• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.50-54
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• 2001

Information systems development entails planning, analysis, design and construction phases. The analysis phase identifying user requirements is the most important of these phases. Since unidentified defects in the early phase causes increased work and costs as development proceeds, the quality of analysis results affects the quality of the resultant system. Major tasks in the analysis phase are data modeling and process modeling. Research on building a knowledge-based system for data modeling have been conducted much, however, not sufficiently for process modeling. As a system environment with high user interaction increases, research on process modeling methods and knowledge- based systems considering such environment are required. In this research, a process modeling framework for information systems with high user interaction is suggested and a knowledge-based system for supporting the suggested framework is implemented. A proposed model consists of the following tasks: event analysis, process analysis, and event/process interaction analysis. Event analysis identifies business events and their responses. Process analysis break down the processes of an enterprise into progressively increasing details. Decomposition begins at the function level and ends when the elementary process level is reached. Event/process interaction analysis verifies the results of process analysis and event analysis. A knowledge-based system for supporting a proposed process modeling framework is implemented in a web-based environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.126-134
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• 2019

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.411-415
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• 2001

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us to extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.609-620
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• 2008

There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

• Journal of the Korean Society of Systems Engineering
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• v.9 no.2
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• pp.83-90
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• 2013

Integrated aircraft synthesis process for rapid analysis and design is described in this paper. Data flow between different analysis fields is described in details. All the data are divided into several groups according to importance and source of the data. Analysis of design requirements and certification regulations is carried out to determine baseline configuration of an aircraft. Overall design process can be divided into initial sizing, conceptual and preliminary design phases. Basic data for conceptual design are obtained from initial sizing, CAD and geometry analysis. Basic data are required input for weight, aerodynamics and propulsion analyses. Results of this analysis are used for stability and control, performance, mission, and load analysis. Feasibility of design is verified based on analysis results of each discipline. Design optimization that involves integrated process for aircraft analysis is performed to determine optimum configuration of an aircraft on a conceptual design stage. The process presented in this paper was verified to be used for light aircraft design.

• Journal of Institute of Convergence Technology
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• v.3 no.1
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• pp.41-44
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• 2013

Process and manufacturing data are numerously accumulated to the enterprise database in industries but little of those data are utilized. Data mining can support a decision to manager in process from the data. However, it is not easy to field managers because a proper adoption of various schemes is very difficult. In this paper, six scenarios are conducted using data mining schemes for the various situations of field claims such as yield problem, trend analysis and prediction of yield according to changes of operating conditions, etc. Scenarios, like templates, of various analysis situations are helpful to users.

• The Journal of Bigdata
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• v.3 no.1
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• pp.47-54
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• 2018

Previous studies of business process analysis have analyzed various factors such as task, customer service, operator convenience, and execution time prediction. To accurately analyze these factors, it is effective to utilize actual historical data recorded in information systems. Process mining is a technique for analyzing various elements of a business process from event log data. In this case study, process mining was applied to the transaction data of a purchase agency to analyze the business process of their procurement process, the execution time, and the operators.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.176-185
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• 2018

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.713-723
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• 2019

Purpose: The purpose of this study is to develop a statistical program for capability analysis of measuring system and measurement process based upon KS Q ISO 22514-7. Methods: R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Therefore, in this study, we will develop the statistical program using R language. Results: The R program developed in this study consists of the following five modules. ① Measuring system capability analysis with Type 1 study data: MSCA_Type1.R ② Measuring system capability analysis with Linearity study(Type 4 study) data: MSCA_Type4.R ③ Measurement process capability analysis with Type 1 study & Gage R&R study data: MPCA_T1GRR.R ④ Measurement process capability analysis with Type 4 study & Gage R&R study data: MPCA_T4GRR.R ⑤ Attribute measurement processes capability analysis : AttributeMP.R Conclusion: KS Q ISO 22514-7 evaluates measuring systems and measurement processes on the basis of the measurement uncertainty that was determined according to the GUM(KS Q ISO/IEC Guide 98-3). KS Q ISO 22514-7 offers precise procedures, however, computations are more intensive. The R program of this study will help to evaluate the measurement process.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.