that could lead to problem improvement.

[0026] [Embodiment 1] (Configuration of Manufacturing Management Support System 10) Figure 2 shows the configuration of the manufacturing management support system 10 according to Embodiment 1. The manufacturing management support system 10 is comprised of a computer that executes a predetermined program. The manufacturing management support system 10 includes a data input unit 11, a specific data location candidate extraction unit 12, a group creation unit 13, a survey priority setting unit 14, a visualization unit 15, and a specific data location candidate storage unit 16.

[0027] The customer flow data storage unit 1 is connected to the data input unit 11. The customer flow data storage unit 1 and the specific data location candidate storage unit 16 are storage devices. The customer flow data storage unit 1 may be an internal device of the manufacturing management support system 10. The visualization unit 15 and the specific data location candidate storage unit 16 may be external devices of the manufacturing management support system 10.

[0028] The customer flow data storage unit 1 stores customer flow data 1d. Figure 3A shows customer flow data 1d. Figure 3B shows visualized customer flow data 1d1. Customer flow data 1d and 1d1 are examples of actual data.

[0029] As shown in Figure 3A, customer flow data 1d has columns for time, product ID, and process ID, and stores the associated process (process ID) where the product identified by the product ID is located at each time point. In the production plan, if the points corresponding to the same product ID shown in customer flow data 1d for the time and process ID are plotted and connected by a single production plan line, it is visualized as shown in customer flow data 1d1 in Figure 3B.

[0030] The data input unit 11 receives input of customer flow data 1d to be analyzed from the customer flow data storage unit 1. If the customer flow data 1d is data for a predetermined period, such as daily or monthly, the data input unit 11 receives input of one or more customer flow data 1d to be analyzed.

[0031] The specific data location candidate extraction unit 12 extracts "specific data location candidates that lead to problem improvement" from the customer flow data 1d received as input by the data input unit 11, for each "type of suggestion for problem improvement / new service creation". Examples of "types of suggestion for problem improvement / new service creation" (hereinafter referred to as "types of suggestion") include the above-mentioned "stagnation", "overtaking", and "discontinuation".

[0032] Furthermore, "candidate data points that can lead to problem improvement" (hereinafter referred to as "candidate data points") are turbulent areas in the production plan line of customer flow data 1d that could become "candidate data points that can lead to problem improvement." However, turbulent areas are not limited to areas caused by "stagnation," "overtaking," or "production discontinuation," but include various suggestions corresponding to areas in customer flow data 1d that are not "laminar flow" or where the production plan line is not parallel. Details of the processing of the candidate data point extraction unit 12 will be described later.

[0033] The specific data location candidate extraction unit 12 stores the extracted "specific data location candidates" in a predetermined format in the specific data location candidate storage unit 16.

[0034] The group creation unit 13 extracts production planning lines that intersect with each "specific data location candidate" from the "specific data location candidate" stored in the specific data location candidate storage unit 16. Then, for each "specific data location candidate," the group creation unit 13 groups together the production planning lines that include the "specific data location candidate" and the production planning lines that intersect with the "specific data location candidate" or that contain at least a portion of the "specific data location candidate" into a single group of production planning lines. Each group of production planning lines includes production planning lines that are affected by the same turbulence factor. Details of the processing by the group creation unit 13 will be described later.

[0035] The group creation unit 13 stores the production plan line group in a predetermined format in the specific data location candidate storage unit 16.

[0036] The investigation priority setting unit 14 reads the groups of production plan lines stored in the specific data location candidate storage unit 16 and sets an investigation priority P for each group of production plan lines based on the "degree of improvement toward laminar flow". The "degree of improvement toward laminar flow" can be said to be based on the area occupied in the production plan table by a group of production plan lines that are simultaneously converted to laminar flow by resolving one turbulence factor.

[0037] For example, the investigation priority setting unit 14 sets the investigation priority P according to the following rules (a1)(a2). (a1) The investigation priority P is set higher the larger the width W in the time axis direction of the group of production plan lines in question. (a2) The investigation priority P is set higher the larger the magnitude W described above is, relative to the average interval between adjacent laminar-flow production plan lines located in the vicinity of the relevant production plan line group within a predetermined time.

[0038] The investigation priority setting unit 14 may also set the investigation priority for specific data location areas based on the following (b1) and (b2), not limited to the above. A specific data location area is an area that defines the range for setting new production plan lines to laminarize production plan lines based on grouped production plan lines. (b1) Time width indicating the width of each process in the specific data area described below (b2) Time differences by process, indicating the interval between processes in production plan lines that are not included in the specified data area and are not grouped.

[0039] The investigation priority setting unit 14 outputs the set investigation priority P to the visualization unit 15. The visualization unit 15 includes information output means (not shown), including a display. The visualization unit 15 outputs the investigation priority P set by the investigation priority setting unit 14, along with, for example, visualized customer flow data 1d1, from the output means. Details of the processing of the investigation priority setting unit 14 will be described later.

[0040] (Investigation priority setting process related to Embodiment 1) Figure 4 is a flowchart showing the investigation priority setting process according to Embodiment 1. The investigation priority setting process is executed by the manufacturing management support system 10, which receives customer flow data 1d via the data input unit 11, triggered by a user instruction input from an input device (not shown).

[0041] First, in step S1, the specific data location candidate extraction unit 12 performs a specific data location candidate extraction process from the input customer flow data 1d. The specific data location candidate extraction unit 12 extracts specific data location candidates according to whether or not they meet predetermined rules. For example, the specific data location candidate extraction unit 12 extracts types of production plan lines that are turbulent locations corresponding to the above-mentioned "stagnation," "overtaking," and "production discontinuation" from the customer flow data 1d, and stores them as specific data location candidate storage units 16.

[0042] Figure 5 is a diagram illustrating the process for extracting candidate data points. In this embodiment, three types of "suggestions for problem improvement"—"stagnation," "overtaking," and "production discontinuation"—are assumed as turbulence points in the production plan line of customer flow data 1d, and turbulence points corresponding to these are extracted.

[0043] Candidate data points 52a, which are turbulent flow locations corresponding to "stagnation," correspond to situations where a product remains in a process for longer than a predetermined time. Candidate data points 52a are extracted in the coordinate system of the first and second axes of the production plan table where the slope of a process on a single production plan line 51a is smaller than a predetermined value. Alternatively, candidate data points 52a are extracted where adjacent production plan lines are not parallel in the second axis direction, or where the slopes of processes differ within a single production plan line 51a.

[0044] The candidate data location 52b, which corresponds to a turbulent flow point that triggers "overtaking," is a situation where a product is processed before a preceding product in a certain process. As illustrated in Figure 5, the candidate data location 52b is a point where a certain production plan line 51b intersects with other production plan lines 51c and 52d.

[0045] The candidate data point 52c, which corresponds to the turbulence point for "discontinuation of production," is a situation where the product finishes processing at a certain step and remains there without transitioning to a subsequent step. As illustrated in Figure 5, the candidate data point 52c is a point where a certain production plan line 51e ends at an intermediate step (step 3 in Figure 5) among multiple processes.

[0046] It should be noted that the above examples of candidate data points are merely illustrative. In other words, the shape of the graph representing "non-laminar flow areas" broadly falls under the category of candidate data points.

[0047] Next, in step S2, the group creation unit 13 extracts production plan lines 51 that include the specific data location candidates 52a, 52b, and 52c extracted in step S1 (production plan line extraction process). The group creation unit 13 groups the production plan lines 51 for each specific data location candidate 52a, 52b, and 52c and stores the grouped information in the specific data location candidate storage unit 16.

[0048] Figure 6A is a diagram illustrating the production plan line extraction process. As shown in Figure 6A, the region of production plan line 51 that includes the specific data location candidate 52a corresponding to "stagnation" (specific data location candidate region 53a) includes production plan line 51a. Also, the region of production plan line 51 that includes the specific data location candidate 52b corresponding to "overtaking" (specific data location candidate region 53b) includes production plan lines 51b, 51c, and 51d. Furthermore, the region of production plan line 51 that includes the specific data location candidate 52c corresponding to "discontinuation" (specific data location candidate region 53c) includes production plan line 51e.

[0049] Figure 6B shows customer flow data 1d1 with grouped production planning lines 51 that have been processed laminarly. When each of the production planning lines 51 contained in the candidate data location areas 53a, 53b, and 53c is grouped into a single production planning line 51, the 13 production planning lines 51 before grouping, as shown in Figure 6A, are consolidated into 11 lines, as shown in Figure 6B. Therefore, the entire data is processed laminarly.

[0050] Next, in step S3, the investigation priority setting unit 14 calculates the average of the process-specific time differences between production plan lines that are not included in the specific data location candidates (process-specific time difference calculation process).

[0051] Figure 7A is a diagram illustrating the process for calculating time differences by process. In the example in Figure 7A, the pairs of production planning lines 51 that do not include the specific data location candidates 52a, 52b, and 52c are production planning lines 51f, 51g, 51h, 51i, 51j, 51k, 51l, and 51m. Of these, the adjacent production planning lines 51 that do not have a production planning line 51 containing the specific data location candidates 52a, 52b, and 52c in between are production planning lines 51f and 51g, 51i and 51j, 51j and 51k, and 51l and 51m.

[0052] Furthermore, the intervals between production planning lines 51 for each process (process-specific time differences) are as shown in Figure 7A, for example, production planning lines 51f and 51g have time differences t11 for process 1, time differences for process 2, ..., process 6.

[0053] Then, as the "average time difference by process" for "Process 1," the average of the time differences t11, t12, t13, and t14 in Figure 7A is calculated. The same applies to "Process 2" through "Process 6."

[0054] In Figure 7A, production planning lines 51f, 51g, 51h, 51i, 51j, 51k, 51l, and 51m are all production planning lines used to calculate the "process-specific time difference average" for specific data location candidates 52a, 52b, and 52c. However, this is not limited to this, and the production planning line used to calculate the "process-specific time difference average" may differ depending on the specific data location candidate. In other words, the "process-specific time difference average" may differ for each specific data location candidate. This case is, for example, when the dates or time zones of the specific data location candidates differ.

[0055] For example, consider the operating conditions of a manufacturing line where the number of units produced per unit time is increased during the day (the interval between production plan lines is narrowed (the "average time difference per process" is small)), and the number of units produced per unit time is decreased at night (the "average time difference per process" is large). Under these operating conditions, suppose that specific data location areas 54, described later, with the same time width are set for both the daytime and nighttime. In this case, by limiting the calculation of the "average time difference per process" to the production plan lines in a predetermined vicinity of each specific data location area 54, the "average time difference per process" corresponding to the specific data location area 54 during the daytime will be smaller than that at night. Therefore, even if the time width of the specific data location area 54 during the daytime is smaller than that at night, the investigation priority, defined as the ratio of the time width of the specific data location area 54 to the "average time difference per process," may be set higher than at night. In this way, even if the time width of the specific data location area 54 is absolutely small, it will not be overshadowed by specific data location areas 54 with absolutely larger time widths, and its investigation priority will be set higher, and the investigation will be given priority.

[0056] Figure 7B shows the results of the process-specific time difference calculation process. The "average process-specific time difference" calculated as described above is stored in a predetermined storage area such as the specific data location candidate storage unit 16 as information as exemplified in Figure 7B. The "average process-specific time difference" shown in Figure 7B is the same value regardless of the specific data location candidates 52a, 52b, and 52c, but as described above, it may differ for each specific data location candidate 52a, 52b, and 52c.

[0057] Next, in step S4, the investigation priority setting unit 14 creates a region (specific data location region 54) of specific data locations excluding the average time difference for each process. Figure 8 shows the specific data location region 54. In the example shown in Figure 8, the specific data location region 54 consists of specific data location regions 54a, 54b, and 54c.

[0058] The specific data location area 54 is an area that is narrowed by expanding each specific data location candidate area 53 in the positive and negative directions of the time axis and offsetting it by the "average time difference per process" (Figure 7B) from the production planning line 51 on both the positive and negative sides just before the first collision towards the specific data location candidate 52.

[0059] In the example shown in Figure 7A, when the candidate data location area 53a is expanded in the positive and negative directions of the time axis, it first intersects with the production plan line 51g on the negative time axis side and with the production plan line 51h on the positive time axis side. Then, as shown in Figure 8, it is offset from the production plan line 51g by "2.8 seconds" for "Process 1", "2.9 seconds" for "Process 2", "3.6 seconds" for "Process 3", "2.3 seconds" for "Process 4", and "3.1 seconds" for "Process 5". Similarly, it is offset from the production plan line 51h by "2.8 seconds" for "Process 1", "2.9 seconds" for "Process 2", "3.6 seconds" for "Process 3", "2.3 seconds" for "Process 4", and "3.1 seconds" for "Process 5". By offsetting the boundary lines in this way, the specific data location area 54a is set by removing the margin area from the candidate data location area 53a. The specific data location areas 54b and 54c are the same as the specific data location area 54a.

[0060] Next, in step S5, the investigation priority setting unit 14 calculates the investigation priority for each specific data location area 54.

[0061] Figure 9 is a diagram illustrating the process for calculating the investigation priority of specific data location areas 54. First, the investigation priority setting unit 14 calculates the time width for each process in each specific data location area 54. The time width for each process in a specific data location area 54 is the value obtained by subtracting twice the "average time difference per process" mentioned above from the time difference for each process of the production planning line 51 on both the positive and negative sides of the time axis of the corresponding specific data location area 54.

[0062] For example, in "Process 1," suppose the time difference between the production planning lines 51g and 51h on both the positive and negative sides of the time axis of a specific data location area 54a is "9.7 seconds." Subtracting "2.8 seconds" as the "average time difference per process" on the production planning line 51g side and "2.8 seconds" as the "average time difference per process" on the production planning line 51h side from "9.7 seconds" results in "4.1 seconds." In other words, the "time width of Process 1" in the specific data location area 54 is "4.1 seconds." The "time width of Process 2" to "time width of Process 6" can be determined in the same way.

[0063] The average Aa of the time width of the specific data location area 54a ("time width before the execution of process 1", "time width of process 1" to "time width of process 6") is "(4.1 + 4.3 + 5.1 + 5.3 + 4.5 + 4.7) / 6 = 4.6 seconds" in the example of Figure 9.

[0064] On the other hand, the average Ba of all "process-specific time difference averages" in the specific data location area 54a is "(2.8 + 2.1 + 2.9 + 3.6 + 2.3 + 3.1) / 6 = 2.8 seconds".

[0065] Therefore, the investigation priority for the specific data location area 54a is calculated to be Pa=Aa / Ba=1.6.

[0066] Similarly, the average Ab of all time widths ("time width of process 1" to "time width of process 6") in the specific data location area 54b is "(7.2 + 6.9 + 7.0 + 6.8 + 6.5 + 6.6) / 6 = 6.8 seconds" in the example in Figure 9. On the other hand, the average Bb of all "average time differences by process" in the specific data location area 54b is "2.8 seconds", the same as the average Ba.

[0067] Therefore, the investigation priority for the specific data location area 54b is calculated as Pb=Ab / Bb=2.4.

[0068] Similarly, the average Ac for all time widths ("time width for process 1" to "time width for process 6") in the specific data location area 54c is "(4.0 + 3.8 + 4.1 + 4.5 + 3.2 + 4.3) / 7 = 4.0 seconds" in the example in Figure 9. On the other hand, the average Bc for all "average time differences by process" in the specific data location area 54d is "2.8 seconds," the same as the average Ba and Bb.

[0069] Therefore, the investigation priority for the specific data location area 54c is calculated as Pc=Ac / Bc=1.4.

[0070] Comparing the investigation priorities Pa to Pc, investigation priority Pb ​​is the highest. Therefore, it can be seen that specific data location area 54b is the area that should be investigated with the highest priority, and the investigation priority decreases in the order of specific data location area 54a and specific data location area 54c.

[0071] Next, in step S6, the visualization unit 15 outputs the candidate specific data locations 52 corresponding to each specific data location area 54, along with the investigation priority set for each specific data location area 54 in step S5, via the GUI (Graphical User Interface) 15D described later, from the output means (not shown).

[0072] Figure 10 shows the GUI15D illustrating the results of the investigation priority setting process according to Embodiment 1. Figure 10 is merely an example of the display configuration and may differ from the actual configuration. GUI15D visualizes and displays a list of specific data locations (turbulence locations) that lead to problem improvement and their investigation priorities in descending order of investigation priority. GUI15D can display three or more specific data locations (turbulence locations) that lead to problem improvement and their investigation priorities by scrolling.

[0073] GUI15D includes a suggestion type selection switch 15D1. The suggestion type selection switch 15D1 sets whether or not to include specific data locations (turbulent areas) and investigation priorities that can lead to problem improvement for each of the three suggestions: "overtaking," "stagnation," and "discontinuation." The visualization unit 15 displays a list of suggestions selected by the user using the suggestion type selection switch 15D1, including specific data locations (turbulent areas) and investigation priorities that can lead to problem improvement, in descending order of investigation priority.

[0074] In the example in Figure 10, customer flow data (diagram chart) 1d11 related to "overtaking" is displayed as the first-highest investigation priority, along with an identification indicator for the production plan line 51y1 included in the specific data location. Customer flow data (diagram chart) 1d12 related to "stagnation" is displayed as the second-highest investigation priority, along with an identification indicator for the production plan line 51y2 included in the specific data location. Customer flow data (diagram chart) 1d13 related to "discontinuation" is displayed as the third-highest investigation priority, along with an identification indicator for the production plan line 51y3 included in the specific data location.

[0075] (Effects of Embodiment 1) According to this embodiment, for turbulent flow locations in the production plan, "candidate specific data locations that can lead to problem improvement" are grouped, and the "degree of improvement to laminar flow" is set as the investigation priority. Therefore, it becomes possible to compare which of the "specific data locations that can lead to problem improvement" with different "types of suggestions for problem improvement" should be investigated first. In addition, it becomes possible to efficiently set the investigation priority and investigate specific data locations that have the potential to lead to business improvement or the creation of new services.

[0076] Furthermore, according to this embodiment, the user can efficiently identify turbulence areas that should be prioritized for investigation, regardless of the "type of insights for problem improvement," through the GUI15D, which displays specific data points that should be investigated in a comparable manner based on investigation priority.

[0077] [Embodiment 2] In Embodiment 1, "stagnation," "overtaking," and "discontinuation" were given as examples of "types of suggestion." In Embodiment 2, instead of "stagnation," "overtaking," and "discontinuation," types based on other indicators are adopted as the categories of "types of suggestion."

[0078] Figure 11 is a diagram showing the types of suggestions related to Embodiment 2. In Embodiment 2, six types are listed as "types of suggestions".

[0079] The first type is "#1: Status value range is constant". In "#1", the status value (#1) is the number of products completed on the relevant manufacturing line per unit time t within the time frame T of the data being analyzed.

[0080] In this state, when the production plan line is "laminar flow," the status value (#1) always falls within a predetermined range (the status value range is constant). However, the status value (#1) may deviate from the predetermined range. Therefore, from the customer flow data 1d, locations where the status value (#1) deviates from the predetermined range are extracted as candidate data locations 52. The process for extracting candidate data locations after extracting candidate data locations 52 is the same as in Embodiment 1.

[0081] The second type is "#2: Status transition order is constant". In "#2", the "current process" in which the product with the corresponding product ID is located is used as the status value (#2).

[0082] In this state, when the production plan line is "laminar flow," the status value (#2) progresses in a predetermined order (the order of status progression is constant). However, the progression of the status value (#2) may deviate from the predetermined order. Therefore, from the customer flow data 1d, locations where the progression of the status value (#2) deviates from the predetermined order are extracted as candidate data locations 52. The process for extracting candidate data locations after the extraction of candidate data locations 52 is the same as in Embodiment 1.

[0083] The third type is "#3: Status duration is constant." In "#3," the status value (#3) is the "duration time for each process for each product ID." "Status duration is not constant" in Embodiment 1 would include, for example, "stagnation" or "discontinuation of production."

[0084] Here, when the production plan line is in a "laminar flow" state, the dwell time at each process of the status value (#3) becomes a constant value (status dwell time is constant). However, there are cases where the dwell time at each process of the status value (#3) does not become a constant value. Therefore, from the customer flow data 1d, locations where the dwell time at each process of the status value (#3) deviates from a predetermined time are extracted as candidate specific data locations 52. The process for extracting candidate specific data locations after the extraction of candidate specific data locations 52 is the same as in Embodiment 1.

[0085] The fourth type is "#4: The difference range of status values ​​for multiple product IDs is constant." In "#4," the status value (#1) is used as the status value (#4). "The difference range of status values ​​for multiple product IDs is not constant" is, in Embodiment 1, for example, "overtaking."

[0086] In this state, when the production plan line is "laminar flow," the difference in the status values ​​(#4) of multiple product IDs processed on the production line is less than or equal to a certain value (the range of status value differences for multiple product IDs remains unchanged). However, there are cases where the difference in status values ​​(#4) is not less than or equal to a certain value. Therefore, from the customer flow data 1d, locations where the difference in status values ​​(#4) deviates from a predetermined range are extracted as candidate data locations 52. The process for extracting candidate data locations after extracting candidate data locations 52 is the same as in Embodiment 1.

[0087] The fifth type is "#5: The trend of increase or decrease in the number of product IDs remains constant." "#5" refers to a situation where the total number of product IDs that remain in all processes (hereinafter referred to as "number of product IDs") in a unit of time t within the time frame T of the data being analyzed remains constant. "The trend of increase or decrease in status values ​​is not constant" would refer to a situation such as "the interval at which products are introduced into the manufacturing line is not constant, and introduction may be delayed."

[0088] In this state, when the production plan line is "laminar flow," the number of product IDs always falls within a predetermined range (the status value range remains unchanged). However, the number of product IDs may deviate from the predetermined range. Therefore, from the customer flow data 1d, locations where the number of product IDs deviates from the predetermined range are extracted as candidate specific data locations 52. The process for extracting candidate specific data locations after extracting candidate specific data locations 52 is the same as in Embodiment 1.

[0089] The sixth type is "#6: The upper limit of the number of product IDs in the same process is constant." "#6" refers to the fact that the number of product IDs remaining in the relevant process within the time frame T of the data being analyzed does not exceed a certain upper limit. "The upper limit of the status value in the same process is not constant" refers to situations such as "a bottleneck is occurring in the relevant process."

[0090] Here, when the production plan line is in a "laminar flow" state, the number of product IDs in the relevant process is always below a predetermined upper limit (the upper limit of the status value for the same process remains unchanged). However, the number of product IDs in the relevant process may exceed the predetermined upper limit. Therefore, from the customer flow data 1d, locations where the number of product IDs in the relevant process exceeds the predetermined upper limit are extracted as candidate specific data locations 52. The process for extracting candidate specific data locations after extracting candidate specific data locations 52 is the same as in Embodiment 1.

[0091] Figure 12 shows GUI15D2, which displays the results of the investigation priority setting process according to Embodiment 2. Figure 12 is merely an example of the display configuration and may differ from the actual configuration. GUI15D2 visualizes and displays a list of specific data locations (turbulence locations) that lead to problem improvement and their investigation priorities in descending order of investigation priority. GUI15D2 can be scrolled to display three or more specific data locations (turbulence locations) that lead to problem improvement and their investigation priorities.

[0092] GUI15D2 differs from GUI15D of Embodiment 1 in that it can display a "stacked bar graph" instead of customer flow data (diagram chart) for all or some of the types of suggestions #1 to #6 described above.

[0093] GUI15D2 includes a suggestion type selection switch 15D2. Depending on the type of suggestion selected by the suggestion type selection switch 15D2, it is set whether or not to include specific data locations (turbulence locations) that can lead to problem improvement and their investigation priority in the list display. The visualization unit 15 displays a list of the suggestion types selected by the user operation of the suggestion type selection switch 15D1, including specific data locations (turbulence locations) that can lead to problem improvement and their investigation priority, in descending order of investigation priority.

[0094] Furthermore, GUI15D2 includes a visualization type selection switch 15D3. Depending on the visualization type selected by the visualization type selection switch 15D3, for a specific type of suggestion among the types #1 to #6, a "stacked bar graph" is specified to be displayed instead of customer flow data (diagram chart).

[0095] A "stacked bar graph" is a graph created by dividing customer flow data (diagram chart) 1d into predetermined time intervals along the time axis, summing the number of product IDs for each process ("Process 1" to "Process 6") for each predetermined time interval, and stacking the resulting bars.

[0096] In the example in Figure 12, customer flow data (diagram chart) 1d21 related to "#4: Status value difference range of multiple product IDs remains unchanged" is displayed as the first-highest investigation priority, along with an identification indicator for the production plan line 51z1 included in the specific data area that needs to be resolved. Also, a stacked bar graph 1d22 related to "#5: Increase / decrease trend of product ID number remains unchanged" is displayed as the second-highest investigation priority, along with an identification indicator for the number of instances (number of product IDs) 51z2 included in the specific data area that needs to be resolved. Furthermore, customer flow data (diagram chart) 1d23 related to "#3: Status dwell time is constant" is displayed as the third-highest investigation priority, along with an identification indicator for the production plan line 51z3 included in the specific data area that needs to be resolved.

[0097] Furthermore, the aforementioned "stacked bar graph" may also display "stagnation," "overtaking," and "discontinuation" in Embodiment 1.

[0098] In this embodiment, as described above, six types of "suggestion types" from "#1" to "#6" have been listed, but the embodiment is not limited to these. In other words, this embodiment only needs to extract the relevant location as a "candidate specific data location that can lead to problem improvement" when various indicators representing the manufacturing performance of a product in a manufacturing line including multiple processes show unusual changes that are different from the norm.

[0099] (Effects of Embodiment 2) According to this embodiment, in addition to the effects of Embodiment 1, the user can intuitively grasp the turbulence areas that should be prioritized for investigation by using the GUI15D2, which displays specific data points to be investigated as a stacked bar graph.

[0100] (Computer 1000 hardware) Figure 13 is a hardware diagram showing an example configuration of computer 1000. For example, the manufacturing management support system 10, or a system with appropriately distributed components of this system, is implemented by computer 1000.

[0101] The computer 1000 comprises a processor 1001 including a CPU, a main memory 1002, an auxiliary memory 1003, a network interface 1004, an input device 1005, and an output device 1006, all interconnected via an internal communication line 1009 such as a bus.

[0102] The processor 1001 controls the overall operation of the computer 1000. The main memory 1002 is composed of, for example, volatile semiconductor memory and is used as the work memory of the processor 1001. The auxiliary storage device 1003 is composed of a large-capacity non-volatile storage device such as a hard disk drive, SSD (Solid State Drive), or flash memory, and is used to retain various programs and data for long periods of time.

[0103] The executable program 1100 stored in the auxiliary storage device 1003 is loaded into the main memory device 1002 when the computer 1000 starts up or when needed, and the processor 1001 executes the executable program 1100 loaded into the main memory device 1002. This realizes various systems that perform various processes.

[0104] The executable program 1100 may be recorded on a non-temporary recording medium, read from the non-temporary recording medium by a media reader, and loaded into the main memory 1002. Alternatively, the executable program 1100 may be obtained from an external computer via a network and loaded into the main memory 1002.

[0105] The network interface 1004 is an interface device for connecting computer 1000 to various networks within the system or for communicating with other computers. The network interface 1004 consists of, for example, a NIC (Network Interface Card) such as a wired LAN (Local Area Network) or a wireless LAN.

[0106] The input device 1005 consists of a keyboard, a pointing device such as a mouse, and is used by the user to input various instructions and information into the computer 1000. The output device 1006 consists of a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display, or an audio output device such as a speaker, and is used to present necessary information to the user when needed.

[0107] The technology disclosed herein is not limited to the embodiments described above, but includes various modifications. For example, the embodiments described above are described in detail to illustrate the technology disclosed herein and are not necessarily limited to having all the configurations described. Furthermore, to the extent that they do not contradict each other, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and to add parts or all of the configuration of one embodiment to parts or all of the configuration of another embodiment. Furthermore, it is possible to add, delete, replace, integrate, or distribute parts of the configuration of each embodiment. In addition, the configurations and processes shown in the embodiments can be distributed, integrated, or rearranged as appropriate based on processing efficiency or implementation efficiency. [Explanation of Symbols]

[0108] 10: Manufacturing management support system, 12: Specific data location candidate extraction unit, 13: Group creation unit, 14: Investigation priority setting unit, 15: Visualization unit, 1000: Computer.

Claims

1. A manufacturing management support method performed by a manufacturing management support system that assists in the management of product manufacturing in a manufacturing line that performs multiple processes, A specific data location candidate extraction step involves extracting specific data location candidates from production plan lines for each product ID, which are formed by connecting the actual data with a time, a product ID that identifies the product, and a process ID that identifies the process in which the product of the product ID was located at that time, in a coordinate system with a first axis and a second axis, with the time taken in the first axis direction and the process in the second axis direction, and extracting specific data location candidates from production plan lines for each product ID, which are formed by connecting the actual data with straight lines in the first axis direction, and which include turbulence locations where adjacent production plan lines in the first axis direction are not parallel in the second axis direction. A group creation step involves extracting and grouping production plan lines that include at least a portion of the same specific data location candidate extracted by the specific data location candidate extraction step, A priority setting step to set the investigation priority of a specific data location area based on the time width indicating the width of each process in a specific data location area that defines the range for setting a new production plan line to laminarize the production plan line based on the production plan line grouped in the group creation step, and the time difference for each process indicating the interval between each process in the production plan line that is not grouped, A specific data location candidate area setting step sets specific data location candidate areas by expanding the area for each group, which includes the production plan lines grouped by the group creation step, to just before it first intersects with other production plan lines in both the positive and negative directions of the first axis; A specific data location area setting step involves calculating the average of the process-specific time differences for the ungrouped production plan line pairs, and setting the specific data location area by excluding the margin area based on the average from the specific data location candidate area, It has, In the aforementioned priority setting step, The first average of the time width of the specific data location area set in the specific data location area setting step, and the second average of the time difference for each process of the margin area, are calculated, and the ratio of the first average to the second average is set as the investigation priority of the specific data location area. A manufacturing management support method characterized by the following features.

2. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, In the coordinate system, locations are extracted where the slope of a process in one of the production plan lines is less than a predetermined value, where adjacent production plan lines are not parallel in the second axis direction, or where the slope of the process differs within one of the production plan lines. A manufacturing management support method characterized by the following features.

3. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, In the aforementioned coordinate system, the points where the production plan lines intersect are extracted. A manufacturing management support method characterized by the following features.

4. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, In the aforementioned coordinate system, the points where the production plan line ends at an intermediate process among the multiple processes are extracted. A manufacturing management support method characterized by the following features.

5. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, The production plan extracts locations where the number of completed products per unit time after completing the aforementioned multiple processes deviates from a predetermined range. A manufacturing management support method characterized by the following features.

6. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, Extract the locations in the production plan where the execution order of the aforementioned multiple processes deviates from a predetermined order. A manufacturing management support method characterized by the following features.

7. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, Extract the locations in the production plan where the time spent in the aforementioned process deviates from a predetermined time. A manufacturing management support method characterized by the following features.

8. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, The production plan extracts locations where the difference between different product IDs in the number of completed products per unit time after the completion of the aforementioned multiple processes deviates from a predetermined range. A manufacturing management support method characterized by the following features.

9. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, Extract the locations in the production plan where the trend of increase or decrease in the number of products remaining in the aforementioned multiple processes deviates from predetermined conditions. A manufacturing management support method characterized by the following features.

10. A manufacturing management support method according to claim 1, In the aforementioned step of extracting candidate specific data locations, As for the aforementioned turbulent flow location, Extract the locations in the production plan where the number of products remaining in each of the processes exceeds a predetermined upper limit. A manufacturing management support method characterized by the following features.

11. A manufacturing management support method according to any one of claims 2 to 10, A visualization step that displays the aforementioned investigation priority, the production plan line included in the specific data location area, and the type of suggestions for problem improvement and new service creation corresponding to the specific data location area, along with the aforementioned actual data, or a bar graph obtained by dividing the aforementioned actual data into predetermined time intervals on the first axis and summing up the number of product IDs that stay in each of the aforementioned processes for each predetermined time interval. A manufacturing management support method characterized by having the following features.

12. A manufacturing management support system that assists in the management of product manufacturing in a manufacturing line that performs multiple processes, A specific data location candidate extraction unit extracts specific data location candidates from production plan lines for each product ID, which are formed by connecting the actual data for each product ID with straight lines, and from the production plan lines for each product ID, which are adjacent in the first axis direction and include turbulent flow locations in the second axis direction, where the production plan lines adjacent in the first axis direction are not parallel in the second axis direction. A group creation unit extracts and groups production plan lines that include at least a portion of the same specific data location candidate extracted by the specific data location candidate extraction unit, A priority setting unit sets the investigation priority of a specific data location area based on the time width indicating the width of each process in a specific data location area that defines the range for setting a new production plan line to laminarize the production plan line based on the production plan line grouped by the group creation unit, and the time difference for each process indicating the interval between each process in the production plan line that is not grouped. It has, The priority setting unit is, A specific data location candidate area is set by expanding the area for each group, which includes the production plan lines grouped by the group creation unit, to just before it first intersects with another production plan line in both the positive and negative directions of the first axis. The average of the process-specific time differences for the ungrouped pairs of production plan lines is calculated, and the margin area based on this average is excluded from the candidate area for the specific data location to set the specific data location area. The system calculates a first average of the time width of the set specific data location area over all the processes, and a second average of the time differences for each process in the margin area over all the processes, and sets the ratio of the first average to the second average as the investigation priority of the specific data location area. A manufacturing management support system characterized by the following features.

13. A manufacturing management support program that enables a computer to function as a manufacturing management support system that assists in managing the production of products in a manufacturing line that performs multiple processes, The aforementioned computer, A specific data location candidate extraction unit extracts specific data location candidates from production plan lines for each product ID, which are formed by connecting the actual data for each product ID with straight lines, and from the production plan lines for each product ID, which are adjacent in the first axis direction and include turbulent flow locations in the second axis direction, where the production plan lines adjacent in the first axis direction are not parallel in the second axis direction. A group creation unit extracts and groups production plan lines that include at least a portion of the same specific data location candidate extracted by the specific data location candidate extraction unit. A priority setting unit sets the investigation priority of a specific data location area based on the time width indicating the width of each process in a specific data location area that defines the range for setting a new production plan line to laminarize the production plan line based on the production plan line grouped by the group creation unit, and the time difference for each process indicating the interval between each process in the production plan line that is not grouped. To make it function as, The priority setting unit is, A specific data location candidate area is set by expanding the area for each group, which includes the production plan lines grouped by the group creation unit, to just before it first intersects with another production plan line in both the positive and negative directions of the first axis. The average of the process-specific time differences for the ungrouped pairs of production plan lines is calculated, and the margin area based on this average is excluded from the candidate area for the specific data location to set the specific data location area. The system calculates a first average of the time width of the set specific data location area over all the processes, and a second average of the time differences for each process in the margin area over all the processes, and sets the ratio of the first average to the second average as the investigation priority of the specific data location area. Manufacturing management support program.

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