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제조업 공급망 온톨로지 기반 적응형 ERP 모듈 시스템 프레임워크
오영광(Yeonggwang Oh),한휘영(Hweeyoung Han),신동민(Dongmin Shin),김동철(Dongchul Kim),김남훈(Namhun Kim) 대한산업공학회 2015 대한산업공학회지 Vol.41 No.4
Recently, an ERP (Enterprise resource Planning) system has been becoming an essential S/W tool for companies to manage their business processes and manufacturing resources. As the information exchange becomes more complex, not only corporate companies but also small- and mid- sized enterprises (SMEs) are required to build an ERP system. However, for small- and middle- sized companies, the adoption of ERP systems becomes challenging due to high cost and long installation time of the system. This paper presents a novel concept of an adaptive ERP system incorporating the ontology structure of the business supply chain information. The proposed ERP installation methodology is illustrated with an example of a door-trim manufacturing company in the automotive supply chain.
실시간 공정 모니터링을 통한 제품 품질 예측 모델 개발
오영광(YeongGwang Oh),박해승(Haeseung Park),유아름(Arm Yoo),김남훈(Namhun Kim),김영학(Younghak Kim),김동철(Dongchul Kim),최진욱(JinUk Choi),윤성호(Sung Ho Yoon),양희종(HeeJong Yang) 대한산업공학회 2013 대한산업공학회지 Vol.39 No.4
In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.