• 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

• 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.

• 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.

• 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.

• 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.

• 중소기업연구
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• 제41권4호
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• pp.1-36
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• 2019

본 연구의 목적은 스마트 팩토리의 전략적 활용을 위한 스마트 팩토리 구축 목적 - 스마트 팩토리 구축내용 - 스마트 팩토리 지속적 활용 간의 관계를 파악하는 것이다. 구체적으로 본 연구는 스마트 팩토리 구축 목적을 두 가지 요인 - (1) 생산성 향상, (2) 유연성 향상 - 으로 구분하고, 이들 각각이 다음의 3가지 측면의 스마트 팩토리 구축내용 - (1) 자동화 영역(설비 자동화, 업무 자동화), (2) 제조 빅데이터 기술 활용영역(생산 프로세스의 재구축을 위한 제조 빅데이터 활용, 생산 프로세스의 점진적 개선을 위한 제조 빅데이터 활용), 그리고 (3) 가치사슬 통합 범위(내부통합, 외부통합) - 에 미치는 영향을 파악하고, 이어서 본 연구는 스마트 팩토리 구축 내용이 스마트 팩토리 지속적 활용에 미치는 영향을 조사하였다. 또한, 기업규모에 따라 스마트 팩토리 구축 목적 - 스마트 팩토리 구축 내용 - 스마트 팩토리의 지속적 활용 간의 관계가 어떻게 달라지는 지를 살펴보았다. 본 연구의 실증분석은 총 151개의 표본기업들을 대상으로 하였다. 표본기업들의 구성은 중소기업 100개사와 대기업 51개사로 구성되었다. 이의 분석결과는 다음과 같다. 첫째, 생산성 및 유연성 향상이라는 스마트 팩토리 구축 목적은 스마트 팩토리의 모든 구축 내용 변수들에 긍정적 영향을 주었다. 둘째, 스마트 팩토리 구축 내용들로서 설비 자동화, 업무 자동화, 생산 프로세스의 재구축을 위한 제조 빅데이터 활용, 내부 가치사슬 통합, 외부 가치사슬 통합은 스마트 팩토리의 지속적 활용에 긍정적 영향을 주었다. 셋째, 스마트 팩토리 구축 목적이 스마트 팩토리 구축 내용에 미치는 영향은 구축 목적이 생산성 향상이냐 혹은 유연성 향상이냐에 따라 차이가 있음을 확인할 수 있었다. 넷째, 기업규모에 따른 조절효과 분석 결과 기업규모에 따라서 스마트 팩토리의 구축 목적과 구축 내용 간에 차이가 있는 것으로 나타났다.

• 한국전자통신학회논문지
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• 제12권6호
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• pp.1071-1080
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• 2017

최근 4차 산업혁명과 스마트 팩토리라는 용어를 뉴스나 매체를 통해 자주 들을 수 있다. 하지만 스마트 팩토리에 대한 정보와 어떻게 스마트 팩토리를 구축해야 하는지에 대한 구체적인 가이드라인이 없기 때문에 기업들로부터 외면 받고 있다. 스마트 팩토리의 구축은 도입 목적을 고려하여 회사의 규모에 적합하게 수행되어야한다. 기존 논문 연구에서 국내 대 중 소기업들을 대상으로 스마트 팩토리 성공구축 사례들을 분석 하였다. 사례분석 결과, 대기업의 경우 일부공장을 대상으로 시범적으로 스마트 팩토리를 구축한 후 성공적으로 평가될 시 전체공장으로 확대시키는 전략이 효과적이다. 중소기업의 경우, 낮은 수준의 스마트 팩토리 구축레벨에서 높은 수준의 구축레벨로 업그레이드하는 것이 효과적이다. 본 논문에서는 전통제조업체를 1개 선정하고, 3D가상공정설계를 통해 병목 구간과 개선이 필요한 공정을 파악한 후 센서를 설치한다. 최종적으로 센서를 통해 수집 된 데이터를 분석한 후 공정을 개선하고, 생산성이 향상된 스마트 팩토리를 구축하여 그 효과를 검증해 보고자 한다.

• 기술혁신학회지
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• 제19권3호
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• pp.545-571
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• 2016

정보통신(ICT)의 발달은 제조업에 있어 큰 변화를 가져왔으며 제조공정에는 사물인터넷, 증강현실, 빅데이터와 같은 최신 정보기술이 적용되어 스마트공장이라는 이름으로 진화하고 있다. 이에 미국, 독일, 일본 등 선진국들은 전기전자, 자동차, 기계 등의 제조업에서 스마트공장을 활발히 도입함에 따라 기존의 제조기업들의 생산품질을 높이고, 생산 환경은 유연한 체제로 변화시키면서 미래 제조업을 앞당기고 있다. 이러한 변화는 국내 제조업에까지 영향을 미치게 되었으며 대기업 중심으로 스마트공장이 이루어지고 있다. 그러나 중소기업들은 기술적, 재무적 어려움으로 인해 스마트공장을 적극적으로 도입하고 있지 못하다. 이같은 배경 속에서 본 논문은 스마트공장의 표준화가 중소기업들의 스마트공장의 활용에 큰 기여를 할 것으로 전제하고 우리나라 중소기업들의 스마트공장의 표준화 전략 및 확산 방안을 도출하는 것을 목표로 하고 있다. 이를 위하여 본 연구에서는 이론분석을 통하여 스마트공장의 확산에 영향을 미치는 요소를 기술적 요인, 조직적 요인, 산업적 요인, 정책적 요인으로 도출 분류하고 사례분석을 실시하였다. 실증분석 대상은 우리나라 제조업의 핵심이 되고 있는 뿌리산업이며, 특히 금형분야의 두 기업을 대상으로 실시하였다. 이를 바탕으로 본 연구에서는 향후 뿌리산업 중소기업들에게 스마트공장 구축을 위한 표준화 전략 4가지, 즉 국제 표준화, 정부주도 표준화, 기업주도 표준화, 비표준화 추진 등을 도출 및 제안하였다.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.273-278
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• 2022

Uncertainty is increasing around the world due to COVID-19 and Ukraine crisis. In this situation, each company is making countless efforts to survive. In Korea, smart factory projects targeting small and medium-sized businesses with difficulties have been continuously promoted. As for the smart factory business that has been promoted so far, the base expansion of the smart factory is also steadily increasing as the number of companies carrying out the project is increasing. It was also found that it contributed to productivity improvement and quality improvement. Despite these positive aspects, difficulties and operational problems are also appearing in the process of promoting smart factories. In this study, we investigated and analyzed operational problems and difficulties in the process of promoting smart factories. In addition, improvement plans for problems were presented according to the contents of this analysis, and improvement plans were presented by classifying them into introduction and supply companies, considering that the smart factory business is formed in the form of a consortium between introduction and supply companies.

• 아태비즈니스연구
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• 제14권1호
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• pp.281-294
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• 2023

Purpose - The purpose of this study is to explore the determinants of the organizational level for the advancement of smart factory. We suggested three determinants of the organizational level such as CEO's entrepreneurship, high-involvement human resource management, and cooperative industrial relations. Design/methodology/approach - The population of our survey was manufacturing SMEs, and we took a sample and conducted a survey of 232 companies. Since the level of smart factory advancement, which is a dependent variable, was measured on an ordinal scale, ordinal logistic regression analysis was used to test the hypothesis. Findings - The higher the level of high-involvement human resource management, the higher the level of smart factory advancement. As the level of high-involvement human resource management increases by one unit, the probability of smart factory advancement increases by 22.8%. On the other hand, the CEO's entrepreneurship did not significantly affect the level of smart factory advancement. Interestingly, the cooperative industrial relations negatively affected to the level of smart factory advancement, contrary to the hypothesis prediction. Research implications or Originality - This study explored determinants at the organizational level that affect the advancement of smart factories. Through this, various implications are presented for related research and policy fields.