• 한국콘텐츠학회논문지
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• 제19권10호
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• pp.407-420
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• 2019

본 연구는 '기업의 환경요인을 통한 기술혁신이 Smart Factory 구축에 미치는 영향'에 대한 실증연구이다. 연구 목적은 Smart Factory를 구축할 때 내부환경이나 중점적으로 추진할 요인을 고려하고 구축하여 Smart Factory의 활용도와 효과를 높이는 것이다. 연구방법은 Smart Factory를 구축한 기업의 관련 인원들에게 설문을 조사하여 SMART PLS로 통계분석 하였다. 연구결과는, 기업 내부 조직 요인과 자기효능감은 기술혁신에 긍정적인 영향을 미치고, 기술혁신은 Smart Factory 구축의 핵심요인에 긍정적인 영향을 미쳤다. 그리고 조절변수인 흡수역량은 기술혁신요인과 상호작용으로 Smart Factory 구축의 핵심요인에 부분적으로 긍정적인 영향을 미쳤다. 본 연구는 Smart Factory를 구축하려는 기업이 활용할 수 있으며, 실증분석을 통한 Smart Factory 구축연구에 이론적 토대를 마련한 의의가 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.205-224
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• 2023

Purpose This study investigates the impact of organizational characteristics on organizational performance through case studies of smart factory implementation in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study adopts the smart factory index of KOSMO (Korea Smart Manufacturing Office) established by Korean Ministry of SMEs and Startups. We visited 3 firms implemented smart factory projects. This study presents the results of field study in detail with evaluation criteria on how organizational competences like AI technology adoption and facility automation can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach There are not so many results of empirical studies related to smart factories in Korea. This is because organizational support and user involvement are required for facility AI platform service beyond factory automation after the start of the 4th Industrial Revolution. Korean government's KOSMO (Korean Smart Manufacturing Office) has developed and proposed a level measurement index for smart factory implementation. This study conducts case studies based on the level measurement method proposed by KOSMO in the process of conducting case studies of three companies belonging to the root and mechanic industries in Korea. Findings The findings indicate that organizational competences, such as facility AI platform adoption and user involvement, are antecedents to influence smart factory implementation, while smart factory implementation has significant relationship with organizational performance. This study provides a better understanding of the connection between organizational competences and organizational performance through smart factory case studies. This study suggests that SMEs should focus on enhancing their organizational competences for improving organizational performance through implementing smart factory projects.

• 한국정보시스템학회지:정보시스템연구
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• 제31권1호
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• pp.197-218
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• 2022

Purpose This study examines the roles of firm-level smart factory implementation in the relationship between organizational competence and organizational performance in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study presents and empirically tests a research model with evaluation data conducted by industrial experts on how organizational competence can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach Organizational competence are based on the research construct developed by Odważny et al.(2018). Research constructs on smart factory are based on the measurement model developed by Korea Technology and Information Promotion Agency for Korea small and medium Enterprises (TIPA) (2020) and organizational performance are based on the performance construct developed by Kwon(2019). To complete the investigation, we collected 31 firm data conducted by industrial experts in Korea from Dec 2018 to Dec 2020. Most of firm was implemented officially by government budget granted for smart factory of Korea SMEs. To test our hypotheses, partial least squares (PLS) method was employed. Findings The findings indicate that organizational competence is antecedent to influence smart factory implementation, while smart factory implementation has significant relationship with organizational performance. This study provides a better understanding of the connection between organizational competence and organizational performance through smart factory implementation. So companies should focus on enhancing organizational competence and implementing smart factory to obtain sustainable competitiveness.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국분말재료학회지
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• 제29권2호
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• pp.166-175
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• 2022

Ppuri or Root technology primarily includes technologies such as casting, mold, plastic working, welding, heat treatment and surface treatment. It is regarded as an essential element for improving the competitiveness of the quality of final products. This study investigates the current status of smart factory implementation for Ppuri companies and analyzes the influencing relationships among various company factors. The factors affecting smart factory implementation for Ppuri companies are sales, exports, number of technical employees, and holding corporate research institutes. In addition, this research shows that even if smart factory implementation is pursued for data collection, data utilization is not implemented properly. Thus, it is suggested that the implementation of smart factories requires not only the availability of facilities and systems but also proper data utilization.

• 디지털융복합연구
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• 제17권7호
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• pp.115-124
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• 2019

본 연구는 '중소기업에서 내부 환경요인을 통한 Smart Factory의 핵심요인이 경영성과에 미치는 영향'에 대한 실증연구이다. 연구 목적은 Smart Factory 구축이 경영성과에 영향을 미쳐서 회사가 지속 발전하는데 기여하는지 검증하고, 국가적으로 추진하고 있는 Smart Factory 구축 확대 정책에 대해 제언하고자 한다. 절차는 Smart Factory를 구축한 중소제조기업의 실무자를 중심으로 설문을 받아 SPSS와 SMART PLS로 통계 분석하였다. 연구결과는 첫째, 기업 내부의 환경요인은 Smart Factory 핵심요인에 긍정적인 영향을 미쳤다. 둘째, Smart Factory 핵심요인은 경영성과에 긍정적인 영향을 미쳤다. 위의 입증을 통해서 기업의 환경요인을 고려한 Smart Factory의 핵심요인은 경영성과에 영향을 주는 것으로 나타나, Smart Factory 구축 성과의 이론적인 토대를 마련했다고 할 수 있다. 향후는 Smart Factory 구축방법 연구를 하고자 한다.

• International journal of advanced smart convergence
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• 제11권3호
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• pp.42-48
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• 2022

Currently, the world is facing severe inflation due to Corona and the war in Ukraine, and it is causing a lot of difficulties for us. Companies are facing a lot of restrictions on their economic activities compared to the past due to supply chain problems and foreign exchange rates. In this situation, many countries have been implementing various smart factory promotion projects to secure competitiveness through productivity improvement in the manufacturing industry. In this study, the contents of smart factory promotion in major countries were reviewed, and problems raised about the implementation of smart factory in Korea, which are being implemented based on this, were described. It is most reasonable to judge the success of a smart factory by the achievement of the performance indicators presented at the time of the project. Therefore, based on the performance index of the business, which is a key factor in determining the success or failure of a smart factory, we investigated whether the company's smart factory promotion can be carried out successfully through examples

• 산업경영시스템학회지
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• 제44권2호
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

• 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.

• 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.