• 한국의료질향상학회지
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• 제8권2호
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• pp.186-207
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• 2001

Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

• 품질경영학회지
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• 제49권1호
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• 한국경영과학회지
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• 제28권3호
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• pp.31-47
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• 2003

Quality design in practice highly depends on human designer's intuition and past experiences due to lack of formal knowledge about the relationship among 10 variables. This paper represents an data mining approach for developing quality design support system that integrates Case Based Reasoning (CBR) and Artificial Neural Networks (ANN) to effectively support all the steps in quality design process. CBR stores design cases in a systematic way and retrieve them quickly and accurately. ANN predicts the resulting quality attributes of design alternatives that are generated from CBR's adaptation process. When the predicted attributes fail to meet the target values, quality design simulation starts to further adapt the alternatives to the customer's new orders. To implement the quality design simulation, this paper suggests (1) the data screening method based on ξ-$\delta$ Ball to obtain the robust ANN models from the large production data bases, (2) the procedure of quality design simulation using ANN and (3) model management system that helps users find the appropriate one from the ANN model base. The integration of CBR and ANN provides quality design engineers the way that produces consistent and reliable design solutions in the remarkably reduced time.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.173-187
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• 2022

A special government agency has been charged with implementing quality management to guarantee the quality of wild-simulated ginseng. However, these processes are carried out by use of documents, and this has resulted in information omission and high document management costs. To solve this problem, this study analyzed the existing quality management process by using a smart contract for the existing offline form and proposed a new quality management system for storing and managing all log data in the blockchain. This system reduced documentation management costs about quality management and recorded information in the previous step through the quality management steps, thus forming a step-by-step record chain. Experiments were conducted by implementing this system, which improved data integrity and reliability. Additionally, sensitive information, such as personal information, was included in the system by use of the off-chain technology.

• 한국융합학회논문지
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• 제12권8호
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• pp.245-256
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• 2021

4차산업혁명시대에 정보통신기술의 비약적인 발전, 고객구매 성향의 다양함, 복잡함은 산업 전체적으로 데이터의 양적 중가를 가져와 '빅데이터' 시대를 맞이하게 되었다. 빅데이터 시대는 데이터를 분석, 활용하여 기업의 전략적 의사결정에 활용하는 것이 기업의 핵심 역량으로 자리 잡게 되었다. 하지만 현재 빅데이터 연구들은 기술적 이슈와 미래 잠재 가치 중심이었다. 반면 기업이 보유한 내.외부 고객 빅데이터의 품질 및 활용 수준관리에 대한 연구와 논의는 부족하였다. 본 연구에서는 기업의 내.외부 빅데이터 품질관리 정보시스템 측면와 품질경영 측면으로 인식하여 영향요인을 도출하였다. 또한 빅데이터 품질관리, 빅데이터 활용 및 수준관리가 기업의 업무 효율화와 기업 경영성과에 유의한 영향을 미치는지 204명의 임직원 설문을 통해 조사하였고, 가설을 설정하여 검증하였다. 연구결과 경영층의 지원, 개인 혁신성, 경영환경변화, 빅데이터 품질활용 지표관리, 빅데이터 거버넌스 체계 마련이 기업 경영성과에 유의한 영향을 미쳤다.

• 품질경영학회지
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• 제34권1호
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• pp.13-19
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• 2006

Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

• 한국전자거래학회지
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• 제15권4호
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• pp.79-99
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• 2010

e-비즈니스의 활성화로 기업과 조직에서 이해당사자 간의 데이터 교환이 활발해 짐에 따라, 신뢰성 있는 데이터의 확보 및 관리가 시급한 과제로 떠오르고 있다. 이러한 문제를 해결하기 위해, 본 논문은 데이터의 품질을 체계적으로 관리할 수 있는 프레임워크를 시나리오와 함께 제시한다. 데이터 품질 관리 프레임워크는 데이터 품질 모니터링, 데이터 품질 개선, 데이터 활용의 3단계로 구분되어 있으며 각 단계마다 3개씩, 총 9개의 프로세스로 구성되어 있다. 각 프로세스에는 필요성, 기능, 역할, 프로세스간의 관계가 명시되어 있다. 또한, 본 프레임워크를 현장에 직접 적용할 수 있도록, e-비즈니스에서 많이 사용되는 상품식별 및 분류 코드체계의 사례를 이용하여 업무 시나리오를 제시하였다.

• 품질경영학회지
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• 제51권4호
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• pp.663-675
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• 2023

Purpose: Skewness is an indicator used to measure the asymmetry of data distribution. In the past, product quality was judged only by mean and variance, but in modern management and manufacturing environments, various factors and volatility must be considered. Therefore, skewness helps accurately understand the shape of data distribution and identify outliers or problems, and skewness can be utilized from this new perspective. Therefore, we would like to propose a statistical quality control method using skewness. Methods: In order to generate data with the same mean and variance but different skewness, data was generated using normal distribution and gamma distribution. Using Minitab 18, we created 20 sets of 1,000 random data of normal distribution and gamma distribution. Using this data, it was proven that the process state can be sensitively identified by using skewness. Results: As a result of the analysis of this study, if the skewness is within ± 0.2, there is no difference in judgment from management based on the probability of errors that can be made in the management state as discussed in quality control. However, if the skewness exceeds ±0.2, the control chart considering only the standard deviation determines that it is in control, but it can be seen that the data is out of control. Conclusion: By using skewness in process management, the ability to evaluate data quality is improved and the ability to detect abnormal signals is excellent. By using this, process improvement and process non-sub-stitutability issues can be quickly identified and improved.

• 한국경영과학회지
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• 제41권3호
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• pp.45-58
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• 2016

Data are important in an organization because they are used in making decisions and obtaining insights. Furthermore, given the increasing importance of data in modern society, data governance should be requested to increase an organization's competitive power. However, data governance concepts have caused confusion because of the myriad of guidelines proposed by related institutions and researchers. In this study, we re-established the concept of ambiguous data governance and derived the top-level components by analyzing previous research. This study identified the components of data governance and quantitatively analyzed the relation between these components by using DEMATEL and context analysis techniques that are often used to solve complex problems. Three higher components (data compliance management, data quality management, and data organization management) and 13 lower components are derived as data governance components. Furthermore, importance analysis shows that data quality management, data compliance management, and data organization management are the top components of data governance in order of priority. This study can be used as a basis for presenting standards or establishing concepts of data governance.

• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.2107-2111
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• 2013

Objectives: To investigate data agreement of cancer registries and medical records as well as the quality of care and assess their relationship in a 5-year period from 2006 to 2011. Methods: The present cross-sectional, descriptive-analytical study was conducted on 443 cases summarized through census and using a checklist. Data agreement of Nemazi hospital-based cancer registry and the breast cancer prevention center was analyzed according to their corresponding medical records through adjusted and unadjusted Kappa. The process of care quality was also computed and the relationship with data agreement was investigated through chi-square test. Results: Agreement of surgery, radiotherapy, and chemotherapy data between Nemazi hospital-based cancer registry and medical records was 62.9%, 78.5%, and 81%, respectively, while the figures were 93.2%, 87.9%, and 90.8%, respectively, between breast cancer prevention center and medical records. Moreover, quality of mastectomy, lumpectomy, radiotherapy, and chemotherapy services assessed in Nemazi hospital-based cancer registry was 12.6%, 21.2%, 35.2%, and 15.1% different from the corresponding medical records. On the other hand, 7.4%, 1.4%, 22.5%, and 9.6% differences were observed between the quality of the above-mentioned services assessed in the breast cancer prevention center and the corresponding medical records. A significant relationship was found between data agreement and quality assessment. Conclusion: Although the results showed good data agreement, more agreement regarding the cancer stage data elements and the type of the received treatment is required to better assess cancer care quality. Therefore, more structured medical records and stronger cancer registry systems are recommended.