• 한국산업정보학회논문지
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• 제10권2호
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• pp.42-47
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• 2005

A single-server queueing model with the Batch Markovian Arrival Process and disaster ow correlated with the arrival process is analyzed. The numerically stable algorithm for calculating the steady state distribution of the system is presented.

• 지역연구
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• 제10권1호
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• pp.19-37
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• 1994

The past studies on the change of industrial location can be reinterpreted as spatial determinism, decision making process determinism and industrial sturcure determinism according to the view of the mutual operation between the economic activity and spatial structure. From the point of the industrial structure determinism which focuses on the fact that the locational type of production activity can be differenciated by the structural change of industry itself, it is proper to explain the change of industrial location not only by spatial condition but also by containing the fundamental changing causes for the reorganization of the spatial condition. One of the most important changing causes for reorganizing economic space is industrial technology. This study aims to explain the spatialization process of industrial technology and consists of the process of suggesting the hypothesis that the spatialization process is differentiated according to the production organization as the user of industrial technology and the user of industrial technology and the production system as using method. In other words, this study attempts to internalize the technology variable, which has been treated as external in the past studies on the industrial location, as an independent one and consider the production organization and production system as a paramenter, so that it can represent 'conceptual framework' for explaining the change of industrial location.

• 대한산업공학회지
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• 제32권4호
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• pp.314-322
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• 2006

Despite its brilliant success, Six Sigma has suffered from two shortcomings, namely, the lack of a systematic method to identify the right projects in the "Define" stage and to sustain the improvement in the "Control" stage. The integration of Six Sigma and Business Process Management(BPM) seems to be a promising way to overcome the shortcomings of Six Sigma. This paper first reviews the existing efforts on this issue, and then proposes a framework for an effective integration of Six Sigma and BPM. The framework consists of five phases - DEFINE, EXECUTE, MONITOR, ANALYZE, and IMPROVE(DEMAI). A detailed description on the procedural model is also presented.

• 한국신뢰성학회지:신뢰성응용연구
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• 제8권3호
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• pp.113-124
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• 2008

In reliability engineering, failure reporting, analysis, and corrective action system (FRACAS) is an useful tool for effective failure reporting and related operations. FRACAS is generally mainly focused on implementation of its closed-loop process, but also includes various related information which has to be effectively managed such as failure types, failure modes, failure mechanisms, and corrective actions. In this study, we adopt and utilize the concept of process knowledge, and create it through abstraction of FRACAS information. At each step of closed-loop process, the necessary type of knowledge, priority and usability are clearly defined. This study also suggests corresponding management tools such as business process management system, knowledge management system, and their key elements and functions to deal with process knowledge. A prototype system using simple closed-loop process with its process knowledge is presented to demonstrate the feasibility of the proposed work.

• 산업공학
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• 제16권spc호
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• pp.76-81
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• 2003

BPMS (Business Process Management Systems) is aninformation system that systematically supports designing, administrating, and improving the business processes. It can execute the business processes by assigning tasks to human or computer agents according to the predefined definitions of the processes. In this research we developed a task assignment algorithm that can maximize overall process efficiency under the limitation of agents' capacity. Since BPMS manipulates the formal and predictable business processes, we can analyze the processes using queuing theory to achieve overall process efficiency. We first transform the business processes into queuing network model in which the agents are considered as servers. After that, workloads of agents are calculated as server utilization and we can determine the task assignment policy by balancing the workloads. This will make the workloads of all agents be minimized, and the overall process efficiency is achieved in this way. Another application of the results can be capacity planning of agents in advance and business process optimization in reengineering context. We performed the simulation analysis to validate the results and also show the effectiveness of the algorithm by comparing with well known dispatching policies.

• 산업경영시스템학회지
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• 제28권4호
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• pp.116-123
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• 2005

The traditional process capability indices Cp, Cpk, Cpm, $Cpm^+$ have been used to characterize process performance on the basis of univariate quality characteristics. Cp, Cpk consider the process variation, Cpm considers both the process variation and the process deviation from target and Cpm+ considers economic loss for the process deviation from target. In manufacturing industry, there is growing interest in quantitative measures of process variation under multivariate duality characteristics. The multivariate process capability index incorporates both the process variation and the process deviation from target or considers expected loss caused by the process deviation from target. This paper proposes multivariate capability index based on the expected loss derived from multivariate normal distribution.

• 산업경영시스템학회지
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• 제31권3호
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• pp.117-124
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• 2008

Ultra-high voltage transformer industry has characteristic of small quantity batch production system by other order processing unlike general mass production systems. In this industry, observance of time deadline is very important in market competitive power security and company continued existence. The transformer winding is a process that rolls a coil is coated with an electric insulation material in order to generate the required voltage using the voltage fluctuation. The winding process is very important production process in the extra-high voltage transformer manufacturing industry because winding process is core process that occupy weight about half of whole process and is process that decide current ratio of transformer. This paper proposes a statistical calculation and control method of planned leadtime on the basis of real data and informations for the A company in transformer winding process. Moreover, we develop unit process scheduling system.

• 대한산업공학회지
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• 제41권6호
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• 대한산업공학회지
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• 제36권3호
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• 산업경영시스템학회지
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• 제33권1호
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• pp.33-40
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• 2010

Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.