JP7027022B2 - Manufacturing process statistical processing system, manufacturing process statistical processing method and program - Google Patents

Description

The present invention relates to a manufacturing process statistical processing system, a manufacturing process statistical processing method and a program.

Various devices for improving the manufacturing process have been proposed by acquiring and analyzing various data at the manufacturing site. For example, Patent Document 1 discloses a platform for acquiring and analyzing data from a sensor provided in a manufacturing apparatus. Further, there is known a system that records the actions and operations of workers working at a manufacturing site in a format that can be directly processed by a computer (for example, Patent Document 2). Further, a method of obtaining a mutual correlation coefficient (ZNCC) including a time delay element from the acquired data is known (for example, Non-Patent Document 1).

JP-A-2015-255648 Japanese Unexamined Patent Publication No. 2006-178583

Luigi Di Stefano, Stefano Mattoccia, and Federico Tombari, "ZNCC-based template matching using bounded partial correlation", Pattern Recognition Letters 26 (2005): 2129-2134.

Many equipment and many workers are involved in the manufacturing process. Therefore, the performance of the manufacturing process varies not only by factors such as the failure rate, quality, and downtime of the manufacturing equipment, but also by the behavioral factors of the workers involved in manufacturing. That is, in order to improve the performance of the manufacturing process, it is necessary to acquire not only the information indicating the state of the manufacturing equipment but also the information indicating the behavior and state of the worker and statistically analyze it. On the other hand, there is a problem that it is difficult to statistically process a large number of types of information indicating the state of the manufacturing apparatus and the behavior and state of the worker to extract information that leads to the performance of the manufacturing process.

The present invention has been made in view of the above circumstances, and is a manufacturing process statistical processing system, a manufacturing process statistical processing method, and a manufacturing process statistical processing system that can notify the user of information that leads to improvement in the performance of the manufacturing process so that the user can easily grasp the information. The purpose is to provide a program.

In order to achieve the above object, the manufacturing process statistical processing system according to the present invention includes an information acquisition device, a correspondence relationship definition device, a designated reception device, a correlation calculation device, an output device, and a selection reception device. It has an information generator . The information acquisition device acquires manufacturing process information obtained in the manufacturing process of a product. The correspondence relationship definition device defines the correspondence relationship between the manufacturing efficiency index indicating the efficiency of the manufacturing process and the manufacturing process information. The designated receiving device receives the designation of any of the manufacturing efficiency indexes. The correlation calculator calculates a correlation coefficient that indicates the degree of correlation between the specified manufacturing efficiency index and the corresponding manufacturing process information, based on the defined correspondence. The output device enables the user to distinguish between the manufacturing process information and other manufacturing process information that are effective for improving the manufacturing efficiency of the product according to the correlation coefficient calculated by the correlation calculation device. It is output for display together with the information shown in. The selection receiving device accepts selections for the correlation coefficient displayed according to the signal from the output device. The information generator generates information showing the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated as the manufacturing process information effective for improving the manufacturing efficiency of the product. The correspondence definition device can set the manufacturing efficiency index as the manufacturing process information. The output device outputs a signal for displaying information indicating manufacturing process information effective for improving the manufacturing efficiency of the product.

According to the present invention, in the manufacturing process, for example, information indicating the state of the manufacturing apparatus and the behavior and state of the worker can be acquired and statistically processed. Therefore, for example, information that leads to improvement in the performance of the manufacturing process can be presented to the user in a manner that can be easily grasped by the user.

Configuration diagram of the first manufacturing process statistical processing system according to the embodiment of the present invention. The hardware configuration diagram of the computer of the first manufacturing process statistical processing system shown in FIG. The figure which shows the structure of the manufacturing process statistical processing program which is executed in the computer of the manufacturing process statistical processing system shown in FIG. 1 and FIG. The first figure which illustrates the setting of the statistical processing accepted by the statistical processing setting module shown in FIG. The first figure illustrating the UI image that the UI module shown in FIG. 3 displays on the input / output device. Flow chart of processing of manufacturing process statistical processing program shown in FIG. Configuration diagram of the second manufacturing process statistical processing system according to the embodiment The second figure exemplifies one of the statistical processing settings accepted by the statistical processing setting module shown in FIG. The second figure which illustrates the UI image which the UI module shown in FIG. 3 displays in the input / output apparatus. A diagram exemplifying a UI image in which the correlation coefficients for indicators of multiple manufacturing process information are graphed in time series on the same time axis. A diagram illustrating a UI image obtained by normalizing and converting the correlation coefficient shown in FIG. 10 and graphing it in time series on the same time axis.

[Embodiment 1]
Hereinafter, the manufacturing process statistical processing system and the method thereof according to the first embodiment of the present invention will be described with reference to the drawings. In each figure, substantially the same components are designated by the same reference numerals.

The manufacturing process statistical processing system of the first embodiment acquires manufacturing process information for statistical processing from the manufacturing line, performs statistical processing, and color-codes each value of the statistical information so that the user can easily understand the UI (User). Interface) Display in the image. By selecting the statistical information displayed in the UI image, the user can select the manufacturing process information effective for improving the performance of the manufacturing process and use it for improving the manufacturing process.

[Configuration of manufacturing process statistical processing system 1]
As shown in FIG. 1, the first manufacturing process statistical processing system 1 performs statistical processing in which the first manufacturing line 10 for acquiring information for statistical processing and the input / output device 48 are connected. It is provided with a computer 4 for performing. The input / output device 48 includes an output device such as a display and a printer, and an input device such as a keyboard and a mouse. The computer 4 processes the information acquired from the production line 10 by the statistical method input from the input device, and outputs the result of the statistical processing to the output device in a form that is easy for the user to recognize.

The production line 10 further includes a material stand 12 on which 0 or more materials 30 of the product 34 manufactured by the production line 10 are carried in and placed. The production line 10 further includes a product stand 14 on which the manufactured product 34 is placed for unloading. The production line 10 further includes workbenches 16a to 16c provided between the material base 12 and the product base 14. The workbenches 16a to 16c are used by the workers 1 to 3 to perform the work 1 to 3 included in the manufacturing process on the material 30 or the work-in-process 32a and 32b using the manufacturing devices 20a to 20c. Hereinafter, when one or more of a plurality of possible components in the manufacturing process statistical processing system 1 such as the workbench 16a to 16c are shown without being specified, it may be simply abbreviated as the workbench 16. The material 30 generally means a material that is the basis of a product manufactured on the production line 10, and broadly includes substances, materials, parts, members, and the like. Hereinafter, the worker may be referred to as a worker. Also in the drawings, the worker may be described as a worker.

The production line 10 further includes work-in-process tables 18a and 18b between the workbenches 16a and 16c. On the work-in-process tables 18a and 18b, the work-in-process 32a and 32b to be conveyed to the work-in-process 16b and 16c for the work 2 and 3 are placed.

As shown in FIG. 1, the material 30, work-in-process 32, and product 34 are tagged with an RFID (Radio Frequency Identifier) tag when the material 30 is brought into the production line 10. The RFID tag is not removed until the product 34 is carried out. RFID detection devices 22a to 22d are attached to the material base 12, the work-in-process bases 18a and 18b, and the product base 14. RFID detection devices 22a to 22d detect and identify signals from RFID tags attached to material 30, work in process 32 and product 34.

Further, the workbenches 16a to 16c are provided with motion sensors 160a to 160c for detecting that the workers 1 to 3 are working on the workbenches 16a to 16c, that is, the workers are seated. Attached. Further, the workbench 16a to 16c are provided with body temperature sensors 162a to 162c for detecting the body temperature of the workers 1 to 3. Further, the operation display devices 200a to 200c are attached to the manufacturing devices 20a to 20c. The operation display devices 200a to 200c are turned on when the manufacturing device 20 is operating and turned off when the manufacturing device 20 is not operating, indicating whether or not the manufacturing device 20 is operating. The production line 10 uses these components to provide information indicating the components required for all the manufacturing processes for manufacturing the product 34 from the material 30 and the states of the manufacturing equipment 20 and the workers used in the manufacturing process. It is acquired and output to the computer 4.

As shown in FIG. 2, the computer 4 includes a main body 40 and an I / O (Input / Output) device 42 that receives information for manufacturing process statistical processing from the manufacturing line 10. The computer 4 further includes a recording device 44 for reading / writing data to / from a non-volatile recording medium such as an HD (Hard Disk) or a DVD (Digital Versaille Disk). The computer 4 further includes an input / output device 48 used for input / output of information. That is, the computer 4 has components as a general computer that can acquire information from the RFID detection device 22 of the production line 10, perform statistical processing, and output the result of statistical processing according to the operation of the user.

FIG. 3 is a diagram showing a configuration of a manufacturing process statistical processing program 5 executed by the computer 4 of the manufacturing process statistical processing system 1 shown in FIGS. 1 and 2 and for obtaining a correlation between indicators indicating the performance of the manufacturing line 10. Is. In FIG. 3, the "module" is omitted from the names of the components of the manufacturing process statistical processing program 5 in order to make the drawings easier to see. The manufacturing process statistical processing program 5 is loaded into the memory 402 of the computer 4 shown in FIG. 2 via a recording medium 46 or the like. The manufacturing process statistical processing program 5 loaded in the memory 402 is executed by specifically utilizing the hardware resources under the control of the CPU (Central Processing Unit) 400. As a result, the manufacturing process statistical processing system 1 exerts the function of the correlation calculation device.

As shown in FIG. 3, the manufacturing process statistical processing program 5 includes a manufacturing equipment information acquisition module 500 that sequentially acquires information indicating an operating state of the manufacturing equipment 20 shown in FIG. 1 every 10 seconds. The ratio of the time during which the manufacturing device 20 is operating to the time zone set by the user via the input / output device 48 is also hereinafter referred to as the device operating rate. Further, the ratio of the time during which the manufacturing apparatus 20 is out of order to this time zone is also hereinafter referred to as the failure rate. Further, the number of materials 30 or work-in-process 32 worked by the worker on the workbench 16 in this time zone is also hereinafter referred to as the volume of the workbench 16.

Further, the manufacturing process statistical processing program 5 further includes a worker information acquisition module 502. The worker information acquisition module 502 sequentially acquires information indicating whether or not workers 1 to 3 are working on the workbench 16 and information indicating the body temperature of workers 1 to 3 every 10 seconds. ..
The ratio of the time during which the workers 1 to 3 are working on the workbench 16 with respect to the predetermined time zone is also described below as the worker attendance rate 1 to 3. The worker attendance rate is an example of the execution time ratio between the time when each of the plurality of workers in charge of the plurality of processes executes one of the plurality of processes in charge and the time other than that.

Further, the manufacturing process statistical processing program 5 further includes a time series information acquisition module 504. The time-series information acquisition module 504 sequentially acquires time-series information indicating the identifier (ID) of the RFID tag attached to the material 30, work-in-process 32, and product 34 detected by the RFID detection device 22 every 10 seconds. .. The information acquired by the manufacturing equipment information acquisition module 500, the worker information acquisition module 502, and the time series information acquisition module 504 is hereinafter collectively referred to as manufacturing process information. Further, the number of work-in-process 32 placed on the material table 12 immediately before the operation of the production line 10 is also referred to as a pre-production work-in-process amount. The manufacturing device information acquisition module 500, the worker information acquisition module 502, and the time series information acquisition module 504 are examples of information acquisition devices that acquire a plurality of manufacturing process information related to product manufacturing.

Further, the manufacturing process statistical processing program 5 further includes a manufacturing apparatus DB (Data Base) 510 connected to the manufacturing apparatus information acquisition module 500, and a worker DB 512 connected to the worker information acquisition module 502. Further, the manufacturing process statistical processing program 5 further includes a time-series DB 514 connected to the time-series information acquisition module 504 and a statistical processing DB 516 connected to the statistical processing setting module 506. The manufacturing apparatus DB 510 stores the manufacturing apparatus information acquired by the manufacturing apparatus information acquisition module 500, and the worker DB 512 stores the worker information acquired by the worker information acquisition module 502. Further, the time-series DB 514 stores the time-series information acquired by the time-series information acquisition module 504.

The manufacturing process statistical processing program 5 further includes a statistical processing setting module 506 that accepts statistical processing settings set by the user in the input / output device 48. The statistical processing setting module 506 is an example of a correspondence relationship definition device that defines a correspondence relationship between each manufacturing efficiency index indicating the efficiency of a manufacturing process and a plurality of manufacturing process information acquired from each of the plurality of processes.

As shown in FIG. 4, in the setting of statistical processing, the manufacturing process is associated with each of a plurality of indexes y ₁ to y _k to y _n indicating the performance of the manufacturing process and the indexes y ₁ to y _k to y _n . Contains one or more directed graphs. In each directed graph, the index y indicating the performance of the manufacturing process corresponds to one or more manufacturing process information s acquired by the manufacturing equipment information acquisition module 500, the worker information acquisition module 502, and the time series information acquisition module 504. Attached.

The index y indicating the performance of the manufacturing process is, for example, the volume y ₁ of the manufactured product 34, the equipment operating rate y _k , and the ratio of the product 34 manufactured from the material 30 without repeating each operation among the products 34. Any index indicating performance, such as the direct rate y _n shown, is arbitrary. The index y is selected from, for example, the manufacturing process information s, or is obtained by processing the manufacturing process information s.

On the other hand, the manufacturing process information s includes the device operating rate, the worker attendance rate, the work-in-progress amount, and the device operating rate acquired by the manufacturing device information acquisition module 500, the worker information acquisition module 502, and the time-series information acquisition module 504. And includes information such as worker body temperature.

In the example of the directed graph shown in FIG. ₄ , the volume y1 and the device operating rate s1'. .. .. The worker attendance rate s _a'is associated with the device operating rate y _k , and the worker attendance rate s ₁ "... work in process amount s _b " is associated with the direct rate y. _n and the device operating rate _s1 '''. .. .. Worker body temperature s _c '''' is associated. The subscripts a, b, and c are natural numbers, and may be 1. Further, the manufacturing process information s is common to all directed graphs, but in order to make it easy to distinguish which directed graph is used, the manufacturing process information s associated with the volume y1 is ". "_" Distinguish. The manufacturing process information s _1'to s _a ', s ₁ "to s _b ", and s ₁ '''to _sc '''are all selected from the set of manufacturing process information s.

The same manufacturing process information s may be included in a plurality of directed graphs, the index y in one directed graph may be the manufacturing process information s in another directed graph, and the manufacturing process information s in one directed graph may be an index y in another directed graph. It is also possible to become. For example, in the example of FIG. 4, the device operating rate y _k is used as the device operating rate s ₁ ′'' in the directed graph of the orthogonal rate y _n , and the worker attendance rate is the volume y ₁ and the device operating rate y _k . It is used as the worker availability rate s _a'and s ₁ "in the directed graph.

The statistical processing DB 516 connected to the statistical processing setting module 506 stores the processing method of the manufacturing process information s accepted by the statistical processing setting module 506.

The information organization module 520 is connected to the manufacturing apparatus DB 510, the worker DB 512, the time series DB 514, and the statistical processing DB 516. The information organizing module 520 processes the index y input from the manufacturing apparatus DB 510, the worker DB 512, and the time series DB 514, and the manufacturing process information s according to the information organizing method stored in the statistical processing DB 516. That is, the information organizing module 520 processes the index y and the manufacturing process information s according to the directed graph illustrated in FIG. 4, and organizes them in a table format as illustrated in Tables 1 to 3.

Tables 1 to 3 show specific examples of information organization executed by the information organization module 520 based on the statistical process settings accepted by the statistical process setting module 506 shown in FIG.
Table 1 shows an example in which a directed graph is set in which the device operating rate of the manufacturing device 1, that is, the manufacturing device 20a is y, and the average number of works in process of the manufacturing device 1 and the processing record of the manufacturing device 1 are s. In this example, the information organization module 520 associates the device operating rate of the manufacturing apparatus 1 with the average number of work in process of the manufacturing apparatus 1 and the processing results s of the manufacturing apparatus 1 for each preset hourly time zone. Organize on the table.

On the other hand, Table 2 shows a specific example of a data organization method when a directed graph is set in which the attendance rate of the worker 1 is y and the body temperature of the worker 1 is s. In this example, the information organizing module 520 organizes the attendance rate y of the worker 1 and the body temperature s of the worker 1 on the table according to the time zone.

Further, Table 3 shows a specific example of a data organization method when a directed graph is set in which the volume of the production line 10 is y and the orthogonality is s. In this example, the information organizing module 520 organizes the volume y and the orthogonality ratio s on the table according to the time zone.

A statistical processing module 522 and a display information generation module 524 are connected to the information organization module 520 shown in FIG.

The statistical processing module 522 reads the statistical processing method from the statistical processing DB 516. Further, the statistical processing module 522 is arranged on a set of an index y indicating the performance of the manufacturing process processed and organized by the information organization module 520 and the manufacturing process information s, that is, on the tables exemplified in Tables 1 to 3. For each information, the statistical processing indicated by the read statistical processing method is performed.

More specifically, the statistical processing module 522 is defined by the following equations 1 to 4 for the combination of each index y indicating the performance organized by the information organization module 520 and the manufacturing process information s. Correlation coefficient ρ _iy , which indicates the degree of correlation between the manufacturing process information s _i and the index y, and ρ _yy , which indicates the degree of correlation between the index y and the j-th manufacturing process information s _j . A correlation coefficient ρ _ij indicating the degree of correlation between the i-th manufacturing process information s _i and the j-th manufacturing process information s _j is calculated. i and j are arbitrary integers such that 0 ≦ i and j ≦ N, where N is the number of manufacturing process information s associated with the index y. For example, in the example of Table 1, N = 2, i and j are 1 or 2, and in the examples of Tables 2 and 3, N = 1 and i = j = 1.

Further, the statistical processing module 522 obtains a symmetric correlation coefficient matrix M in the form of (N + 1) × (N + 1) defined by the following equation 5 using the calculated correlation coefficients ρ _iy , ρ _yy , and ρ _ij . It should be noted that ρ _iy is the same as ρ _yi , and can be obtained by setting j = i in the equation 1.

Further, in the examples of Tables 1 to 3, since the data is organized by time zone, the variable τ that specifies the time zone is introduced. Further, T indicates the number of time zones. For example, in the examples of Tables 1 to 3, since the measurement time zone is one hour unit, T is 24 if the measurement period is one day.

Further, y _τ is an index y indicating performance in the time zone τ. Therefore, y _τ shown below corresponds to an index different from y ₁ to y _n shown in FIG. Similarly, s _iτ indicates the i-th manufacturing process information in the time zone τ, and s _jτ indicates the j-th manufacturing process information in the time zone τ. Explaining in Table 1, y is the device operating rate, y _τ = y ₁ is the device operating rate at 0:00, and s ₁ is the average number of work in process before the device, s _1τ = S. ₁₂ is the average number of work in process before the equipment in the time zone of 1:00, 1409.7, s ₂ is the actual processing of the equipment, and s _2τ = S ₂₃ is the actual processing of the equipment in the time zone of 2:00. be.

As described above, ρ _ij = ρ _ji holds. Therefore, if the row number of the matrix M is i'and the column number is j', then ρ _i'j' = ρ _j'i' holds. Further, due to the nature of the correlation coefficient, in the correlation coefficient matrix M, ρ _i'j'is obtained by rounding off the second decimal place, and -1 ≤ ρ _i'j'≤ 1.

In the example of the information in Table 1, since the number N = 2 of the manufacturing process information s, the matrix M is a matrix in the form of 3 × 3, and in the examples of the information in Tables 2 and 3, the number N = 1 of the manufacturing process information s. Therefore, the matrix M is a 2 × 2 format matrix.

The correlation coefficient matrix M is an element of how much the manufacturing process information s associated with the index y has an influence in order to improve the index y indicating the performance of the manufacturing process selected by the user. It is shown by the correlation coefficient of. Since ρ _i'j' = ρ _j'i' holds in the correlation coefficient matrix M, in order to show the user all the information contained in the correlation coefficient matrix M, the upper half is an input / output device. All you have to do is display it on 48.

However, if the upper half of the correlation coefficient matrix M is shown only by parentheses and coefficients, the user can directly determine which manufacturing process information s has a large relationship in order to improve the index y. It is difficult to grasp. Therefore, the display information generation module 524 processes the information in the upper half of the correlation coefficient matrix M for display. The display information generation module 524 further causes each element of the correlation coefficient matrix M to have its value in order to directly grasp which manufacturing process information s has a large relationship in order to improve the index y. Depending, as illustrated in Table 4, colors are added in the HSV color space or the HLS color space.

That is, as shown in Table 4, the elements in the upper half of the correlation coefficient matrix M exemplified in Table 5 are red, orange, yellow, and so on, in the order of the elements having the largest value to the elements having the smallest value. A color that gradually changes from yellowish green to green is added. Each element of the correlation coefficient matrix M shown in each of the following tables is given a color exemplified in Table 4.

The UI module 526 displays the information generated by the display information generation module 524 on the display screen of the input / output device 48.

The correlation between the index y obtained from the production line 10 and the production process information s may include a time delay as an element. For example, in the example of Table 1, for the workbench 16a in which the apparatus 1 is arranged, the number of work-in-process 32s at the stage before receiving the work is the number of materials 30 placed on the materialbench 12. There is a time lag. At this time, the statistical processing module 522 is not a correlation coefficient matrix M that does not consider the time delay shown in Equations 1 to 5, but a symmetric mutual phase relationship of N × N format defined by Equations 6 and 7. The number matrix M [l] is obtained.
In the intercorrelation coefficient _Rij [l] included as an element in the intercorrelation coefficient matrix M [l], a time delay is taken into consideration. The cross-correlation coefficient when the time delay shown in Equations 6 and 7 affects the correlation is abbreviated as ZNCC (Zero mean Normalized Cross-Correlation function).

In the mutual correlation coefficient matrix M [l], R _i'j' [l] = R _{i'j' [} l] and -1≤R i'j'for any i', j', l. [L] ≦ -1. Further, as shown in Equations 1 to 5, when the index y to be statistically processed and the manufacturing process information s are the same, R _i'j' [0] = ρ _i'j' . Further, the unit of the time delay l differs depending on the index y to which the formulas 6 and 7 are applied and the method of organizing the manufacturing process information s, and is 1 hour in the case illustrated in Tables 1 to 3.

The display information generation module 524 also assigns the colors exemplified in Table 4 to each element of the mutual correlation coefficient matrix M [l] shown in each of the following tables. Further, since the mutual correlation coefficient matrix M [l] when l = 0 is the same as the correlation coefficient matrix M, these are hereinafter collectively referred to as the mutual correlation coefficient matrix M [l]. Further, the statistical processing methods shown in Equations 1 to 7 and the like are set by the user in the statistical processing setting module 506 and stored in the statistical processing DB 516.

Tables 6 to 8 show the mutual correlation coefficient matrix M [l] obtained with the time delay l = 0,2,4 for the manufacturing process information s associated with the index y to be improved selected by the user. ] Is illustrated. By referring to the mutual correlation coefficient matrix M [l] exemplified in Tables 6 to 8, the user has a strong positive effect between the index y and the manufacturing process information s3 when the time delay l = 2. Find the existence of a correlation.

The display information generation module 524 shown in FIG. 3 is generated by the statistical processing module 522, and is added to each element of the correlation coefficient matrix M and the mutual correlation coefficient matrix M [l] shown in Table 5 and the like in Table 4. A display image is generated with the colors shown in.

FIG. 5 is a diagram illustrating a UI image displayed by the user interface (UI) module 526 shown in FIG. 3 on the input / output device 48 shown in FIG. 1 and the like. A UI module 526 is connected to the display information generation module 524. As shown in FIG. 5, the UI module 526 displays a UI image including the mutual correlation coefficient matrix M [l] generated by the display information generation module 524 on the input / output device 48.

As shown in FIG. 5, the UI image includes an image showing the mutual correlation coefficient matrix M [l] in which each element is colored as shown in Table 4, and the index y to be analyzed. Includes an image for selecting. In FIG. 5, colors are pseudo-expressed by gradation. Further, as shown in FIG. 5, this UI image includes an image for setting the time delay l used for generating the mutual correlation coefficient matrix M [l] and an image for setting the analysis period. Is included.

The user of the manufacturing process statistical processing system 1 operates on this UI image displayed on the input / output device 48, and sets the analysis target, the time delay l, and the analysis time. Each component of the manufacturing process statistical processing program 5 processes according to the operation of the user, and the image of the upper half of the mutual correlation coefficient matrix M [l] shown in FIG. 5 is the UI image of the input / output device 48. Display inside. Further, the UI module 526 outputs the time delay l set by the user to the statistical processing setting module 506.

Further, the UI module 526 accepts an operation for the user to select each element included in the image of the upper half of the mutual correlation coefficient matrix M [l] displayed in the UI image. Upon accepting the operation, the UI module 526 generates the displayed intercorrelation coefficient matrix M [l] and selection information indicating the selected element. The UI module 526 is an example of a selection receiving device that accepts selections for one or more of a plurality of correlation coefficients displayed on the display device when performing such processing. The information organization module 520, the statistical processing module 522, and the improvement information generation module 528 are connected to the UI module 526.

The improvement information generation module 528 generates improvement information indicating the manufacturing process information s for improving the manufacturing line 10 shown in FIG. 1 from the selection information input from the UI module 526. The improvement information generation module 528 provides information indicating manufacturing process information indicating a manufacturing process to be changed in each of the plurality of processes included in the manufacturing process based on one or more of the selected plurality of correlation coefficients. This is an example of an information generation device to be generated. The improvement information DB 532 is connected to the improvement information generation module 528, and stores the improvement information input from the improvement information generation module 528. The improvement information stored in the improvement information DB 532 is appropriately output from the input / output device 48 according to the user's operation on the input / output device 48. The information organization module 520 and the statistical processing module 522 perform processing triggered by the input of selection information from the UI module 526.

[Overall operation of manufacturing process statistical processing program 5]
FIG. 6 is a flowchart showing the overall operation of the manufacturing process statistical processing program 5 shown in FIG. As shown in FIG. 6, in step S100, the manufacturing apparatus information acquisition module 500, the worker information acquisition module 502, and the time series information acquisition module 504 are various from the respective components of the production line 10 shown in FIG. Acquire manufacturing process information s.

The manufacturing equipment information acquisition module 500, the worker information acquisition module 502, and the time-series information acquisition module 504 store various acquired manufacturing process information s in the manufacturing equipment DB 510, the worker DB 512, and the time-series DB 514. The statistical processing setting module 506 accepts the statistical processing settings input to the input / output device 48 by the user. Further, the statistical processing setting module 506 accepts a time delay l according to the user's operation on the UI image shown in FIG. The statistical processing setting module 506 stores the accepted settings and the time delay in the statistical processing DB 516.

In step S102, the information organization module 520 and the statistical processing module 522 determine whether or not selection information has been input from the UI module 526 and information processing has been requested. The manufacturing process statistical processing program 5 proceeds to the process of step S104 when the information processing request is requested (step S102: Yes), and returns to the process of step S100 at other times (step S102: No).

In step S104, the information organization module 520 reads out the information organization method from the statistical processing DB 516. Further, as shown in Tables 1 to 3, the information organization module 520 processes the manufacturing process information s for each index y to generate a table, and outputs the table to the statistical processing module 522.

In step S106, the statistical processing module 522 statistically processes the index y arranged by the information organizing module 520 and the manufacturing process information s, as described with reference to the equations 1 to 7. The statistical processing module 522 outputs the correlation function counting matrix M [l] obtained as a result of this statistical processing to the display information generation module 524.

In step S108, the display information generation module 524 generates information for displaying the table and / or the correlation function counting matrix M [l]. The display information generation module 524 outputs the generated information to the UI module 526. The UI module 526 generates an image of the table and / or a UI image containing the correlation function counting matrix M [l] illustrated in FIG. The UI module 526 displays the generated table image and / or UI image on the input / output device 48.

In step S110, the improvement information generation module 528 operates on the UI image displayed on the input / output device 48 shown in FIG. 5, and the user correlates with the index y for the index y. It is determined whether or not the element of the function counting matrix M [l] is selected. The manufacturing process statistical processing program 5 proceeds to the processing of S112 when the element of the correlation function counting matrix M [l] is selected (step S110: Yes), and proceeds to the processing of S112 at other times (step S110: No). Return to the process of S100.

In step S112, the improvement information generation module 528 corresponds the element of the correlation function counting matrix M [l] selected for improving the index y displayed in the UI image with the correlation function counting matrix M [l]. In addition, improvement information is generated. Further, the improvement information generation module 528 stores the generated improvement information in the improvement information DB 532. The improvement information stored in the improvement information DB 532 is appropriately displayed on the input / output device 48 according to the user's operation on the input / output device 48.

As described above, according to the present embodiment, even if the index y indicating many types of performance and the manufacturing process information s affect the performance of the production line 10, the user can use the index y. The correlation with the manufacturing process information s can be grasped directly. That is, the user can easily find the manufacturing process information s having a strong positive or negative correlation with the index y to be improved. Specifically, the user sees the display exemplified in Table 5 and has a strong positive correlation between the time-series volume y selected as the index y to be improved and the manufacturing process information s3, and s7. It can be seen that the negative correlation with is strong.

As a result, in order to improve the volume y, the user sets the value of the correlation coefficient between the volume y and the manufacturing process information s7 so as to increase the value of the correlation coefficient between the volume y and the manufacturing process information s3. It can be understood that the production line 10 needs to be improved so as to make it smaller. That is, according to the manufacturing process statistical processing system 1, the user can easily narrow down the index y to be improved and the manufacturing process information s from the many indexes y and the manufacturing process information s obtained from the manufacturing line 10. can.

[Embodiment 2]
The production line that is the subject of the invention is not limited to the production line 10 shown in FIG. 1, and the configuration of the production line is arbitrary. Hereinafter, a second embodiment for production lines having different configurations will be described.

FIG. 7 is a diagram illustrating the configuration of the manufacturing process statistical processing system 6 of the second embodiment. As shown in FIG. 7, in the manufacturing process statistical processing system 6, the production line 10 of the first manufacturing process statistical processing system 1 shown in FIG. 1 is replaced with the second production line 36.

Further, in the production line 36, the work-in-process table 18 is removed from the production line 10 of the production process statistical processing system 1 shown in FIG. The configuration in which the RFID detection devices 24a to 24c are provided is adopted. That is, in the production line 36, when the workbenches 16b and 16c cannot receive the material 30 or the work in process from the workbenches 16a and 16c, the work on them is stopped.

Further, in the production line 36, the production devices 20a to 20c are provided with state display devices 202a to 202c. The status display device 202 has light sources of three colors such as blue, yellow, and red, and displays the status of work on the workbench 16 in different colors. That is, the status display devices 202a to 202c display the three states of "work", "waiting for hand", and "failure" with three colors of light.

Of the three states, the "work" state indicates a state in which the manufacturing apparatus 20 is operating and work is being performed on the material 30 or the work-in-process 32 on the workbench 16. Further, among these, in the "handheld" state, the work on the workbench 16 is performed even though the manufacturing apparatus 20 can be operated because the material 30 or the work-in-process is not sent from the previous stage. Indicates a state of being unable to do so. Further, among these, the state of "failure" indicates that the manufacturing apparatus 20 of the workbench 16 is out of order. In addition to the information output by the first production line 10 to the computer 4, the second production line 36 outputs information indicating which of these three states each workbench 16 is in every 10 seconds. It is output to the computer 4 in series.

FIG. 8 is a second diagram illustrating one of the statistical processing settings accepted by the statistical processing setting module 506 shown in FIG. In the manufacturing process statistical processing system 6, the directed graph illustrated in FIG. 8 is stored as statistical processing information in the statistical processing DB 516 of the manufacturing process statistical processing program 5 shown in FIG. In this directed graph, "volume" is used as an index y for improving the performance of the manufacturing line 36 in the manufacturing process information s. Further, as the manufacturing process information s corresponding to this index y, the "equipment failure rate" of the manufacturing devices 20a and 20b and the "worker attendance rate" of the workers 1 to 3 are used.

Table 9 is a table illustrating the table generated by the information organization module 520 shown in FIG. 3 based on the directed graph shown in FIG. The manufacturing device information acquisition module 500 and the worker information acquisition module 502 acquire the "equipment failure rate" and the "worker attendance rate" from the manufacturing line 36, and use the manufacturing device DB 510 and the worker DB 512 as manufacturing process information s. Remember. The information organizing module 520 processes the stored manufacturing process information s using the directed graph read from the statistical processing DB 516 shown in FIG. 8, and organizes the stored manufacturing process information s into the format of the table shown in Table 9. However, for the sake of clarification of the explanation, Table 9 shows the case where no defective product occurs.

Tables 10 to 12 show the phase relationship between the volume y as an index shown in Table 9 and the manufacturing process information s shown in Equations 6 to 8 using the directed graph shown in FIG. The mutual correlation coefficient matrix M [l] indicating the number is shown. Tables 10 to 12 show the case of l = 0 to 2.

The statistical processing module 522 shown in FIG. 3 has a time delay of l = 0 to 2 hours, as shown in Tables 10 to 12, depending on the user's operation on the UI image shown in FIG. A mutual correlation coefficient matrix M [l] is generated. The intercorrelation coefficient matrix M [l] generated by the statistical processing module 522, or the intercorrelation coefficient matrix M [l] and the tables shown in Table 9 are sent to the input / output device 48 via the UI module 526. , Are displayed in color as shown in Table 4.

By referring to columns 7 and 8 of Table 10, the correlation value between the volume of the manufacturing apparatus 20c and the volume of the manufacturing apparatus 20a when the time delay l = 0 is 0.67, but the correlation value with the volume of the manufacturing apparatus 20b. It can be judged that 0.97 is larger. That is, when the time delay l = 0, the user understands that the correlation between the volume of the manufacturing apparatus 20c and the volume of the manufacturing apparatus 20b is stronger than the correlation between the volume of the manufacturing apparatus 20c and the volume of the manufacturing apparatus 20a. can do. That is, the user can determine that the trading volume in the manufacturing apparatus 20b has the strongest influence on the final trading volume of the manufacturing line 36 and is a bottleneck in the manufacturing line 36.

Further, the volume of the entire production line 36 has a strong correlation with the attendance rate (3 rows; 0.95) of the worker 2 working on the production apparatus 20b. Therefore, the user can determine that reducing the time for the worker 2 to leave the manufacturing apparatus 20b is important for improving the volume of the entire manufacturing line 36. On the other hand, the failure rate of the manufacturing apparatus 20a (5 rows; −0.07) and the failure rate of the manufacturing apparatus 20b (6 rows; −0.12) both have a very small correlation with the volume of the manufacturing apparatus 20c. Therefore, the user can determine that the failure rate of the manufacturing devices 20a to 20c has a very small effect on the volume of the entire manufacturing line 36.

Further, a manufacturing process having a large correlation with the volume of the manufacturing apparatus 20c shown in the first row of the mutual correlation coefficient matrix M [l] when the time delay l = 1 and 2 shown in Tables 11 and 12 are obtained. There is no information s. That is, the user can determine such a thing by referring to the first line of the mutual correlation coefficient matrix M [l] when the time delay l = 1 and 2. Further, by referring to the mutual correlation coefficient matrix M [l] when the time delay l> 2, the user can be sure whether or not there is manufacturing process information s having a large correlation with the volume of the manufacturing apparatus 20c. Can be judged.

The manufacturing process statistical processing program 5 in the manufacturing process statistical processing system 6 shown in FIG. 7 generates a kind of mutual correlation coefficient matrix M [l] according to the value of the time delay l. That is, as the types of the mutual correlation coefficient matrix M [l] increase, the number of elements to be referred to also increases. Therefore, compared to the case where only one correlation coefficient matrix M needs to be referred to, simply displaying many types of mutual correlation coefficient matrix M [l] means the values of the elements contained therein. It becomes more difficult for the user to grasp.

On the other hand, as shown in Tables 10 to 12, the elements included in the mutual correlation coefficient matrix M [l] are color-coded as shown in Table 4 according to their values. Therefore, the user can easily grasp the magnitude of the correlation between the manufacturing process information s and the meaning thereof. As described above, according to the manufacturing process statistical processing program 5, the user can easily grasp the manufacturing process information s that can greatly affect the production volume of the entire manufacturing line 36, and take measures for improving the information s.

[Modification example]
In the above embodiment, the manufacturing process information that has a great influence on the performance is colored and highlighted in order to display it in an easy-to-see manner, but the display form is not limited to the color. For example, the background of letters or numbers may be colored and displayed, or letters or numbers may be colored. In addition, the color combination of letters or numbers and the background may be adjusted. Further, it is not limited to coloring, and other types of highlighting such as bold display, character size enlargement, shading, blinking, and inversion may be adopted.

In the embodiment, the case where three operations are included from the material 30 to the production of the product 34 is illustrated, but the manufacturing process statistical processing of the present invention includes less than three operations or four or more operations. It can also be applied in some cases. Further, FIG. 1 shows a case where the RFID detection device 22 is attached only to the material table 12, the product table 14, and the work-in-process table 18 in the manufacturing process statistical processing system 1, but the work table 16 also has an RFID. The detection device 22 may be attached. Further, FIG. 1 shows a case where the work-in-process table 18 is provided in all of the workbenches 16 in the production line 10, but the work-in-process table 18 may be omitted as appropriate.

Further, in the embodiment, a case where the manufacturing process statistical processing is performed by software by the computer 4 is exemplified, but one or more of the manufacturing process statistical processing programs 5 shown in FIG. 3 are appropriately hardware-like. It may be realized by any means. Further, the manufacturing device information acquisition module 500 of the manufacturing process statistical processing program 5 can acquire information from the manufacturing line 10 at arbitrary time intervals other than 10 seconds according to the user's operation via the input / output device 48. .. The statistical processing setting module 506 of the manufacturing process statistical processing program 5 accepts other processing of the processing exemplified in FIG. 4 and Tables 1 to 3, and the statistical processing DB 516 is also shown in FIGS. 4 and 1 to 3. Other processes of the exemplified process may be stored.

Further, the correlation coefficient matrix M shown in Table 5 and the like and displayed on the input / output device 48 is an example, and the format of the correlation coefficient matrix M is not limited to the format shown in Table 5 and the like. Further, the colors attached to each element of the correlation coefficient matrix M shown in Table 4 are examples, and each element of the correlation coefficient matrix M is given a color other than those shown in Table 4. You may. Further, in the manufacturing process statistical processing systems 1 and 6, instead of the RFID detection devices 22 and 24, a bar code reader for reading the bar code attached to the material 30, work in process 32 and product 34 may be used.

[Embodiment 3]
In the present embodiment, the manufacturing process statistical processing program 5 displays the correlation coefficients processed by the Zero-mean Normalized Cross-Correlation function in the second embodiment in a time series graph. In the second embodiment, the correlation coefficient of the index determined by the user to confirm the correlation coefficient and to be highly relevant is displayed in a time series graph in the present embodiment, and the user can display the correlation coefficient in the time series. You can see the changes.

In the second embodiment, the manufacturing process statistical processing program 5 shows the manufacturing process information s that affects the performance by using the mutual correlation coefficient matrix M [l]. The method of the second embodiment is effective when the user extracts an index having a high degree of influence on performance, but for further analysis, the correlation coefficient ρ between the index y and the manufacturing process information s. It is useful for the user to know when and how the number increased or decreased. Therefore, in the present embodiment, the user can obtain an opportunity for further analysis by confirming the time-series data of the correlation coefficient ρ.

FIG. 9 illustrates a UI image displayed on the input / output device 48 shown in FIG. 1 and the like by the user interface (UI) module 526 of the manufacturing process statistical processing program 5 shown in FIG. 3 in the present embodiment. It is a figure to do. The manufacturing process statistical processing program 5 in the present embodiment has basically the same configuration as the first and second embodiments. The UI image has an analysis target selection unit 50, an analysis period selection unit 52, and a time series graph display unit 54. The user can use any plurality of manufacturing process information s ₁ to s _τ ( _m ≧ τ ≧ 2) from the same manufacturing process information s ₁ to sm as in the second embodiment shown in the analysis target selection unit 50. Can be selected. The manufacturing process information s ₁ to s _τ is the manufacturing process information s in which a meaningful numerical value is found in the correlation coefficient ρ with the index y for the user in the mutual correlation coefficient matrix M [l] in the second embodiment. Is. Similar to the second embodiment, the analysis period set by the user is displayed on the analysis period selection unit 52. The time-series graph display unit 54 sets the correlation coefficients ρ ₁ to ρ ₃ between the index y selected by the analysis target selection unit 50 and the manufacturing process information s ₁ to s ₃ in the period displayed on the analysis period selection unit 52. Display as a time series graph.

Among the index y acquired by the manufacturing process statistical processing program 5 of the second embodiment and the plurality of manufacturing process information s ₁ to _sm , the phase of the manufacturing process information s ₁ to s ₃ selected by the user. When the relation numbers ρ ₁ to ρ ₃ are graphed as they are on the same time axis, as shown in FIG. 10, each phase relationship with the index y of the manufacturing process information s ₁ to s ₃ to be analyzed. Since the scales and biases of the numbers ρ ₁ to ρ ₃ are different, it is difficult for the user to intuitively read the meaning even if the graph is visually recognized. Therefore, in order to make it easier to recognize the correspondence between the increase / decrease of the correlation coefficient on the time axis among the plurality of correlation coefficients, in the present embodiment, the manufacturing process statistical processing program 5 normalizes the correlation coefficient ρ. ..

That is, the following equation 9 is defined for the correlation coefficients ρ ₁ to ρ ₃ with respect to the indexes y of the manufacturing process information ₁ to s ₃ represented by the following equation 8, and the normalization represented by the following equation 10 is performed. , Ρ ₁ ′ to ρ ₃ ′.

Next, the operation of the manufacturing process statistical processing program 5 when generating the graph displayed on the time series graph display unit 54 shown in FIG. 9 in the present embodiment will be described. The statistical processing module 522 reads out the normalization processing method stored in the statistical processing DB 516 for the correlation coefficients ρ ₁ to ρ ₃ with respect to the index y of the manufacturing process information s ₁ to s ₃ selected by the user, and performs the above-mentioned normalization. Performs conversion and generates a time series graph on the same time axis. An example of the generated time series graph is FIG. The statistical processing module 522 outputs the generated time series graph to the display information generation module 524. The display information generation module 524 outputs the time series graph shown in FIG. 11 to the UI module 526. The UI module 526 generates an image to be displayed on the analysis target selection unit 50, an image to be displayed on the analysis period selection unit 52, and an image to be displayed on the time series graph display unit 54 shown in FIG. Displayed on the input / output device 48. Compared with the graph of FIG. 10, the graph of FIG. 11 allows the user to easily compare between a plurality of manufacturing process information, and enables a more intuitive analysis for the user. In the present embodiment, the manufacturing process statistical processing program 5 normalizes the correlation coefficients ρ ₁ to ρ ₃ with respect to the index y of the three manufacturing process information s ₁ to s ₃ , but two or more manufacturing processes. The correlation coefficients ρ ₁ to ρ _τ with respect to the index y of the information s ₁ to s _τ (τ ≧ 2) may be normalized. Further, the manufacturing process information s ₁ to _sm shown in the analysis target selection unit 50 was the same as that displayed in the image exemplified in the second embodiment, but is exemplified in the second embodiment. It may be different from what was displayed in the image that was being displayed. Further, in the second embodiment, the correlation coefficient of the index that the user confirmed the correlation coefficient and judged to be highly relevant was displayed in a time series graph in the present embodiment, but in the second embodiment, the correlation coefficient is displayed. Regardless, the correlation coefficient arbitrarily selected by the user may be displayed in a time series graph.

The present invention allows for various embodiments and modifications without departing from the broad spirit and scope of the invention. Further, the above-described embodiments are for explaining the present invention, and do not limit the scope of the present invention. That is, the scope of the present invention is shown not by the embodiment but by the scope of claims. And, various modifications made within the scope of the claims and the equivalent meaning of the invention are considered to be within the scope of the present invention.

This application is based on Japanese Patent Application No. 2017-252454 filed on December 27, 2017. The specification, claims, and drawings of Japanese Patent Application No. 2017-252454 are incorporated herein by reference.

1,6 Manufacturing process statistical processing system, 5 Manufacturing process statistical processing program, 10,36 Manufacturing line, 500 Manufacturing equipment information acquisition module, 502 Worker information acquisition module, 504 Time series information acquisition module, 506 Statistical processing setting module, 520 Information organization module, 522 statistical processing module, 524 display information generation module, 526 UI module, 528 improvement information generation module.

Claims (13)

An information acquisition device that acquires manufacturing process information obtained in the manufacturing process of a product,
A correspondence definition device that defines a correspondence between a manufacturing efficiency index indicating the efficiency of the manufacturing process and the manufacturing process information, and a correspondence definition device.
A designated reception device that accepts the designation of any of the manufacturing efficiency indicators, and
A correlation calculator that calculates a correlation coefficient that indicates the degree of correlation between the specified manufacturing efficiency index and the corresponding manufacturing process information based on the defined correspondence.
The correlation coefficient calculated by the correlation calculation device is shown so that the user can distinguish between the manufacturing process information effective for improving the manufacturing efficiency of the product and other manufacturing process information according to the value. An output device that outputs information along with information for display,
A selection receiving device that accepts selections for the correlation coefficient displayed according to a signal from the output device, and
An information generator that generates information indicating the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated is shown as the manufacturing process information effective for improving the manufacturing efficiency of the product.
Have,
The correspondence definition device can set the manufacturing efficiency index as manufacturing process information .
The output device outputs a signal for displaying information indicating the manufacturing process information effective for improving the manufacturing efficiency of the product .
Manufacturing process statistical processing system. The correspondence definition device defines the correspondence between each of the manufacturing efficiency indexes and the manufacturing process information in the form of a directed graph.
The manufacturing process statistical processing system according to claim 1. The manufacturing efficiency index includes one or more of the operating rate of the manufacturing equipment used in the manufacturing process, the direct rate of the product, and the volume of the product.
The manufacturing process statistical processing system according to claim 1 or 2. The manufacturing process information includes the execution time ratio between the time for executing any of the processes in charge of each worker in charge of the manufacturing process and the other time, the body temperature and operating rate of each worker, and the manufacturing process. Includes two or more of the number of work-in-process that can be placed in and the number of work-in-process between processes.
The manufacturing process statistical processing system according to any one of claims 1 to 3. The correlation calculation device calculates the correlation between the designated manufacturing efficiency index and the corresponding manufacturing process information, taking into account a time delay factor.
The manufacturing process statistical processing system according to any one of claims 1 to 4. The output device outputs a signal for displaying the correlation coefficient between the designated manufacturing efficiency index and the corresponding manufacturing process information in a color corresponding to the value of these correlation functions. ,
The manufacturing process statistical processing system according to any one of claims 1 to 5. The output device outputs a signal for displaying the correlation coefficient between the specified manufacturing efficiency index and the corresponding manufacturing process information with emphasis corresponding to the value of these correlation functions. ,
The manufacturing process statistical processing system according to any one of claims 1 to 5. The output device outputs a signal for displaying the correlation coefficient between the specified manufacturing efficiency index and the corresponding manufacturing process information in the form of a matrix.
The manufacturing process statistical processing system according to any one of claims 1 to 7. The correlation calculation device normalizes the correlation coefficient of each of the first and second manufacturing process information corresponding to any of the designated manufacturing efficiency indexes in chronological order.
The output device outputs a signal for displaying the correlation coefficient in a time-series graph.
The manufacturing process statistical processing system according to any one of claims 1 to 8. Obtain multiple manufacturing process information from each of the multiple processes included in the manufacturing process,
Define the correspondence between the manufacturing efficiency index and manufacturing process information,
Based on the defined correspondence, calculate a correlation coefficient that indicates the degree of correlation between the specified manufacturing efficiency index and the corresponding manufacturing process information.
The correlation coefficient is output for display together with information indicating that the manufacturing process information effective for improving the manufacturing efficiency of the product and other manufacturing process information can be identified by the user according to the value.
Accepts the selection for the displayed correlation coefficient and accepts
Information is generated to show the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated as the manufacturing process information effective for improving the manufacturing efficiency of the product.
When defining the correspondence between the manufacturing efficiency index and the manufacturing process information, the manufacturing efficiency index can be set as the manufacturing process information .
A signal for displaying information indicating the manufacturing process information effective for improving the manufacturing efficiency of the product is output .
Manufacturing process statistical processing method. On the computer
Processing to acquire multiple manufacturing process information from the manufacturing process,
Processing that defines the correspondence between manufacturing efficiency indicators and manufacturing process information,
A process that calculates a correlation coefficient that indicates the degree of correlation between a specified manufacturing efficiency index and the corresponding manufacturing process information, based on a defined correspondence.
A process of outputting the correlation coefficient for display together with information indicating that the manufacturing process information effective for improving the manufacturing efficiency of the product and other manufacturing process information can be identified by the user according to the value. ,
Processing to accept selection for the displayed correlation coefficient,
A process of generating information indicating the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated as the manufacturing process information effective for improving the manufacturing efficiency of the product.
A process of outputting a signal for displaying information indicating the manufacturing process information effective for improving the manufacturing efficiency of the product.
To execute,
In the process of defining the correspondence between the manufacturing efficiency index and the manufacturing process information, the manufacturing efficiency index can be set as the manufacturing process information .
program. An information acquisition device that acquires manufacturing process information obtained in the manufacturing process of a product,
A correspondence definition device that defines a correspondence between a manufacturing efficiency index indicating the efficiency of the manufacturing process and the manufacturing process information, and a correspondence definition device.
A designated reception device that accepts the designation of any of the manufacturing efficiency indicators, and
A correlation calculator that calculates a correlation coefficient that indicates the degree of correlation between the specified manufacturing efficiency index and the corresponding manufacturing process information based on the defined correspondence.
The correlation coefficient calculated by the correlation calculation device is shown so that the user can distinguish between the manufacturing process information effective for improving the manufacturing efficiency of the product and other manufacturing process information according to the value. An output device that outputs information along with information for display,
A selection receiving device that accepts selections for the correlation coefficient displayed according to a signal from the output device, and
An improvement information generator that generates information indicating the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated is shown as the manufacturing process information effective for improving the manufacturing efficiency of the product.
Have,
The correlation calculation device calculates the correlation between the designated manufacturing efficiency index and the corresponding manufacturing process information, taking into account a time delay factor .
The output device outputs a signal for displaying information indicating the manufacturing process information effective for improving the manufacturing efficiency of the product .
Manufacturing process statistical processing system. An information acquisition device that acquires manufacturing process information obtained in the manufacturing process of a product,
A correspondence definition device that defines a correspondence between a manufacturing efficiency index indicating the efficiency of the manufacturing process and the manufacturing process information, and a correspondence definition device.
A designated reception device that accepts the designation of any of the manufacturing efficiency indicators, and
A correlation calculator that calculates a correlation coefficient that indicates the degree of correlation between the specified manufacturing efficiency index and the corresponding manufacturing process information based on the defined correspondence.
The correlation coefficient calculated by the correlation calculation device is shown so that the user can distinguish between the manufacturing process information effective for improving the manufacturing efficiency of the product and other manufacturing process information according to the value. An output device that outputs information along with information for display,
A selection receiving device that accepts selections for the correlation coefficient displayed according to a signal from the output device, and
An improvement information generator that generates information indicating the manufacturing process information related to the correlation from which the selected correlation coefficient is calculated is shown as the manufacturing process information effective for improving the manufacturing efficiency of the product.
Have,
The correlation calculation device normalizes the correlation coefficient of each of the first and second manufacturing process information corresponding to any of the designated manufacturing efficiency indexes in chronological order.
The output device outputs a signal for displaying the correlation coefficient in a time-series graph .
The output device outputs a signal for displaying information indicating the manufacturing process information effective for improving the manufacturing efficiency of the product .
Manufacturing process statistical processing system.

Images