• International journal of advanced smart convergence
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• 제13권2호
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• pp.265-275
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• 2024

Smart factory is a remarkable development from traditional manufacturing systems to data-based smart manufacturing systems that can connect and process data continuously, collected from machines, production equipment to production and business processes, capable of supporting workers in making decisions or performing work automatically. Smart factory is the key and center of the fourth industrial revolution, combining improvements in traditional manufacturing activities with digital technology to help factories achieve greater efficiency, contributing to increased revenue and reduce operating costs for businesses. Besides, the importance of smart factories is to make production more quality, efficient, competitive and sustainable. Businesses in Vietnam are in the process of learning and applying smart factory models. However, the number of businesses applying the pine factory model is still limited due to many barriers and difficulties. Therefore, in this paper we conduct a survey to assess the needs and current situation of businesses in applying smart factories and propose some specific solutions to develop and promote application of smart factory model in Vietnamese businesses.

• 응용통계연구
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• 제24권3호
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• pp.465-475
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• 2011

본 연구에서는 공장용지 면적을 예측하기 위한 통계적 추정을 다루었다. 공장용지에 대해서는 1981년부터 2003년까지 자료가 존재하며, 2004년 이후에는 공장용지보다 좁은 개념인 산업단지 면적에 대한 조사 자료만 존재한다. 한국산업단지공단에서는 2009년 10월 표본조사를 실시하여 당해의 공장용지 면적을 추정하였으며, 동 조사 시 향후 5개년의 공장용지면적에 대한 수요를 조사한 바 있다. 본 연구에서는 과거 절단된 자료를 여러 통계모형을 사용하여 적절히 대체할 수 있는 수요예측모형을 도출하고, 표본조사에 의한 추정치와 통계적 모형에 의한 대체값들을 융합하는 평활기법으로 향후 공장용지 수요를 예측하는 방법을 다루었다.

• 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 advanced smart convergence
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• 제11권1호
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• pp.42-47
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• 2022

As smart factory is the factory which produces the products according to the customer's diverse demand and the changing conditions in it, it can be characterized by flexible production, dynamic reconstruction, and optimized production environment. To implement these characteristics, many kind of configuration elements in the smart factory should be connected to and communicated with each other. So the network is responsible for playing this role in the smart factory. As SDN (Software Defined Network) is the technology that can dynamically cope with the explosive increasing data amount and the hourly changing network condition, it is one of network technologies that can be applied to the smart factory. In this paper, we address SDN function and operation, SDN model suitable for the smart factory, and then performs the simulation for measuring this model.

• 융합정보논문지
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• 제9권3호
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• pp.16-21
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• 2019

우리나라 중소기업은 현재 국내 외 다양한 환경 요인(경쟁력 확보 및 우수 제품 개발 등)으로 인하여 산업 구조가 과거에 비해 빠르게 변화하고 있는 상황이다. 특히, 인공지능과 관련된 다양한 장비가 제조현장에 투입되면서 스마트팩토리 환경에서 생산되는 데이터 수집 및 활용의 중요성이 점점 증가하고 있다. 본 논문에서는 최근 중소기업 제조 현장이 스마트팩토리화 되면서 제조 현장에서 생산되는 제품의 프로세스를 향상시키기 위한 인공지능 기반 스마트팩토리 모델을 제안한다. 제안 모델은 갈수록 치열해지는 제조 환경의 경쟁력 확보 및 생산 비용 절감을 최소화시키는 것이 목적이다. 제안 모델은 인공지능 기반의 스마트팩토리 현장에서 생산되는 제품의 정보뿐만 아니라 제품 생산에 소비되는 노동력, 노동 근무 시간 및 가동 공장기계 상태 등을 모두 고려하여 관리한다. 또한, 제안 모델에서 생산되는 데이터는 유사 기업과 시스템 연계 및 정보 공유가 가능하기 때문에 제조 현장 운영의 기업간 전략적 협력이 가능하다.

• 한국식생활문화학회지
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• 제21권4호
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• pp.414-419
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• 2006

The purpose of this study was to measure the factor influencing tourist preferences for leaf mustard iimchi. Among 250 questionnaires, 230 questionnaires were utilized for the analysis. Frequencies, conjoint model, max. utility model, BTL model, Logit model, K-means cluster analysis, and one-way ANOVA analysis were used for this study. The findings from this study were as follows. First, the Pearson's R and Kendall's tau statistics showed that the model fitted the data well. Second, it was found that total respondents and three clusters regarded taste and price as the very important factor. Third, it was found that the first cluster most preferred product with light red color, plain package, and mild taste sold at a cheap price in factory. The second cluster most preferred product with light red color, plain package, and moderately pungent taste sold at a expensive price in factory. The third cluster most preferred product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory. Fourth, it was found that the first cluster most preferred simulation product with light red color, shaped package, and mild taste sold at a cheap price in factory. The second cluster most preferred simulation product with light red color, shaped package, and moderately pungent taste sold at a cheap price in factory. The third clutter most preferred simulation product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.586-592
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• 2000

There is currently no satisfactory way to optimize supplemental lighting in a greenhouse-type plant factory especially concerning plant production. In a commercial plant factory, we got outside radiation data, inside radiation data and lamp running data. They have a correlation, but have much disorder. By using regression, tendency between the outside and the inside including supplemental lighting was found. We could estimate the average transmittance of this plant factory. From this estimation, we could admit the amount of inside radiation was supplied as much supplied compared to natural radiation. Then we are trying to investigate of the production amount and the supplemental lighting. Plant factory is environmentally controlled, the temperature and humidity are not actually controlled stable. We propose a design of neural network model could be useful to estimate the profit resulting from the operation of supplemental lighting.

• 융합경영연구
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• 제11권6호
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• pp.43-55
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• 2023

Purpose: The purpose of this study was to identify the relationship between smart factory utilization and continued use intention between UTAUT, dynamic capabilities of smart factory construction companies and present the company's strategic direction. Research design, data, and methodology: In this study, a structured research model was derived to confirm the relationship between UTAUT, dynamic capabilities, smart factory utilization and continued use intention and the difference according to Technology perception. For analysis a total of 223 valid questionnaires from e-commerce users were used. Confirmatory factor analysis, correlation analysis, and structural equations were conducted to verify. Results: Both UTAUT, dynamic capabilities had a significant effect on smart factory utilization as well as continued use intention. It was found that the relationship between UTAUT, dynamic capabilities, smart factory utilization, and continued use intention. differed depending on the technology perception. Conclusions: Organizational members utilize the smart factory in anticipation of effects such as work performance and various improvements. Smart factory data will be used continuously when it is useful for business processes and operations. It is necessary to establish strategies and provide training to improve the technical level and capabilities of organizational members. Through this, a strategy is needed that can be continuously used by utilizing the information obtained through smart factory to improve work efficiency, productivity and efficiency increase is needed

• 산업경영시스템학회지
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• 제42권3호
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

• 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.