• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.146-151
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• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.79-84
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• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.1-12
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• 2009

Distribution centers in a distribution system that consists of the distribution centers and retailers supplies products to retailers. At the present, although total capacity of the distribution centers are enough to supply total demand of retailers, capacity of the distribution centers need to be expanded to satisfy the demand of retailers in case that future demand of the retailers will be increased. Capacity expansion model in a distribution system is to determine the location and size of expansion distribution centers that minimize costs among given distribution centers. Transportation amount from distribution center to retailers also is determined. The costs factors are the capacity expansion costs of the distribution centers and the transportation costs from the distribution centers to the retailers. A model is formulated, and a genetic algorithm based solution procedure is developed. A numerical example is shown and the algorithm is analyzed through examples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.98-105
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• 2012

This paper describes the Bayesian approach for reliability demonstration test based on the samples from a finite population. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to produce the posterior distribution. In this paper, the hypergeometric distribution is adopted as a likelihood function for a finite population. The conjugacy of the beta-binomial distribution and the hypergeometric distribution is shown and is used to make a decision about whether to accept or reject the finite population judging from a viewpoint of faulty goods. A numerical example is also given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.97-103
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• 2006

Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs. And most of industrial processes are known to follow the non-normal distribution. Therefore, the control chart is required to be developed for monitoring the processes with non-normal short production runs by the CV characteristics for a non-normal distribution. In this paper, we suggest the control chart for monitoring the processes with a gamma short runs by the CV characteristics for a gamma distribution. This control chart is denoted by the gamma CV control chart. Futhermore evaluated the performance of the gamma CV control chart by average run length(ARL).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.71-79
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• 2010

This paper addresses capacity expansion planning model of distribution center under usability of public distribution center. For discrete and finite time periods, demands for distribution center increase dynamically. The capacity expansion planning is to determine the capacity expansion size of private distribution center and usage size of public distribution center for each period through the time periods. The capacity expansion of private distribution center or lease usage of public distribution center must be done to satisfy demand increase for distribution center. The costs are capacity expansion cost and excess capacity holding cost of private distribution center, lease usage cost of public distribution center. Capacity expansion planning of minimizing the total costs is mathematically modelled. The properties of optimal solution are characterized and a dynamic programming algorithm is developed. A numerical example is shown to explain the problem.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.52-60
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• 2007

Estimating parameters of the lifetime distribution is investigated when field failure data are not completelyreported. To take into account the reality and the accuracy of the estimates in such a case, the failure reportingprobability is incorporated in estimating parameters, Firstly, method of maximum likelihood estimate (MLE) isused to estimate parameters of the lifetime distribution when failure reporting probability is known, Secondly,Expectation and Maximization (EM) algorithm is used to estimate the failure reporting probability and parame-ters of the lifetime distribution simultaneously when failure reporting probability is unknown. For both cases,procedures of estimation are illustrated for single Weibull distribution and mixed Weibull distribution. Simula-tion results show that MLE obtained by the proposed method is more accurate than the conventional MLE.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2575-2577
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• 1999

This paper describes the engineering process for analyzing the simulation result and deciding the site in which Distribution STATCON operates more effectively. For this purpose the modeling method of industrial loads, equipments and STATCON was represented. Models of motor, furnace and so on are presented for the modeling of industrial loads. The distribution system models include the parameters of the distribution line and transformer. The models of PESS(Power Electronics Subsystem), controllers and maginetics are consist of STATCON model.

• Fourth Industrial Review
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• v.1 no.2
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• pp.9-16
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• 2021

Purpose - O2O (online to off-line) is a new economic concept being emerged in the 4^th industrial age. Automobile distribution is also undergoing a change to the O2O system. In overseas, vehicles are already being sold through various online platforms. The purpose of this study is to analyze the domestic and foreign automobile distribution structure and intends to suggest the desirable direction of automobile sales in the 4th industrial age. Research design, data, and methodology - Through the investigation of previous studies and the actual cases, the advantages and disadvantages and trends of different types of automobile distribution structures. Result - It can be seen that domestic and foreign automobile brands have quite diverse distribution structures such as direct managed system, agency system, dealer system, direct sales system, online system and online/offline parallel system. Each type has its own characteristics. Conclusion - The distribution structure of automobiles is very diverse, and in particular, it is rapidly changing to an online sales system thanks to the development of information and communication technology and Internet technology and the popularization of smartphones. Through the online system, consumers can quickly and conveniently obtain vehicle information. Although the online sales system has many advantages, it requires continuous management