Analysis system, system, and information presentation method

Abstract

To provide a technique for specifying a cause of a quality problem that occurs when an operation is performed across a plurality of steps.SOLUTION: An analysis system comprises a processor and a storage device. The storage device defines a chain of a plurality of steps and stores a data model associated with a work result for each task included in a step. The processor acquires information about a first task in which a defect has occurred; extracts a related task from the information about the first task; refers to the data model; and extracts work result data of a second task included in the related task. The processor calculates a correlation between the work result data of the first task and the second task, and outputs information about the correlation.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an analysis system, a system, and a method for presenting information. [Background technology]

[0002] From the perspective of business sustainability, a wide range of industrial fields require more advanced quality assurance. With this demand for more advanced quality assurance, the importance of identifying the root cause and considering countermeasures when there is a quality problem is increasing. However, factors such as dependency on personal work that relies on experience and intuition and the difficulty of collecting quality-related data can impede the efficiency of quality assurance. The following prior art documents are related prior art. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-018526 A Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 describes a technology for identifying the cause of a pump equipment failure by calculating correlations from past measurement values ​​for a combination of actual measurement data from multiple devices and comparing them with the most recent measurement values, and inferring the cause of the failure when the correlation is no longer valid.

[0005] However, in cases where work is carried out across multiple processes, the work data for each process may not be recorded, or even if it is recorded, it is extremely costly to combine all the data linked to each process to investigate the cause of a quality problem.

[0006] From this perspective, there is a need to provide a technique for identifying the cause of quality problems that occur when work is carried out across multiple processes. [Means for solving the problem]

[0007] According to the present invention, the following analysis system is provided as an example. The analysis system includes a processor and a storage device. The storage device defines a chain of a plurality of processes and stores a data model in which work results are linked to each task included in the process. The processor acquires information about a first task in which a defect has occurred, and extracts related tasks from the information about the first task. The processor refers to the data model and extracts work results data of a second task included in the related tasks. The processor calculates a correlation between the work results data of the first task and the second task, and outputs information about the correlation. A system is also provided that includes the analysis system and a terminal connected to the analysis system, and the analysis system and the terminal are capable of wireless communication by 5G. The following information presentation method is also provided using the analysis system. This information presentation method includes the steps of: the quality analysis system acquiring information about a first task in which a defect has occurred, which is input by a user; the quality analysis system performing processing according to the input and presenting information about correlation of the results to the user; the quality analysis system acquiring information about a first task, which is input by the user after a measure has been implemented for a process, and which covers a period after the implementation of the measure; and the quality analysis system performing processing according to the input and presenting information about correlation of the results to the user. Effect of the Invention

[0008] According to the present invention, it is possible to provide a technique for identifying the cause of quality problems that occur when work is performed across multiple processes. Note that problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a diagram illustrating an example of a functional configuration of the estimation system. [Diagram 2]FIG. 13 is a diagram showing an example of a defect event and business matrix. [Figure 3A] FIG. 2 illustrates an example of a data model. [Figure 3B] 1 is a diagram for explaining a data model structure and how to use it. FIG. [Figure 4] 13 is a flowchart showing an example of an entire process. [Diagram 5] 11 is a flowchart for explaining a detailed example of processes from a parameter setting unit to an output unit. [Figure 6A] 1 is an example of an image of an input unit. [Figure 6B] 13 is an example of an image of an output unit. [Figure 6C] 13 is an example of an image of an output unit. [Figure 6D] FIG. 11 is a diagram showing an example of a screen transition mode. [Figure 7] FIG. 2 is a diagram illustrating an example of a hardware configuration of the estimation system. [Figure 8] FIG. 1 is a diagram illustrating an example of a system configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment is an example for explaining the present invention, and appropriate omissions and simplifications are made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. As examples of various information, the various information may be described using expressions such as "table," "list," and "queue," but the various information may be expressed using data structures other than these. For example, various information such as "XX table," "XX list," and "XX queue" may be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are mutually interchangeable. When there are multiple components having the same or similar functions, they may be described by using the same reference numerals with different subscripts, or when there is no need to distinguish between these multiple components, the subscripts may be omitted. In the embodiment, a process performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., CPU, GPU), and performs the process defined by the program while using a storage resource (e.g., memory) and an interface device (e.g., communication port), etc. Therefore, the subject of the process performed by executing the program may be the processor. Similarly, the subject of the process performed by executing the program may be a controller, device, system, computer, or node having a processor. The subject of the process performed by executing the program may be a calculation unit, and may include a dedicated circuit that performs a specific process. Here, the dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device), etc. The program may be installed in the computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and a storage resource for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0011] In this embodiment, an example will be described in which event information regarding product defects is received, relevant business information is extracted, and correlation coefficients regarding the defects are output, but this may also be implemented as part of daily quality improvement work.

[0012] An example of the functional configuration of the system will be described with reference to Fig. 1. This figure shows an example of the functional configuration of the estimation system.

[0013] The estimation system 1a (analysis system) includes an input unit 11, a parameter setting unit 12, a related task extraction unit 13, a 4M data extraction unit 14, a correlation calculation unit 15, a graph creation unit 16, and an output unit 17. The estimation system 1a also stores, as data, a correlation 21 between a defective event and a task, and a data model 22.

[0014] The input unit 11 is a functional block that receives user operations. The parameter setting unit 12 is a functional block that sets the information received by the input unit 11 as parameters to be used by the related task extraction unit 13 and the 4M data extraction unit .

[0015] The related task extraction unit 13 is a functional block that extracts task information related to the parameters set in the parameter setting unit 12. Specifically, the related task extraction unit 13 is a functional block that extracts related task information from the event information related to defects set in the input unit 11 using the relationship between defect events and tasks 21, extracts related tasks from the item information and task information set in the input unit 11 using a data model 22, and sets tasks that meet both conditions as related tasks.

[0016] The 4M data extraction unit 14 is a functional block that extracts 4M information linked to the related tasks based on the parameters set in the parameter setting unit 12 and the related task information in the related task extraction unit 13. Specifically, the 4M data extraction unit 14 is a functional block that extracts various 4M data linked to the related tasks extracted by the related task extraction unit 13 using a data model 22, and narrows down the data to those that fall within the target period set in the parameter setting unit 12, thereby extracting 4M data to be used in the correlation calculation unit 15.

[0017] The correlation calculation unit 15 is a functional block that uses the 4M data extracted by the 4M data extraction unit 14 to calculate correlations with the target data in a brute-force manner, and puts the results in a form that can be output by the output unit 17. The graph creation unit 16 is a functional block that creates a scatter plot when the correlation calculation unit 15 calculates the correlation, and puts the results in a form that can be output by the output unit 17. The output unit 17 is a functional block that outputs the results of the correlation calculation unit 15 and the graph creation unit 16, and visualizes them so that the user can recognize them.

[0018] The association 21 between defective events and tasks is a DB (database) referenced by the related task extraction unit 13. Using the association 21 between defective events and tasks, the estimation system receives as input the parameters of the defective events, which are parameters set by the parameter setting unit 12, extracts tasks related to the relevant event from a matrix consisting of a list of defective events and tasks, and returns the extracted tasks to the related task extraction unit 13.

[0019] The data model 22 is a DB (database) referenced by the related task extraction unit 13 and the 4M data extraction unit 14. In the related task extraction unit 13, the estimation system uses the data model 22 to extract tasks that the item passed through before reaching the task, using item information and task information, which are parameters set in the parameter setting unit 12, as input. In the 4M data extraction unit 14, the estimation system uses the data model 22 to extract various 4M data linked to the task, using the related tasks extracted by the related task extraction unit 13 as input, and narrows down the data that falls within the target period set in the parameter setting unit 12 to obtain the target 4M data.

[0020] The target systems and facilities 31 are systems and facilities that serve as inputs of the business and 4M data accumulated in the data model 22.

[0021] Figure 2 is an example of a matrix showing the relationship 21 between defect events and operations. The vertical axis shows operations such as "molding," "heat treatment," "processing," and "assembly," while the horizontal axis shows defect events such as "defective printing." In Figure 2, it is shown that "molding," "heat treatment," and "processing" are operations related to the defect event "defective printing." This matrix is ​​referenced when inputting the parameters of a defect event and outputting the operations related to that event.

[0022] FIG. 3A is a diagram showing an example of a data model. The data model 22 defines a chain of a plurality of processes. The symbol A in FIG. 3A indicates the structure of the business data included in the data model 22. The business data is linked to the 4M (Man, Machine, Material, Method) data related to the business, that is, the data of the worker, the equipment, the materials, and the work procedure. For example, when the business is image output inspection, an example of the 4M data corresponds to the performance information such as the print output value obtained from the inspection equipment. In the case of a manufacturing process, there is also a case where the performance information such as the processing result of the manufacturing equipment and the input quantity of materials is linked. In this way, quantitative information is associated with each business as 4M data.

[0023] Symbol B in Fig. 3A indicates the structure of the data model 22. In the data model 22, for the business data of a certain business, the output parts of the previous business are used as the input parts of the business, and the previous and next business are related with "output parts / input parts". Such a data model structure can be created in correspondence with an actual production site such as a factory, and data acquired from the site is mapped to this data model, and related data that also relates the site data to each other is generated.

[0024] Fig. 3B is a diagram to explain the data model concept and how to use it. The estimation system expresses each business operation with its own data model and associates data between businesses, thereby enabling data exploration while tracing the manufacturing process.

[0025] In Fig. 3B, symbol A indicates operations 7 and 8 related to the finished product and the 4M data associated with each operation, symbol B indicates operations 1, 2, and 3 related to a sub-part and the 4M data associated with each operation, and symbol C indicates operations 4, 5, and 6 related to a main part and the 4M data associated with each operation. In the example shown in Fig. 3B, if a defect occurs in the finished product obtained as a result of operation 8, it is possible to trace back operations 1 to 7 that are related to the defect and have been passed through, and search for and extract the 4M performance data associated with the operations.

[0026] For example, if a defect is found in a finished product and the defect is a printing defect, the inference system extracts the related tasks in which the printing defect is created (e.g., coating) based on the master information defined in the relationship between the defect event and tasks 21. Then, when task 8 is used as the starting point, the inference system traces back tasks 1 to 7 that have passed through the process related to the defect. For example, the inference system searches based on information such as an ID (serial number, lot number, etc.) that identifies the product in which a defect was found in the 4M data. In addition, regarding tracing between tasks, the inference system can trace based on, for example, an ID that identifies a process defined in each task.

[0027] Fig. 4 is a flow chart showing an example of the overall process. With reference to Fig. 4, a flow will be described in which event information related to a product defect is received, relevant business information is extracted, a correlation coefficient related to the defect is output, and defect improvement is implemented based on the correlation coefficient. Note that defect improvement can also be implemented by implementing a part of the flow.

[0028] First, the user sets search conditions in the input unit 11 (S401). At this time, the user sets item information / defective event / business information / target data / target period. In addition, the user adds 4M information as necessary to narrow down the search.

[0029] Next, the estimation system causes the parameter setting unit 12 to set parameters based on the information from the input unit 11 (S402).

[0030] Next, the estimation system extracts related task information based on the parameters set in the parameter setting unit 12 in the related task extraction unit 13 (S403). Specifically, the estimation system extracts task information that is related from the event information related to the defect set in the parameter setting unit 12 using the defect event and task relationship 21, extracts tasks that are related from the item information and task information set in the parameter setting unit 12 using the data model 22, and sets those that meet both conditions as related tasks.

[0031] Next, the estimation system uses the 4M data extraction unit 14 to extract 4M information linked to those tasks based on the parameters set by the parameter setting unit 12 and the related task information from the related task extraction unit 13 (S404). Specifically, the estimation system uses the data model to trace the tasks through which the product has passed, and extracts various 4M data linked to the related tasks extracted by the related task extraction unit 13, using the data model 22. The estimation system then extracts 4M data to be used in the correlation calculation unit 15 by narrowing down the data to those that fall within the target period set by the parameter setting unit 12.

[0032] Next, the estimation system uses the 4M data extracted by the 4M data extraction unit 14 to calculate the correlation with the target data in a round-robin manner in correlation calculation unit 15 (S405).

[0033] Next, the estimation system causes graph creation unit 16 to create a scatter diagram when correlation calculation unit 15 calculates the correlation (S406).

[0034] Next, the estimation system displays the correlation coefficients calculated as the results by the correlation calculation section 15 in descending order on the output section 17 (S407).

[0035] The user investigates the cause while looking at the output result (S408).

[0036] Furthermore, the estimation system displays a correlation graph between certain 4M data and the target data as a result of the graph creation unit 16 as necessary (S409).

[0037] The user investigates the cause and considers measures for improvement while referring to the graph on the output unit 17, and reflects the results in the design (S410).

[0038] The collection target system / equipment 31 operates based on the revised new design values ​​(S411).

[0039] At the timing of validity verification after a certain period of operation, the user sets, in the input unit 11, information similar to that in S401 and the period since the design change (S412).

[0040] The same processes as those in S402 to S406 are carried out, and the estimation system displays the correlation coefficient result on the output unit 17 (S413).

[0041] The user checks the validity of the implemented measures by comparing the correlation coefficient before the design change with the value after the design change (S414).

[0042] A detailed example of the process from the parameter setting unit 12 to the output unit 17 will be described with reference to FIG.

[0043] The estimation system sets parameters in the parameter setting unit 12 based on the search conditions of item information / defective event / business information / target data / target period / 4M input by the user in the input unit 11 (S501). At this time, 4M can be either quantitative data or qualitative data, but qualitative data is assumed.

[0044] For example, if a user inputs "Item A" as the item, "printing defect" as the defective event, "image output inspection" as the business information, "print output value" as the target data, "2018 / 1 / 1-2020 / 11 / 30" as the target period, and "operator S" as 4M, the estimation system sets each of these values ​​as a parameter.

[0045] The estimation system uses the related task extraction unit 13 to extract task information related to the parameters set in the parameter setting unit 12 (S502). Specifically, the estimation system extracts task information related to the defect-related event information set in the parameter setting unit 12 using the defect event and task correlation 21, extracts tasks related to the item information and task information set in the parameter setting unit 12 using the data model 22, and sets tasks that meet both conditions as related tasks.

[0046] For example, the estimation system extracts the tasks of "molding," "heat treatment," and "processing" from the relationship between the defect event and the task 21 based on the parameter-set defect information "printing defect," and by extracting "processing" and "molding" from the data model 22 based on the parameter-set "item A" and "image output inspection," it determines "processing" and "molding" as related tasks.

[0047] The estimation system uses the 4M data extraction unit 14 to extract 4M information linked to the related tasks based on the parameters set by the parameter setting unit 12 and the related task information from the related task extraction unit 13 (S503). Specifically, the estimation system uses the data model 22 to extract various 4M data linked to the related tasks extracted by the related task extraction unit 13, and narrows down the data to the target period and 4M set by the parameter setting unit 12, thereby extracting 4M data to be used in the correlation calculation unit 15. When 4M is qualitative data, the estimation system extracts data by narrowing down to performance data related to the 4M information, for example, performance data of work performed by a certain worker.

[0048] For example, the estimation system extracts linked 4M information from the data model 22 for two tasks extracted by the related task extraction unit 13. At that time, the estimation system narrows down the 4M information to tasks that fall within the set period of "2018 / 1 / 1-2020 / 11 / 30" and were performed by "Worker S". As a result, it is possible to extract performance data for the work performed by "Worker S" for related tasks during the period, and even if the 4M data is qualitative data such as a worker, it is possible to extract performance data (quantitative data) linked to the qualitative data. For example, it is possible to extract performance data such as the "stay time" of "Worker S". Therefore, even qualitative data included in the 4M data can be associated with quantitative data, making it possible to calculate a correlation coefficient with other quantitative data.

[0049] The estimation system uses the 4M data extracted by the 4M data extraction unit 14 to calculate the correlation with the target data in a brute-force manner in the correlation calculation unit 15. During the calculation, the correlation coefficient is calculated by finding the covariance and standard deviation from all plots in the scatter diagram (S504).

[0050] In this case, the estimation system skips 4M data for which the absolute value of the correlation coefficient does not meet a certain number for the first 100 items, and moves on to the calculation of the next data, thereby reducing the calculation speed. The estimation system performs calculations with the target data for all data. For example, for the "print output value" set by the parameter setting unit 12, the system calculates the correlation with all 4M information extracted by the 4M data extraction unit 14, such as "molding tool temperature," "device rotation speed," and "polishing angle," in a brute-force manner. Here, for data for which a strong correlation is not expected (for example, "polishing angle"), the correlation coefficient is calculated for only the first 100 items. This makes it possible to reduce the amount of calculation required to calculate correlation coefficients for which a strong correlation is not expected.

[0051] The estimation system creates a scatter plot in graph creation unit 16 when correlation calculation unit 15 calculates the correlation (S505). For example, based on the 4M information extracted by 4M data extraction unit 14, a scatter plot is created with the X-axis representing "molding tool temperature" and the Y-axis representing "print output value."

[0052] The estimation system displays the correlation coefficients calculated as the results of the correlation calculation unit 15 in descending order on the output unit 17. For example, the "molding tool temperature" having the largest absolute value of the correlation coefficient is displayed at the top. Also, a correlation graph which is the result of the graph creation unit 16 is displayed (S506).

[0053] In the above example, the correlation coefficient was calculated by extracting the performance data of the work performed by worker S, but the correlation coefficient can also be calculated by extracting the performance data of the work performed by worker T. As a result, the "molding tool temperature" that had a strong correlation in the performance data performed by worker S may not have a correlation in the performance data performed by worker T. By calculating the correlation coefficient with the parameter related to "printing defect" for each worker, the correlation between the worker and the defect information can be quantitatively compared. In this case, the correlation coefficient of the parameter related to "printing defect" for each worker may be averaged, or the correlation coefficient of the parameter related to "printing defect" between workers may be compared.

[0054] 6A to 6D show examples of images of the input unit 11 and the output unit 17. When the user inputs search parameters into the input unit 11 at the top of the main screen and presses "Execute", the above-mentioned processing is executed internally by the parameter setting unit 12 to the graph creation unit 16, the association between defective events and operations 21, and the data model 22, and the output unit 17 returns the results, allowing the user to recognize the results.

[0055] As shown in FIG. 6A, the user specifies, in the input unit 11, item information for recognizing the target item, defective event information, business information, target data for correlation calculation, and a target period.

[0056] The output unit 17 has two types of output. As shown in Fig. 6B, the correlations calculated by the correlation calculation unit 15 are displayed in descending order of absolute values ​​of the correlation coefficients. As shown in Fig. 6C, the graph created by the graph creation unit 16 and related numerical values ​​such as the correlation coefficients are also displayed.

[0057] That is, as shown in Fig. 6D, when the user inputs search parameters into the input section 11 at the top of the main screen and presses "Execute", the information on the absolute values ​​of the correlation coefficients in descending order is displayed. Then, when the user presses "Display", a graph is displayed.

[0058] The user identifies the cause of the event and considers improvement measures while viewing the information in the output unit 17. When identifying the cause or taking improvement measures, the user can conduct an investigation starting from data with a large absolute value of the correlation coefficient from the list of correlation calculation results, and when considering measures, the user can also approach starting from points with a large absolute value, that is, points that are highly related to the event.

[0059] The correlation graph allows the user to see trends in the associations between data, enabling the user to identify improvement measures by understanding what the correlation data looks like when a defect occurs.

[0060] The estimation system can also be used to verify the effects of measures after they have been implemented. By setting the target period in the input unit 11 to after the measures have been implemented and calculating the correlation again under the same conditions, it is possible to verify whether there has been a change in the correlation coefficient before and after the measures have been implemented, that is, whether there is a tendency for improvement.

[0061] Furthermore, when a new causal relationship becomes clear from the correlation output results, the correlation between the defect event and the operation 21 can be changed, and the processing of Figures 4 and 5 can be carried out the next time the defect occurs, thereby improving the accuracy of the correlation calculation. For example, since the "polishing angle", which is not expected to have a strong correlation with the "print output value", is not the cause of the defect event "defective printing", the matrix showing the relationship between the defect event and the operation shown in Figure 2 is amended to show that "defective printing" and the "polishing accuracy" of the "processing" operation are not related. As a result, when "defective printing" is input as a parameter of the defect event, the "polishing accuracy" of the "processing" operation, which has a low correlation, will not be output. By reflecting the actual situation of the correlation between the defect event and the operation, the accuracy of the correlation calculation can be improved.

[0062] An example of a hardware configuration of the estimation system will be described with reference to Fig. 7. As shown in Fig. 7, the estimation system 1a includes a processor 71, a storage device 72, an input device 73, and an output device 74.

[0063] The processor 71 is a main body that executes predetermined processes, and can be configured using an appropriate semiconductor device, for example. The processor 71 can be configured using a CPU (Central Processing Unit), for example.

[0064] The storage device 72 appropriately stores data such as programs used in a predetermined process. The storage device 72 can be configured using a main storage device and an auxiliary storage device, for example. The main storage device 72 can be a RAM (Random Access Memory), for example. The auxiliary storage device can be a HDD (Hard Disk Drive), for example.

[0065] The input device 73 is a device for inputting the contents of a user's operation, and can be configured using a keyboard, a mouse, buttons, etc. The output device 74 is a device for presenting appropriate information to the user, and can be configured using an appropriate display.

[0066] The input unit 11, parameter setting unit 12, related task extraction unit 13, 4M data extraction unit 14, correlation calculation unit 15, graph creation unit 16, output unit 17, correlation between defective events and tasks 21, and data model 22 can be realized, as an example, by appropriately using the above configuration.

[0067] The estimation system 1a may also include a communication device used for receiving and transmitting data. Data of the collection target system / equipment 31 may be input using an input device. Data of the collection target system / equipment 31 may also be input using a communication device.

[0068] According to this embodiment, it is possible to reduce the cost of identifying the cause of a problem that occurs when work is performed across multiple processes. Therefore, it is possible to contribute from an economic perspective. In addition, by considering improvement measures for the process and reviewing the design, it is possible to improve the process, for example, and to contribute from an environmental perspective.

[0069] Although the embodiments have been described above, the present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the configurations described. In addition, for example, other configurations may be added, deleted, or replaced with respect to part of the configuration of the embodiment.

[0070] The estimation system may be placed at a site where equipment or the like is installed, or may be placed on the cloud. As shown in Fig. 8, a system including an estimation system 1b and a terminal 2 capable of communicating with the estimation system 1b may be realized.

[0071] In this system, the estimation system 1b includes, for example, a processor 81, a storage device 82, and a communication device 83, and realizes a parameter setting unit 12, a related task extraction unit 13, a 4M data extraction unit 14, a correlation calculation unit 15, a graph creation unit 16, a correlation between defective events and tasks 21, and a data model 22. The storage device 82 can be appropriately configured using, for example, a RAM, a HDD, etc.

[0072] Moreover, the terminal 2 includes, for example, a processor 85, a storage device 86, a communication device 87, an input device 88, and an output device 89, and realizes the input unit 11 and the output unit 17. The storage device 86 can be appropriately configured using, for example, a RAM, a HDD, or the like.

[0073] For example, a fifth generation mobile communication system, so-called 5G (5th Generation), which enables "multiple simultaneous connections" and "ultra-low latency", can be used for communication between the estimation system 1b and the terminal 2. By utilizing the characteristics of new systems after 5G, for example, when a large number of terminals 2 are connected simultaneously and the amount of data communication increases, the data stored in the storage device 82 of the estimation system 1b becomes large, and even when the amount of data communication between the terminals 2 increases, communication delays can be suppressed.

[0074] Although an example in which a plurality of terminals 2 are connected to the estimation system 1b has been described in FIG. 8, the number of terminals 2 connected to the estimation system 1b is not particularly limited.

[0075] Although an example in which the estimation system 1b is arranged on the cloud has been described, the estimation system 1b may be used in an on-premise environment. In this case, communication between the estimation system 1b and the terminal 2 may be wired communication. [Explanation of symbols]

[0076] 1a Estimation System 11 Input section 12 Parameter setting section 13 Related Business Extraction Department 14 4M Data Extraction Section 15 Correlation calculation section 16 Graph Creation Section 17 Output section 21 Relationship between the defect and the business 22 Data Model 31 Systems and equipment to be collected

Claims

1. A processor; A storage device; Equipped with The storage device includes: Define the chain of multiple processes, store a data model that links the work results for each task included in the process, The processor, Acquiring information about a first task in which a defect has occurred, and extracting a related task from the information about the first task; extracting work performance data of a second task included in the related task by referring to the data model; Calculating a correlation between work performance data of the first task and the second task; outputting information regarding the correlation; 1. An analysis system comprising:

2. 2. The analysis system of claim 1, The processor, extracting performance data of the work performed by the worker as the work performance data of the second task; 1. An analysis system comprising:

3. 2. The analysis system of claim 1, The storage device includes: Store a matrix linking operations and defective events, The processor, Using the matrix to identify the relevant tasks; 1. An analysis system comprising:

4. 4. The analysis system according to claim 3, The processor, Extracting a task related to the first task from the data model; and performing the identification using a result of the extraction.

1. An analysis system comprising:

5. 2. The analysis system of claim 1, The processor, Calculating a correlation between the work performance data associated with the second task and all data; 1. An analysis system comprising:

6. 2. The analysis system of claim 1, The processor, extracting work performance data of the second task by tracing back from a process related to the first task in the data model; 1. An analysis system comprising:

7. 2. The analysis system of claim 1, Work performance data is related to 4M data.

1. An analysis system comprising:

8. 2. The analysis system of claim 1, The processor, generating data indicating the correlation data arranged in descending order of absolute values ​​of the correlation coefficients as the information regarding the correlation; 1. An analysis system comprising:

9. 2. The analysis system of claim 1, The processor, generating data for displaying a graph showing the correlation as information about the correlation; 1. An analysis system comprising:

10. The analysis system of claim 1 ; A terminal connected to the analysis system; Equipped with The analysis system and the terminal are capable of communicating via 5G. A system characterized by:

11. 11. The system of claim 10, The analysis system is deployed on a cloud. A system characterized by:

12. An information presentation method using the analysis system according to claim 1, The analysis system acquires information about a first operation in which a defect has occurred, the information being input by a user; the analysis system performing processing in response to the input and presenting information regarding correlations of the results to a user; The analysis system acquires information on the first task, the information being input by a user after the implementation of the measure for the process; and the analysis system performing processing in response to the input and presenting information regarding correlations of the results to a user; An information presentation method comprising:

Images