Data processing system, data processing method, and recording medium

Abstract

The present invention provides a data processing system, a data processing method, and a recording medium. The data processing system includes: an operation data acquisition unit that acquires operation data representing actual performance related to production operations; an evaluation data acquisition unit that acquires evaluation data representing actual performance related to production evaluation; a benchmark storage unit that stores the management benchmarks to be used for each target management parameter; a data classification unit that classifies the performance data representing actual production performance based on a determination result of whether the operation data complies with the management benchmark for the management parameter and the evaluation data; and an output unit that outputs the classification result.

Description

Technical Field

[0001] The present invention relates to a data processing system, a data processing method, and a recording medium having a data processing program recorded thereon. Background Art

[0002] Patent Document 1 describes a "method for analyzing a manufacturing process for identifying factors that hinder product performance variations and stabilizing product performance."

[0003] Prior art literature

[0004] Patent Document 1: Japanese Patent Application Laid-Open No. 2016-177794 Summary of the Invention

[0005] (Item 1)

[0006] In a first embodiment of the present invention, a data processing system is provided. The data processing system may include an operation data acquisition unit that acquires operation data representing actual performance related to production operations. The data processing system may include an evaluation data acquisition unit that acquires evaluation data representing actual performance related to production evaluation. The data processing system may include a benchmark storage unit that stores management benchmarks to be used for each management parameter as an object. The data processing system may include a data classification unit that classifies performance data representing actual production performance based on a judgment result of whether the operation data is based on the management benchmark and the evaluation data for the management parameter. The data processing system may include an output unit that outputs the classification result.

[0007] (Item 2)

[0008] The data classification unit may classify the performance data into at least four types based on whether the work data complies with the management standard in all items related to the operating parameters among the management parameters and whether the evaluation data satisfies a predetermined standard.

[0009] (Item 3)

[0010] The output unit may output a display screen that displays the frequencies of each of the at least four categories in a graph.

[0011] (Item 4)

[0012] The data classification unit may classify the performance data according to whether the evaluation data satisfies a predetermined standard for each item in the management parameter, for each of the cases where the operation data is in compliance with the management standard, deviating upward, and deviating downward.

[0013] (Item 5)

[0014] The output unit may output a display screen that displays, in a graph, the frequency of whether the evaluation data satisfies a predetermined criterion in each case for each item in the management parameter.

[0015] (Item 6)

[0016] The output unit may output a display screen indicating which of the corresponding situations the data, which is evaluation data in the performance data and does not satisfy a predetermined criterion, corresponds to with respect to each item in the management parameter.

[0017] (Item 7)

[0018] The output unit may output a display screen indicating which of the corresponding situations the data of the evaluation data in the performance data that satisfies a predetermined criterion corresponds to with respect to each item in the management parameter.

[0019] (Item 8)

[0020] The data processing system may further include a benchmark updating unit configured to update at least one of an evaluation benchmark and a management benchmark for determining the evaluation index based on the evaluation data.

[0021] (Item 9)

[0022] The data classification unit may reclassify the performance data using the updated criteria based on the update of at least one of the evaluation criteria and the management criteria, and the output unit may output the reclassified classification result.

[0023] (Item 10)

[0024] The data processing system may further include an input unit for receiving a user input, and the benchmark updating unit may update at least one of the evaluation benchmark and the management benchmark based on the user input.

[0025] (Item 11)

[0026] The data processing system may further include an update determination unit that determines an update of at least one of the evaluation criteria and the management criteria based on the classification result, and the criteria update unit updates at least one of the evaluation criteria and the management criteria based on the determination of the update determination unit.

[0027] (Item 12)

[0028] The update determination unit may search for a combination with a high frequency of evaluation data satisfying a predetermined criterion from among combinations of conditions of a plurality of items in the management parameters, and determine the updated management criterion.

[0029] (Item 13)

[0030] The evaluation data may include data evaluating the quality of the produced product.

[0031] (Item 14)

[0032] The evaluation data may include data evaluating at least any one of productivity, cost, delivery time, and safety of production.

[0033] (Item 15)

[0034] In a second embodiment of the present invention, a data processing method is provided. The data processing method may include obtaining operation data representing actual performance related to production operations. The data processing method may include obtaining evaluation data representing actual performance related to production evaluation. The data processing method may include storing management benchmarks to be used for each management parameter as an object. The data processing method may include classifying the performance data representing actual production performance based on a judgment result of whether the operation data complies with the management benchmark for the management parameter and the evaluation data. The data processing method may include outputting the classification result.

[0035] (Item 16)

[0036] In a third aspect of the present invention, a recording medium having a data processing program recorded thereon is provided. The data processing program can be executed by a computer. By executing the data processing program, the computer can function as a work data acquisition unit that acquires work data representing actual performance related to production work. By executing the data processing program, the computer can function as an evaluation data acquisition unit that acquires evaluation data representing actual performance related to production evaluation. By executing the data processing program, the computer can function as a benchmark storage unit that stores management benchmarks to be followed for each management parameter that is an object. By executing the data processing program, the computer can function as a data classification unit that classifies performance data representing actual production performance based on a judgment result of whether the work data is based on the management benchmark and the evaluation data for the management parameters. By executing the data processing program, the computer can function as an output unit that outputs the classification result.

[0037] In addition, the above summary of the invention does not list all the necessary features of the present invention. In addition, sub-combinations of these feature groups may also constitute inventions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 An example of a block diagram of a data processing system 100 and a production management object 10 according to this embodiment are shown.

[0039] Figure 2 An example of a QM matrix stored in the data processing system 100 according to this embodiment is shown.

[0040] Figure 3 An example of performance data recorded by the data processing system 100 according to this embodiment is shown.

[0041] Figure 4 An example of the flow of data processing by the data processing system 100 according to the present embodiment is shown.

[0042] Figure 5 An example of the classification result output by the data processing system 100 according to this embodiment is shown.

[0043] Figure 6 An example of another classification result output by the data processing system 100 of this embodiment is shown.

[0044] Figure 7 An example of another classification result output by the data processing system 100 of this embodiment to support the discovery of deviation patterns is shown.

[0045] Figure 8 An example of other classification results output by the data processing system 100 of this embodiment to support the discovery of a restoration method is shown.

[0046] Figure 9 An example of a flow of updating evaluation criteria and management criteria using the data processing system 100 of this embodiment is shown.

[0047] Figure 10 An example of changes in classification results when the evaluation criteria range is narrowed using the data processing system 100 of this embodiment is schematically shown.

[0048] Figure 11 An example of changes in classification results when the management criteria range is narrowed using the data processing system 100 of this embodiment is schematically shown.

[0049] Figure 12 An example of changes in classification results when the QM matrix is ​​set for each deviation pattern using the data processing system 100 of this embodiment is schematically shown.

[0050] Figure 13 An example of a block diagram of a data processing system 100 according to a modification of the present embodiment is shown.

[0051] Figure 14 An example of analysis results when the data processing system 100 according to a modification of the present embodiment uses decision tree analysis to narrow down the management criteria range is shown.

[0052] Figure 15 An example of a computer 2200 is shown that can implement various aspects of the present invention in whole or in part.

[0053] Description of Reference Numerals

[0054] 10 production management object, 100 data processing system, 110 operation data acquisition unit, 120 evaluation data acquisition unit, 130 data recording unit, 140 benchmark storage unit, 150 data classification unit, 160 output unit, 170 input unit, 180 benchmark update unit, 1310 update determination unit, 2200 computer, 2201 DVD-ROM, 2210 main controller, 2212 CPU, 2214 RAM, 2216 graphics controller, 2218 display device, 2220 input / output controller, 2222 communication interface, 2224 hard disk drive, 2226 DVD-ROM drive, 2230 ROM, 2240 input / output chip, 2242 keyboard. DETAILED DESCRIPTION

[0055] The present invention will be described below by way of embodiments of the invention, but the following embodiments do not limit the invention as defined in the claims. Furthermore, not all combinations of features described in the embodiments are essential to the solution provided by the invention.

[0056] Figure 1 This figure shows an example of a block diagram of a data processing system 100 according to this embodiment and a production management object 10. The data processing system 100 according to this embodiment obtains and classifies performance data representing the actual production performance of the production management object 10, and outputs the classification results. The data processing system 100 according to this embodiment classifies the performance data based on the results of determining whether the work in the production management object 10 complies with the management standards and the evaluation of the production in the production management object 10.

[0057] The production management object 10 is an object managed by the data processing system 100. An example of a production management object 10 is a factory. In addition to chemical and other industrial plants, such factories may also manage and control wellheads and their surrounding areas, such as gas or oil fields; factories that manage and control hydropower, thermal power, or nuclear power generation; factories that manage and control environmental power generation such as solar or wind power; and factories that manage and control water supply and drainage systems or dams. However, this is not a limitation. The data processing system 100 may also manage any industrial facility that processes raw materials to produce products.

[0058] The data processing system 100 can be a computer such as a PC (personal computer), a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or a computer system comprising multiple computers. This type of computer system is also a computer in a broad sense. Furthermore, the data processing system 100 can also be implemented as one or more virtual computer environments that can be executed within a computer. Alternatively, the data processing system 100 can be a dedicated computer designed for data processing, or dedicated hardware implemented with dedicated circuits. Furthermore, if the data processing system 100 can be connected to the internet, the data processing system 100 can also be implemented through cloud computing.

[0059] The data processing system 100 includes an operation data acquisition unit 110, an evaluation data acquisition unit 120, a data recording unit 130, a benchmark storage unit 140, a data classification unit 150, an output unit 160, an input unit 170, and a benchmark update unit 180. These modules are functionally separate and do not necessarily correspond to the actual device structure. That is, although they are shown as a single module in this figure, they do not necessarily consist of a single device. Furthermore, although they are shown as different modules in this figure, they do not necessarily consist of different devices.

[0060] The operation data acquisition unit 110 acquires operation data representing actual performance related to production operations. For example, the operation data acquisition unit 110 can acquire data representing actual performance related to production factors in the production management object 10 as operation data. Here, production factors refer to factors used to produce products. Among these production factors, "raw materials (Material)", "equipment (Machine)", "people (Man)", and "process (Method)" are referred to as the "four elements of production", and they are also referred to as "4M". For example, the operation data acquisition unit 110 can acquire operation data representing actual performance related to the "4M" in the production management object 10 in a time series.

[0061] Here, items related to "process" within the "4Ms" are defined as operating parameters. In other words, operating parameters can be considered to be parameters that can be controlled during operation. On the other hand, items related to "raw materials," "equipment," and "people" within the "4Ms" are defined as part of the operating conditions. Furthermore, these operating conditions, in addition to "raw materials," "equipment," and "people," can also include various conditions that may affect the operation of the production management object 10, such as season, weather, temperature, and time of day. In other words, operating conditions can be considered to be parameters that cannot be controlled during operation.

[0062] The operation data acquisition unit 110 can be, for example, a communication unit that acquires operation data from the production management object 10 in a time series via a communication network. Such a communication network can be a network that connects multiple computers. For example, the communication network can be a global network that connects multiple computer networks to each other, and as an example, it can be the Internet using the Internet Protocol. Instead of this, the communication network can also be implemented by a dedicated line. In addition, in the above description, as an example, the operation data acquisition unit 110 acquires operation data from the production management object 10 in a time series via a communication network, but it is not limited to this. The operation data acquisition unit 110 can also acquire operation data in the production management object 10 through other means different from the communication network, such as user input or various storage devices. The operation data acquisition unit 110 supplies the acquired operation data to the data recording unit 130.

[0063] The evaluation data acquisition unit 120 acquires evaluation data representing actual performance related to production evaluation. Here, production evaluation refers to the evaluation of the target production. Stably achieving the target PQCDS (Productivity, Quality, Cost, Delivery, and Safety) is a critical issue in many manufacturing industries. Therefore, the evaluation data acquisition unit 120 may, for example, acquire data evaluating at least one of the PQCDS performance indicators for the production management target 10 as evaluation data. Previously, as an example, the evaluation data acquisition unit 120 acquired data evaluating the quality of products produced in the production management target 10 (e.g., actual measured values ​​of product quality) for each batch of products as evaluation data. Thus, the evaluation data may include data evaluating the quality of the produced products. However, this is not limiting. As described above, the evaluation data acquisition unit 120 may also acquire data evaluating at least one of productivity, cost, delivery, and safety of the production in the production management target 10 as evaluation data, in addition to or in lieu of product quality. Therefore, the evaluation data may include data evaluating at least any one of the productivity, cost, delivery date, and safety of production.

[0064] Like the work data acquisition unit 110, the evaluation data acquisition unit 120 can be a communication unit. For example, it can acquire evaluation data evaluating product quality for each batch of products from the production management object 10 via a communication network. Furthermore, like the work data acquisition unit 110, the evaluation data acquisition unit 120 can acquire evaluation data from the production management object 10 through other means other than a communication network, such as user input or various storage devices. The evaluation data acquisition unit 120 supplies the acquired evaluation data to the data recording unit 130.

[0065] The data recording unit 130 records performance data indicating actual production performance in the production management target 10. For example, the data recording unit 130 acquires operation data supplied from the operation data acquisition unit 110. Furthermore, the data recording unit 130 acquires evaluation data supplied from the evaluation data acquisition unit 120. Furthermore, the data recording unit 130 associates the acquired operation data and evaluation data for each product batch and records them as performance data.

[0066] The benchmark storage unit 140 stores the management benchmarks to be used for each of the management parameters as the object. In addition, the benchmark storage unit 140 stores the evaluation benchmarks for determining the evaluation index based on the evaluation data for each of the evaluation items as the object (for example, a good quality benchmark range for judging that the quality is good when the measured value of the product quality is within this range). Here, the management benchmark refers to, for example: in order to maintain the quality characteristics of the product well in the production management object 10, the important parameters that may affect the quality characteristics are selected as management parameters, and the range of values ​​that the parameters should take is defined. The relationship between the management benchmarks of each management parameter and the quality characteristics is also called the QM matrix. That is, the benchmark storage unit 140 can store the management benchmarks to be used for the management parameters selected as important parameters that may affect the quality characteristics from the multiple items contained in the operation data. In addition, such management parameters can be selected from both operating conditions and operating parameters.

[0067] In the past, production was based on the supply of raw materials with stable properties, the use of equipment that exhibited stable performance, and the operation of the system by experienced personnel. Under these circumstances, in principle, the production management object 10 operated according to the management benchmark. However, in recent years, due to changes in operating conditions (such as the globalization of raw materials, aging of equipment, and the mobility of personnel), it has become difficult to maintain the quality characteristics of products even when operating according to the management benchmark. In addition, in response to higher quality requirements from customers, it is necessary to prevent not only large (fatal) abnormalities but also small abnormalities (such as quality deviations). Under these circumstances, the production management object 10 is sometimes intentionally operated in a manner that deviates from the management benchmark based on the wisdom of the site in response to changes in operating conditions. The data processing system 100 of this embodiment classifies and outputs performance data based on the results of determining whether the operations in the production management object 10 are in accordance with the management benchmark and the evaluation of the production in the production management object 10 (such as the evaluation of product quality), thereby supporting the improvement of production in the production management object 10.

[0068] The data classification unit 150 accesses the benchmark storage unit 140 and compares the evaluation benchmarks for each of the evaluation items. Furthermore, the data classification unit 150 accesses the data recording unit 130 and compares the evaluation data recorded for each of the evaluation items with the evaluation benchmarks to determine the evaluation indicators for each of the evaluation items. The data recording unit 130 writes the determined evaluation indicators into the data recording unit 130.

[0069] Furthermore, the data classification unit 150 accesses the standard storage unit 140 to refer to the management standard to be followed for each of the target management parameters. Furthermore, the data classification unit 150 accesses the data recording unit 130 to compare the operation data recorded for each of the target management parameters with the management standard to determine whether the operation data complies with the management standard.

[0070] Furthermore, the data classification unit 150 classifies the performance data recorded in the data recording unit 130 based on the results of the determination of whether the operation data complies with the management standards and the evaluation indicators. Thus, the data classification unit 150 classifies the performance data representing production performance based on the results of the determination of whether the operation data complies with the management standards and the evaluation data for the management parameters. In other words, the data classification unit 150 classifies the performance data based on both the perspective of whether the operation was performed in accordance with the management standards and the perspective of the performance evaluation. This will be explained in detail later. The data classification unit 150 supplies the classification results to the output unit 160.

[0071] Output unit 160 outputs the classification results. For example, output unit 160 can display the classification results supplied by data classification unit 150. Displaying the results here is not limited to direct display on a monitor; for example, it can also include creating a screen for display on another device or functional unit and then transmitting the results. While the above description illustrates the case where output unit 160 displays the classification results, this is not a limitation. When outputting the classification results, output unit 160 can also transmit the data to another device or functional unit that prints the classification results, or output the results through any other means, such as audio output.

[0072] The input unit 170 receives user input. The input unit 170 can receive input from, for example, a user who has studied the classification results displayed by the output unit 160. As an example, the input unit 170 can be an interface for exchanging information between a computer and a user, and in particular, it can be a GUI (Graphical User Interface) that uses computer graphics and a pointer device. The input unit 170 supplies commands corresponding to the received user input to the output unit 160 and the benchmark update unit 180. The output unit 160 can update the output method of the classification results according to the commands from the input unit 170. In this way, the output unit 160 can output the classification results in a manner desired by the user.

[0073] The benchmark update unit 180 updates at least one of the evaluation benchmark and the management benchmark used to determine evaluation indicators based on the evaluation data. For example, the benchmark update unit 180 can update at least one of the evaluation benchmark and the management benchmark based on user input. Specifically, the benchmark update unit 180 updates at least one of the evaluation benchmark and the management benchmark stored in the benchmark storage unit 140 in accordance with a command corresponding to the user input received by the input unit 170. The term "update" here is not limited to actually updating the benchmark but also includes attempting to change the benchmark.

[0074] Then, the data classification unit 150 reclassifies the performance data using the updated criteria based on at least one of the evaluation criteria and the management criteria, and the output unit 160 outputs the reclassified classification result.

[0075] Figure 2 The figure shows an example of a QM matrix stored in the data processing system 100 of this embodiment. For example, the reference storage unit 140 may store a QM matrix showing the relationship between the management reference and the quality characteristics in each management parameter as shown in this figure.

[0076] The benchmark storage unit 140 can store such a QM matrix for each product produced (for example, by "product X", "product Y" and "product Z"). That is, a management parameter can be selected for each product produced, and a management benchmark can be defined for each management parameter. In addition, the benchmark storage unit 140 can store the QM matrix not only for each product, but also for each operating condition (for example, by "summer", "winter", "spring / autumn"), so that the best management benchmark can be defined according to changes in operating conditions. That is, a management parameter can be selected for each operating condition, and a management benchmark can be defined for each management parameter. Therefore, when classifying performance data, the data classification unit 150 can select a QM matrix suitable for the target product and operating condition from the multiple QM matrices stored in the benchmark storage unit 140 and refer to it. In addition, in this figure, as an example, the QM matrix when "Y" is selected as the product and "summer" is selected as the operating condition is shown.

[0077] This figure shows the selection of "Raw Material B. Characteristic 3," "Addition Amount," and "Hot Water Temperature" as management parameters for the important parameter that may affect "pH," a quality characteristic. Similarly, this figure shows the selection of "Raw Material A. Characteristic 1," "Raw Material B. Characteristic 3," and "Addition Amount" as management parameters for the important parameter that may affect "Viscosity," a quality characteristic. This allows different management parameters to be selected for each quality characteristic in the QM matrix.

[0078] Furthermore, for example, for the management parameter "Raw material A. Characteristic 1", "Lower limit value: 6.0" and "Lower limit condition: greater than" are defined as management benchmarks. That is, when producing product Y in the summer, in order to well maintain the viscosity quality of product Y, raw material A. Characteristic 1 greater than 6.0 is defined as an important parameter. Similarly, for the management parameter "warm water temperature", "Lower limit value: 42", "Lower limit condition: above", "Upper limit value: 43" and "Upper limit condition: less than" are defined as management benchmarks. That is, when producing product Y in the summer, in order to well maintain the pH quality of product Y, making the warm water temperature greater than 42 degrees and less than 43 degrees is defined as an important parameter. Thus, in order to well maintain the evaluation characteristics (such as quality characteristics) of production in the production management object 10, important parameters that may affect the evaluation characteristics are used as management parameters and the range of values ​​that the parameters should take is stored in the benchmark storage unit 140.

[0079] Figure 3 This figure shows an example of performance data recorded by the data processing system 100 according to this embodiment. For example, as shown in this figure, the data recording unit 130 can associate the operation data supplied by the operation data acquisition unit 110 and the evaluation data supplied by the evaluation data acquisition unit 120 with each product's batch ID and record them as performance data. Furthermore, the data recording unit 130 can associate the evaluation indicators determined by the data classification unit 150 by comparing the evaluation data with the evaluation criteria with the evaluation data and record them separately. This figure shows, as an example, the performance data corresponding to batches #001 through #005 of product Y.

[0080] As shown in this figure, the data recording unit 130 can record, for example, data representing the performance of the "4Ms" ("Raw Materials," "Equipment," "People," and "Processes") within the production management object 10 as operational data. As mentioned above, items related to "Raw Materials," "Equipment," and "People" within the "4Ms" are defined as part of the operating conditions. Furthermore, items related to "Processes" within the "4Ms" are defined as operating parameters.

[0081] In this figure, as examples, data representing performance related to "Raw Materials" includes "Raw Material A. Property 1," which examines the property of Raw Material A for Property 1, and "Raw Material B. Property 3," which examines the property of Raw Material B for Property 3. Furthermore, data representing performance related to "Equipment" and "People" is omitted from this figure. Similarly, data representing performance related to "Process" includes "Starting Temperature," "Hot Water Temperature," "Addition Amount," and "Heating Time," as examples.

[0082] Furthermore, the data recording unit 130 can record data evaluating the performance of the PQCDS in the production management object 10 as evaluation data. For example, as shown in this figure, the data recording unit 130 can record data evaluating the quality of the pH and viscosity of Product Y as evaluation data. In this figure, the measured pH value of Product Y is shown as an example of evaluation data evaluating the pH of Product Y. Furthermore, in this figure, as an example of an evaluation index for evaluating the pH of Product Y, an index indicating whether the measured pH value meets a predetermined evaluation standard (Good) or (Bad) is shown. Furthermore, in this figure, the evaluation data evaluating the viscosity of Product Y is omitted. While the above description shows, as an example, an evaluation index categorized as a binary value (Good / Bad) based on whether the measured value meets a predetermined evaluation standard, this is not limiting. An evaluation index can also be a multi-valued index (e.g., a grade or level) categorized by comparing the measured value with a predetermined evaluation standard.

[0083] The data recording unit 130 records the performance data acquired for multiple batches in the above manner as data processing targets. The data processing system 100 of this embodiment categorizes this performance data and outputs the categorized results. At this point, the data processing system 100 of this embodiment categorizes the performance data based on the results of determining whether the operations in the production management target 10 comply with the management standards and the evaluation of the production in the production management target 10. This usage process will be described in detail.

[0084] Figure 4 An example of the flow of data processing by the data processing system 100 according to the present embodiment is shown.

[0085] In step 410, the data processing system 100 acquires operation data. For example, the operation data acquisition unit 110 acquires operation data representing actual performance related to production operations from the production management object 10 in a time series via a communication network. As an example, the operation data acquisition unit 110 may acquire operation data representing actual performance related to the "4M" elements of the production management object 10: "Raw Materials," "Equipment," "People," and "Processes."

[0086] The operation data acquisition unit 110 can, for example, acquire inspection data on raw materials inspected in the production management object 10 as operation data related to "raw materials." Furthermore, the operation data acquisition unit 110 can acquire data indicating the health of equipment in the production management object 10 as operation data related to "equipment." Furthermore, the operation data acquisition unit 110 can acquire data indicating the schedule of workers in the production management object 10 as operation data related to "people." Furthermore, the operation data acquisition unit 110 can acquire measurement data from sensors installed in the production management object 10 or control data for actuators as operation data related to "processes." The operation data acquisition unit 110 supplies the acquired operation data to the data recording unit 130.

[0087] In step 420, the data processing system 100 obtains evaluation data. For example, the evaluation data acquisition unit 120 obtains evaluation data representing the performance related to the production evaluation for each batch of products via the communication network. As an example, the evaluation data acquisition unit 120 can obtain data that evaluates at least any one of the performance of the PQCDS in the production management object 10. Here, the evaluation data acquisition unit 120 obtains evaluation data that evaluates the quality of the products produced in the production management object 10 for each batch of products. That is, the evaluation data can include data that evaluates the quality of the produced products. However, this is not limited to this. As described above, the evaluation data can also include data that evaluates at least any one of the productivity, cost, delivery time, and safety of the production. The evaluation data acquisition unit 120 supplies the obtained evaluation data to the data recording unit 130.

[0088] In step 430 , the data processing system 100 records the performance data. For example, the data recording unit 130 associates the operation data acquired in step 410 and the evaluation data acquired in step 420 with each lot of the product and records the associated data as performance data.

[0089] As an example, the data recording unit 130 associates the operation data obtained in step 410 with data of the same time period. This association is made because the output timing of the obtained operation data is sometimes different for each production factor. Next, the data recording unit 130 grasps the start and end points of the process in the production management object 10 based on the obtained operation data, and distinguishes the operation data for each batch. In addition, the data recording unit 130 corresponds the operation data distinguished for each batch with the evaluation data obtained for each batch in step 420 and records them as performance data. In addition, the data recording unit 130 corresponds the evaluation index determined by the data classification unit 150 by comparing the evaluation data with the evaluation benchmark to the evaluation data and records them separately.

[0090] In step 440, the data processing system 100 categorizes the performance data. For example, the data categorization unit 150 accesses the benchmark storage unit 140, selects a QM matrix appropriate for the target product and operating conditions from among the multiple stored QM matrices, and references the matrix. Furthermore, the data categorization unit 150 accesses the data recording unit 130 and references the performance data recorded in step 430. Furthermore, the data categorization unit 150 categorizes the performance data representing production performance based on the results of the management parameter determination of whether the operating data complies with the management benchmark and the evaluation data. This will be described in detail.

[0091] The data classification unit 150 accesses the reference storage unit 140, for example, Figure 2 The QM matrix shown in FIG. As a result, the data classification unit 150 identifies that "Raw Material B. Characteristic 3," "Addition Amount," and "Warm Water Temperature" are selected as important management parameters that may affect "pH," a quality characteristic. Furthermore, the data classification unit 150 identifies the range of values ​​that should be taken by each of the management parameters, "Raw Material B. Characteristic 3," "Addition Amount," and "Warm Water Temperature."

[0092] Furthermore, the data classification unit 150 accesses the data recording unit 130 and refers to, for example, Figure 3 Furthermore, the data classification unit 150 uses, for example, Figure 2 The QM matrix shown is Figure 3 Analyze the performance data shown.

[0093] For example, if we look at the performance data corresponding to batch ID "Y001," the operating data for "Raw Material B. Characteristic 3," which is related to the operating conditions in the management parameters, complies with the management benchmark. Furthermore, the operating data for "Amount Added" and "Hot Water Temperature," which are related to the operating parameters in the management parameters, also complies with the management benchmark. Furthermore, "pH" is evaluated as "Good," which meets a predetermined benchmark. For example, in the production management object 10, such performance data can be obtained when raw material B, which complies with the management benchmark, is supplied and the product is operated according to the management benchmark, resulting in a good pH. Thus, the performance data corresponding to batch ID "Y001" indicates that good quality was achieved as a result of operation in compliance with the management benchmark. Therefore, the data classification unit 150 classifies performance data such as "Category 1" in which the operating data for all items related to the operating parameters in the management parameters complies with the management benchmark and the evaluation data meets the predetermined benchmark. In this "Category 1," the goal is to achieve higher quality targets (e.g., reducing variation).

[0094] Similarly, focusing on the performance data corresponding to batch ID "Y002," the operating data for "Raw Material B. Characteristic 3" deviates from the management benchmark. Furthermore, the operating data for "Warm Water Temperature" complies with the management benchmark, and the operating data for "Addition Amount" deviates from the management benchmark. Furthermore, "pH" is evaluated as "Good." For example, in production management object 10, this type of performance data can be obtained when, despite supplying raw material B that deviates from the management benchmark, the addition amount is adjusted to deviate from the management benchmark (for example, to a value greater than 50, the upper limit of the management benchmark) through on-site intelligence, resulting in a good product pH. Thus, the performance data corresponding to batch ID "Y002" indicates that good quality was achieved despite operations that failed to adhere to the management benchmark. Therefore, the data classification unit 150 classifies performance data in which the operating data deviates from the management benchmark in at least one of the management parameters related to the operating parameters, and the evaluation data meets predetermined criteria, as "Category 2." In this "Category 2," the challenge is to standardize the experience gained through on-site intelligence to achieve good quality.

[0095] Similarly, focusing on the performance data corresponding to batch ID "Y003," the operating data for "Raw Material B. Characteristic 3" deviates from the management benchmark. Furthermore, the operating data for "Warm Water Temperature" and "Addition Amount" all conform to the management benchmark. Furthermore, "pH" is evaluated as "Bad," meaning it does not meet the predetermined benchmark. For example, in production management object 10, this type of performance data can be obtained when, despite supplying raw material B that deviates from the management benchmark, no action is taken on-site and operations are performed in accordance with the management benchmark, resulting in a poor pH for the product. Therefore, the performance data corresponding to batch ID "Y003" indicates that operations in compliance with the management benchmark resulted in poor quality. Therefore, the data classification unit 150 classifies performance data such as this, where the operating data for all items related to operating parameters within the management parameters conform to the management benchmark and the evaluation data does not meet the predetermined benchmark, as "Category 3." In this "Category 3," the challenge is to adjust the operating parameters in response to changes in operating conditions.

[0096] Similarly, in this figure, focusing on batch ID "Y004," the operating data for "Raw Material B. Characteristic 3" deviates from the management benchmark. Furthermore, the operating data for "Warm Water Temperature" is in compliance with the management benchmark, and the operating data for "Addition Amount" deviates from the management benchmark. Furthermore, "pH" is evaluated as "Bad." For example, in production management object 10, if raw material B is supplied that deviates from the management benchmark, and the addition amount is adjusted to deviate from the management benchmark through on-site intelligence, and operation is performed, but the product pH is poor, such performance data can be obtained. In other words, batch ID "Y004" indicates that operation without adhering to the management benchmark resulted in poor quality. Therefore, the data classification unit 150 classifies such performance data as "Category 4" when the operating data deviates from the management benchmark in at least one item related to the operating parameters, and the evaluation data does not meet the predetermined benchmark. The challenge with this "Category 4" is to ensure accurate recovery when operating conditions change.

[0097] Thus, the data classification unit 150 classifies the performance data into at least four categories based on whether the work data for all items related to the operating parameters in the management parameters conform to the management standards and whether the evaluation data meets the predetermined standards. This allows the data classification unit 150 to classify the performance data from the overall perspective of the operations in the production management object 10.

[0098] Based on this, the data classification unit 150 can classify performance data based on the perspective of each management parameter. For example, the data classification unit 150 focuses on "Raw Material B. Characteristic 3" and compares the operating data for "Raw Material B. Characteristic 3" with the management criteria defined in the QM matrix, classifying the data into three categories. As an example, the data classification unit 150 classifies performance data with operating data for "Raw Material B. Characteristic 3" that is 2.0 or higher and less than 10.0 as "Category C," indicating that the operating data complies with the management criteria for the target management parameter.

[0099] Similarly, the data classification unit 150 classifies the performance data having operation data of 10.0 or greater in "Raw Material B. Characteristic 3" into "Classification U" indicating that the operation data in the target management parameter deviates upward from the management standard.

[0100] Similarly, the data classifying unit 150 classifies the performance data of the operation data less than 2.0 in "Raw Material B. Characteristic 3" into "Classification L" indicating that the operation data in the target management parameter deviates downward from the management standard.

[0101] The data classifier 150 then determines whether the evaluation data for "Category C," "Category U," and "Category L" meet predetermined criteria, and classifies the performance data into two categories. For example, the data classifier 150 classifies performance data classified into "Category C" into two categories: one in which the "pH" value is evaluated as "Good" and one in which it is evaluated as "Bad." The data classifier 150 similarly classifies performance data classified into "Category U" and "Category L" into two categories. The data classifier 150 performs this classification for all items selected as management parameters in the QM matrix. Thus, for each item in the management parameter, the data classifier 150 classifies performance data based on whether the evaluation data meets predetermined criteria, for each case where the operating data is below the management criteria, deviates upward, or deviates downward. This allows the data classifier 150 to distinguish whether the "pH" value is good or poor, for example, for each case where the operating data for "Raw Material B. Characteristic 3" is below the management criteria, deviates upward, or deviates downward.

[0102] In step 450, the data processing system 100 outputs the classification results. For example, the output unit 160 displays the classification results obtained in step 440 on a display. As an example, the output unit 160 may output the classification results obtained in step 440 by the data classifier 150 based on the overall perspective of the operations in the production management object 10. In this case, the output unit 160 may output a display screen that displays the frequency of each of the at least four classifications as a graph.

[0103] Based on this, the output unit 160 can output the classification results of the performance data classified by the data classification unit 150 from the perspectives of each management parameter in step 440. In this case, the output unit 160 can output a display screen that graphically displays the frequency of whether the evaluation data meets predetermined criteria for each item in the management parameter. Details of the display screen output by the output unit 160 will be described later.

[0104] In addition, the output unit 160 can switch the classification results output according to the command from the input unit 170 between the classification results that classify the performance data from the overall perspective of the operation in the production management object 10 and the classification results that classify the performance data from the individual perspectives of each management parameter.

[0105] In step 460, data processing system 100 determines whether to update the baseline. For example, baseline update unit 180 may determine whether to update the baseline based on whether a baseline update command has been received from input unit 170. If, in step 460, it is determined that the baseline should not be updated, data processing system 100 terminates the process.

[0106] On the other hand, if it is determined in step 460 that the benchmark should be updated, data processing system 100 updates the benchmark in step 470. For example, benchmark update unit 180 updates at least one of the evaluation benchmark and the management benchmark used to determine the evaluation indicators based on the evaluation data in accordance with a command corresponding to the user input received by input unit 170. Thus, benchmark update unit 180 can update at least one of the evaluation benchmark and the management benchmark based on the user input, for example.

[0107] If the criteria are updated in step 470, data processing system 100 returns to step 440 and continues the process. Specifically, in step 440, following step 470, data classification unit 150 reclassifies the performance data using the updated criteria based on at least one of the updated evaluation criteria and management criteria. Furthermore, in step 450, following step 470, output unit 160 outputs the reclassified results.

[0108] Figure 5 This figure shows an example of the classification results output by the data processing system 100 of this embodiment. This figure shows an example of the classification results output from the overall perspective of the operations within the production management object 10. The data processing system 100 of this embodiment classifies performance data based on two perspectives: whether operations were performed in accordance with the management standard and performance evaluation. As described above, as an example, the data classifier 150 classifies performance data into "Category 1" if the operation data for all items related to operation parameters in the management parameters conform to the management standard and the evaluation data meets the predetermined standard. Furthermore, the data classifier 150 classifies performance data into "Category 2" if the operation data for at least one item related to operation parameters deviates from the management standard and the evaluation data meets the predetermined standard. Furthermore, the data classifier 150 classifies performance data into "Category 3" if the operation data for all items related to operation parameters in the management parameters conform to the management standard and the evaluation data does not meet the predetermined standard. Furthermore, data classification unit 150 classifies performance data for which the operating data deviates from the management standard in at least one item related to operating parameters within the management parameters and the evaluation data does not meet the predetermined standard into "Category 4." The left side of the figure schematically illustrates the classification of performance data into four categories based on the two viewpoints described above.

[0109] The data processing system 100 of this embodiment can summarize the classification results classified in the above manner and display them as a chart such as the one on the right side of this figure. That is, the output unit 160 can output a display screen that displays the frequencies of at least four categories as a chart. In this figure, as an example, the output unit 160 displays a pie chart that shows the frequencies of each category as a ratio. However, this is not limited to this. The output unit 160 can also display any form of chart that can show the frequencies of each category, such as a bar chart, a strip chart, a histogram, or a radar chart, instead of a pie chart.

[0110] Figure 6 This figure shows another example of classification results output by the data processing system 100 of this embodiment. This figure shows an example of the classification results of performance data classified from the perspective of each management parameter. As an example, this figure shows the classification of 80 batches of performance data from the perspective of each management parameter. This figure shows that, for "pH," 53 of the 80 batches were evaluated as "Good," while 27 were evaluated as "Bad." To enable more detailed analysis, the data processing system 100 of this embodiment classifies each item in the management parameter into situations where the operating data falls below the management standard, deviates upward, or deviates downward. As described above, as an example, the data classification unit 150 classifies performance data with operating data of 2.0 or greater and less than 10.0 in "Classification C," performance data with a value of 10.0 or greater into "Classification U," and performance data with a value less than 2.0 into "Classification L." The data classification unit 150 then classifies "Classification C," "Classification U," and "Classification L" into two categories: "Good" and "Bad" for "pH." The data classification unit 150 performs this classification on all items selected as management parameters in the QM matrix.

[0111] For example, this figure shows that, for "Raw Material B. Characteristic 3," 27 of 80 batches of operating data deviated upward from the management benchmark. Of these, 14 batches were ultimately evaluated as having a good pH, while the remaining 13 batches were evaluated as unacceptable. Similarly, this figure shows that, for "Raw Material B. Characteristic 3," 26 of 80 batches of operating data met the management benchmark. Of these, 24 batches were ultimately evaluated as having a good pH, while the remaining 2 batches were evaluated as unacceptable. Similarly, this figure shows that, for example, 27 of 80 batches of operating data for "Raw Material B. Characteristic 3" deviated downward from the management benchmark. Of these, 15 batches were ultimately evaluated as having a good pH, while the remaining 12 batches were evaluated as unacceptable. The same applies to other management parameters. As shown in this figure, the output unit 160 can output a display screen that graphically displays, for each item in the management parameter, the frequency with which the evaluation data met the predetermined benchmark in each case. While this figure shows, as an example, the output unit 160 displays a pie chart, any form of chart can be used. Furthermore, the output unit 160 can display the graphs in the order in which the management parameters were displayed, arranged from left to right, in the chronological order in which they were identified by the operator. This facilitates understanding of the spread of the phenomenon. Furthermore, the output unit 160 can also omit some of the displayed management parameters. This allows, even with many management parameters, to identify only those that are important and impact quality.

[0112] Figure 7 This section shows an example of other classification results output by the data processing system 100 of this embodiment to support the discovery of deviation patterns. Here, when comparing work data with management benchmarks for each item in a management parameter, points where the work data deviates from the management benchmark, and points estimated to be factors causing the evaluation data to fail to meet the predetermined benchmark, are defined as "deviation points." Furthermore, combinations of conditions for multiple items in the management parameter, including at least one "deviation point," are defined as "deviation patterns."

[0113] For example, in Figure 6In the display of the classification results shown, the user selected and clicked the chart (the lower right chart in this figure) indicating the situation where the final "pH" became "Bad" via the input unit 170. In this case, the output unit 160 can output the display screen shown in this figure. That is, the output unit 160 can display the path and its batch number until the final "pH" is evaluated as "Bad". Here, the output unit 160 can also display the path with a thickness corresponding to the number of batches, for example. That is, the output unit 160 can also display the path with a large number of batches as thicker than the path with a small number of batches. Thus, the output unit 160 can output data indicating that the evaluation data in the performance data does not meet the predetermined benchmark, and a display screen showing which of the corresponding relationships of each situation is met for each item in the management parameter.

[0114] As shown in this figure, it can be seen that in 13 of the 27 batches that ultimately received a "Bad" pH evaluation, which accounts for approximately half of the batches, "Raw Material B. Characteristic 3" deviated upward. Therefore, the upward deviation in "Raw Material B. Characteristic 3" can be considered one of the deviation points. Furthermore, in this figure, for example, the path from "Classification U" in "Raw Material B. Characteristic 3" to "Classification C" in "Addition Amount" is shown as having 13 batches. This indicates that in 13 of the 27 batches that ultimately received a "Bad" pH evaluation, the operating data in "Raw Material B. Characteristic 3" deviated upward, while the "Addition Amount" operated in accordance with the management standard. Therefore, the combination of the upward deviation in "Raw Material B. Characteristic 3" and the compliance with the standard in "Addition Amount" can be considered one of the deviation patterns. Thus, by studying the classification results output by the data processing system 100 of this embodiment, users can identify deviation patterns.

[0115] Figure 8 This shows an example of other classification results output by the data processing system 100 of this embodiment to support the discovery of a recovery method. Here, a recovery method refers to a method for recovering a deviation pattern. For example, Figure 7 The user of the classification results shown has discovered that the deviation pattern that is presumed to be the cause of the final "pH becoming "Bad" is the combination of the upward deviation in "Raw Material B. Characteristic 3" and the reference basis in "Addition Amount". Figure 7 In the display of the classification results shown, the user selects and clicks the graph (the upper left graph in this figure) showing the deviation point, that is, the case where "Raw Material B. Characteristic 3" deviates upward via the input unit 170. In this case, the output unit 160 can output the display screen shown in this figure. That is, the output unit 160 can display the path and the number of batches through the selected case to the final "pH" evaluation of "Good". At this time, the output unit 160 can be connected to the output unit 160. Figure 7The display screen shown also displays the route with a thickness corresponding to the batch number. Thus, the output unit 160 can output a display screen indicating whether the evaluation data in the performance data meets the predetermined benchmark and which of the corresponding conditions is met for each item in the management parameter.

[0116] For example, in this figure, the path from "Category U" in "Raw Material B. Characteristic 3" to "Category U" in "Addition Amount" is shown as having 12 batches. Similarly, the path from "Category U" in "Raw Material B. Characteristic 3" to "Category C" in "Addition Amount" is shown as having 2 batches. This indicates that even when "Raw Material B. Characteristic 3" deviated upward, 14 batches were ultimately evaluated as having a good pH. Twelve of these batches were operated with the "Addition Amount" adjusted upward, while the remaining two batches were operated with the "Addition Amount" adjusted according to the management standard. Therefore, it can be assumed that when "Raw Material B. Characteristic 3" deviated upward, adjusting the "Addition Amount" upward increased the frequency of good pH evaluations. In other words, the user can identify that adjusting the "Addition Amount" upward is a method for recovering from the aforementioned deviation pattern. Thus, by studying the classification results output by the data processing system 100 of this embodiment, the user can identify the recovery method for each deviation pattern.

[0117] Figure 9 An example of a flow of updating evaluation criteria and management criteria using the data processing system 100 of this embodiment is shown.

[0118] Steps 900 to 920 are steps for solving the problem in the aforementioned "Category 1." That is, for example, in order to meet high quality demands from customers and further reduce product quality variations, steps 900 to 920 are performed.

[0119] In step 900 , the data processing system 100 determines whether to reduce the deviation of the quality characteristic. For example, the data processing system 100 may determine whether to reduce the deviation of the quality characteristic by receiving a user input requesting to reduce the deviation of the quality characteristic via the input unit 170 .

[0120] If it is determined in step 900 that the variation in quality characteristics is not to be reduced, the data processing system 100 proceeds to step 930 . On the other hand, if it is determined in step 900 that the variation in quality characteristics is to be reduced, the data processing system 100 proceeds to step 910 .

[0121] In step 910, the data processing system 100 narrows the evaluation benchmark range. For example, the data processing system 100 displays the classification results of the performance data from the overall perspective of the operation in the production management object 10. At this time, as an example, the data processing system 100 can also display a histogram with the horizontal axis representing the measured values ​​in the evaluation items that are the targets of the evaluation benchmark update and the vertical axis representing the frequency of each measured value. In addition, if the data processing system 100 receives user input requesting a change in the good quality benchmark range via the input unit 170, for example, it can narrow the good quality benchmark range, that is, the evaluation benchmark range, in accordance with the command corresponding to the input.

[0122] In step 920, the data processing system 100 reclassifies the performance data. The data processing system 100 reclassifies the performance data using the evaluation criteria updated in step 910. As a result, some of the performance data classified as "Category 1" is reclassified as "Category 3" under the updated evaluation criteria, and some of the performance data classified as "Category 2" is reclassified as "Category 4" under the updated evaluation criteria. This will be explained in detail later. Thus, the data processing system 100 updates the evaluation criteria to minimize product quality variations.

[0123] Steps 930 through 960 address the aforementioned issue of "Category 3." Specifically, if there is performance data classified as "Category 3," steps 930 through 960 are executed with the goal of narrowing the management criteria so that conforming products are consistently produced as long as the product is operated within the criteria.

[0124] In step 930 , the data processing system 100 determines whether “Category 3” exists. For example, the data processing system 100 can determine whether “Category 3” exists based on whether there is performance data classified as “Category 3” in the classified performance data.

[0125] If it is determined in step 930 that “classification 3” does not exist, the data processing system 100 advances the process to step 970 . On the other hand, if it is determined in step 930 that “classification 3” exists, the data processing system 100 advances the process to step 940 .

[0126] In step 940, for example, the user discovers the separation or interval between good and bad products. As an example, the data processing system 100 may display a histogram or distribution graph of the actual performance values ​​of the management parameter. The user, after examining the displayed screen, discovers that the distribution of good and bad product quality is a parameter that represents the separation or interval.

[0127] In step 950, the data processing system 100 narrows the management baseline range. For example, the data processing system 100 may display a histogram with the horizontal axis representing the actual performance values ​​of the management parameters found in step 940 and the vertical axis representing the frequency of each performance value. Furthermore, if the data processing system 100 receives user input requesting a change in the management baseline range via the input unit 170, for example, it may narrow the management baseline range in accordance with the command corresponding to the input.

[0128] In step 960, the data processing system 100 reclassifies the performance data. The data processing system 100 reclassifies the performance data using the management criteria updated in step 950. As a result, some of the performance data classified as "Category 1" is reclassified as "Category 2" under the updated management criteria, and all of the performance data classified as "Category 3" is reclassified as "Category 4" under the updated management criteria. This will also be explained in detail later. As a result, the data processing system 100 updates the management criteria so that no performance data classified as "Category 3" remains.

[0129] The processing from steps 970 to 990 is used to resolve the issue in "Category 4" described above. Specifically, steps 970 to 990 are executed to discover deviation patterns based on the performance data classified as "Category 4" and to find a method to restore them based on the performance data classified as "Category 2." This is done with the goal of setting a new QM matrix.

[0130] In step 970, for example, the user finds a deviation pattern. As an example, the data processing system 100 outputs a display screen showing the classification results (for example, Figure 6 ). And, for example, in Figure 6 In the display of the classification results shown, the user selects and clicks the graph showing the final "pH" being "Bad" via the input unit 170. In response, the data processing system 100 outputs data indicating that the evaluation data in the performance data does not meet the predetermined criteria, and a display screen showing which of the corresponding relationships for each item in the management parameter is met (e.g., Figure 7 ). Furthermore, the user's perception deviation pattern of the display screen was studied.

[0131] In step 980, for example, the user discovers a recovery method. As an example, Figure 7In the display of the classification results shown, the user selects and clicks the graph showing the deviation point, that is, "Raw Material B. Characteristic 3" deviating upward, via the input unit 170. In response, the data processing system 100 outputs data indicating whether the evaluation data in the performance data meets the predetermined criteria, and a display screen showing which of the corresponding relationships for each item in the management parameter meets each of the conditions (e.g., Figure 8 ). Furthermore, the recovery method for each deviation mode discovered by the user of the display screen was studied.

[0132] In step 990, the data processing system 100 sets the QM matrix for each deviation pattern. For example, if the data processing system 100 receives user input from the user who discovered the recovery method in step 980 via the input unit 170 requesting the setting of a management standard for each deviation pattern, the data processing system 100 may reset the QM matrix for each deviation pattern in accordance with the command corresponding to the input. This will be described in detail later. The data processing system 100 then completes the process of updating the evaluation standard and management standard.

[0133] Figure 10 An example of a change in the classification result when the evaluation benchmark range is narrowed using the data processing system 100 of the present embodiment is schematically shown. The classification result before narrowing the evaluation benchmark range (good quality benchmark range) is shown at the top of this figure. In addition, the classification result after narrowing the evaluation benchmark range is shown at the bottom of this figure. In addition, the left side of this figure shows a histogram in which the horizontal axis represents the measurement values ​​in the evaluation items that become the update objects of the evaluation benchmark and the vertical axis represents the frequency of each of the measurement values. In addition, the right side of this figure shows a pie chart that shows the classification results of the performance data from the overall perspective of the operation in the production management object 10.

[0134] As shown in this figure, as a result of narrowing the good quality standard range, some performance data classified as "Category 1" has been reclassified as "Category 3" under the updated evaluation criteria, and some performance data classified as "Category 2" has been reclassified as "Category 4" under the updated evaluation criteria. Thus, data processing system 100 updates the evaluation criteria to further reduce product quality variations.

[0135] Figure 11This figure schematically illustrates an example of how classification results change when the management benchmark range is narrowed using the data processing system 100 of this embodiment. The top portion of this figure shows the classification results before the management benchmark range is narrowed. Furthermore, the bottom portion of this figure shows the classification results after the management benchmark range is narrowed. Furthermore, the left side of this figure shows a histogram with the horizontal axis representing the actual performance values ​​of the management parameters that are the targets of the management benchmark update, and the vertical axis representing the frequency of each performance value. Furthermore, the right side of this figure shows a pie chart showing the classification results of the performance data from the overall perspective of the operation of the production management object 10.

[0136] As shown in this figure, as a result of narrowing the scope of the management criteria, some performance data categorized as "Category 1" has been reclassified as "Category 2" under the updated management criteria, and all performance data categorized as "Category 3" has been reclassified as "Category 4" under the updated management criteria. Thus, data processing system 100 updates the management criteria so that no performance data categorized as "Category 3" remains.

[0137] Figure 12 This diagram schematically illustrates an example of how classification results change when a QM matrix is ​​set for each deviation pattern using the data processing system 100 of this embodiment. The top of the figure shows the classification results before the QM matrix is ​​set for each deviation pattern. Furthermore, the bottom of the figure shows the classification results after the QM matrix is ​​set for each deviation pattern. The left side of the figure shows the QM matrix for each set operating condition. Furthermore, the right side of the figure shows a pie chart showing the classification results of performance data from the overall perspective of operations within the production management object 10.

[0138] The data processing system 100 of this embodiment identifies deviation patterns based on performance data classified as "Category 4" and identifies recovery methods based on performance data classified as "Category 2." A new QM matrix is ​​then created for each deviation pattern. For example, in this figure, "Pattern 1" could be a situation where "Raw Material B. Characteristic 3" deviates upward, meaning that "Raw Material B. Characteristic 3" is "10 or greater." Furthermore, in the newly created QM matrix for "Pattern 1," for example, the management criteria for "Addition Amount" could be "Lower Limit: 50," "Lower Limit: Greater than," "Upper Limit: 55," and "Upper Limit: Less than," respectively.

[0139] As a result of setting the QM matrix for each deviation pattern, all performance data classified as "Category 4" is reclassified as "Category 2" under the QM matrix for each deviation pattern. Thus, the data processing system 100 can set the QM matrix for each deviation pattern so that no performance data classified as "Category 4" exists. In other words, the data processing system 100 of this embodiment uses the discovered deviation pattern as a new operating condition and resets the QM matrix for each corresponding operating condition. This allows the data processing system 100 to operate according to the QM matrix set for each deviation pattern even when the same pattern as previously encountered occurs.

[0140] In the past, due to changes in operating conditions, even if operations were conducted in compliance with management benchmarks, it was sometimes impossible to maintain the production evaluation characteristics. Furthermore, it was sometimes unclear how to change management benchmarks to maintain the production evaluation characteristics. Consequently, management benchmarks became a formality, relying on on-site intelligence, and unskilled operators could not achieve stable operation. In contrast, the data processing system 100 of this embodiment classifies performance data based on the management parameter judgment results and evaluation characteristics to determine whether the work data complies with the management benchmarks, and outputs the classification results. Thus, the data processing system 100 of this embodiment can notify users of whether the relationship between management benchmarks and evaluation characteristics is adhered to.

[0141] Furthermore, the data processing system 100 of this embodiment categorizes performance data into at least four types based on whether the work data for all items related to operating parameters within the management parameters conform to management standards and whether the evaluation data satisfies predetermined standards, and displays the respective frequencies in a graph. Thus, the data processing system 100 of this embodiment can notify the user of the occurrence frequency within each category.

[0142] Furthermore, the data processing system 100 of this embodiment categorizes performance data based on whether the evaluation data meets predetermined criteria for each management parameter item, including whether the operation data conforms to the management benchmark, deviates upward, or deviates downward. The system then displays the frequency of each category in a graph. Thus, the data processing system 100 of this embodiment allows users to understand the relationship between compliance with the management benchmark for each management parameter and the evaluation characteristics during the operation process.

[0143] Furthermore, the data processing system 100 of this embodiment outputs data indicating that the evaluation data does not meet predetermined criteria, and displays a corresponding relationship indicating which of the following situations applies to each item in the management parameters. Thus, the data processing system 100 of this embodiment can support the user in inferring the factors that lead to poor evaluation characteristics.

[0144] Furthermore, the data processing system 100 of this embodiment outputs data indicating whether the evaluation data meets predetermined criteria, and displays a corresponding relationship display indicating which of the following conditions each item in the management parameters corresponds to. Thus, the data processing system 100 of this embodiment can help users discover operating parameter adjustment methods to improve evaluation characteristics.

[0145] Furthermore, data processing system 100 of this embodiment includes a benchmark updating unit that updates at least one of the evaluation benchmark and the management benchmark. For example, upon updating at least one of the evaluation benchmark and the management benchmark based on user input, the unit reclassifies performance data using the updated benchmark and outputs the reclassified results. Thus, data processing system 100 of this embodiment can notify users of the expected evaluation characteristics of benchmark updates before formal improvements are implemented.

[0146] Furthermore, the data processing system 100 of this embodiment uses data evaluating at least one of product quality, production productivity, cost, delivery time, and safety as evaluation data. Therefore, the data processing system 100 of this embodiment can support stable implementation of PQCDS.

[0147] Thus, the data processing system 100 of this embodiment enables the discovery and resolution of issues even without advanced data analysis knowledge and skills. Furthermore, the data processing system 100 of this embodiment supports the continuous updating of evaluation and management standards, enabling the stable implementation of PQCDS by operating in compliance with the management standards.

[0148] In the above description, the user of data processing system 100 is shown as an example in which the evaluation and management criteria are updated. However, this is not limiting. Alternatively, data processing system 100 itself may determine the evaluation and management criteria to be updated and automatically update or propose them.

[0149] Figure 13 An example of a block diagram of a data processing system 100 according to a modified example of the present embodiment is shown. Figure 1Components having the same function and structure are given the same reference numerals, and description thereof will be omitted except for the following differences. The data processing system 100 of this modified example includes an update determination unit 1310. In addition, in this figure, as an example, a case where the data processing system 100 includes the update determination unit 1310 instead of the input unit 170 is shown, but the present invention is not limited to this. The data processing system 100 may also include the update determination unit 1310 in addition to the input unit 170. That is, the data processing system 100 may also include both a function of updating the benchmark based on user input and a function of automatically updating the benchmark itself.

[0150] In this variation, output unit 160 supplies the classification results from data classification unit 150 to update determination unit 1310. Update determination unit 1310 then determines to update at least one of the evaluation criteria and the management criteria based on the classification results output by output unit 160. Update determination unit 1310 then supplies update information determined for at least one of the evaluation criteria and the management criteria to benchmark update unit 180. Benchmark update unit 180 then updates at least one of the evaluation criteria and the management criteria stored in benchmark storage unit 140 based on the update information supplied by update determination unit 1310. That is, benchmark update unit 180 updates at least one of the evaluation criteria and the management criteria based on the determination made by update determination unit 1310.

[0151] For example, in the above step 910, when narrowing the evaluation criterion range, the update determination unit 1310 may determine the updated good quality criterion range based on the frequency distribution of the measurement values. Figure 10 The updated good quality standard range is determined based on the histogram shown in FIG. 1 , so that the measured values ​​within the range where the deviation from the mean in the frequency distribution of the measured values ​​is greater than or equal to a predetermined threshold (e.g., greater than or equal to 1σ) are changed from "Good" to "Bad." Thus, in the data processing system 100 of this variation, the update determination unit 1310 can automatically determine the update of the evaluation standard based on the classification results.

[0152] Furthermore, for example, in step 940, when the separation or interval between good and bad products is discovered, update determination unit 1310 may use a decision tree analysis. For example, update determination unit 1310 may use performance data (tabular data) as input to perform a decision tree analysis to determine which parameters and values ​​can be used to distinguish between good and bad product quality. Furthermore, in step 950, update determination unit 1310 may determine an updated management benchmark range based on the analysis results.

[0153] Figure 14This figure shows an example of analysis results when data processing system 100, according to a variation of this embodiment, uses decision tree analysis to narrow down the management criteria. For example, update determination unit 1310 inputs tabular data (shown on the left side of the figure) containing a batch ID, actual values ​​of operating parameters for that batch, and quality evaluation results for that batch. Update determination unit 1310 then receives this tabular data as input and outputs the analysis results (shown on the right side of the figure).

[0154] The right side of this figure shows how the 37 batches of product X can be classified as Good or Bad. Specifically, the right side of this figure shows that when parameter 1 ≥ 31.7, 27 batches were Good; when parameter 1 < 31.7 and parameter 2 ≥ 46.7, 1 batch was Good; and when parameter 1 < 31.7 and parameter 2 < 46.7, 9 batches were Good. After performing the above analysis, the update determination unit 1310 determines, for example, parameter 1 ≥ 31.7 and / or parameter 2 ≥ 46.7 as the updated management benchmark range. Thus, in the data processing system 100 of this variation, the update determination unit 1310 can automatically determine whether to update the management benchmark based on the classification results.

[0155] In addition, for example, when a deviation pattern is found in the above step 970, the update determination unit 1310 may automatically find the deviation pattern. As an example, the update determination unit 1310 may Figure 7 The path with the largest number of corresponding batches is determined as the deviation pattern. In this case, the update determination unit 1310 may, for example, determine the path with the largest number of corresponding batches as the deviation pattern. Alternatively, the update determination unit 1310 may determine the paths with the largest number of corresponding batches up to the nth highest as the deviation pattern, the paths with a number of corresponding batches exceeding a predetermined threshold as the deviation pattern, or all discovered paths as the deviation pattern.

[0156] Furthermore, in step 980, the update determination unit 1310 may select a deviation point in the determined deviation pattern, Figure 8 , automatically discovering a recovery method by searching for the path with the largest number of corresponding batches. Specifically, update determination unit 1310 can search for combinations of conditions for multiple items in the management parameters, searching for combinations with a high frequency of evaluation data meeting a predetermined benchmark, and determine an updated management benchmark. Thus, in data processing system 100 of this variation, update determination unit 1310 can automatically determine an update to the management benchmark based on the classification results.

[0157] Thus, data processing system 100 according to this variation further includes update determination unit 1310 that determines whether to update at least one of the evaluation criteria and the management criteria based on the classification results, and benchmark update unit 180 updates at least one of the evaluation criteria and the management criteria based on the determination made by update determination unit 1310. Thus, data processing system 100 according to this variation can automatically optimize the evaluation criteria and the management criteria used when classifying performance data.

[0158] Furthermore, in data processing system 100 of this variation, update determination unit 1310 searches for combinations of conditions for multiple items in the management parameters, searching for combinations with a high frequency of evaluation data satisfying a predetermined benchmark, and determines an updated management benchmark. Thus, data processing system 100 of this variation can automatically discover a recovery method and optimize the management benchmark.

[0159] In addition, various embodiments of the present invention may be described with reference to flow charts and block diagrams, where a module may represent (1) a stage of a process for performing an operation or (2) a portion of a device having the function of performing an operation. Specific stages and portions may be implemented by dedicated circuits, programmable circuits supplied together with computer-readable instructions stored on a computer-readable medium, and / or processors supplied together with computer-readable instructions stored on a computer-readable medium. Dedicated circuits may include digital and / or analog hardware circuits, and may also include integrated circuits (ICs) and / or discrete circuits. Programmable circuits may include reconfigurable hardware circuits that include logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and other memory elements.

[0160] A computer-readable medium may include any tangible device capable of storing instructions for execution by an appropriate device. Consequently, a computer-readable medium having instructions stored therein includes an article containing instructions that can be executed to produce a means for performing the operations specified by the flowchart or block diagram. Examples of computer-readable media include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable media include floppy disks, magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disks (DVD), Blu-ray discs (RTM), memory sticks, integrated circuit cards, and the like.

[0161] Computer-readable instructions include any of source code and object code described by any combination of one or more programming languages ​​including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or object-oriented programming languages ​​such as Smalltalk (registered trademark), JAVA (registered trademark), C++, and existing procedural programming languages ​​such as the "C" programming language or similar programming languages.

[0162] The computer-readable instructions may be provided to a processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device via a local area network (LAN) or a wide area network (WAN) such as the Internet, and the computer-readable instructions may be executed to create a means for performing the operations specified by the flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0163] Figure 15 This figure illustrates an example of a computer 2200 capable of implementing various aspects of the present invention in whole or in part. Programs installed on computer 2200 enable computer 2200 to perform operations associated with devices according to embodiments of the present invention or functions of one or more components of such devices, or to execute such operations or such one or more components, and / or enable computer 2200 to perform processes according to embodiments of the present invention or stages of such processes. Such programs can be executed by CPU 2212 to cause computer 2200 to perform specific operations associated with some or all of the modules in the flowcharts and block diagrams described in this specification.

[0164] The computer 2200 of this embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected via a main controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the main controller 2210 via an input / output controller 2220. The computer also includes conventional input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0165] The CPU 2212 controls each unit by operating according to programs stored in the ROM 2230 and the RAM 2214. The graphics controller 2216 acquires image data generated by the CPU 2212 from a frame buffer or the like provided in the RAM 2214 or from the graphics controller itself, and displays the image data on the display device 2218.

[0166] The communication interface 2222 is capable of communicating with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0167] The ROM 2230 stores therein a boot program or the like executed by the computer 2200 upon activation and / or programs that depend on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0168] The program is provided on a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from the computer-readable medium, installed in the hard disk drive 2224, RAM 2214, or ROM 2230, also examples of computer-readable media, and executed by the CPU 2212. The information processing described in these programs is read into the computer 2200, thereby enabling the program to cooperate with the various types of hardware resources described above. An apparatus or method can be constructed by implementing information manipulation or processing with the use of the computer 2200.

[0169] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 can execute a communication program loaded in the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer area provided in a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer area provided on the recording medium.

[0170] Furthermore, the CPU 2212 can read all or a necessary portion of a file or database stored in an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), or an IC card into the RAM 2214, and perform various types of processing on the data in the RAM 2214. The CPU 2212 then writes the processed data back to the external recording medium.

[0171] Various types of information such as various types of programs, data, tables, and databases can be stored in a recording medium and subjected to information processing. The CPU 2212 performs various types of processing described in various places of this disclosure on the data read from the RAM 2214 and writes the results back to the RAM 2214. The various types of processing include various types of operations specified by the instruction sequence of the program, information processing, conditional judgment, conditional branching, unconditional branching, information retrieval / replacement, etc. In addition, the CPU 2212 can retrieve information in files, databases, etc. in the recording medium. For example, in the case where a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 can retrieve an entry that is consistent with the condition specifying the attribute value of the first attribute from the plurality of entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute that meets the predetermined condition.

[0172] The programs or software modules described above can be stored in a computer-readable medium on or near the computer 2200. In addition, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as the computer-readable medium, thereby providing the program to the computer 2200 via the network.

[0173] While the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various modifications or improvements can be made to the above embodiments. As can be seen from the claims, embodiments with such modifications or improvements are also included in the technical scope of the present invention.

[0174] The order of execution of actions, processes, steps, and stages, etc., in the apparatus, system, program, and method described in the claims, specifications, and drawings is not specifically indicated as "before," "before," or the like. Furthermore, it should be noted that the actions, processes, steps, and stages may be executed in any order as long as the output of the previous process is not used in the subsequent process. Even if the action flow in the claims, specifications, and drawings is described using the phrases "first," "next," or the like for ease of explanation, it does not necessarily mean that the actions must be executed in that order.

Claims

1. A data processing system, characterized in that include: an operation data acquisition unit that acquires operation data indicating actual performance related to production operations; an evaluation data acquisition unit that acquires evaluation data indicating actual performance related to the production evaluation; a reference storage unit for storing management references to be used for each of the management parameters to be managed; a data classification unit that classifies performance data indicating actual production performance based on a determination result of whether the operation data complies with the management standard with respect to the management parameter and the evaluation data; as well as Output part, output classification results, The data classification unit classifies the performance data according to whether the evaluation data satisfies a predetermined standard for each item in the management parameter, based on whether the operation data is in accordance with the management standard, deviates upward, or deviates downward. The output unit outputs a display screen that displays the frequency of whether the evaluation data meets the predetermined benchmark in each case as a graph for each item in the management parameters, and also outputs a display screen that shows the corresponding relationship between the data in the performance data that does not meet the predetermined benchmark and which of the cases the data meets for each item in the management parameters.

2. The data processing system according to claim 1, wherein: The data classification unit classifies the performance data into at least four types based on whether the work data complies with the management standard for all items related to the operating parameters in the management parameters and whether the evaluation data satisfies a predetermined standard.

3. The data processing system according to claim 2, wherein: The output unit outputs a display screen that displays the frequencies of each of the at least four categories in a graph.

4. The data processing system according to any one of claims 1 to 3, characterized in that: The output unit outputs a display screen indicating which of the respective situations corresponds to which of the respective items in the management parameters the data of the performance data that satisfies a predetermined criterion.

5. The data processing system according to any one of claims 1 to 3, characterized in that: The system further includes a benchmark updating unit configured to update at least one of an evaluation benchmark for determining an evaluation index based on the evaluation data and the management benchmark.

6. The data processing system according to claim 5, characterized in that The data classification unit reclassifies the performance data using the updated criteria based on the update of at least one of the evaluation criteria and the management criteria. The output unit outputs the reclassified classification result.

7. The data processing system according to claim 5, wherein: It also includes an input unit for receiving user input, The criterion updating unit updates at least one of the evaluation criterion and the management criterion based on the user input.

8. The data processing system according to claim 5, wherein: further comprising an update determination unit configured to determine an update of at least one of the evaluation criterion and the management criterion based on the classification result; The criterion updating unit updates at least one of the evaluation criterion and the management criterion based on the determination of the update determination unit.

9. The data processing system according to claim 8, wherein: The update determination unit searches for a combination with a high frequency of the evaluation data satisfying a predetermined criterion from among combinations of conditions of a plurality of items in the management parameters, and determines the updated management criterion.

10. The data processing system according to any one of claims 1 to 3, characterized in that: The evaluation data includes data evaluating the quality of the produced product.

11. The data processing system according to any one of claims 1 to 3, characterized in that: The evaluation data includes data evaluating at least any one of productivity, cost, delivery time, and safety of production.

12. A data processing method, characterized in that include: Acquiring operational data showing actual performance related to production operations; obtaining evaluation data indicating performance related to the evaluation of the production; The management criteria to be followed are stored for each of the management parameters to be managed; classifying performance data indicating actual production performance based on a determination result of whether the operation data complies with the management standard with respect to the management parameter and the evaluation data; as well as Output classification results, In the step of classifying the performance data representing the actual production performance, for each item in the management parameter, the operation data is classified according to whether the evaluation data satisfies a predetermined benchmark, based on each of the management benchmark, the upward deviation, and the downward deviation. In the step of outputting the classification result, a display screen is output that displays the frequency of whether the evaluation data meets the predetermined benchmark in each case as a graph for each item in the management parameter, and a display screen is also output that shows the corresponding relationship between the data in the performance data that does not meet the predetermined benchmark and which of the cases it meets for each item in the management parameter.

13. A recording medium having a data processing program recorded thereon, characterized in that: The computer functions as a work data acquisition unit, an evaluation data acquisition unit, a reference storage unit, a data classification unit, and an output unit by executing the data processing program. The operation data acquisition unit acquires operation data indicating actual performance related to production operations. The evaluation data acquisition unit acquires evaluation data indicating actual performance related to the production evaluation. The reference storage unit stores the management reference to be used for each of the management parameters to be managed. The data classification unit classifies the performance data indicating the actual performance of the production based on the result of the determination of whether the operation data complies with the management standard with respect to the management parameter and the evaluation data. The output unit outputs the classification result, The data classification unit classifies the performance data according to whether the evaluation data satisfies a predetermined standard for each item in the management parameter, based on whether the operation data is in accordance with the management standard, deviates upward, or deviates downward. The output unit outputs a display screen that displays the frequency of whether the evaluation data meets the predetermined benchmark in each case as a graph for each item in the management parameters, and also outputs a display screen that shows the corresponding relationship between the data in the performance data that does not meet the predetermined benchmark and which of the cases the data meets for each item in the management parameters.

Images