• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.425-429
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• 2004

Process adjustment Is a complimentary tool to process monitoring in process control. Process adjustment directs on maintaining a process output close to a target value by manipulating another controllable variable, by which significant process improvement can be achieved. Therefore, this approach can be applied to the 'Improve' stage of Six Sigma strategy. Though the optimal control rule minimizes process variability in general, it may not properly function when special causes occur in underlying process, resulting in off-target bias and increased variability in the adjusted output process, possibly for long periods. In this paper, we consider a responsive feedback control system and the minimum mean square error control rule. The bias in the adjusted output process is investigated in a general framework, especially focussing on stationary underlying process and the special cause of level shift type. Illustrative examples are employed to illustrate the issues discussed.

• Management & Information Systems Review
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• v.26
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• pp.1-26
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• 2008

Since Capital Asset Pricing Model(CAPM) was proposed in the early 1960s by William Sharpe(1964) and John Lintner(1965) researchers have investigated the validity of the model. The results of empirical researches do not show that expected returns of stocks seem to be determined solely by systematic risk of the stocks as precicted by CAPM. In this paper the relationship between transaction volume and expected returns of stocks was investigated. Empirical cross-sectional analysis about the data collected from Stock Market of Korea Exchange shows transaction volume and variability of stock returns play an important role in pricing assets. The well-known variables which were used traditionally to explain the differences of expected returns among stocks such as the size and beta of a stock seems to be unimportant in pricing assets.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.39-44
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• 2004

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.43-59
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• 1995

This paper presents a method for estimating the population variability distribution of the failure parameter (failure rate or failure probability) for each failure mode considered in PSA (Probabilistic Safety Assessment). We focus on the utilization of generic estimates from various industry compendia for the estimation. The estimates are complicated statistics of failure data from plants. When the failure data referred in two or more sources are overlapped, dependency occurs among the estimates provided by the sources. This type of problem is first addressed in this paper. We propose methods based on ML-II estimation in Bayesian framework and discuss the characteristics of the proposed estimators. The proposed methods are easy to apply in real field. Numerical examples are also provided.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.81-89
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• 2002

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems； EPC, EPC combined with EWMA, and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

• Management & Information Systems Review
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• v.11
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• pp.91-109
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• 2002

This study provides evidence on the determinants of variability in corporate ETRs(effective x rates). Specially, this study examined the association between ETRs, firm size, and variables proxying for firms' capital structure and asset mixes, while controlling for firms' profitability. Overall, results suggest that ETRs are associated with many firm-specific characteristics such as size, capital structure, asset mix, and profitability, and that some of these associations continued after Corporate Tax Law changes. In addition, although the results indicate that the association between ETRs and firm-specific characteristics have undergone a shift since tax law change, these firm-specific characteristics have continued to be associated with ETRs.

• Proceedings of the Safety Management and Science Conference
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• 2003.05a
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• pp.277-283
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• 2003

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. Tn the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressed disturbance. We compare three control systems; EPC, EPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

• Journal of the Korea Safety Management & Science
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• v.7 no.2
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• pp.141-151
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• 2005

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems; EPC, EPC combined with EWMA. This paper shows through simulation that tlhe performance of the integrated model of EPC and EWMA is more preferable than that of EPC.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.599-610
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• 2008

It is an important and urgent issue to improve process capability in quality control. Process capability refers to the uniformity of the process. The variability in the process is a measure of the uniformity of output. A simple, quantitative way to express process capability, the degree of variability from target in specification is defined by process capability index(PCI). Almost process capability indices are defined under normal distribution. However, these indices can not be applied to the process of non-normal distribution including reliability. We investigate current research on the process of non-normal distribution, and advanced method and technology for developing more reliable and efficient PCI. Finally we suggest the perspective for future study.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.169-173
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• 2015

The linear scheduling method or line-of-balance (LOB) is a popular choice for projects that involve repetitive tasks during project execution. The method, however, produces deterministic schedule that does not convey a range of potential project outcomes under uncertainty. This results from the fact the basic scheduling parameters such as crew production rates are estimated to be deterministic based on single-point value inputs. The current linear scheduling technique, therefore, lacks the capability of reflecting the fluctuating nature of the project operation. In this paper the authors address the issue of how the variability of operation and production rates affects schedule outcomes and show a more realistic description of what might be a realistic picture of typical projects. The authors provide a solution by providing a more effective and comprehensive way of incorporating the crew performance variability using a Monte Carlo simulation technique. The simulation outcomes are discussed in terms of how this stochastic approach can overcome the shortcomings of the conventional linear scheduling technique and provide optimum schedule solutions.