KR102351991B1 - Process of Big Data Analysis and Change Management and its method by Smart Factory FOMs Package - Google Patents

Abstract

Disclosed are a big data analysis and change management process of a smart-factory operation management (FOM) analysis unit in a package of FOMs. The present invention relates to a process for analyzing big data collected from a package of FOMs, and the process includes: a comparison and analysis step (S300) in which periods of As-Is and To-Be are set in order to perform comparison and analysis of indicators of a company according to a change in production process by period, comparison and analysis is performed on a production achievement rate/non-operation rate/defect rate/input cost by hour, and when a 4M1E information-related process is changed, a target corresponding thereto is selected to perform comparison and analysis before and after the change; an outlier step (S400) in which analysis is performed on a difference between a 4M-related production/non-operation/defect/nonconformity, which is analyzed by a normal data group, and a 4M-related production/non-operation/defect/nonconformity, which is analyzed by an outlier data group, in addition to supplement a reliability issue of collected raw data; and a production process change management step (S500) in which because changes in the 4M-related production process occur frequently depending on the sales and order environment due to features of a manufacturing company, the changes are managed to minimize factors that impede productivity of the company and improve the productivity, in order to cope with the frequent changes in a 4M-related production process. The present invention can provide a package of FOMs for smart manufacturing innovation.

Description

Process of Big Data Analysis and Change Management and its method by Smart Factory FOMs Package}

The present invention relates to the big data analysis process and change management (indicator management) of the smart factory, and more specifically, data gathering necessary for on-site diagnosis of the smart factory and analysis of each company, and informatization/automation/ It relates to the big data analysis and change management process and method of the FOM analysis unit in the smart factory FOMs Package for optimizing into an intelligent new production system by performing the steps of optimization/efficiency/intelligence.

The inventor of the present application has applied for a smart factory FOMs package and method for smart manufacturing innovation published in Patent Document 1.

1 shows a block diagram for explaining the configuration and operation of the smart factory FOMs package for smart manufacturing innovation previously filed by the applicant of the present invention.

Patent Document 1 in the smart-Factory Operation Managements (FOMs) package of a smart factory for smart manufacturing innovation,

Data input unit 100 for collecting 4M1E information including manpower (Man), machine (Machine), method (Method), material (Material), and energy (Energy) information input to the production and manufacturing of the factory; a FOM check sheet analysis unit 120 for diagnosing the level of the factory and strengthening the capacity based on the information collected from the data input unit 100; a corporate DB unit 150 that collects and organizes information generated from time to time in the data input unit 100; FOM analysis unit 300 for analyzing the information flow of the resource information of the factory in question collected and stored in the enterprise DB unit 150; a CPS (Cyber Physical System) analysis unit 400 that analyzes the information collected in the enterprise DB unit 150 to automate the logistics flow; The data input unit 100, the FOM check sheet analysis unit 120, and the information provided and collected from the CPS (Cyber Physical System) analysis unit 400 and the information collected by the data input unit 100 are fused. FOMs convergence analysis unit 200 that comprehensively analyzes production and manufacturing information and level diagnosis information of the factory, informatization information and automation information of the company; It is about a smart factory FOMs package for smart manufacturing innovation that performs monitoring.

In the smart-factory operation managements (FOMs) package configuration of a smart factory for such smart manufacturing innovation, the inventor of the present application uses a pre-developed FOM system for ① manufacturing site raw data for FOM integrated management for the FOM analysis unit. FOM-Logic/Algorithm design and development to interlock with and big data analysis, ② FOM-Logic/Algorithm development and FOM SW advanced development equipped with AI/big data analysis technology, ③ FOM diagnosis analysis and process and business convergence While developing a standard manual for management, we recognized the need to continuously develop and upgrade industrial AI big data-based manufacturing data analysis and smart factory models.

Korean Patent Application No./Date: 10-2020-0032342 (2020.03.17), "Smart Factory FOMs Package and Method for Smart Manufacturing Innovation"

The present invention has been devised to solve the above-mentioned need for advancement, and the purpose is to provide a smart-factory operation management (FOMs) package for smart manufacturing innovation, To provide a factory operation management package for smart factories for smart manufacturing innovation that performs big data analysis and change management through data refining and processing processing that is refined and classified into production information, non-operational information, defective information and non-conforming information it is for

Another object of the present invention is to provide a big data analysis process and method of the FOM analysis unit in the smart factory FOMs package.

Another object of the present invention is to provide a change management process and method of the FOM analysis unit in the smart factory FOMs package.

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The process for analyzing big data collected from the smart-Factory Operation Managements (FOMs) package of a smart factory for smart manufacturing innovation according to an embodiment of the present invention is,

In order to perform comparative analysis of the company's indicators according to the change of the production process by period, the period of As-Is and To-Be is set, and comparative analysis is performed on the production achievement rate/non-operation rate/defect rate/hourly input amount, and 4M1E information A comparative analysis step (S300) of selecting a target for a related process change and performing a comparative analysis before and after the change; In addition to supplementing the reliability issue of collected raw data, 4M-related production/non-operation/defective/nonconformity analyzed in the normal data group and 4M-related production/non-operation/defective/nonconformity analyzed in the outlier data group occur differently. an outlier analysis step (S400) of analyzing for it; And in order to respond to frequent changes in the production process related to 4M information, the production process related to 4M information frequently changes depending on the sales and order environment due to the nature of the manufacturing company. A production process change management step to minimize factors and improve productivity (S500); This can be achieved by including

According to an embodiment of the present invention, in a smart-Factory Operation Managements (FOMs) package of a smart factory for smart manufacturing innovation, classified and collected production amount information, non-operation information, defective information, and nonconformity information It is possible to realize the effect of providing a factory operation management package of a smart factory for smart manufacturing innovation that performs big data analysis and change management through data refining and processing that is refined and classified with

In addition, according to the embodiment of the present invention, the period of As-Is and To-Be is set in order to perform comparative analysis of the overall index of the enterprise according to the change of the production process for each period of the enterprise, and the production achievement rate/non-operation rate/defect rate/time Comparative analysis can be performed on the input amount, etc., and when a 4M-related process is changed, the effect of providing a smart factory big data analysis method can be realized by selecting a target and performing comparative analysis before and after the change.

In addition, according to the embodiment of the present invention, as well as supplementing the reliability issue of collected raw data, 4M-related production/non-operation/defective/nonconformity analyzed in the normal data group and 4M-related production/non-operation analyzed in the outlier data group Because /defectiveness/nonconformity will be different, it is possible to implement the effect of providing a smart factory big data analysis method that analyzes each.

In addition, according to the embodiment of the present invention, in order to respond to frequent changes in the production process related to 4M, changes in the production process related to 4M occur frequently depending on the sales and order environment due to the nature of the manufacturing company. It is possible to realize the effect of providing a smart factory big data analysis method that can minimize the factors that inhibit productivity and waste factors and improve productivity.

1 is a block diagram for explaining the configuration and operation of the smart factory FOMs package for smart manufacturing innovation presented by the present inventor's earlier application;
Figure 2 is a block diagram for specifically explaining the configuration and operation of the FOMs package platform according to an embodiment of the present invention;
5 is a schematic diagram for explaining the configuration of a big data solution for industrial AI execution at a manufacturing site and organic analysis of data in the smart factory big data analysis process according to an embodiment of the present invention;
6 is a flowchart for explaining the comparative analysis step in the smart factory big data analysis process according to an embodiment of the present invention;
7 is a flowchart for explaining outlier analysis in the smart factory big data analysis process according to an embodiment of the present invention;
8 is a flowchart for explaining a production process change management step in the smart factory big data analysis process according to an embodiment of the present invention.

The invention disclosed herein may be embodied in various forms and is not limited to the examples set forth herein. In addition, the terms used herein are only used to describe specific embodiments, and are not intended to limit the scope or form of the present invention. Hereinafter, embodiments of the present invention will be described in more detail with reference to the drawings.

(Example)

Smart factory factory operation management package big data analysis method according to a preferred embodiment of the present invention, in the factory operation management package of a smart factory for smart manufacturing innovation,

A data input unit 100 that collects 4M1E information including manpower, machine, method, material, and energy information input to the production and manufacturing of the factory 100 and:

A FOM analysis unit 200 for analyzing the information flow of the resource information of the factory in question collected and stored through the data input unit 100;

A CPS (Cyber Physical System) analysis unit 300 that analyzes and processes the information collected in the data input unit 100 to automate the logistics flow:

By fusion of the data input unit 100 and the information provided and collected from the CPS (Cyber Physical System) analysis unit 300, the factory's production and manufacturing information, level diagnosis information, and the company's informatization information and automation information are comprehensively integrated. FOMs fusion analysis unit 400 to analyze: made including,

The data input unit 100,

a process for classifying and collecting production information (110) for classifying and collecting production information by extracting the 4M1E information;

non-moving information classification and collection 120 processing for classifying and collecting non-moving information by analyzing the 4M1E information;

Defect information classification and collection 130 processing for classifying and collecting defective information by analyzing the 4M1E information; and

After analyzing the 4M1E information and performing the nonconformity information classification and collection 140 processing for classifying and collecting information that is not suitable for the quality standard,

It is characterized in that it is made by performing a data refining and processing processing 150 for refining and classifying the classified and collected production amount information, non-operational information, and defective information and non-conforming information in each process.

The configuration and operation of a factory operation management package of a smart factory for smart manufacturing innovation according to an embodiment of the present invention having the above configuration will be described in more detail with reference to FIG. 2 .

2 is a block diagram for specifically explaining the configuration and operation of the FOMs package platform according to an embodiment of the present invention.

The data input unit 100 is a device for collecting 4M1E information including manpower (Man), machine (Machine), method (Method), material (Material), and energy (Energy) information input to production and manufacturing of a factory to be.

The data input unit 100 is a device for inputting 4M1E information generated at the production and manufacturing site using tools such as ERP, MES, POP, and Excel.

The FOM analysis unit 200 is configured to analyze the information flow of the resource information collected and stored through the data input unit 100 .

The CPS (Cyber Physical System) analysis unit 300 performs an analysis and processing operation so as to automate the logistics flow by utilizing the information collected from the data input unit 100 .

The FOMs fusion analysis unit 400 fuses the data input unit 100 and the information provided and collected from the CPS (Cyber Physical System) analysis unit 300 to provide production and manufacturing information of the factory and level diagnosis information and the company It performs the function of comprehensively analyzing the informatization information and automation information of

As described above, the main components of the present invention include a data input unit 100 , a FOM analysis unit 200 , a CPS (Cyber Physical System) analysis unit 300 and a FOMs fusion analysis unit 400 .

At this time, the data input unit 100 extracts the 4M1E information and performs the process of classifying and collecting production information by classifying and collecting production information (110). Here, '4M1E information' includes information about manpower (Man), machine (Machine), method (Method), material (Material), and energy (Energy) input to production and manufacturing of the factory (hereinafter the same) .

In addition, the data input unit 100 analyzes the 4M1E information and performs the non-moving information classification and collection 120 processing for classifying and collecting the non-moving information.

In addition, the data input unit 100 analyzes the 4M1E information to classify and collect the defective information, and performs the processing for collecting and classifying defective information 130; After analyzing the 4M1E information and performing the nonconformity information classification and collection 140 processing for classifying and collecting information that is not suitable for the quality standard,

Refinement and classification into production information (FOM CODE #1,000), non-operational information (FOM CODE #2,000), defective information (FOM CODE #3,000) and nonconforming information (FOM CODE #4,000) It is characterized in that it is performed by performing the data refining processing process 150 to be processed.

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Next, a process for analyzing big data collected from a factory operation management package of a smart factory for smart manufacturing innovation according to an embodiment of the present invention will be described in detail with reference to FIGS. 5 to 6 .

5 is a schematic diagram for explaining the configuration of a big data solution for industrial AI execution at the manufacturing site and organic analysis of data in the smart factory big data analysis process according to an embodiment of the present invention, and FIG. 6 is this In the smart factory big data analysis process according to an embodiment of the invention, a flowchart for explaining the comparative analysis step is shown.

The smart factory factory operation management package big data analysis method for smart manufacturing innovation according to an embodiment of the present invention is a process for analyzing big data collected from a factory operation management package of a smart factory, a company according to a production process change by period In order to perform comparative analysis of the indicators of A comparative analysis step (S300) of selecting and performing comparative analysis before and after the change; In addition to supplementing the reliability issue of collected raw data, 4M-related production/non-operation/defective/nonconformity analyzed in the normal data group and 4M-related production/non-operation/defective/nonconformity analyzed in the outlier data group occur differently. an outlier analysis step (S400) of analyzing for it; And in order to respond to frequent changes in the production process related to 4M information, the production process related to 4M information frequently changes depending on the sales and order environment due to the nature of the manufacturing company. A production process change management step to minimize factors and improve productivity (S500); It is characterized in that it comprises a.

In this case, the comparative analysis step (S300) is a FOM defining production information as FOM CODE #1000, non-operational information as FOM CODE #2000, defective information as FOM CODE #3000, and nonconformity information as FOM CODE #4000. A comparative analysis type setting step (S310) of setting a characteristic code (FOM Code), performing an analysis for each FOM characteristic code (FOM Code) when changing a management method for each period, and establishing a comparative analysis period and comparative analysis target criteria;

Key Performance Indicator (KPI) analysis by period is prioritized, and company information according to 4M production process changes by period of annual analysis, first/second half analysis, quarterly analysis, monthly analysis, weekly analysis, and daily analysis is comparatively analyzed. Periodic KPI analysis step 320 and;

With respect to the target criteria established in S300 and 310, when the target criteria are different from target comparison analysis by period (S330), large/medium/small classification category classification (S340), and the target criteria are different, the category of the information is classified by shift (shift category) ) (S350), and performing product / equipment / operator category setting (S360);

Comparing the established target with an input amount per defective/non-operation/hour and a target set value (S370);

As a result of the comparison of the step (S370), if any one did not exceed the target set value (N), a countermeasure for this was established, the production process 4M change and company-customized education were performed, and the process returned to the S300 and proceeded again (S370) -2, S370-4) and;

When the comparison result of the step (S370) exceeds the target set value for all (Y), the FOM management control step (S380) of providing the analyzed result to the FOM Management Control Tower to manage;

In the comparative analysis step (S300), comparative analysis is performed based on the following criteria.

1) By comparing the current status (As-is) and the goal to achieve (To-be), the analysis is performed to increase the achievement rate/operation rate and decrease the amount of defective/non-operation/hourly input.

2) After performing analysis by FOM Code, comparative analysis is performed by overall period, or comparative analysis is performed on the top 5% or bottom 5% of each category element. In this case, the upper or lower % setting can be set differently depending on the characteristics of each company.

3) The comparative analysis is performed according to the purpose by varying the comparative analysis period according to the target to be used for comparative analysis.

In this case, the comparative analysis period criterion of the comparative analysis type setting step (S310) is set to a period in which the production achievement rate is low compared to other periods or the defective rate/non-operation rate/nonconformity rate is higher than other periods, and the target criterion is, product/equipment / It is characterized in that it is set to be any one of the production achievement rate or the defective rate/non-operation rate/nonconformity rate for the operator.

According to an embodiment of the present invention, in this way, the comparative analysis period criterion and the target criterion are performed in parallel, and the productivity improvement can be visually classified by comparing the indicators of the bank plant.

Next, in the smart factory big data analysis process according to an embodiment of the present invention, an outlier analysis procedure will be described in detail with reference to FIG. 7 .

7 is a flowchart illustrating an outlier analysis in a smart factory big data analysis process according to an embodiment of the present invention.

Referring to FIG. 7 , in the outlier analysis step (S400), after adding 19 FOM columns and alpha data classified by FOM characteristic codes, parameters for setting reference parameters for outlier analysis setting step (S410) and;

A group classification step (S430) of applying a big data classification method using an IQR method or a machine learning method (S420), classifying it into a normal data group and an outlier data group;

an analysis step (S440, S445, S450) for each FOM characteristic code of dividing the group into a normal data group (Y) and an outlier data group (N) in the group classification step (S430) and performing an analysis for each FOM code for each group;

It is characterized by including; deriving a technical management indicator management KPI through the analyzed contents for each FOM code (S460), and managing an outlier (S470) of managing the analyzed result in the FOM Management Control Tower (S470). .

In this case, the parameter setting step (S410) is characterized in that it is set for each facility by capacity (Capacity) and by cycle time (Cycle Time).

Next, in the smart factory big data analysis process according to an embodiment of the present invention, the production process change management step will be described in detail with reference to FIG. 8 .

8 is a flowchart for explaining the production process change management step in the smart factory big data analysis process according to an embodiment of the present invention.

The production process change management step (S500) by the smart factory big data analysis method is to change the raw data collected at the manufacturing site to respond to the frequent occurrence of 4M changes in the production process depending on the business and order receiving environment of the manufacturing company. Raw data change step (S510) for; FOM characterization performance management step (S520, S530) of applying FOM characterization information logic and algorithm (FOM-Logic/Algorithm) or changing FOM column data through FOM characterization information performance management function; performing analysis for each FOM code according to FOM Code Management (#1000, #2000, #3000, #4000) (S540); Performing a comparative analysis before and after the production process change for the changed 4M (S550) and; A step of deriving a technology management change management KPI through analysis (S540) and comparative analysis (S550) for each FOM code (S560); and managing the analyzed results in the FOM Management Control Tower (S570).

The raw data changing step (S510) is a step for changing the raw data of information collected at the manufacturing site in order to respond to the frequent occurrence of 4M changes in the production process depending on the business and order receiving environment of the manufacturing company.

The FOM characterization performance management step (S520, S530) is a step of applying the FOM characterization information logic and algorithm (FOM-Logic/Algorithm) or changing the FOM column data through the FOM characterization information performance management function.

Referring to FIG. 5 , by providing a manufacturing site big data analysis solution for Industrial AI according to an embodiment of the present invention, it is possible to organically connect and analyze the FOM Code component of the entire system.

In addition, it is possible to enable smart factory operation management through continuous change management at the manufacturing site.

As shown in Fig. 5, according to the embodiment of the present invention, FOM Code #1000 indicates production information, and the production information is #1100 (comprehensive), #1200 (product), #1300 (facility), #1400 ( worker), etc.

And, FOM Code #2000 indicates non-operational information, and this non-operational information includes information such as #2100 (comprehensive), #2200 (product), #2300 (facility), #2400 (worker), #2500 (factor), etc. includes

In addition, FOM Code #3000 indicates defect information, and this defect information includes information such as #3100 (comprehensive), #3200 (product), #3300 (equipment), #3400 (worker), #3500 (factor), etc. do.

In addition, FOM Code # 4000 denotes the inadequate information, and inadequate information # 4100 (general), # 4200 (product), # 4300 (facilities), # 4400 includes information such as the (operator), # 4500 (factor) do.

In this case, the FOM solution according to the embodiment of the present invention can organically connect and analyze various information as described above.

For example, FOM Code #1000 may be connected to #1200 (product) while indicating production information to derive production amount information of the product. After that, #1200 (product) is configured and operated so that FOM Code #2000 is linked with non-movable information, and specific non-movable information of #2400 (operator) can be connected for analysis.

In this way, according to the embodiment of the present invention, the period of As-Is and To-Be is set in order to perform comparative analysis of the overall index of the enterprise according to the change of the production process by period of the enterprise, and the production achievement rate/non-operation rate/defect rate/ It is possible to perform comparative analysis on hourly input amount, etc., and when changing 4M-related processes, it is possible to realize the effect of providing a smart factory big data analysis method that selects a target and performs comparative analysis before and after the change.

In addition, according to the embodiment of the present invention, as well as supplementing the reliability issue of collected raw data, 4M-related production/non-operation/defective/nonconformity analyzed in the normal data group and 4M-related production/non-operation analyzed in the outlier data group Because /defectiveness/nonconformity will be different, it is possible to implement the effect of providing a smart factory big data analysis method that analyzes each.

In addition, according to the embodiment of the present invention, in order to respond to frequent changes in the production process related to 4M, changes in the production process related to 4M occur frequently depending on the sales and order environment due to the nature of the manufacturing company. It is possible to realize the effect of providing a smart factory big data analysis method that can minimize the factors that inhibit productivity and waste factors and improve productivity.

Although the present invention has been described with reference to one embodiment as described above, those of ordinary skill in the art may have various modifications, variations, and additions within the same or similar scope, as well as those of ordinary skill in the art. All should be considered to be included in the scope of the present application.

100: data input unit
110: production information collection
120: Non-operational information collection
130: collection of bad information
140: collection of nonconforming information
150: data refining processing
200: FOMs fusion analysis unit
300: FOM analysis unit
400: CPS analysis unit
1000: FOMs package platform

Claims (13)

delete delete delete delete delete delete delete In a big data analysis method using a smart-Factory Operation Management (FOMs) package of a smart factory for smart manufacturing innovation,
In order to perform comparative analysis of company indicators according to changes in the production process by period, the period of As-Is and To-Be is set, and comparative analysis is performed on the production achievement rate/non-operation rate/defective rate/hourly input amount, 4M1E information A comparative analysis step (S300) of selecting a target for a related process change and performing a comparative analysis before and after the change;
In addition to supplementing the reliability issue of collected raw data, 4M-related production/non-operation/defective/nonconformity analyzed in the normal data group and 4M-related production/non-operation/defective/nonconformity analyzed in the outlier data group occur differently. an outlier analysis step (S400) of analyzing for it; and
In order to respond to frequent changes in the production process related to 4M information, the production process related to 4M information frequently changes depending on the sales and order environment due to the nature of the manufacturing company. production process change management step (S500) to minimize the problems and improve productivity;
Big data analysis method using the factory operation management package of a smart factory, characterized in that it includes a. The FOM characteristic according to claim 8, wherein the comparative analysis step (S300) defines production information as FOM CODE 1000, non-operation information as FOM CODE 2000, defective information as FOM CODE 3000, and nonconformity information as FOM CODE 4000. A comparative analysis type setting step (S310) of setting a code (FOM Code), performing an analysis for each FOM characteristic code (FOM Code) when changing a management method for each period, and establishing a comparative analysis period and comparative analysis target criteria;
Key Performance Indicator (KPI) analysis by period is prioritized, and company information according to 4M production process changes by period of annual analysis, first/second half analysis, quarterly analysis, monthly analysis, weekly analysis, and daily analysis is comparatively analyzed. Periodic KPI analysis step 320 and;
With respect to the target criteria established in S300 and 310, target comparative analysis by period (S330), large/medium/small classification category classification (S340), and when the target criteria are different, the category of the information is classified by shift (S350) , and performing product / equipment / operator category setting (S360);
Comparing the established target with an input amount per defective/non-operation/hour and a target set value (S370);
As a result of the comparison of the step (S370), if any one did not exceed the target set value (N), a countermeasure for this was established, the production process 4M change and company-customized education were performed, and the process returned to the S300 and proceeded again (S370) -2, S370-4) and;
When the comparison result of the step (S370) exceeds the target setting value for all (Y), the FOM management control step (S380) of providing the analyzed result to the FOM Management Control Tower and managing it; Big data analysis method using the factory operation management package of the factory. The method of claim 9, wherein the comparative analysis period criterion of the comparative analysis type setting step (S310) is set to a period in which the production achievement rate is low compared to other periods or the defective rate/non-operation rate/nonconformity rate is higher than other periods,
The target criterion is a big data analysis method using a factory operation management package of a smart factory, characterized in that it is set to be any one of a production achievement rate or a defective rate/non-operation rate/nonconformity rate for products/equipment/workers.
The parameter of claim 8, wherein, in the outlier analysis (S400), 19 kinds of FOM columns classified by FOM codes and alpha data are added, and then a reference parameter for outlier analysis is set. setting step (S410) and;
A group classification step (S430) of applying a big data classification method using an IQR method or a machine learning method (S420), classifying it into a normal data group and an outlier data group;
an analysis step (S440, S445, S450) for each FOM characteristic code of dividing the group into a normal data group (Y) and an outlier data group (N) in the group classification step (S430) and performing an analysis for each FOM code for each group;
The outlier management step (S470) of deriving a technology management change management KPI through the analyzed contents for each FOM code (S460), and managing the analyzed result in the FOM Management Control Tower (S470); Big data analysis method using factory operation management package. [Claim 12] The method of claim 11, wherein the parameter setting step (S410) is set for each facility by capacity and by cycle time. According to claim 8, wherein the production process change management step (S500), Raw data for changing the raw data collected at the manufacturing site to respond to the frequent occurrence of 4M changes in the production process depending on the sales and order environment of the manufacturing company data changing step (S510) and;
FOM characterization performance management step (S520, S530) of applying FOM characterization information logic and algorithm (FOM-Logic/Algorithm) or changing FOM column data through FOM characterization information performance management function;
performing analysis for each FOM code according to FOM Code Management (#1000, #2000, #3000, #4000) (S540);
Performing a comparative analysis before and after the production process change for the changed 4M (S550) and;
A step of deriving a technology management indicator management KPI through analysis (S540) and comparative analysis (S550) for each FOM code (S560);
Managing the analyzed results in the FOM Management Control Tower (S570); Big data analysis method using a factory operation management package of a smart factory, characterized in that it comprises a.

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