• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2004년도 추계학술대회
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• pp.453-457
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• 2004

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable. 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 first-order autoregressive disturbance.

• 한국산업정보학회논문지
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• 제5권3호
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• pp.77-86
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• 2000

The main objectives of this dissertation is to propose a forth generation index C for the case where the target value T is not equal to the midpoint of the specification limits (i.e. asymmetric tolerances), and show that this index is more sensitive compared to the standard PCI's in detacting small shifts of the process mean from the target value. In conclusion, in this dissertation , a new methods for estimating a measure of process capability for non-normally distributed variable data is proposed using the percentage nonconforming.

• 한국유기농업학회지
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• 제22권1호
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• pp.81-96
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• 2014

This study is aimed to examine the mediated effects of consumer recognition in relationship of local food tour experience and intention of action in the revitalization of local food. Questionnaire survey target was women in 30s and 40s. The local food tour experience is independent variable, intention of action is dependent variable, and consumer recognition is analyzed as mediated variable. As a result, consumer recognition which is mediating variable has two subordinated variables. One is direct affect and the other is indirect affect. Between local food tour experience and intention of action, there was partial mediating effect. Thus, through tour experience, consumer can have positive recognition of freshness, safety, health, taste, price, job creation and relationship. That affects to the intention of action. Based on the results of the study, it is necessary to learn success case for marketing revitalization, and develop and operate experiencing tour education program to manage customer continuously.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

• 보건행정학회지
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• 제28권2호
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• pp.186-193
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• 2018

Background: The aim of our study was to review the findings of health insurance fraud investigations and to evaluate their impacts on medical costs for target and non-target organizations. An interrupted time series study design using generalized estimation equations was used to evaluate changes in cost following fraud investigations. Methods: We used National Health Insurance claims data from 2009 to 2015, which included 20,625 medical institutions (1,614 target organizations and 19,011 non-target organizations). Outcome variable included cost change after fraud investigation. Results: Following the initiation of fraud investigations, we found statistically significant reductions in cost level for target organizations (-1.40%, p<0.001). In addition, a reduction in cost trend change per month was found for both target organizations and non-target organizations after fraud investigation (target organizations, -0.33%; non-target organizations of same region, -0.19%; non-target organizations of other regions, -0.17%). Conclusion: This study suggested that fraud investigations are associated with cost reduction in target organization. We also found similar effects of fraud investigations on health expenditure for non-target organizations located in the same region and in different regions. Our finding suggests that fraud investigations are important in controlling the growth of health expenditure. To maximize the effects of fraud investigation on the growth of health expenditure, more organizations needed to be considered as target organizations.

• 한국물환경학회지
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• 제35권6호
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• pp.520-538
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• 2019

In this study, it was proposed that a method of setting the target water quality for TOC using the watershed model and the load duration curves to manage non-biodegradable organics in the total water load management system. To simulate runoff and water quality of the watershed, the HSPF model is used which is appropriate for urban and rural areas. Additionally, the load duration curve is used to reflect the variable water quality correlated with various river flow rates in preparing the TMDL plans in the U.S. First, the model was constructed by inputting the loads calculated from the pollutant sources in 2015. After the calibration and verification process, the water quality by flow conditions was analyzed from the BOD and TOC simulation results. When the BOD achieved the target water quality by inputting the target year loads for 2020, the median and average values of TOC were proposed for the target water quality. The provisional method of TOC target water quality for the management of non-biodegradable organics, which is one of the challenges of the total water load management system, was considered. In the future, it is expected to be used as basic data for the conversion of BOD into TOC in the total water load management system.

• 유통과학연구
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• 제20권6호
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• pp.1-10
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• 2022

Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

• 산업경영시스템학회지
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• 제40권2호
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• 한국콘텐츠학회논문지
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• 제20권12호
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• pp.13-22
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• 2020

본 연구는 영화 분야 크라우드펀딩 성공요인 실증분석을 위하여 국내 크라우드펀딩 플랫폼 텀블벅의 영화 프로젝트 중, 총 583개 데이터를 수집하여 분석을 진행하였다. 구체적으로 목표 금액, 게시글 정보, 리워드 선택 옵션, 창작자 펀딩 파워, 에디터 추천 여부, 창작자 콘텐츠 파워, 영화 유형, 코멘트 수, 댓글 수, SNS 정보 수 등 10개의 독립변인을 설정하고 크라우드펀딩 최종 달성률을 종속변인으로 설정하여 영향관계를 검증하였다. 연구 결과 영화 크라우드펀딩 프로젝트 달성률에 목표금액, 텍스트 수, 동영상 수, 에디터 추천 여부, 후원자 댓글 수, SNS 정보 수가 유의미한 영향을 미치는 것으로 드러났다. 본 연구는 '에디터 추천 여부'와 '창작자의 SNS 정보 수' 변인을 크라우드펀딩 연구 분야에 접목시켜 두 변인 모두 크라우드펀딩 달성률에 정(+)적인 영향을 미치는 것을 검증했다는 점에서 시사점을 가진다.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.386-390
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• 2003

데이터마이닝은 많은 양의 데이터로부터 의사결정에 유용한 패턴을 발견하는 과정으로서 최근 경영 및 공학 분야의 폭넓은 영역에서 많은 관심을 모으고 있다. 어떤 그룹을 여러 하위그룹으로 분류해내는 일은 데이터마이닝의 주요 내용 중 하나이다. 의사결정나무로 알려진 트리기반 기법은 그러한 분류모형을 수립하는 데 효율적인 방안을 제공한다 트리학습에 있어서 우선적인 관건은 목표변수에 의해 측정되는 노드불순도를 최소화하는 것이다. 하지만 공정관측, 마케팅과학, 임상분석 등과 같은 문제에서는 여러 목표변수를 동시에 고려해야 하는 상황이 쉽게 등장하는 데, 본 논문의 목적은 이처럼 다변량 목표변수를 갖는 데이터셋에서 활용할 수 있는 노드불순도 측정방안을 제시하는 데 있다. 아울러 수치 예를 이용하여 적용결과에 대해 논의한다.