• 실천공학교육논문지
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• 제14권1호
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• pp.195-203
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• 2022

최근 스마트공장 도입이 확산됨에 따라 스마트공장 도입에 대한 긍정적, 부정적 영향에 대한 관심과 연구가 증가하고 있다. 본 연구에서는 750개 기업을 대상으로 일터혁신지수 하위 4개 부문(노사관계, 인적자원관리, 인적자원개발, 작업조직)별로 스마트공장 도입에 따른 일터혁신지수의 변화를 정량적으로 분석하였다. 전반적으로 스마트공장을 도입한 기업의 일터혁신지수가 그렇지 않은 경우보다 높게(0.5~9.4점) 나타났으며 특히 작업조직부분에서는 통계적으로 유의미한 차이를 보였다. 또한 스마트공장 도입과 일터혁신 컨설팅의 영향을 함께 분석한 결과, 노사관계부문과 작업조직부문의 경우 스마트공장을 도입하고 컨설팅을 받은 것이 일터혁신지수 향상에 부합하는 결과를 보였다.

• 사물인터넷융복합논문지
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• 제6권4호
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• pp.81-87
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• 2020

본 논문은 스마트 팩토리(Smart Factory) 디지털 트윈(Digital Twin) 구축을 위해서 다양한 산업용 센서(IoT/비 IoT)로부터 센서데이터를 획득하기 위한 표준 노드(Node)를 생성하고 그룹별/공정별 존(Zone)을 연동하여 상호호환적인 데이터의 교환 기능을 제공함으로써 데이터의 안정성을 확보하고 스마트 팩토리의 디지털 트윈(Digital Twin)을 위한 효과적인 존 마스터(Zone Master) 플랫폼 구축 방안을 제시하였다. 존 마스터(Zone Master) 플랫폼의 Process는 독립된 시스템 간의 센서 객체 및 센서 상호작용이 어떻게 수행하는지를 정의해 주기 위한 인터페이스 명세를 포함하고 있으며 고유의 데이터 교환 규칙에 대한 개별 정책들을 수행하고 있다. 존 마스터 플랫폼 프로세서의 실행 통제를 위한 인터페이스는 시스템관리, 자료교환의 협상(publish-subscribe)을 위한 선언 관리, 센서 객체의 등록 및 상태 정보를 통신하기 위한 객체 관리, 속성 소유권 공유를 위한 소유권 관리, 데이터 동기화를 위한 시간 관리, 데이터 교환에 대한 Route 정보를 위한 자료 분배 관리를 제공한다.

• Journal of Information Technology Applications and Management
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• 제26권2호
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• pp.13-26
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• 2019

The purpose of this study is to examine the effect of each of the two technology acceptance factors(employees' attitude toward smart factory technology, and ease of smart factory technology use) on the introduction level of each of the three smart factory technologies (manufacturing big data technology, automation technology, and supply chain integration technology), and in turn, the effect of each of the three smart factory technologies on manufacturing performance. This study employed PLS statistics software package to empirically validate a structural equation model with survey data from 100 domestic small-and medium-sized manufacturing firms (SMMFs). The analysis results revealed the followings. First, it is founded that employees' attitude toward smart factory technology influenced all of the three smart factory technology introduction levels in a positive manner. In particular, SMMFs of which employees had more favorable attitude toward smart factory technology tended to increase introduction levels of both automation technology and supply chain integration technology more than in the case of manufacturing big data technology. Second, ease of smart factory technology use also had a positive impact on each of the three smart factory technology introduction levels, respectively. A noteworthy finding is this : SMMFs which perceived smart factory technology as easier to use would like to elevate the introduction level of manufacturing big data technology more than in the cases of either automation technology or supply chain integration technology. Third, smart factory technologies such as automation technology and supply chain integration technology had affirmative impacts on manufacturing performance of SMMFs. These results shed some valuable insights on the introduction of smart factory technology : The success of smart factory heavily depends on organization-and people-related factors such as employees' attitude toward smart factory technology and employees' perceived ease of smart factory technology use.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• Journal of Information Technology Applications and Management
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• 제30권5호
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• pp.41-57
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• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• Journal of information and communication convergence engineering
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• 제18권1호
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• pp.61-68
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• 2020

In the era of the fourth industrial revolution, many production plants are gradually evolving into smart factories that apply information and communication technology to manufacturing, distribution, production, and quality management. The conversion from conventional factories to smart factories has resulted in the automation of production sites using the internet and the internet of things (IoT) technology. Thus, labor-intensive production can easily collect necessary information. However, implementing a smart factory required a significant amount of time, effort, and money. In particular, labor-intensive production industries are not automated, and productivity is determined by human skill. A representative industry of such industries is sewing the industry. In the sewing industry, wherein productivity is determined by the operator's skills. This study suggests that production performance, inventory management and product delivery of the sewing industries can be managed efficiently with existing production method by using smart buttons incorporating IoT functions, without using automated machinery.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2002년도 금형가공 심포지엄
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• pp.26-33
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• 2002

Die Factory follows typical order adaptive manufacturing, and delaying delivery affects directly product development of customer, Manufacturing Management System is tried to comply with the appointed date of delivery. It acquires running signal from NC milling, calculates manufacturing results, and offers the basic data to manage the operation ratio. Thus it offers Production data necessary to accomplish the objective of progress improvement for Unmanned Manufacturing. Manufacturing Management System runs on Web Environment, and is composed of electronic work order, operation ratio data acquisition and totaling module.

• 벤처창업연구
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• 제15권3호
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• pp.59-71
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• 2020

본 연구는 중소벤처기업의 스마트팩토리 기술적용이 품질과 혁신성과에 미치는 영향력을 밝히는데 목적을 갖고 실증분석 하였다. 연구 결과 중소벤처기업에서의 스마트팩토리 기술적용이 품질과 혁신성과에 미치는 영향에서 유의적인 영향을 미치는 것으로 나타나 가설은 부분 채택되었다. 스마트팩토리 기술로서의 디바이스와 어플리케이션 기술이 정보품질과 시스템품질에 긍정적인 영향을 미치는 반면, 플랫폼 기술은 정보품질과 시스템품질에 유의하지 않아 기각되었다. 또한, 스마트팩토리 기술이 혁신성과에 미치는 영향에서도 디바이스 기술은 유의한 영향을 미치는 반면, 플랫폼과 애플리케이션은 유의하지 않아 기각되었다. 시스템품질은 혁신성과에 유의하지만 정보품질은 혁신성과에 유의하지 않은 영향을 미쳤다. 스마트팩토리의 디바이스 기술이 혁신성과에 미치는 영향에서 품질은 부분 매개효과를 보이는 것으로 나타났다. 이와 같은 결과는 중소벤처기업의 4차 혁명시대 스마트팩토리의 핵심으로서의 상호 연결을 통해 수준 높은 정보 품질관리를 구현해야 한다. 또한, 제조설계에서부터 실행, 분석에 이르기까지 상호 컴포넌트 연동과 중소벤처기업의 필요에 따라 디바이스로부터 수집된 체계적인 정보를 통합 관리함으로서 기업의 성과를 높일 수 있음을 시사한 것으로 평가할 수 있다.

• 대한안전경영과학회지
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• 제26권2호
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• pp.107-115
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• 2024

This study conducted frequency analysis, reliability analysis, descriptive statistics, and correlation analysis to determine the impact of quality control managers' perception and implementation of KS certification factory inspection evaluation items on follow-up management. Through a multiple linear regression model, the influence of KS certification officer's awareness and implementation of KS certification factory inspection on post management was found to have a positive (+) influence on post management, with implementation having a greater influence on post management than awareness. It was having an impact. The independent variable (perception) has a statistically significant impact on the mediating variable (execution), and in the stage of verifying the mediating effect, the influence of the independent variable (perception) on the dependent variable (follow-up management) has a statistically significant impact. , In the stage where the independent variable (perception) and the mediator (implementation) are input simultaneously, both the independent variable and the mediator have a statistically significant effect on the dependent variable, indicating that there is a mediation effect.

• 한국정보전자통신기술학회논문지
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• 제11권1호
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• pp.70-75
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• 2018

스마트 팩토리는 설계 개발, 제조, 유통 물류 등 생산 전체 과정에 정보 통신 기술을 적용하여 생산성, 품질, 고객만족도 등을 향상시킬 수 있는 지능형 공장이다. 스마트 팩토리에서 발생되는 데이터의 양은 공장의 규모 및 시설 수준에 따라 많은 차이를 보이지만, 기존의 생산관리시스템을 활용하여 방대한 양의 데이터를 발생시키는 스마트 팩토리 환경에 적용하기에 어려움이 있다. 이로 인해 방대한 양의 빅데이터 처리할 수 있는 빅데이터 분산 처리 시스템의 필요성이 요구되고 있다. 따라서 본 논문에서는 스마트 팩토리 환경에서의 GlusterFS 기반 빅데이터 분산 처리 시스템 설계하였다. 제안하는 빅데이터 분산 처리 시스템은 기존 분산 처리 시스템에 비해 네트워크 트래픽 분산 및 관리를 통해 부하와 데이터 소실 위험도를 감소시켰다.