• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.539-544
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• 2006

This study has been attempted to find that factors for successful six-sigma implementation influence non-financial performance & financial performance in korean finance industry. In addition, goal of this study is to find out core factor in korean finance industry. To achieve the aim of this study, a document study and interview and an empirical analysis were performed. The collected questionnaires for the empirical analysis were processed statistically through data cording. Cronbach'a was conducted to get the construct reliability. To identify which factors for successful six-sigma implementation influence performances of six-sigma implementation, factor analysis was conducted to get the construct validity. After factor analysis, multiple regressions were utilized to identify the core factors (or factors for successful six-sigma implementation). The result of the study that has been derived through this process is summarized below. Firstly, by analyzing the effect factors for successful six-sigma implementation has on non-financial performance of finance industry, it shows that Process-integration & standardization variable has influenced. Secondly, by analyzing the effect factors for successful six-sigma implementation has on financial performance of finance industry, it shows that 'Process-integration & standardization' variables and 'Customer & Market mind' variables have influenced. The results of this study show that 'Process-integration & standardization' and 'Customer & Market mind' are core factors to influence non-financial performance & financial performance in korean finance industry

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Transactions of Materials Processing
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• v.7 no.3
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• pp.225-232
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• 1998

This article is aimed primarily at establishing a process planning method for complex circular shells. For the deep drawing of complex shaped shell, the optimum process design is required to reduce the trial cost improve the quality, increase the productivity and shorten the delivery. The present approach which is related to the optimum process planning is based on the empirical knowledge through trial and error in the industrial field. In order to check the validity and the effectiveness of proposed work a sample process design has been applied to the multi-stepped deep drawing of com-plex shell considering the process variables such as drawing rate radius and blank development. In particular the difference between the limiting drawing rate and to optimum drawing rate has been discussed and the usefulness of present suggestion has been shown.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.03a
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• pp.160-167
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• 1998

This article is aimed primarily at establishing a process planning method for complex circular shells. For the deep drawing of complex shaped shell, the optimum process design is required to reduce the trial cost, improve the quality, increase the productivity and shorten the delivery. The present approach which is related to the optimum process planning is based on the empirical knowledge through trial and error in the industrial field. In order to check the validity and the effectiveness of proposed work, a sample process design has been applied to the multi-stepped deep drawing of complex shell considering the process variables such as drawing rate, radius and blank development. In particular, the difference between the limiting drawing rate and the optimum drawing rate has been discussed and has been shown the usefulness of present suggestion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.983-987
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• 2001

This paper deals with an automated computer-aided process planning system by which designer can determine operation sequences even if they have little experience in process planning of wheel bolt products by a multi-stage former. The approach to the system is based on knowledge-based rules and a process knowledge base consisting of design rules is built. Knowledge for the system is formulated from plasticity theories, empirical results and the empirical knowledge of field experts. Programs for the system have been written in AutoLISP for the AutoCAD using a personal computer and are composed of two main modules. An attempt is made to link programs incorporationg a number of expert design rules to form a useful package. Results obtained using the modules enable the designer and manufacturer of wheel bolt product to be more efficient in this field.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.80-85
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• 2016

This study suggests a process design using cognitive processes. Job characteristic model for job design and recent cognitive engineering studies for process design are reviewed briefly. By using these concepts, the lean production system is re-interpreted in terms of cognitive engineering and the latent dimensions of the lean production system are revealed as the application of cognitive engineering principles. An integrated process design framework for cognitive manufacturing system using job characteristic model is suggested for the effective design of manufacturing system. Propositions for empirical analysis of this model are also analyzed through a questionnaire survey. Propositions are (1) experiential cognition and motivation potential affect the ability, role perception, and need for achievement of the operator in the manufacturing system, (2) the ability, role perception, and need for achievement of the operator affect the job performance. Both propositions are supported by correlation analysis and path analysis.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.149-156
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• 2022

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.407-418
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• 2001

Buhlmann(1944) established the validity of the block bootstrap proposed by Kunsch when it is applied to p-dimensional $\alpha$-mixing dependent sequence. But his result requires a rather restrictive condition on p in the sense that p is entangled with dependence structure. We address that such restriction on p(or complication of dependence structure with p) could be removed completely when the underlying dependence structure is replace by more weakly dependent structure such as ø-mixing.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.401-409
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• 2007

This paper addresses a performance evaluation model for ERP system's implementation using Analytic Network Process (ANP) technique. In this study, the performance variables are identified as the perspectives of cost, business process, systems operation, and change management, respectively. The empirical study also investigated factors that affect the performance variables to find out the causal relationship between them using the ANP approach. The data for the empirical analysis were collected from manufacturing companies that have implemented ERP systems. The research findings indicate the proposed model is powerful in proposing that the indirect relationship between influencing factors and managerial effectiveness, mediated by employee satisfaction, is an important one.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.191-197
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• 2003

This paper considers the independence test for two stationary infinite order autoregressive processes. For a test, we follow the empirical process method devised by Hoeffding (1948) and Blum, Kiefer and Rosenblatt (1961), and construct the Cram${\acute{e}}$r-von Mises type test statistics based on the least squares residuals. It is shown that the proposed test statistics behave asymptotically the same as those based on true errors.