• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.1-8
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• 2021

This study proposes a systems engineering approach for the development of an advanced planning & scheduling (APS) system for a cosmetic case manufacturing factory. The APS system makes production plans and schedules based on the injection process, which consists of 27 plastic injection machines in parallel to control recommended inventory of products. The system uses machine operation/failure information and defective product/work-in-process tracking information to support intelligent scheduling. Furthermore, a genetic algorithm model is applied to handle the complexity of heuristic rules and machine/quality constraints in this process. As a result of the development, the recommended inventory compliance rate is improved by scheduling the 30-day production plan for 15 main products.

• 한국측량학회지
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• 제19권3호
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• pp.253-261
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• 2001

현재 하천의 수질관리를 위하여 여러 수질모델이 개발되어 있으며, 이러한 수질예측모델에 각종 수질관리에 따른 대안을 적용시킴으로써 그 효과를 사전에 모의 평가하고 있다. 그러나 이러한 수질모델을 적용하기 위해서는 복잡한 형식의 입력자료 구축단계가 요구되고 있으며 모델을 통한 타당한 분석결과를 산출하였음에도 불구하고 모델 자체의 표현의 한계성으로 인하여 효과적인 의사결정 자료로서의 활용이 미약한 실정이다. 본 연구는 GSIS를 이용한 하천수질모의에 관한 연구로서, 기존 수질모델의 이러한 제약을 극복하고자 GSIS환경에서 유역별 오염부하량을 산정하고 입력자료를 생성하며 모의결과를 시각화하는 인터페이스를 개발함에 있어 모델의 전ㆍ후처리과정에 GSIS를 적용하는 유연한 통합(Flexible coupling) 방법을 이용하여 수질모델과 GSIS를 통합하였다. 수질모델로는 기존의 하천수질모델 중 우리나라의 실정에 적합하여 비교적 정확하고 또한 그 적용이 간단하여 많은 지역에서 실제 적용되어 그 적용성이 검증된 QUAL2E 모델을 사용하였다.

• 품질경영학회지
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• 제38권3호
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• pp.393-407
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• 2010

The purpose of this study is to analysis relative efficiency and efficiency in process of time. Thus we use panel data of 34 local public medical centers between 2003 and 2005 to use DEA and Malmquist analysis. The result of our this study is as flow; first, The results of static efficiency of 34 local public medical centers show 10 CCR model and 23 BCC model which is difference of efficiency by economic of scale. Second, a cause of increased efficiency is not only change of technology but also change of efficiency to management system index show between 2003 and 2005 by Malmquist analysis and contracting-out is higher than direct management between 2004 and 2005. That means efficiency of local public medical centers is their own effort and innovation not government subsidies.

• 한국전자거래학회지
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• 제27권2호
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• pp.37-64
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• 2022

인공지능의 사회적수용도가 증가하면서 머신러닝 기법을 기업에 적용하는 사례가 증가하고 있다. 머신러닝 기법의 선정에는 주로 정확성이나 해석 가능성 등 기술적 요인이 주로 기준이 되어왔다. 그러나 머신러닝 채택의 성공은 개발부서, 사용부서, 리더십과 조직문화 등 경영관리 요인도 영향을 주기도 한다. 아쉽게도 기술적 요인과 경영관리적 요인이 함께 고려된 머신러닝 선정의 성공 요인을 이해하는 통합 연구가 거의 존재하지 않는다. 이에 본 논문의 목적은 기업 내 머신러닝 선정을 이해하기 위해 John Rice의 algorithm selection process model과 task-technology fit, 그리고 IS Success Model 이론을 결합한 기술-경영관리 통합 모형을제안하고 실증적 분석을 하는 것이다. 머신러닝을 도입한 국내 기업 240곳을 대상으로 설문 분석을 실시한 결과 알고리즘 품질과 데이터 품질이 높을수록 문제-알고리즘 적합성에 높게 영향을 주는 것으로 나타났으며, 문제-알고리즘 적합성은 조직의 생산성과 혁신성에도 유의한 영향을 미치는 것으로 검증되었다. 또한 외주화와 경영진 지원이 머신러닝 시스템 품질에 긍정적인 영향을 미치고, 데이터 중심 경영 및 동기화와 같은 조직문화 요인은 활용성과에 높은 영향을 미치는 것으로 확인되었다.

• 산경연구논집
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• 제10권7호
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• pp.71-82
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• 2019

Purpose - This study aims to identify the priorities of medical service quality improvement by customer satisfaction characteristics and potential customer satisfaction improvement (PCSI) index based on the dualistic quality classification of Kano Model (1984) for Comprehensive Health Screeening Center in General Hospitals and Centers only for Comprehensive Health Screening and suggest a direction for future improvement. Research design, data, and methodology - Through advanced research on health screening medical service quality, this study set four service quality factors, including tangible, human, process and supportive factors, and 39 measurement items. Based on these items, the study used 117 questions, which consist of dualistic quality factors, customer satisfaction coefficients, positive and negative questions for PCSI index and questions for current satisfaction. 300 effective samples were collected for adults in their 20s who experienced health screening service in Seoul, Gyeonggi-do and Incheon within the past two years. Collected data were input in the quality evaluation duality table to categorize quality factors and calculate customer satisfaction coefficients by Timko(1993). The study also analyzed PCSI index in comparison with current satisfaction and identified priorities in quality improvement. Results - It was found that the most urgent factors to improve the quality in both groups were adequate waiting hours and emergency response for complications, which are process factors classified as unitary quality. It is urgently needed to improve the quality as the PCSI index was high in supportive factors (complaint response team) as attractive quality in Comprehensive Health Screening Center in General Hospitals and in process factors (prevention of infection) as unitary quality in Centers only for Comprehensive Health Screening. As the PCSI index was low in space use as a tangible factor, it was found that the current level can be maintained instead of improvement. Conclusions - To improve the health screening medical service quality, it is required to focus on process factors (adequate waiting hours, emergency response for complications, prevention of infection) and supportive factors (complaint response team) among service qualities perceived by users. It is proposed to ensure continuous efforts to manage and reinforce priorities as a direction for future improvement in health screening service.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.426-432
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• 2013

The construction industry consists of various and massive architectural information as an architectural process includes a variety of design stages with cooperation of many disciplines. Particularly, architectural information is generated and managed through the life cycle of a building, from conceptual design stage to the construction and maintenance. A Building Information Model (BIM) serves as a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life-cycle from inception onward. BIM technology accomplished quantitative development being utilized in various disciplines. However, it is necessary to develop environment and requirement for qualitative improvement of BIM based project. Particularly, requirement is very important for architectural design evaluations. The purpose of this study is to develop and apply of quality control requirement for improving the quality of architectural design in open BIM environments. To achieve this purpose, the authors have investigated case study for open BIM data quality control (software, guideline and application case) and classified quality control targets according to physical/logical quality and data quality. In addition, the authors have defined open BIM based quality control process and developed quality control requirements. Finally, the authors have developed rule based quality check system using requirements for efficient quality control based on open BIM.

• 경영정보학연구
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• 제7권2호
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• pp.117-128
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• 2005

본 연구는 철강제조 분야에서 제품 생산 중 발생하는 모든 데이터를 실시간으로 수집하여 데이터베이스화하고, 웹 기반 시스템을 구축함으로써 시간과 장소의 제약 없이 필요 데이터를 조회, 분석할 수 있는 시스템 개발에 대한 연구이다. 본 시스템의 구축 결과, 실시간으로 데이터의 조회 및 분석이 가능하게 됨으로써 생산 제품에 대한 보다 효율적인 품질개선 활동이 가능하게 되었고, 제품의 신뢰도를 높일 수 있게 되었다. 또한 장기간 누적된 데이터를 데이터베이스화하고 그것을 통계적으로 분석 가능하게 함으로써 새로운 제어모델의 개발과 조업기술의 개발이 보다 용이하게 되었고, 신제품 개발의 기초자료로도 활용할 수 있게 되었다.

• 정보기술과데이타베이스저널
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• 제3권2호
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• pp.97-118
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• 1996

Information systems quality engineering is one of the most problematic areas in practice and research, and needs cooperative efforts between practice and theory [Glass, 1996]. A model for evaluating the quality of system development process and ensuing success is proposed based on information processing theory of project unit design. A nomological net among a set of quality variables is identified from prior research in the areas of organization science, software engineering, and management information systems. More specifically, system development success was modelled as a function of project complexity, system development modelling environment, user participation, project unit structure, resource availability, and the level of iterative nature of development methodology. Based on the model developed from the information processing theory of project unit design in organization science. appropriate quality metrics for each variable in the proposed model are matched. In this way, a framework of relevant systems development and success quality metrics for controlling systems development processes and ensuing success is proposed. The causal relationships among the constructs in the proposed model are proposed as future empirical research for academicians and as managerial tools for quality managers. The framework and propositions help quality manager to select more parsimonious quality metrics for controlling information systems development processes and project success in an integrated way. Also this model can be utilized for evaluating software quality assurance programmes, which are developed and marketed by many vendors.

• 산업경영시스템학회지
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• 제46권3호
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• pp.109-122
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• 2023

As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users' abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.