• Journal of Distribution Science
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• v.15 no.12
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• pp.41-51
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• 2017

Purpose - This study proposes the impact of the US and Korean economic policy uncertainty on macroeconomy, and its effect on Korea. The economic policy uncertainty index of the US and Korea is used to represent the economic policy uncertainty on Korean economy. Research design, data, and methodology - In this paper, we collect the eight variables to find out the interrelationship among the US and Korean economic policy uncertainty index of the US and macroeconomic indicators during 1990 to 2016, and use Vector Error Correction Model. Result - The distribution industry stock index in Korea is influenced by the economic policy uncertainty index of the US rather than of Korea. All variables are related negatively to the economic policy uncertainty index of the US and Korea from Vector Error Correction Model. This study shows that the economic policy uncertainty index of the US and Korea has the dynamic relationships on the Korean economy. Conclusions - A higher economic policy uncertainty shows a greater economy recession of a country. Finally, the economic policy uncertainty of the Korea has an intensive impact on Korea economy. Particularly, the economic policy uncertainty of the US has a strong impact on distribution industry stock market in Korea.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.46-50
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• 2013

In insurance statistics, the sum of homogeneous insurance is often needed. The sum is called individual risk model which is a fundamental model in risk analysis for insurance. This paper first presents an individual risk model based on the uncertainty theory. Then its uncertainty distribution is provided. Finally, its arithmetic is shown by a numerical example.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.758-763
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• 2006

Statistical concepts and methods are routinely utilized in a number of design and management problems in engineering hydrology. This is because most of hydrological processes have some degree of randomness and uncertainty. Thus, the concepts of risk and uncertainty are commonly utilized for designing and evaluating hydraulic structures such as spillways and dikes. Therefore, in this study, uncertainty analysis considering the variance of design floods is performed to evaluate the uncertainty of the hydrologic risk of flood related hydraulic structures using frequency analysis.

• Journal of Distribution Science
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• v.16 no.6
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• pp.25-35
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• 2018

Purpose - Although marketing networks are crucial competitive advantage in terms of firm's new information and resource acquisition ability, their impact on new product development performance remains vague, especially under environmental uncertainty. The principal objective of this research is to provide a better understanding of effects of technological uncertainty and volume uncertainty on first tier supplier's perceived performance of new product development under conditions reflecting varying levels of structural holes. Specifically, this research examines the moderating effect of structural holes on the relationship between environmental uncertainty and new product development performance. Research design, data, and methodology - To test the hypotheses, a questionnaire survey was conducted with a Korean engineering firm's major first-tier suppliers in the context of internal network entities, manufacturer-supplier-subsupplier relationships, and to verify the proposed hypotheses, structural equation modeling was established. Construct measures were based on existing measures and previous research. Results - The survey results indicate that technological uncertainty and volume uncertainty differentially affect NPD performance under conditions of high and low structural holes. Conclusions - This study offer some theoretical and practical implications among distribution channel members, especially, this study suggests that interfirm networks have critical competitive advantage in uncertain environments. The distinctiveness of engineering industry might limit the generalizability of the results. Thus, future research should consider a wider range of industries.

• Journal of Distribution Science
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• v.13 no.5
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• pp.71-82
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• 2015

Purpose - The purpose of this study is to investigate risk price implied from the pricing kernel of Korean stock distribution market. Recently, it is considered that the quantitative easing programs of major developed countries are contributing to a reduction in global uncertainty caused by the 2007~2009 financial crisis. If true, the risk premium as compensation for global systemic risk or economic uncertainty should show a decrease. We examine whether the risk price in the Korean stock distribution market has declined in recent years, and attempt to provide practical implications for investors to manage their portfolios more efficiently, as well as academic implications. Research design, data and methodology - To estimate the risk price, we adopt a non-parametric method; the minimum norm pricing kernel method under the LOP (Law of One Price) constraint. For the estimation, we use 17 industry sorted portfolios provided by the KRX (Korea Exchange). Additionally, the monthly returns of the 17 industry sorted portfolios, from July 2000 to June 2014, are utilized as data samples. We set 120 months (10 years) as the estimation window, and estimate the risk prices from July 2010 to June 2014 by month. Moreover, we analyze correlation between any of the two industry portfolios within the 17 industry portfolios to suggest further economic implications of the risk price we estimate. Results - According to our results, the risk price in the Korean stock distribution market shows a decline over the period of July 2010 to June 2014 with statistical significance. During the period of the declining risk price, the average correlation level between any of the two industry portfolios also shows a decrease, whereas the standard deviation of the average correlation shows an increase. The results imply that the amount of systematic risk in the Korea stock distribution market has decreased, whereas the amount of industry-specific risk has increased. It is one of the well known empirical results that correlation and uncertainty are positively correlated, therefore, the declining correlation may be the result of decreased global economic uncertainty. Meanwhile, less asset correlation enables investors to build portfolios with less systematic risk, therefore the investors require lower risk premiums for the efficient portfolio, resulting in the declining risk price. Conclusions - Our results may provide evidence of reduction in global systemic risk or economic uncertainty in the Korean stock distribution market. However, to defend the argument, further analysis should be done. For instance, the change of global uncertainty could be measured with funding costs in the global money market; subsequently, the relation between global uncertainty and the price of risk might be directly observable. In addition, as time goes by, observations of the risk price could be extended, enabling us to confirm the relation between the global uncertainty and the effect of quantitative easing. These topics are beyond our scope here, therefore we reserve them for future research.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.329-340
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• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.415-420
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• 1996

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.20-28
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• 2008

This paper proposes a method for solving distribution system planning problems taking into account demand uncertainty and geographical information. The proposed method can automatically select appropriate location and size of a substation, routing of feeders, and appropriate sizes of conductors while satisfying constraints, e.g. voltage drop and thermal limit. The demand uncertainty representing load growth is modeled by fuzzy numbers. Feeder routing is determined with consideration of existing infrastructure, e.g. streets and canals. The method integrates planner's experience and process optimization to achieve an appropriate practical solution. The proposed method has been tested with an actual distribution system, from which the results indicate that it can provide satisfactory plans.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.77-87
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• 2012

Recently, Load and Resistance Factor Design (LRFD) based on reliability analysis has become a global trend for economical and rational design. In order to implement the LRFD, quantification of uncertainty for load and resistance should be done. The reliability of result relies on input variable, and therefore, it is important to obtain exact uncertainty properties of load and resistance. Since soil stress is the main reason causing the settlement or deformation of ground and load on the underground structure, it is essential to clarify the uncertainty of soil stress distribution for accurately predict the uncertainty of load in LRFD. In this study, laboratory model test on silty sand bed under probabilistic load is performed to observe propagation of upper load uncertainty. The results show that the coefficient of variation (COV) of soil stress are varied depending on location due to non-linear relationship between upper load increment and soil pressure increment. In addition, when the load uncertainty is transmitted through ground, COV is decreased by damping effect.