WO2023171197A1 - Production management system, production management method, and production management program - Google Patents

Abstract

Provided is a technology that makes it possible to improve the quality and productivity of a production line more efficiently. A production management system (1) for a production line (10) that has at least one manufacturing device (10a, 10c, 10e) and inspection device (10b, 10d, 10f) and in which the manufacturing device and inspection device are used to perform a product manufacturing step and an inspection step, comprising: a change detection unit (110a) that detects a change in a prescribed factor relating to the manufacturing step and/or the inspection step; an indicator change acquisition unit (110b) that acquires the change in an indicator temporally before and after the change in the factor that was detected by the change detection unit, said indicator relating to the productivity and/or the manufacturing quality of the manufacturing step; and an information generation unit (110c) that uses the change in the indicator that was acquired by the indicator change acquisition unit to generate information indicating the relationship between the indicator and the change in the factor.

Description

Production management system, production management method, and production management program

The present invention relates to a production management system, a production management method, and a production management program that manage production on a product production line.

On product production lines, product inspection equipment is placed in intermediate and final processes of the line to detect defects and sort out defective products. For example, in a production line for component mounting boards, there is generally a device that prints cream solder on printed wiring boards (printing device), a device that mounts components on the board printed with cream solder (mounting device), and a device that mounts components on the board printed with cream solder. This includes equipment (reflow equipment) that heats the board to solder components to the board. Then, an inspection device placed after each production device inspects whether the work in each device is being performed correctly as planned.

In addition, on the above production line, a production management system is in operation that collects information from each manufacturing device and inspection device and comprehensively manages defect rates, production volume, etc., which improves the productivity of the entire production line. Contributing to improvement.

Regarding the state of the production line mentioned above, in addition to intentional changes planned in advance by line managers, there are many changes that occur inevitably or accidentally during production, and the fact of their occurrence and its impact are not always known. Unidentified and unexplained fluctuations in quality and productivity occur. For example, even if data exists about changes that occur inevitably or accidentally, the amount of data is enormous and cannot be grasped by line managers. In addition, many intentional changes are implemented at the operator's discretion, and are often not understood by line managers. Trying to understand these changes requires a huge amount of man-hours and is not realistic.

In the situation described above, when the quality or productivity of a production line deteriorates, it is required to be able to quickly identify the cause and make corrections. In particular, there was a need to be able to discover hidden areas for improvement when it comes to changes that occur either inevitably or accidentally. In addition, when confirming the effects of intentional changes due to improvements, it is required to be able to confirm the presence or absence of other fluctuation factors and to confirm the effects in the absence of disturbances.

International Publication No. 2012/165275 International Publication No. 2015/122272 Japanese Patent Application Publication No. 2004-198148

The present invention was made in view of the above circumstances, and its purpose is to provide a technology that makes it possible to more efficiently improve the quality and productivity of a production line.

The present invention for solving the above-mentioned problems has a production line that has a single or plural manufacturing equipment and inspection equipment, and in which a product manufacturing process and an inspection process are performed by the manufacturing equipment and the inspection equipment. A production management system relating to,
a change detection unit that detects a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition unit that acquires a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected by the change detection unit;
an information generation unit that generates information indicating a relationship between a change in the factor and the index based on the change in the index acquired by the index change acquisition unit;
This is a production management system that is characterized by comprising:
Note that the changes in the predetermined factors include planned changes that are carried out in a planned manner and other changes,
The information generated by the information generation unit and indicating the relationship between the change in the factor and the index may include information as to whether the change in the factor is the planned change or another change.

According to this, it is possible to obtain knowledge of how planned or accidental changes or unplanned changes in the manufacturing process or inspection process affect productivity and manufacturing quality in the manufacturing process. It is possible to convert information into information. As a result, it is possible to improve the productivity and manufacturing quality of the manufacturing process more reliably. More specifically, for example, when quality or productivity deteriorates, the cause can be identified and corrected. Note that information regarding whether the factor is a planned change or something else may be stored in association with the above-mentioned factor. In addition, hidden improvement targets can be discovered, especially for changes that occur inevitably or accidentally, as examples of changes other than planned changes. Furthermore, when confirming the effects of intentional changes due to improvements, it is possible to confirm the presence or absence of other fluctuation factors, and the effects can be confirmed in the absence of disturbances. Furthermore, the change in a predetermined factor related to the manufacturing process and/or the inspection process detected by the change detection section may be a change in inspection conditions. In addition, the indicators related to productivity and/or manufacturing quality in the manufacturing process are numerical values such as the false judgment rate and direct delivery rate, which are calculated based on the inspection results of the board or parts in a predetermined period, and are based on changes in the factors described above. The relationship with the index may be an increase or decrease in a numerical value calculated based on the inspection results of the board or component based on the 4M change. According to this, it is possible to obtain knowledge of how planned changes to the inspection process or changes made at the discretion of the person in charge will affect productivity and inspection accuracy in the manufacturing process, and to convert this information into information. It is possible to do so. As a result, productivity and accuracy of the inspection process can be improved more reliably. Note that "the manufacturing process and/or the testing process" herein refers to at least one of the manufacturing process and the testing process. The same applies to "productivity and/or manufacturing quality" and similar expressions below.

Further, in the present disclosure, information on a change in the predetermined factor detected by the change detection unit, information on a change in the index acquired by the indicator change acquisition unit, and a relationship between the change in the factor and the index are provided. It is also possible to further include a database unit that stores and accumulates the information in association with the information indicating the information.

According to this, it is possible to obtain knowledge of how planned or accidental changes or unplanned changes in the manufacturing process or inspection process affect productivity and manufacturing quality in the manufacturing process. It is possible to store information using a database.

Further, in the present disclosure, information on a change in the predetermined factor detected by the change detection unit, information on a change in the index acquired by the indicator change acquisition unit, and a relationship between the change in the factor and the index are provided. It is also possible to further include an output unit that outputs the information in association with the information indicating the information. According to this, it is possible to report findings on how planned or accidental changes to manufacturing processes and inspection processes, as well as unplanned changes, affect productivity and manufacturing quality in manufacturing processes. , it is possible to share within the organization more reliably.

Further, in the present disclosure, the change in the predetermined factor is a 4M change in the manufacturing process,
The index related to productivity and/or manufacturing quality in the manufacturing process is a numerical value calculated based on the number of defects or the number of defects in a predetermined period,
The relationship between the change in the factor and the index may be an increase or decrease in the number of defects or a numerical value calculated based on the number of defects based on the 4M change.
In addition, the index related to productivity and/or manufacturing quality in the manufacturing process is a numerical value indicating the production amount or production speed calculated based on the production number or production time in a predetermined period, and is a numerical value indicating the production amount or production speed, and The relationship with the index may be an increase or decrease in numerical values indicating production volume or production speed based on the 4M change. Examples of numerical values indicating the above-mentioned production amount and production speed include the number of products produced per unit time or the average time required for production per product.
In addition, the index related to productivity and/or manufacturing quality in the manufacturing process is a numerical value indicating productivity and quality calculated based on the number of products produced and the number of defects in a predetermined period, and changes in the factors and the index The relationship may be an increase or decrease in numerical values indicating productivity and quality based on the 4M change. Note that, as the numerical value indicating the productivity and quality mentioned above, for example, OEE (Overall Equipment Efficiency) can be exemplified.
Furthermore, the index related to productivity and/or manufacturing quality in the manufacturing process is a numerical value that is calculated based on the measured value of product quality and indicates process capability such as not being defective, but close to defective, or not close to defective. The relationship between the change in the factor and the index may be an increase or decrease in the numerical value indicating the process capability based on the 4M change. Examples of numerical values indicating this process capability include Cpk and Cp (process capability index).

That is, in the present disclosure, the index related to productivity and/or manufacturing quality in the manufacturing process is calculated based on or at least one of the number of defects, process capacity, production volume, and production speed in a predetermined period. is a numerical value,
The relationship between the change in the factors and the index may be an increase or decrease in at least one of the number of defects, process capacity, production volume, and production speed based on the 4M change, or a numerical value calculated based on them. . According to this, it is possible to more reliably understand the impact of 4M changes in the manufacturing process on quality, such as the number of defects, defective rate, and process capacity, and the impact on productivity, such as production volume and production speed. It is possible.

Further, in the present disclosure, information indicating a relationship between a change in the factor and the index newly generated by the information generation unit;
an information confirmation unit that acquires a relationship between a change in the factor and information that has been generated in the past by the information generation unit in response to a change in the same factor and indicates a relationship between a change in the same factor and the index; It is also possible to prepare further.

The present disclosure also provides a production control method relating to a production line that includes a single or multiple manufacturing device and inspection device, and in which a product manufacturing process and an inspection process are performed by the manufacturing device and inspection device,
a change detection step of detecting a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition step of acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected in the change detection step;
an information generation step of generating information indicating a relationship between the change in the factor and the index from the change in the index acquired in the index change acquisition step;
It may be a production control method characterized by having the following.

Further, the present disclosure provides that the change in the predetermined factor includes a planned change that is carried out in a planned manner and a change other than that,
The information generated in the information generation step and indicating the relationship between the change in the factor and the index is characterized in that it includes information as to whether the change in the factor is the planned change or another change. , the above production control method may be used.

The present disclosure also provides information on a change in the predetermined factor detected in the change detection step, information on a change in the index acquired in the index change acquisition step, and information on a change in the factor and the index. The production management method described above may further include a database creation step of storing and accumulating the information in association with the information indicating the relationship.

The present disclosure also provides information on a change in the predetermined factor detected in the change detection step, information on a change in the index acquired in the index change acquisition step, and information on a change in the factor and the index. The production management method described above may further include an output step of correlating and outputting information indicating a relationship.

Further, the present disclosure provides that the change in the predetermined factor is a so-called 4M change in the manufacturing process,
The index related to productivity and/or manufacturing quality in the manufacturing process is at least one of the number of defects, process capacity, production volume, and production speed in a predetermined period, or a numerical value calculated based on them,
The relationship between the change in the factors and the index is an increase or decrease in at least one of the number of defects, process capacity, production volume, and production speed, or a numerical value calculated based on them, based on the 4M change. The above production control method may be characterized in that:

The present disclosure also provides information indicating a relationship between a change in the factor newly generated in the information generation step and the index;
an information confirmation step of acquiring the relationship between the change in the factor and information that has been generated in the past in the information generation step and indicates the relationship between the change in the same factor and the index; The above production control method may further include the following.

A production management program relating to a production line that has a single or multiple manufacturing equipment and inspection equipment, and executes a product manufacturing process and an inspection process using the manufacturing equipment and inspection equipment,
a change detection step of causing a computer to detect a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition step of acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected in the change detection step;
an information generation step of generating information indicating a relationship between the change in the factor and the index from the change in the index acquired in the index change acquisition step;
It may be a production management program characterized by causing the execution of the following.

Further, the change in the predetermined factor includes a planned change that is carried out in a planned manner and other changes,
The information generated in the information generation step and indicating the relationship between the change in the factor and the index includes information as to whether the change in the factor is the planned change or another change. , or the above-mentioned production management program.

Note that each of the above configurations and processes can be combined with each other to constitute the present invention unless technical contradiction occurs.

According to the present invention, the quality and productivity of the production line can be improved more efficiently.

1 is a schematic configuration diagram of a production management system according to an embodiment of the present invention. FIG. 1 is a functional block diagram of a production management system according to an embodiment of the present invention. FIG. 3 is a diagram for explaining planned change information and accidental change information when operating a production line according to an embodiment of the present invention. 1 is a diagram showing features of a production management system according to an embodiment of the present invention. 1 is a flowchart of processing in the entire production management system according to an embodiment of the present invention. 5 is a flowchart of a data accumulation process and a case accumulation process according to an embodiment of the present invention. 2 is a detailed flowchart of a knowledge extraction process and a process for setting narrowing conditions according to an embodiment of the present invention. 2 is a flowchart of a process for continuous evaluation of knowledge according to an embodiment of the present invention. 1 is a flowchart of a process for continuing evaluation of knowledge according to an embodiment of the present invention. It is a figure which shows the example of the report of a fluctuation|variation and its influence based on the Example of this invention. FIG. 3 is a diagram showing an example of a table in which cases are extracted and listed according to an embodiment of the present invention. It is a figure which shows the example of the table which extracts and lists the cases based on the Example of this invention, and is further narrowed down. It is a figure which shows the example of the table which extracts and lists the cases based on the Example of this invention, and is further narrowed down. It is a figure which shows the example of the table which extracts and lists the cases based on the Example of this invention, and is further narrowed down. It is a figure which shows the example of the table which extracts and lists the cases based on the Example of this invention, and is further narrowed down. It is a figure which shows the example of the table which extracts and lists the cases based on the Example of this invention, and is further narrowed down. It is an example of the report of the improved case based on the Example of this invention. 3 is an example of a report of an aggravated case according to an embodiment of the present invention.

<Application example>
As shown in FIG. 1, a production line 10 to which the present invention is applied includes a solder printing device 10a, a solder printing post-inspection device 10b, a mounter 10c, a post-mounting inspection device 10d, a reflow oven 10e, and a post-reflow inspection device 10f. . Each device in the production line 10 is connected to a mounting machine server 1c, an inspection machine server 1e, and a line management server 1a via a network such as a LAN.

FIG. 2 shows a schematic block diagram of the production management system 1 according to this embodiment. The production management system 1 includes a control unit 11b for managing information on the influence of the 4M change on the production line 10 on the quality or productivity of the production line 10. The control unit 11c also includes a database unit 11c that stores information generated by the control unit 11b regarding the influence of 4M changes on the production line 10 on the quality or productivity of the production line 10 in a database. The control unit 11b further includes a change detection unit 110a, an index change acquisition unit 110b, and an information generation unit 110c as functional modules.

The change detection unit 110a detects changes in the manufacturing process and inspection process in the production line 10 during a specified period. The index change acquisition unit 110b acquires changes in the number of defects, defective rate, etc. before and after the changes related to the manufacturing process and inspection process. Then, the information generation unit 110c generates changes in the manufacturing process and inspection process based on the change information detected by the change detection unit 110b and changes in the number of defects, defective rate, etc. acquired by the index change acquisition unit. and information indicating the relationship between changes in indicators such as the number of defectives and defective rate.

Next, using FIG. 4, the features of the production management system 1 in this application example will be described. This application example starts with a change in the cause, accumulates examples of its effects, and converts it into knowledge. In other words, planned changes such as work (setup changes, parameter changes) based on production plans, work standards, and instructions for improvement, or work that naturally occurs in the progress of production (replacement when parts are out of stock ⇒ parts lot changes) ) or fluctuations that occur within the production equipment (vacuum pressure, temperature, etc.). Then, data is used to confirm improvement/deterioration/no change in quality, improvement/deterioration/no change in productivity, and improvement/deterioration/no change in cost.

As a result, it is possible to detect factors that cannot be recognized using conventional methods, and it is possible to more reliably improve the quality and productivity of the production line. In addition, in this example, we will not only deal with individual cases, but will aim to improve the process based on the basis obtained by accumulating data, so it is necessary to clearly state the basis of the measures for improvement. It is possible to improve the process more efficiently.

Hereinafter, embodiments of the present invention will be described based on the drawings. However, the scope of the present invention is not intended to be limited to the constituent elements described in the following examples unless otherwise specified.

<Example>
A production management system 1 according to the present invention includes, for example, a production line 10 as shown in FIG. 1, and performs production management of this production line 10. The production line 10 in FIG. 1 is a surface mounting line for printed circuit boards. As shown in FIG. 1, the production line 10 according to the present embodiment includes, in order from the upstream side, a solder printing device 10a, a post-solder printing inspection device 10b, a mounter 10c, a post-mount inspection device 10d, a reflow oven 10e, and a solder post-printing inspection device 10d. An inspection device 10f is provided.

The solder printing device 10a is a device that prints solder paste for electrode parts on a printed circuit board. The mounter 10c is a device for placing a large number of electronic components to be mounted on a printed circuit board onto solder paste. Further, the reflow oven 10e is a heating device for soldering electronic components placed on a printed circuit board to printed wiring on the board. The post-solder printing inspection device 10b, the post-mount inspection device 10d, and the post-reflow inspection device 10f inspect the condition of the printed circuit board at the exit of each process, and automatically detect defects or potential defects.

The solder printing device 10a, mounter 10c, and reflow oven 10e (hereinafter also collectively referred to as manufacturing devices) described above are connected to the mounting machine server 1c via a network such as a LAN. A maintenance device 1g for maintaining the manufacturing device is connected to this mounting machine server 1c. Further, the solder printing post-inspection device 10b, the post-mount inspection device 10d, and the post-reflow inspection device 10f (hereinafter also collectively referred to as inspection devices) are connected to the inspection machine server 1e via a network. Moreover, the mounting machine server 1c and the inspection machine server 1e are further connected to a line management server 1a, which is a server that collectively manages the production line 10.

The line management server 1a, the mounting machine server 1c, and the inspection machine server 1e are composed of general-purpose computer systems equipped with a CPU (processor), a main storage device (memory), an auxiliary storage device (hard disk, etc.), and the like. Ru. Note that a line manager terminal 1b operated by a line manager and a work instruction/work record terminal 1f operated by a worker are connected to the line management server 1a. The line manager terminal 1b is equipped with an application for checking and analyzing the situation. The work instruction/work record terminal 1f is equipped with a work instruction/record application.

A terminal 1d for a mounting machine staff operated by a mounting machine staff is connected to the mounting machine server 1c. The mounting machine personnel terminal 1d is equipped with a mounting program creation application. The line manager's terminal 1b, the mounting machine person's terminal 1d, and the work instruction/work record terminal 1f are equipped with input devices (keyboard, mouse, controller, touch panel, etc.), output devices (display, printer, speakers, etc.) as appropriate. , equipped. A production management system 1 includes these entire systems. In the production management system 1 described above, various parameters related to the production of the production line 10 are determined, and variations in these parameters affect the quality and productivity of the production line 10. Therefore, the production management system 1 manages these parameters while constantly changing them appropriately.

FIG. 2 shows a schematic block diagram of the production management system 1 according to this embodiment. As shown in FIG. 2, the production management system 1 includes a data acquisition unit 11a that receives data from each manufacturing device and each inspection device on the production line 10. The control unit 11b also includes a control unit 11b for managing information on the influence of the 4M change on the production line 10 on the quality or productivity of the production line 10 based on the data acquired by the data acquisition unit 11a. The database unit 11c also includes a database unit 11c that stores data acquired by the data acquisition unit 11a and information generated by the control unit 11b regarding the influence of the 4M change on the production line 10 on the quality or productivity of the production line 10. have Furthermore, it has an output section 11d that can output information generated by the control section 11b regarding the influence of the 4M change on the production line 10 on the quality or productivity of the production line 10. The control unit 11b further includes a change detection unit 110a, an index change acquisition unit 110b, and an information generation unit 110c as functional modules. Each functional module is realized, for example, by a CPU (not shown) reading and executing a program stored in a database unit 11c having an information storage function. The 4M changes mentioned above are changes related to people, machines (equipment), methods, materials, and measurements in the manufacturing process or inspection process, and include planned changes and accidental changes. In addition to physical changes, the meaning includes all changes that occur in the manufacturing process or inspection process, such as changes in conditions and parameters.

The change detection unit 110a detects changes related to manufacturing processes and inspection processes in the production line 10, mainly 4M changes that occur intentionally or accidentally, during a specified period. The index change acquisition unit 110b obtains indicators related to productivity and manufacturing quality before and after the changes related to the manufacturing process and inspection process, based on the data from each manufacturing device and each inspection device acquired by the data acquisition unit 11a. Changes in the number of defects, defective rate, etc. are obtained. Then, the information generation unit 110c generates changes in the manufacturing process and inspection process based on the change information detected by the change detection unit 110b and changes in the number of defects, defective rate, etc. acquired by the index change acquisition unit. and information indicating the relationship between changes in indicators such as the number of defectives and defective rate.

By the way, as described above, as information for appropriately managing the parameters of the production management system 1, there are planned change information and accidental change information, as described below. Next, planned change information and accidental change information when operating the production line 10 will be explained using FIG. 3. First, planned change information refers to information for work that is studied in advance and systematically implemented to improve the quality and productivity of the production line 10 when producing a predetermined product. This information is information that is known in advance to the line manager. For example, when executing a component mounting program for a printed circuit board that is a product, work such as changing the type of nozzle or feeder used for a specific component number, or changing the mounting conditions for a specific component number. Then, the mounting program in the mounting machine server 1c is changed. Further, for example, when a nozzle, feeder, head, etc. are removed from the mounter 10c and taken to the maintenance device 1g to perform regular maintenance, the information is sent from the maintenance device 1g to the mounting machine server 1c.

Next, accidental change information refers to information related to work that is not planned and is performed depending on the situation. From the line manager's perspective, this information is a task that cannot be grasped because it is neither planned nor reported one by one. Regarding this, for example, when checking a nozzle with many defects, the nozzle is clogged, or when checking a feeder with many mounting errors, the feeder is distorted. When recorded on the person in charge terminal 1d, the information is sent to the mounting machine server 1c or the line management server 1a. Alternatively, if the person in charge of the mounting machine performs tasks such as replacing the reel when a component runs out, replacing the feeder when a number of defects occur, or temporarily changing mounting conditions when a number of mounting errors occur, that information may be transferred to the mounter. 10c to the mounting machine server 1c. There is also a mounter 10c that automatically switches to a spare feeder when a component runs out, and in this case, change information is automatically sent to the mounting machine server 1c without any work.

Next, a specific example of the range on the production line 10 affected by each of the above-mentioned planned change information and accidental change information will be described.
(1) Regarding planned change information (1-1) Nozzle replacement: The scope of influence is that each of the nozzle ID before replacement and the nozzle ID after replacement is a component (1-2) Change in mounting parameters of part part number: Impact The scope is the entire part before and after the changed part number (1-3) Changes in the nozzle or feeder model assigned to the part number: The affected range is the part number (including the combination of the part number and nozzle or feeder model) good)
(1-4) Implementation of mask cleaning: The scope of influence is likely to be the entire part before and after the changed part number.
For example, if the feeder format is changed from FeederA to FeederB, the mounter program should be changed to Rev. A to Rev. B (assignment change). Then, when determining the effect of this feeder change, the mounter program Rev. A to Rev. A method of comparing components C1 and C2 before being changed to B (allocation change) and components C1 and C2 after the allocation change, and components C1 and C2 mounted on Feeder A and component C1 mounted on Feeder B. It is conceivable to compare it with C2.

In addition, when replacing the feeder model, check the mounting error rate, actual defective rate, Cpk of deviation amount, etc. for the entire part before and after the change (it is necessary to check the same effect for changes to the mounter in general). When performing mask cleaning, check the volume of solder after printing, the height of solder wetting after reflow, etc. (it is necessary to check the same effect for changes to the printing machine in general).

(2) About accidental change information (2-1) Reel replacement when parts run out: The affected area is the parts mounted on the feeder in the same slot (position where the feeder is installed) (2-2) The mounter has a mounting error warning The feeder stopped and the worker inserted and removed the feeder (without replacing it): The affected area is likely to be the parts mounted with that feeder ID.
For example, when the component reel ID is changed from ReelA to ReelB, the feeder ID is maintained as FeederID1. When determining the influence of this reel replacement, there is a method of comparing the parts C1 and C2 before the reel replacement with the parts C1 and C2 after the reel replacement.
Furthermore, when the feeder is inserted and removed, there is a method of comparing the parts C1 and C2 before the feeder is inserted and removed, and the parts C1 and C2 after the feeder is inserted and removed.

Next, a specific example of changes to be made in the production line 10 will be described.
(1) Specific examples of work types include the following.
(1-1) Maintenance (head, nozzle, feeder)
(1-2) Change mounting parameters (change component size, change component recognition parameters, change component recognition tolerance range)
(1-3) Changing the assignment of part numbers and nozzle/feeder models (nozzles with different diameters, feeders that use magnets to prevent parts from standing up, and feeders that do not)
(1-4) Replacement of nozzle and feeder (2) Specific examples of change types include the following.
(2-1) Switching of parts lot due to parts shortage (2-2) Temporary change of mounting parameters by operator (limited scope)
(2-3) Resetting parameters temporarily changed by the operator due to setup change

In this embodiment, as information accompanying the change information, nozzle and feeder specifications, component part numbers, component libraries, and component types and sizes may be linked and managed. Further, in order to have an overview of the entire production process, each change may be managed in association with changes in quality, productivity, and cost at the time of occurrence. Further, by extracting and reporting common points between changes that improve the quality and productivity of the production line and incidental information, it may be possible to contribute to the work of changing production rules. In this case, the target of the report is the creator of production rules and work standards. Degrading changes may also be reported as well. This makes it possible to clarify tasks that should not be performed. In addition, the basis for improvement and deterioration should be clarified by showing correlation coefficients, etc. The itemization of this embodiment is that hidden know-how can be clarified and the entire process can be improved. Another point is that the entire process can be improved by improving unseen deteriorating factors.

The production rules may be changed based on the knowledge obtained in this example. Specific production rules include selection of nozzles and feeders for parts, image processing methods for parts (determining which nozzles to use for parts of the same shape, etc.), and maintenance intervals (such as determining quality between the previous maintenance and the next maintenance). comparison). In addition, there are tasks that should be improved (method of attaching the component reel to the feeder) and tasks that should be done (replacing the feeder and resetting incorrect learning of the suction position). Furthermore, in this embodiment, the points of change are found, and the results are confirmed and accumulated. Further, in this embodiment, a recommended new production rule may be presented based on a comparison before and after the change. In that case, we will indicate recommended production conditions and data (number of improvements/number of cases, etc.) (for example, it is recommended to use this nozzle for 1608 chip resistors).

Next, the features of the production management system 1 in this embodiment will be described using FIG. 4. In conventional production management, the starting point was the result, such as a decline in quality, and only intentional (planned) changes were considered as the cause. In conventional production management, a change in the result is used as a starting point to identify and correct the cause, or a change in the cause is used as a starting point to check whether there is a change in the result. They then identified the common causes of good or bad results and turned it into know-how.

On the other hand, this embodiment is characterized by starting from a change in the cause, accumulating examples of its effects, and turning it into knowledge. In other words, planned changes such as work (setup changes, parameter changes) based on production plans, work standards, and instructions for improvement, or work that naturally occurs in the progress of production (replacement when parts are out of stock ⇒ parts lot changes) ) or fluctuations that occur within the production equipment (vacuum pressure, temperature, etc.). Then, data is used to confirm improvement/deterioration/no change in quality, improvement/deterioration/no change in productivity, and improvement/deterioration/no change in cost. As a result, it is possible to detect factors that cannot be recognized using conventional methods, and it is possible to more reliably improve the quality and productivity of the production line. In addition, in this example, we will not only deal with individual cases, but will aim to improve the process based on the basis obtained by accumulating data, so it is necessary to clearly state the basis of the measures for improvement. It is possible to improve the process more efficiently.

Next, the flow of the entire system according to this embodiment will be explained using FIG. 5. The processing from step S101 to step S111 is performed by the line management server 1a, and the processing from step S112 to step S119 is performed by the situation confirmation/analysis application on the line manager terminal 1b. In step S101 of this flow, newly arrived data regarding mounting, inspection, and changes is confirmed. In step S102, mounting/inspection/change data is collected, and in step S103, the mounting/inspection/change data is saved. Then, in step S104, defects and mounting errors are totaled and stored for each time period or for each predetermined range.

Then, in step S105, it is determined whether there is a change in the mounting/inspection/change data. If it is determined in step S105 that there is a change, the process advances to step S106. On the other hand, if it is determined in step S105 that there is no change, the process returns to step S101. In step S106, the influence range (data collection range) of the change is specified based on the content of the change. In step S107, the defect/mounting error tally results before and after the change are obtained for the affected range specified in step S106. In step S108, a determination is made as to whether the defects or mounting errors are improving or worsening. In step S109, the improvement/deterioration determination results for defects/mounting errors are saved.

In step S110, it is determined whether the improvement/deterioration determination result of defects/mounting errors obtained in step S108 meets the notification conditions. If it is determined in step S110 that the facility conditions are not met (negative determination), the process returns to step S101. On the other hand, if it is determined that the notification conditions are met, the process proceeds to step S111, and notification of improvement or deterioration of the defect/mounting error is made.

In step S112, a notification of improvement or deterioration of defects or mounting errors is received in the situation confirmation/analysis application of the line manager terminal 1b. In step S113, a regular timing occurs other than the timing at which the defect/mounting error improvement/deterioration notification is received. Or something you want to investigate occurs. In step S114, a situation confirmation/analysis application is opened. In step S115, improvement/deterioration status of defects/mounting errors is confirmed. In step S116, improvement/deterioration results when the same type of change occurs are collected. In step S117, a list of improvement and deterioration results in the same type of change is displayed. In step S118, common change points are extracted. In step S119, conditions for improvement are reported.

Next, the data accumulation process in this embodiment will be explained using FIG. 6A. This is a flow in which data is continuously accumulated in the process of producing products on the production line 10. In step S201, new mounting, inspection, and change data is confirmed. In step S202, data collection regarding mounting, inspection, and changes is performed. In step S203, mounting/inspection/change data is saved. This is repeated continuously. As long as production continues, data will continue to accumulate.

Next, the case accumulation process will be explained using FIG. 6B. This is a process of periodically reading out the change information collected in FIG. 6A once a day, accumulating cases, and extracting knowledge, for example. In this flow, case accumulation is first started in step S301. Then, in step S302, change information within the target period is searched. In step S303, it is determined whether the change information is finished (whether all change information has been searched). If it is determined that the change information is not finished, the process advances to step S304.

In step S304, the range of influence of the change (data collection range) is specified. In step S305, the total results of defects and mounting errors before and after the change are obtained. In step S306, it is determined whether the defect or mounting error is improved or worsened. In step S307, the improvement/deterioration determination results for defects/mounting errors are saved. When the process in step S307 is completed, the process returns to step S302. Then, in step S303, it is determined whether the change information is finished (whether all change information has been searched). If it is determined that the change information is finished, the process advances to step S308. In step S308, knowledge is extracted.

Next, the overall flow of the knowledge extraction process for converting cases into knowledge will be explained using FIG. 7A. In this flow, if there are n conditions, the n conditions are sequentially executed. In step S401, cases in the target period are read. In step S402, narrowing conditions are set. Here, conditions are set to narrow down the cases to be displayed based on conditions such as the subject of change and the details of the change, and select and display only the cases that the user wants to check. In step S403, cases are narrowed down based on the conditions set in step S402. In step S404, it is determined whether or not there are two or more cases whose results have improved due to changes among the narrowed down cases. Here, since one improved case cannot be considered as knowledge, it is determined whether there are two or more improved cases. If it is determined in step S404 that the number of improved cases among the narrowed down cases is not two or more, the process returns to step S402. If it is determined that there are two or more improved cases among the cases narrowed down in step S404, the process advances to step S405.

In step S405, it is determined whether the sum of the narrowed down cases is an improvement determination. Here, when the narrowed down cases include a mixture of improved cases, worsened cases, or unchanged cases, it is determined whether improvement has been observed as the total value of those cases. If it is determined in step S405 that the total is not an improvement determination, the process returns to step S402. If it is determined in step S405 that the total is an improvement determination, the process advances to step S406. That is, if the total has improved, it is determined that there is an effect. In step S406, it is saved as knowledge of the improvement effect. In step S407, it is determined whether there is a next narrowing pattern. If it is determined that there is a next narrowing pattern, the process returns to step S402. If it is determined in step S407 that there is no next narrowing pattern, the process advances to step S408. In step S408, the findings are written in a report. It may be possible to report only those cases where the improvement effect is greater than or equal to a predetermined value.

Next, details of the process of step S402 for setting narrowing conditions will be described using FIG. 7B. The loop of steps S501, S502, and steps S510 to S514 is a loop for setting conditions regarding the category to be changed. The loop from step S503 to step S509 is a loop for setting conditions regarding the change content category. When this flow is executed, first, in step S501, one category to be changed is selected. Here, one category is selected from categories such as "change target," "target name," and "target attribute." In step S502, one content is further selected from among the selected categories to be changed.

Here, for example, items such as "parts product number" and "nozzle ID" are selected. In step S503, the cases are narrowed down and the change content category and its content are extracted. Here, change content categories such as "change content", "before change", and "after change" and their specific contents are extracted. In step S504, one content is selected from the changed content. Specifically, contents such as "nozzle model change" and "maintenance" are selected as the change contents. In step S505, the category to be changed is added to the narrowing down condition list.

In step S506, one is selected from the contents before and after the change included in the selected change contents. For example, when the selected change content is "nozzle type change", content such as "nozzle diameter 0.5 mm" is selected as the "before change" content. In step S507, the selected change content, before change content, and after change content are added to the "change content narrowing condition list". In step S508, it is determined whether there is the following content before and after the change.

Here, if it is determined that there is the next content before and after the change, the process returns to step S506. If it is determined that there is no next content before or after the change, the process advances to step S509. In step S509, it is determined whether there is the next change content. If it is determined in step S509 that there is the next change content, the process returns to step S504. If it is determined in step S509 that there is no next change, the process advances to step S510. This creates a filtered list for the selected change target category.

In step S510, a narrowing condition with the category to be changed is created. In step S511, the filtering conditions for the selected change target category are added to each condition of the "change content filtering condition list" and added to the "refining condition list". In step S512, it is determined whether there is the next content of the selected change target category. In step S512, if it is determined that there is the next content of the selected change target category, the process returns to step S502.

In step S512, if it is determined that there is no next content for the selected change target category, the process advances to step S513. In step S513, it is determined whether there is a next category to be changed. In step S513, if it is determined that there is the next category to be changed, the process returns to step S501. If it is determined in step S513 that there is no next category to be changed, the process advances to step S514. In step S514, the creation of the "narrowing down condition list" is completed.

Next, referring to FIG. 8, the continuous evaluation process of knowledge will be explained. In step S601, new knowledge is extracted as described in step S308. In step S602, knowledge is added to the list. In step S603, past findings under the same conditions are extracted. In step S604, it is determined whether the new knowledge and the past knowledge under the same conditions match the determination of improvement or deterioration. If it is determined in step S604 that the determinations of improvement and deterioration match, the process advances to step S606. If it is determined in step S604 that the determinations of improvement and deterioration do not match, the process advances to step S605. In step S605, it is notified that a case contrary to past knowledge has occurred.

In step S607, it is confirmed whether or not past knowledge is to be deleted. If it is determined in step S607 that past knowledge is to be deleted, the process advances to step S608. If it is determined in step S607 that past knowledge is to be maintained, the process advances to step S609. In step S608, past knowledge of the same conditions is deleted. In S609, knowledge of the same past conditions is maintained. Note that in step S606, which is proceeded to when it is determined in step S604 that the determinations of improvement and deterioration are consistent, it is notified that a case supported by past knowledge has occurred. When the processing in steps S606, S608, and S609 is completed, the process advances to step S610. In step S610, a command to proceed to the next knowledge extraction cycle is issued, and the process proceeds to step S601.

In FIG. 9, a routine for continuing evaluation of findings will be described. In this routine, first, knowledge is extracted in step S701. In step S702, past findings under the same conditions are extracted. In step S703, cases that are the basis of past knowledge are extracted. In step S704, knowledge is extracted again including cases that are the basis of past knowledge. In step S705, it is determined whether the determinations of improvement and deterioration match. If it is determined in step S705 that the determinations of improvement and deterioration match, the process advances to step S706. If it is determined in step S705 that the determinations of improvement and deterioration do not match, the process advances to step S708.

In step S706, it is notified that the past knowledge is still valid. In step S708, it is notified that the validity of past knowledge is questionable. In step S709, it is confirmed whether to change past knowledge. If it is determined in step S709 that past knowledge is to be changed, the process advances to step S710. On the other hand, if it is determined in step S709 that past knowledge is not to be changed, the process advances to step S712. In step S710, past knowledge is rewritten. In step S712, past knowledge is maintained and new knowledge is added. When the processing in steps S706, S710, and S712 is completed, the process advances to step S707. In step S707, an instruction to proceed to the next knowledge extraction cycle is received. When the process in step S707 is completed, the process returns to step S701.

Next, a method for evaluating cases will be explained. Possible ways to evaluate a case are as follows.
(1) Test the difference in average values If there is a difference in the defective rate before and after the change, if the defective rate before the change is > the defective rate after the change, it is judged as an improvement, and the defective rate before the change is < If it is the defective rate after the change, it may be determined that the defect rate has deteriorated. Alternatively, if there is no difference in the defective rate, it may be left unchanged.
(2) Compare data for 24 hours before and after the change.
In this case, a restriction may be set such that there is data for 500 or more parts both before and after the change. As a determination method in this case, for example, if the defective rate has decreased by 25% or more, it is determined that the condition has improved, if the defective rate has increased by 50% or more, it is determined that the condition has deteriorated, and otherwise it may be determined that the condition has not changed.
(3) Compare the data of more than 500 parts before and after the change.
In this case, the method for determining improvement/deterioration may be the same as (2). In addition, regarding the case where the number of parts is insufficient, if the number of parts is insufficient before the change, no evaluation will be made. If there is a shortage after the change, the next evaluation will be performed if the number of parts is sufficient. You may decide to do so.

Next, an example of a report of fluctuations and their effects will be described with reference to FIG. FIG. 10A is a table displaying change history. That is, this is an example of a report of change information, in which the upper row displays the number of times work execution has been completed for each device type, and the lower row displays a list of information on changes that have occurred. All device types may be displayed in a chronological list, or they may be displayed sorted by device type or work content. FIG. 10B is a table showing the termization of changes. In this table, Up means worse (increase in error rate), Down means better (decreased error rate), and Stay means no change (no change).

Additionally, StartToWorkDone indicates before the change, and ToWorkDoneToEnd indicates after the change. In the upper part of FIG. 10B, due to changes (nozzle maintenance in this example), the actual defective rate in AOI after mounting, the actual defective rate in AOI after reflow increase, respectively, the number of parts that are Up, Down, Stay, and parts for which there is no data. The number of is shown. This makes it clear whether an improvement or deterioration was observed in the actual defect rate in the post-mount AOI and in the post-reflow AOI. In the lower part of FIG. 10B, for each device (nozzle), the actual defective rate in the AOI after mounting, the defective rate in the AOI after reflow, the recognition error rate, and the results of Up, Down, and Stay, respectively, are displayed.

FIG. 11 shows an example of a table in which cases are extracted and listed. In this table, "change target" in "change target category" indicates to which target the 4M variation has occurred. The "target name" is the component part number name if the "change target" is a part part number, if it is a nozzle ID, it is the name of the nozzle ID, and if it is a feeder ID, it is the name of the feeder ID. The target attribute is a library name when the "change target" is a part number, and is the name of the library used for the change target. This library is defined for multiple product numbers with the same specifications and implementation conditions. As the attribute of the object, specifications such as size and color of the object to be changed may be used. That is, as in the case where the "change target" is a nozzle ID, the attribute may be displayed using the nozzle diameter, which is the specification itself to be changed. When the "change target" is a feeder ID, the attribute of the target may be defined, such as the width of the feeder.

Furthermore, although the table shown in FIG. 11 describes the contents of changes for each part number, in reality, the contents of the library will be changed, and the program will be rewritten in accordance with the changes. The change time is also stated. In the table of FIG. 10, changes in the number of parts, actual number of defectives, and actual defective rate before and after the change are described. You can use this table to get a bird's-eye view of improvement, deterioration, and no change, and then focus on any of the data to check more detailed information. At the bottom, the entire data before and after the change for the corresponding working day is also listed.

Here, for example, consider the case where the nozzle type used for part number COMP001, which is the change target in the top row in FIG. 11, is changed. This part indicates a capacitor (0.6 mm x 0.3 mm square chip) with part number COMP001, and its specifications and production conditions are defined by a library with part library name C0603. When this nozzle was changed from a diameter of 0.5 mm to a diameter of 0.7 mm, a change in the mounting conditions for the component with part number COMP001 occurred, and when comparing before and after the change, it was found that there was an effect of reducing actual defects.

There are other component numbers that use the component library C0603, such as COMP004 and COMP005, and the above change also changes the nozzles used for these component numbers COMP004 and COMP005. As a result, changes were made to the part numbers for these parts, and when comparing before and after the changes, it was found that each had an effect on reducing actual defects. Further, in the table of FIG. 10, nozzle maintenance is also performed for NOZZLE002, 006, and 007. As a result, when comparing before and after the change, the effect of reducing actual defects was recognized. Then, those changes and the results of comparison before and after the changes are registered in the database. Note that this data group for each row corresponds to a "case". Also, at the bottom, the results of a before-and-after comparison of these changes as a whole are displayed. In the example of FIG. 10, it can be seen that the actual defective rate has decreased as a result of the changes as a whole, and the quality has improved. This shows that the improvement efforts were effective.

Next, the table in FIG. 12 will be explained. This is a table when the display contents of the table shown in FIG. 11 are further narrowed down by the "part part number" to be changed. In addition to limiting the display content to "part part number," the display content may be narrowed down to "nozzle model change," and the nozzle diameter before the change may be narrowed down to "nozzle diameter 0.5 mm." Conditions can be narrowed down. As a result, 5 cases were extracted, of which 2 cases improved, 2 cases deteriorated, and 1 case remained unchanged. In this example, as a result of the total change in nozzle type, the effect of reducing actual defects remains unchanged before and after the change. Note that a general statistical method is used to determine whether the change in the calculated actual defective rate is significant, so the explanation will be omitted here.

Next, the table shown in FIG. 13 is a diagram obtained by further narrowing down the table shown in FIG. 12. In this example, changes made simultaneously to parts with part numbers COMP001, 004, and 005 whose target attribute is "C0603" (library name) are extracted. In both changes, the nozzle diameter was changed from 0.5 mm to 0.7 mm. In this example, three cases were extracted. Of these, two cases improved, one case worsened, and multiple cases improved. In this case, the result was that the actual defective rate improved before and after the change for all extracted cases. As a result, it is found that for the parts with part numbers COMP001, 004, and 005 for which the C0603 library is used, it is better to have a nozzle diameter of 0.7 mm than 0.5 mm.

In the extraction conditions in this example, the change target has the same "target attribute" as the extracted case. Regarding the change content category, the contents of "change content", "before change", and "after change" are the same. After this, the extraction conditions are relaxed and confirmed, and if the results are effective under broader conditions (conditions with a large number of extracted items), the findings can be made effective over a wider range.

Next, FIG. 14 will be explained. In FIG. 14, cases are extracted in which the "change target" corresponds to the nozzle ID and the "change content" corresponds to maintenance. In this example, three cases are extracted, and three cases are improved. The overall actual defect rate has also improved. There are also multiple cases of improvement. As a result, it is possible to obtain knowledge that nozzle maintenance is effective.

Next, FIGS. 15A and 15B will be explained. In FIG. 15A, an example is extracted in which the "change target" is the feeder ID and the "change content" is reel exchange. Here, 3 cases were extracted, 2 cases of deterioration and 1 case of no change. Overall, things were getting worse. As a result, it has been found that when parts are accidentally out of stock, replacing the reel to the feeder is likely to cause deterioration.

Next, in FIG. 15B, cases are extracted where the "change target" is the feeder ID and the "change content" is reel set state correction. In this case, two cases are extracted. There were two cases where the condition improved. Overall, things are improving. There are also multiple cases of improvement. From this, it can be found that it is effective to correct the reel set state to the feeder that has occurred accidentally (at the operator's own discretion).

If such knowledge is obtained, although the system does not notify the user, the user can draw the following two conclusions: (1) There is a problem with the content of the reel replacement work, so educate the user. (2) Add a work procedure to correct the reel set condition if a defect occurs after replacing the reel. This can be done.

Next, FIGS. 16A and 16B will be explained. FIG. 16A is an example in which a filter is applied based on each narrowing-down pattern and its effect, and results with a clear conclusion of improvement, deterioration, or no change are filtered and notified. FIG. 16B is an example of displaying these items in order of priority in descending order of effectiveness in reducing actual defects. From this, it is possible to clarify effective countermeasures for urgently reducing the defective rate.

Next, FIG. 17 will be explained. FIG. 17 is an example of a report of improved cases. Examples of filtering cases described in this improvement notification include (1) X or more improvement cases, (2) Y or less deterioration cases, and (3) reduction in actual defective rate by X% or more. (4) A combination of these. For example, the number of improvement cases is 3 or more, the number of deterioration cases is 0 or less, and the number of actual defects has been reduced by 50% or more.

In addition, examples of prioritization in the table include (1) descending order of reduction rate of actual defects, (2) descending order of most improved cases, (3) descending order of improved cases - most deteriorating cases, (4) Combinations of these etc. are possible. In this way, the rules to be used are selected and the order in which the order is determined is determined. For example, the highest priority may be given to the order with the most improvement cases, and then the order with the largest reduction rate of actual defects. In the example of FIG. 16, two cases were extracted. The contents of the notification are as follows: ``There was an effect of reducing actual defects with ``change target = nozzle ID'' and ``change content = maintenance'' (3 improvement cases, 80.4% reduction in actual defects).

Next, the table in FIG. 18 will be explained. FIG. 18 is an example of a deterioration report. The filtering in this case is (1) the number of worse cases is X or more, (2) the number of improved cases is Y or less, (3) the actual defective rate increases by X% or more, and (4) a combination of these. For example, the number of deterioration cases is two or more, the number of improvement cases is zero or less, and the number of actual defects has increased by 50% or more.

Next, as an example of prioritization, (1) descending order of increasing rate of actual defects, (2) descending order of increasing number of worsening cases, (3) descending order of worsening cases - descending order of improving cases, (4) order of these It may be in the order of combination. In this way, in the case of prioritization, the rules to be used are selected and the order for determining the order is determined. For example, the order with the highest number of deterioration cases may be given the highest priority, and then the order with the highest increase rate of actual defects may be given. In the example of FIG. 17, one item was extracted. Then, the content of the report is "Actual defects increased for 'Change target = Feeder ID' and 'Change content = Reel replacement' (2 cases of deterioration, actual defects increased by 240.7%)." In addition, in the above embodiment, an example was explained in which the number of defects and defective rate were used as indicators related to productivity and/or manufacturing quality in the manufacturing process. The average time required for production per item, OEE (overall equipment efficiency), Cpk, and Cp (process capability index) may be used.

In addition, in order to make it possible to compare the constituent features of the present invention and the configurations of the embodiments, the constituent features of the present invention will be appended with reference numerals in the drawings.
<Additional note 1>
A production line ( 10 ) A production management system (1) according to
a change detection unit (110a) that detects a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition unit (110b) that acquires a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected by the change detection unit;
an information generation unit (110c) that generates information indicating a relationship between the change in the factor and the index from the change in the index acquired by the index change acquisition unit;
A production management system comprising:
<Additional note 6>
A production line ( 10 ), the production control method according to
a change detection step (S101 to S103) of detecting a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition step (S106 to S107) of acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected by the change detection unit;
an information generation step (S108 to S116) of generating information indicating a relationship between the change in the factor and the index from the change in the index acquired by the index change acquisition unit;
A production control method characterized by having the following.
<Additional note 11>
A production line (10) that has a single or multiple manufacturing equipment (10a, 10c, 10e) and inspection equipment (10b, 10d, 10f), and executes a product manufacturing process and an inspection process using the manufacturing equipment and inspection equipment. A production management program relating to,
a change detection step (S101 to S103) in which the computer detects a change in a predetermined factor related to the manufacturing process and/or the inspection process;
an index change acquisition step (S106 to S107) for acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected in the change detection step;
an information generation step (S108 to S116) of generating information indicating a relationship between the change in the factor and the index from the change in the index acquired in the index change acquisition step;
A production management program that executes.

1... Production management system 1a... Server for line management 1c... Server for mounting machine 1e... Server for inspection machine 10... Production line 10a... Solder printing device 10b... Solder printing Post-inspection device 10c... Mounter 10d... Post-mount inspection device 10e... Reflow oven 10f... Post-reflow inspection device 11a... Data acquisition section 11b... Control section 11c... Database section 11d ... Output section 110a... Change detection section 110b... Index change acquisition section 110c... Information generation section

Claims (14)

1. A production management system relating to a production line that has a single or multiple manufacturing equipment and inspection equipment, and in which product manufacturing processes and inspection processes are executed by the manufacturing equipment and inspection equipment,
   a change detection unit that detects a change in a predetermined factor related to the manufacturing process and/or the inspection process;
   an index change acquisition unit that acquires a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected by the change detection unit;
   an information generation unit that generates information indicating a relationship between a change in the factor and the index based on the change in the index acquired by the index change acquisition unit;
   A production management system characterized by:
2. The changes in the predetermined factors include planned changes that are carried out in a planned manner and other changes,
   The information generated by the information generation unit and indicating the relationship between the change in the factor and the index includes information as to whether the change in the factor is the planned change or another change, The production management system according to claim 1.
3. associating information on a change in the predetermined factor detected by the change detection unit, information on a change in the index acquired by the indicator change acquisition unit, and information indicating a relationship between a change in the factor and the index; 3. The production management system according to claim 1, further comprising a database section for storing and accumulating data.
4. associating information on a change in the predetermined factor detected by the change detection unit, information on a change in the index acquired by the indicator change acquisition unit, and information indicating a relationship between a change in the factor and the index; The production management system according to any one of claims 1 to 3, further comprising an output section for outputting.
5. The change in the predetermined factor is a 4M change in the manufacturing process,
   The index related to productivity and/or manufacturing quality in the manufacturing process is at least one of the number of defects, process capacity, production volume, and production speed in a predetermined period, or a numerical value calculated based on them,
   The relationship between the change in the factors and the index is an increase or decrease in at least one of the number of defects, process capacity, production volume, and production speed, or a numerical value calculated based on them, based on the 4M change. The production management system according to any one of claims 1 to 4, characterized by:
6. Information indicating a relationship between a change in the factor and the index newly generated by the information generation unit;
   an information confirmation unit that acquires a relationship between a change in the factor and information that has been generated in the past by the information generation unit in response to a change in the same factor and indicates a relationship between a change in the same factor and the index; The production management system according to any one of claims 1 to 5, further comprising: a production management system according to claim 1;
7. A production control method relating to a production line that has a single or multiple manufacturing equipment and inspection equipment, and in which a product manufacturing process and an inspection process are performed by the manufacturing equipment and the inspection equipment,
   a change detection step of detecting a change in a predetermined factor related to the manufacturing process and/or the inspection process;
   an index change acquisition step of acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected in the change detection step;
   an information generation step of generating information indicating a relationship between a change in the factor and the index from the change in the index acquired in the index change acquisition step;
   A production control method characterized by having the following.
8. The changes in the predetermined factors include planned changes that are carried out in a planned manner and other changes,
   The information generated in the information generation step and indicating the relationship between the change in the factor and the index is characterized in that it includes information as to whether the change in the factor is the planned change or another change. , The production control method according to claim 7.
9. Information on a change in the predetermined factor detected in the change detection step, information on a change in the index obtained in the index change acquisition step, and information indicating a relationship between a change in the factor and the index. The production management method according to claim 7 or 8, further comprising a database creation step of storing and accumulating the data in association with each other.
10. Information on a change in the predetermined factor detected in the change detection step, information on a change in the index obtained in the index change acquisition step, and information indicating a relationship between a change in the factor and the index. The production management method according to any one of claims 7 to 9, further comprising an output step of outputting in association with each other.
11. The change in the predetermined factor is a 4M change in the manufacturing process,
    The index related to productivity and/or manufacturing quality in the manufacturing process is at least one of the number of defects, process capacity, production volume, and production speed in a predetermined period, or a numerical value calculated based on them,
    The relationship between the change in the factors and the index is an increase or decrease in at least one of the number of defects, process capacity, production volume, and production speed, or a numerical value calculated based on them, based on the 4M change. The production control method according to any one of claims 7 to 10, characterized by:
12. information indicating a relationship between a change in the factor newly generated in the information generation step and the index;
    an information confirmation step of acquiring the relationship between the change in the factor and information that has been generated in the past in the information generation step and indicates the relationship between the change in the same factor and the index; The production control method according to any one of claims 7 to 11, further comprising:
13. A production management program relating to a production line that has a single or multiple manufacturing equipment and inspection equipment, and executes a product manufacturing process and an inspection process using the manufacturing equipment and inspection equipment,
    a change detection step of causing a computer to detect a change in a predetermined factor related to the manufacturing process and/or the inspection process;
    an index change acquisition step of acquiring a change in an index related to productivity and/or manufacturing quality in the manufacturing process before and after the change in the factor detected in the change detection step;
    an information generation step of generating information indicating a relationship between the change in the factor and the index from the change in the index acquired in the index change acquisition step;
    A production management program that executes.
14. The changes in the predetermined factors include planned changes that are carried out in a planned manner and other changes,
    The information generated in the information generation step and indicating the relationship between the change in the factor and the index includes information as to whether the change in the factor is the planned change or another change. , the production management program according to claim 13.

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