• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.219-225
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• 2003

Two­dimensional warranty data can be modelled using two different approaches： two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.

• 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.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.347-358
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• 2012

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.140-159
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• 2001

The purpose of this study is to analyse the effectiveness of Quality Leadership on the Business Performance through Quality System for Manufacturing Companies in the North-east China, and research into the model of Quality Leadership is related with the Business Performance. A new questionnaire of QM based on Conti's Quality System thesis, was developed and tested. A statistical analysis of data was collected from Manufacturing Firms in the North-east China. As a result, Quality Leadership is the important drivers of Total Quality Management, and is related with the Quality System. Then, the Quality System is directly related with process, and process is also related with the Product Quality and the Business Performance. Of course, the Product Quality is directly related with the Business Performance in the end.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.33-40
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• 2020

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.87-101
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• 2018

The purpose of this study is to develop the measuring instruments for evaluation criteria for Malcolm Baldrige National Quality Award(MBNQA), suitable for ICT Industries, and to analyze the cause-effect relationship between those criteria through aforementioned instruments. MBNQA is formed with seven categories: Leadership, Strategic planning, Focus on patients, other customers and markets, Measurement, analysis and knowledge management, Human resource focus, Process management and Results. As excluding the Human Resource Focus category, this study empirically examined the cause-effect relationship among six categories. In order to empirically examine the research model, this study calculated Cronbach's alpha and reliability index, thus examined the reliability and executed Exploratory Factor Analysis. Furthermore, Average Variance Extracted(AVE) is used to verify the discriminant validity. Lastly, the hypothesis testing was made complete through significance test on the paths between variables. The result of this study shows that both leadership and social responsibility have direct cause-effect relationship with Measurement, analysis and knowledge management, Human resource focus, Process management and also that this relationship has direct impact on Human resource focus, Measurement, analysis and knowledge management as well, consequently exerting influence on the result through Process management, Finance and Market data.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.74-97
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• 2005

IDEF0 is the IEEE standard for functional enterprise modeling and has been used for business process modeling or process mapping in US and Europe. But it does not reflect the potential benefits of modeling and simulation of the dynamic aspects of an enterprise or a system. On the other hand, simulation tools concentrate mostly on the simulation of material flows and are difficult to include information flows and control flows. Additionally, the simulation models that include elements such as queues, event generators and process nodes is a visual interactive representation for the model builder, but is inconvenient for the domain expert. In an attempt to fill that void, we provide an integration of business process and simulation models in this paper. An enhancement of the IDEF0, called parameterized IDEF0, is proposed and its conversion mechanism to network simulation model is developed. Using this methodology, business process models for alternative systems can be evaluated and compared through simulation on time, cost, and quality metrics. As an application of the proposed methodology, economic evaluation of EDI (Electronic Data Interchange) for time-based BPR (Business Process Redesign) is demonstrated. In addition to BPR, the developed methodology may be further integrated with ABC (Activity Based Costing), TQM (Total Quality Management), and economic evaluation of information systems.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.