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WO2019030945A1 - Procédé et programme d'estimation de cause - Google Patents

Procédé et programme d'estimation de cause Download PDF

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Publication number
WO2019030945A1
WO2019030945A1 PCT/JP2018/001561 JP2018001561W WO2019030945A1 WO 2019030945 A1 WO2019030945 A1 WO 2019030945A1 JP 2018001561 W JP2018001561 W JP 2018001561W WO 2019030945 A1 WO2019030945 A1 WO 2019030945A1
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Prior art keywords
event
manufacturing
product
production
time
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English (en)
Japanese (ja)
Inventor
昭 森口
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Hitachi Solutions Ltd
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Hitachi Solutions Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to a method and program for estimating causes of production loss.
  • Patent Document 1 describes a display method for specifying the location of loss occurrence by centrally visualizing the manufacturing time of each process.
  • a time axis is provided for each process, a processing time for each product is described by vertical bars on the time axis, and a graph is generated in which vertical bars of processing times of each process are connected by line segments. .
  • Patent Document 2 describes a display method for analyzing loss factors for overall optimization instead of individual optimization such as planning and quality by displaying process planning, process progress, production results, and quality inspection all at once. ing.
  • Patent Document 1 only displays the treatment time of the individual product on a time axis with a bar for each production process, and repeatedly occurs under specific conditions across days and weeks. It is difficult to detect manufacturing loss.
  • Patent Document 1 has no means for correlating a plurality of manufacturing losses, and the visualization of the influence on production efficiency or the cause It is difficult to estimate. For example, when the abnormal stop of the equipment occurs, the worker is in charge of the restoration work of the equipment, and the process which the worker was originally in charge is delayed. As described above, production losses that cause other production losses in a chained manner have to be preferentially addressed, since they greatly impede production efficiency.
  • the cause estimating method of the manufacture loss which a computer performs, and the step of acquiring the production track record information of a production process about each of a plurality of product individuals, and the above-mentioned plurality based on the above-mentioned Calculating the processing time in the production process for each of the individual products, acquiring information on the production event indicating the status of the production process, and based on the production record information and the information on the production event
  • the step of associating the manufacturing event with the individual product, the distribution of the processing time of the plurality of individual products, and the distribution of the processing time of the individual product linked with the manufacturing event are displayed in a superimposed manner Providing a method of estimating the cause of a manufacturing loss including the step of displaying on a device.
  • a program is provided for causing a computer to execute a step of superimposing the distribution of the processing time and the distribution of the processing time of the individual product associated with the manufacturing event on a display device.
  • the present specification includes the disclosure content of Japanese Patent Application No. 2017-152739 on which the priority of the present application is based.
  • FIG. 1 is a diagram showing the configuration of an estimation system for estimating the cause of manufacturing loss according to the present embodiment.
  • the estimation system includes a production planning system 101, a manufacturing execution system 102, a manufacturing / inspection facility 103, a facility abnormality detection system 104, an arithmetic device 110, and a display device 120.
  • the production planning system 101 is a system for formulating types of products to be manufactured and the number thereof, work schedules of workers and equipment, raw materials and parts necessary for manufacturing the products, and has been introduced in many manufacturing industries. Is a well known system.
  • the manufacturing execution system 102 is a system that collects information on work instructions and manufacturing results for equipment and workers.
  • the equipment abnormality detection system 104 is a system that monitors the state of equipment through a sensor or the like and detects an abnormality that leads to equipment failure.
  • the arithmetic unit 110 acquires a data acquisition unit 111 that acquires data related to the manufacture of individual products, a manufacturing event processing unit 112 that generates a manufacturing event from the data acquired by the data acquisition unit 111, and associates a manufacturing result with a manufacturing event.
  • the arithmetic unit 110 is provided with a recording unit (not shown), and the recording unit is recorded with mathematical expressions for determining parameters ⁇ , ⁇ and variance v of log normal distribution described later.
  • various programs are recorded in the recording unit, and the arithmetic device 110 can function as the data acquisition unit 111, the manufacturing event processing unit 112, and the statistical processing unit 113 by executing the programs.
  • the arithmetic device 110 is connected to the display device 120.
  • the data acquisition unit 111 acquires data (production record information and information on manufacturing events) relating to the manufacture of individual products from the production planning system 101, the manufacturing execution system 102, the manufacturing and inspection facility 103, and the equipment abnormality detection system 104.
  • the result of the cause estimation process stored in the calculation result DB 115 is output to the display device 120.
  • FIG. 2 is a diagram showing the configuration of the manufacturing information DB 114.
  • the manufacturing information DB 114 includes a manufacturing result table 201, an equipment error information table 202, a equipment operation information table 203, a manufacturing event list table 204, a manufacturing event time table 205, and a manufacturing event period table 206.
  • the data held by each table will be described below.
  • FIG. 3 is a diagram showing an example of the manufacturing result table 201.
  • the manufacturing results table 201 includes product numbers for identifying individual products or lots, process IDs for uniquely identifying manufacturing processes, start times and completion times of the individual products in processes identified by the process IDs, start times Stores the processing time which is the difference between the and the completion time.
  • processing time for example, the processing time for each of a plurality of individual products in the production process, which is calculated based on the start time and the completion time by the arithmetic device 110, is stored. Moreover, although only process1 is described as process ID in FIG. 3, the data regarding another process ID are also recorded.
  • FIG. 4 is a diagram showing an example of the equipment error information table 202.
  • the facility error information table 202 stores a facility ID uniquely identifying a facility in which an error has occurred, an error occurrence time, and error information representing the content of the error. In the example of FIG. 4, it is recorded that a tool damage error has occurred in the facility whose facility ID is mac_a at March 10, 2017 10:10 in the first row.
  • FIG. 5 is a diagram showing an example of the equipment operation information table 203.
  • the facility operation information table 203 includes a facility ID uniquely identifying the facility, a facility state such as operation or stop, an event causing a stop or operation such as maintenance and production, and a duration of the facility state (a facility state start date and time And end date and time) and.
  • a facility state start date and time And end date and time For example, in the first row of the facility operation information table 203, it is recorded that the facility whose facility ID is mac_a is operating from 9:00 to 12:00 on March 2, 2017, and a product is produced. ing.
  • FIG. 6 is a diagram showing an example of the manufacturing event list table 204.
  • the manufacturing event list table 204 is a table for registering a manufacturing event, and includes an event ID uniquely indicating the event and an event name.
  • an event ID that includes “t” in the ID name string is an event that occurs with a temporal width, and the other event IDs are events that occur at a temporary point.
  • FIG. 7 is a diagram showing an example of the manufacturing event time table 205.
  • the manufacturing event time table 205 stores the occurrence date and time of a manufacturing event (manufacturing event whose event ID does not include “t”) among manufacturing events registered in the manufacturing event list table 204. It is a table. In the manufacturing event time table 205, manufacturing events are arranged in order of occurrence date and time.
  • FIG. 8 is a diagram showing an example of the manufacturing event period table 206.
  • the manufacturing event period table 206 is a table for storing the start date and the end date of a manufacturing event (manufacturing event having an event ID including "t") among manufacturing events registered in the manufacturing event list table 204. It is.
  • FIG. 9 is a diagram showing the configuration of the calculation result DB 115.
  • the calculation result DB 115 includes an event-added manufacturing result table 901, a log normal distribution information table 902, and an event correlation table 903. The data held by each table will be described below.
  • FIG. 10 is a diagram showing an example of the manufacturing result table with event 901.
  • the manufacturing result table with event 901 is the manufacturing result table 201 with information on whether or not a manufacturing event has occurred, true for the column of the manufacturing event that has occurred, and false for the column of the manufacturing event that has not occurred. Is stored. The method of determining the occurrence of the manufacturing event will be described below.
  • the arithmetic device 110 determines a period in which the manufacturing event is considered to be influenced.
  • the period is, for example, a predetermined period determined based on experience for each process, and is a period during which a manufacturing event is considered to affect the processing time of a product individual.
  • the computing device 110 links a product individual and a manufacturing event in which the determined period and the manufacturing period overlap, and sets the column of the manufacturing event to true.
  • step 1 For example, if the above period in step 1 is 600 seconds before and after the occurrence date of a production event, the start time of the product individual with product number 0001 and the occurrence date of eve_1 are both 9:00, and the production event is affected.
  • the column of eve_1 becomes true because the possible period of time overlaps with the period of manufacture of the individual product.
  • the arithmetic unit 110 determines, for each product individual, a time difference indicating temporal perspective between the generated manufacturing event and the manufacturing time of the product individual linked to the manufacturing event, and stores the time difference in the manufacturing result table with event 901 .
  • the time difference is determined based on at least one of the occurrence date and time and the end date and time of the manufacturing event, and at least one of the start time and the completion time of the individual product linked to the manufacturing event.
  • the difference between the occurrence date and time of the start of the product individual is taken as the time difference.
  • the time difference For example, in the case of the product individual of the product number 0033, since the start time of manufacture is 13:05 and the occurrence time of the manufacture event is 13:00, a time difference of -300 seconds is recorded.
  • the start time of manufacture is in the past rather than the manufacture event, it is considered as a positive time difference, and when the start time of manufacture is future than the manufacture event, it is considered as a negative time difference.
  • an event having a temporal width that is, an event that has a time interval from occurrence to end
  • the minimum value of the difference between any time point of the time interval and the start time of the product individual Time difference is assumed. For example, if the start time or completion time of the product individual is within the occurrence period of the event, the time difference is zero.
  • FIG. 11 is a diagram showing an example of the lognormal distribution information table 902.
  • the log normal distribution information table 902 stores the results of calculating the parameters, variances, and log likelihoods of the probability density function corresponding to the log normal distribution by approximating the distribution of the processing time of each step with the log normal distribution.
  • ⁇ and ⁇ in the probability density function described below are stored.
  • ⁇ and ⁇ are parameters characterizing the probability density function, and e represents the number of Napiers.
  • the process including the production loss has a large variation in processing time, that is, dispersion.
  • FIG. 12 is a diagram showing an example of the event correlation table 903.
  • the event correlation table 903 stores a process ID, an event ID, an event log likelihood, a log likelihood difference, and a time difference (seconds).
  • the event log likelihood indicates the log likelihood when it is assumed that the distribution of the population based on the processing time of the product individual linked to the manufacturing event follows the determined log normal distribution.
  • the above-mentioned population extracts only the processing time of the product individual linked to the specific manufacturing event from the first population of the processing time of one process so that it becomes the same number as the number of elements of the first population. It is a second population obtained by randomly duplicating elements of processing time.
  • the log likelihood in the case where it is assumed that the set obtained by duplicating in the second population is the second population, and the distribution of the second population follows the lognormal distribution obtained by fitting to the distribution of the first population is It is an event log likelihood.
  • the log likelihood difference is the difference between the log likelihood stored in the log normal distribution information table 902 and the event log likelihood stored in the event correlation table 903. That is, the difference between the log likelihood assuming that the first population conforms to the log normal distribution and the log likelihood assuming that the second population obeys the same log normal distribution is the log likelihood Degree difference.
  • FIG. 13 is a diagram showing a sequence of acquisition processing of manufacturing information. Each step of the sequence will be described below.
  • Step 1301 the data acquisition unit 111 acquires information on the manufacturing results of individual products from the manufacturing execution system 102 or the manufacturing / inspection facility 103, and stores the information in the manufacturing results table 201.
  • the data acquisition unit 111 acquires facility error information from the manufacturing / inspection facility 103 or the facility abnormality detection system 104, and stores the facility error information in the facility error information table 202.
  • the data acquisition unit 111 acquires facility operation information from the production planning system 101, the manufacturing execution system 102 or the manufacturing / inspection facility 103, and stores the facility operation information table 203.
  • Step 1302 The data acquisition unit 111 calculates the processing time in the production process for each of the plurality of product individuals based on the start time and completion time of the production process of the plurality of product individuals acquired in step 1301 and (production performance information). , And stored in the manufacturing result table 201.
  • the processing time of the process 1 of the product number 0001 is 600 seconds which is the difference between the start time 09:00:00 and the completion time 09:10:00.
  • the manufacturing event processing unit 112 converts the information on the manufacturing event included in the equipment error information and the equipment operation information acquired in step 1301 into an event ID registered in the manufacturing event list table 204.
  • the manufacturing event processing unit 112 stores, in the manufacturing event time table 205, the occurrence date and time of a manufacturing event having no time width and the event ID of the manufacturing event. Further, the manufacturing event processing unit 112 stores, in the manufacturing event period table 206, the event ID, the occurrence date and time, and the end date and time for a production event having a temporal width.
  • FIG. 14 is a diagram showing a sequence of log normal distribution approximation processing. Each step of the sequence will be described below.
  • the statistical processing unit 113 sets, as a population, a set of processing times of one step among the processing times of the plurality of product individuals calculated in step 1302, and performs all the processing of fitting the histogram of the population with the lognormal distribution. Run through the steps. Then, the statistical processing unit 113 obtains parameters ⁇ and ⁇ characterizing the lognormal distribution for each process. Further, the statistical processing unit 113 calculates the log likelihood in the case where it is assumed that the histogram of the above-mentioned population conforms to the fitted log normal distribution. Note that the fitting with the lognormal distribution is performed using, for example, the least squares method or the maximum likelihood estimation method.
  • FIG. 15 is a diagram showing an example of the approximated lognormal distribution.
  • the histogram of the processing time for one process and the approximated log normal distribution are drawn together.
  • the vertical axis represents a ratio, and indicates the number of samples of processing time included in a specific section with respect to the number of samples of total processing time. Thus, the sum of the values across all the sections of the histogram is one.
  • the statistical processing unit 113 obtains the variance v from the parameters ⁇ and ⁇ calculated in step 1401 according to the following equation, and stores the parameters ⁇ , ⁇ , the variance v, and the log likelihood in the log normal distribution information table 902.
  • FIG. 16 is a diagram showing a sequence of processing for adding event information to a manufacturing result. Each step of the sequence will be described below.
  • the manufacturing event processing unit 112 repeats the processing of the subsequent steps 1602 and 1603 for all the events described in the manufacturing event time table 205 and the manufacturing event period table 206.
  • Step 1602 First, the manufacturing event processing unit 112 searches for a product individual manufactured at the same time as the manufacturing event described in the manufacturing event time table 205 and the manufacturing event period table 206.
  • the manufacturing event processing unit 112 searches for a product individual manufactured at the same time as the manufacturing event based on at least one of the occurrence date and end time of the manufacturing event and at least one of the start time and the completion time of the product individual. Do.
  • the manufacturing event processing unit 112 searches for a product individual whose manufacturing period overlaps with a predetermined period considered to be influenced by the manufacturing event.
  • the predetermined period is a predetermined time width defined for each production process centered on at least one of the occurrence date and the end date of the production event.
  • the predetermined time width is determined, for example, by the experience of the process manager, because the influence of the manufacturing event on the processing time differs depending on the product to be manufactured or the manufacturing process.
  • a predetermined period and a manufacturing period overlap means that, for example, at least one of the start time and the completion time of the individual product is included in the predetermined period.
  • the event: eve_1 described in FIG. 7 occurs at 2017/03/02 09:00:00, and the step of the product number 0001 described in FIG. 3:
  • the start time at process 1 is 09:00: It is 00. That is, the occurrence time of the event: eve_1 and the start time of the process of the product number 0001: process 1 are the same. Therefore, since the organization affected by the manufacturing event overlaps with the manufacturing period, the search by the manufacturing event processing unit 112 is hit.
  • the event described in FIG. 8: eve_t_6 is generated at 2017/03/02 02 10:10:00 and then ended at 2017/03/02 10:20:00 and is described in FIG. Step of the product number 0011:
  • the end time of process 1 is included in the event occurrence period. Therefore, since the organization affected by the manufacturing event overlaps with the manufacturing period, the search by the manufacturing event processing unit 112 is hit. (Step 1603) Subsequently, the manufacturing event processing unit 112 adds manufacturing event information used for the search to the information on the manufacturing results of the product unit hit in the search in step 1601, and merges the information into the manufacturing result table with event 901. Store. That is, the manufacturing event processing unit 112 links the manufacturing event to the individual product based on the production record information and the information on the manufacturing event.
  • the record eve_1 of the product number 0001 as shown in FIG. Stores true.
  • the search of eve_t_6 exemplified in the step 1601 since the start time of the process of the product 0011: process1 overlaps, true is stored in the eve_t_6 of the record of the product number 0011 as well.
  • FIG. 17 is a diagram showing a sequence of processing for determining the correlation between the manufacturing loss and the manufacturing event. Each step of the sequence will be described below.
  • Step 1701 The subsequent processing from step 1702 to step 1705 is repeatedly performed for all the events described in the manufacturing event list table 204.
  • manufacturing events will be described as event i (0 ⁇ i ⁇ total number of events + 1).
  • Step 1702 The processes from step 1703 to step 1705 are repeatedly executed for all process IDs.
  • the j-th process ID is described as process j (0 ⁇ j ⁇ total number of processes + 1).
  • the statistical processing unit 113 acquires, for the process j, the manufacturing results (the manufacturing results at the time of event) of the individual product whose flag of event i is true. Specifically, the statistical processing unit 113 acquires, for the process j, the processing time of the individual product whose event i flag is true.
  • the statistical processing unit 113 determines that the histogram of the processing time set of the product individual linked to the manufacturing event is represented by the log normal distribution parameter of process j stored in the log normal distribution information table 902.
  • Log likelihood event log likelihood
  • the number of processing times for process j related to the product individual associated with event i is equal to the number of processing times for all product individual related to process j And randomly duplicate the processing time.
  • the statistical processing unit 113 obtains the difference between the event log likelihood calculated in step 1703 and the log likelihood of process j stored in the log normal distribution information table 902, and stores the difference in the event correlation table 903.
  • the statistical processing unit 113 calculates an average time difference (an average value of time differences of each product individual) between a product individual having a true event i flag of process j and the event i, and stores the calculated average time difference in the event correlation table 903.
  • the statistical processing unit 113 may store, instead of the average value of the time differences, an intermediate value, a maximum value, or a minimum value of the time differences in the event correlation table 903.
  • FIG. 18 is an example of a GUI that the arithmetic device 110 outputs to the display device 120.
  • the arithmetic unit 110 refers to, for example, the variances stored in the lognormal distribution information table 902, and displays the inefficient processes on the display unit 120 in descending order of variance of processing time.
  • a cause analysis button is attached to each process, and when the button is pressed, the screen changes to a screen displaying the cause estimation result.
  • FIG. 19 is an example of a GUI showing the cause estimation result of the manufacturing loss.
  • the arithmetic device 110 extracts, for example, a manufacturing event having a log likelihood difference equal to or larger than a predetermined value from the event correlation table 903 for the process to which the cause analysis button pressed on the screen of FIG. 18 belongs. Then, the arithmetic device 110 acquires the log likelihood difference and the average time difference associated with the extracted manufacturing event, arranges the manufacturing event names in the order of the magnitude of the average time difference, and displays the same on the display device 120. In the example shown in FIG. 19, manufacturing events with small average time differences are displayed in order from the left.
  • an arrow from the manufacturing event name to the process name and the log likelihood difference are displayed.
  • Each arrow is displayed changing in thickness based on the magnitude of the log likelihood difference. Specifically, thicker arrows are displayed for manufacturing events with larger log likelihood differences. That is, the production event in which the bold arrow is displayed is a production event in which the distribution of the processing time is largely deviated, which is a production event that causes a large production loss. Also, each manufacturing event name is displayed from top to bottom in descending order of log likelihood difference.
  • the GUI indicating the cause estimation result of the manufacturing loss determines the two-dimensional arrangement of the manufacturing event name and the process name according to the magnitude of the log likelihood difference and the average time difference. Moreover, the process name and each manufacturing event name are connected by the arrow, and the thickness of the arrow is changed. By displaying in this manner, the user can visually understand the magnitude of the influence of the manufacturing event on the processing time of the process and the strength of the correlation between the manufacturing event and the manufacturing loss.
  • FIG. 20 is an example of a detail screen showing the correlation between the manufacturing loss and the manufacturing event.
  • a histogram of the processing time of a plurality of product individuals not limited to the occurrence of a manufacturing event (normal time histogram) and a histogram of the processing time of a product individual linked to a manufacturing event are displayed superimposed There is.
  • the user can visually recognize which manufacturing event has occurred and how long the normal processing time is extended.
  • a log normal distribution is given as an example of the distribution curve fitted to the processing time histogram.
  • the processing time histogram may be fitted using a distribution curve (probability distribution function) other than the lognormal distribution.
  • a distribution curve probability distribution function
  • fitting using a general probability distribution such as a normal distribution, a Poisson distribution, or a gamma distribution is possible.
  • the log likelihood is used as an example of the evaluation method of the relationship between the manufacturing loss and the manufacturing event, but the evaluation may be performed using the likelihood.
  • a general method for detecting outliers can be used, such as a method in which a value with a predetermined difference from the average value of the treatment time is taken as an outlier, Tukey-Kramer test, Sumirnov-Grubbs test, or the like.
  • the histogram of the processing time of the entire product individual manufactured in a certain process, and the processing time of the individual product associated with the manufacturing event among the entire product individual manufactured in the above process And the histogram of the above are superimposed and displayed on the display device 120. Therefore, the user can visually understand the influence of the production event on the processing time of the individual product.
  • the estimation system of the present disclosure includes, for example, the likelihood (or log likelihood) of the probability distribution function fitted to the histogram of the processing time of the entire product individual manufactured in a certain process, and the product individual manufactured in the above process Calculate the event likelihood (or event log likelihood) when assuming that the histogram of the processing time of the product individual linked to the manufacturing event in the whole follows the above probability distribution function, and the difference (the likelihood Calculate the degree difference or log likelihood difference).
  • the user can understand the degree of influence of a certain manufacturing event on the processing time of a product individual by confirming the above difference in likelihood (or difference in log likelihood).
  • the estimation system of the present disclosure determines a time difference indicating temporal perspective of the manufacturing event and the product individual linked to the manufacturing event for each product individual and calculates the average. By confirming the average time difference, the user can understand how long the production event affects the processing time of production of the product individual.
  • the estimation system of the present disclosure arranges one or more manufacturing events together with information indicating the difference between the likelihood and the event likelihood and displays them on the display device 120 in order of the average magnitude of the time difference.
  • the user can at first glance understand the temporal influence and degree of the production event on the production processing time of the product individual.
  • the user can obtain auxiliary knowledge that recognizes the relationship between a plurality of manufacturing events.
  • the estimation system of the present disclosure links, for example, a production event with a production event whose production period overlaps with a predetermined period during which the production event is considered to have affected the production of the production product. That is, the estimation system of the present disclosure effectively extracts the processing time that production loss is considered to have occurred due to a manufacturing event from a set of overall processing times, and what manufacturing event affected the processing time in the production process Can be effectively shown to the user.
  • the predetermined period is, for example, a predetermined time width defined for each production step centered on at least one of the occurrence date and time and the end date and time of the production event. Since the processing time of a product event varies depending on the type of production process, setting a predetermined period as described above makes it possible to properly extract a sample of processing time affected by the manufacturing event.
  • the estimation system of the present disclosure when fitting a predetermined probability distribution function to the distribution of the processing time of a plurality of product individuals, a predetermined outlier detection method from the distribution of the processing times of the plurality of product individuals.
  • the predetermined probability distribution function is fitted to the distribution from which the outliers have been removed by. As described above, removing outliers can improve the estimation accuracy of the cause of manufacturing loss.
  • the present invention is not limited to the embodiments described above, but includes various modifications.
  • the embodiments described above are described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described.
  • part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • each of the configurations, functions, processing units, processing means, etc. described above may be realized by hardware, for example, by designing part or all of them with an integrated circuit. Further, each configuration, function, etc.
  • Information such as a program, a table, and a file for realizing each function can be placed in a memory, a recording device such as a hard disk or SSD, or a recording medium such as an IC card, an SD card, or a DVD.
  • 101 production planning system
  • 102 production execution system
  • 103 production / inspection equipment
  • 104 equipment abnormality detection system
  • 110 arithmetic device
  • 111 data acquisition unit
  • 112 production event processing unit
  • 113 statistical processing unit
  • 114 manufacturing information DB
  • 115 calculation result DB
  • 120 display device
  • 201 manufacturing results table
  • 202 equipment error information table
  • 203 equipment operation information table
  • 204 manufacturing event list table
  • 205 manufacturing event time table
  • 206 manufacturing event period table
  • 901 manufacturing result table with event
  • 902 log normal distribution information table
  • 903 event correlation table

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Abstract

L'invention concerne un procédé d'estimation de la cause d'une perte de fabrication, comprenant : une étape consistant à d'acquérir des informations de performance de production relatives à un processus de production de chaque produit d'une pluralité de produits individuels ; une étape consistant à calculer une durée de processus du processus de production pour chaque produit de la pluralité de produits individuels, d'après les informations de performance de production ; une étape consistant à d'acquérir des informations relatives à un événement de fabrication, indiquant un état du processus de production ; une étape consistant à associer l'événement de fabrication à des produits individuels d'après les informations de performance de production et les informations relatives à l'événement de fabrication ; et une étape consistant à afficher en superposition, sur un dispositif d'affichage, une distribution des durées de processus de la pluralité de produits individuels et une distribution des durées de processus des produits individuels auxquels l'événement de fabrication est associé.
PCT/JP2018/001561 2017-08-07 2018-01-19 Procédé et programme d'estimation de cause Ceased WO2019030945A1 (fr)

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JP2017152739A JP6802122B2 (ja) 2017-08-07 2017-08-07 原因推定方法およびプログラム

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JP7335154B2 (ja) 2019-12-17 2023-08-29 株式会社東芝 情報処理装置、情報処理方法、およびプログラム
JP7473410B2 (ja) * 2020-07-07 2024-04-23 株式会社日立製作所 作業指示装置、作業指示システムおよび作業指示方法
JP7463250B2 (ja) * 2020-10-07 2024-04-08 三菱重工業株式会社 減速材温度係数測定システム、減速材温度係数測定方法及びプログラム
JP7471334B2 (ja) * 2022-02-17 2024-04-19 株式会社日立製作所 生産性改善システム、生産性改善方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006202255A (ja) * 2004-12-24 2006-08-03 Omron Corp 工程改善支援システム
JP2009080844A (ja) * 2005-10-21 2009-04-16 Omron Corp データ収集システム、解析装置、解析方法およびプログラム
JP2013030021A (ja) * 2011-07-28 2013-02-07 Mitsubishi Heavy Ind Ltd 生産管理装置、工程分析方法および工程分析プログラム
JP2013080458A (ja) * 2011-09-21 2013-05-02 Nippon Steel & Sumitomo Metal 品質予測装置、操業条件決定方法、品質予測方法、コンピュータプログラムおよびコンピュータ読み取り可能な記憶媒体

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006202255A (ja) * 2004-12-24 2006-08-03 Omron Corp 工程改善支援システム
JP2009080844A (ja) * 2005-10-21 2009-04-16 Omron Corp データ収集システム、解析装置、解析方法およびプログラム
JP2013030021A (ja) * 2011-07-28 2013-02-07 Mitsubishi Heavy Ind Ltd 生産管理装置、工程分析方法および工程分析プログラム
JP2013080458A (ja) * 2011-09-21 2013-05-02 Nippon Steel & Sumitomo Metal 品質予測装置、操業条件決定方法、品質予測方法、コンピュータプログラムおよびコンピュータ読み取り可能な記憶媒体

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