• 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.

• 산업경영시스템학회지
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• 제42권4호
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• pp.76-83
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• 2019

The South Korean government is actively assisting the supply of the smart factory solutions to SMEs (Small & Medium-sized Enterprises) according to its manufacturing innovation 3.0 policy for the smart manufacturing as the 4th industrial revolution era unfolds. This study analyzed the impacts of the smart factory solutions, which have been supplied by the government, on the companies performances. The effects of the level of smart factory and the operation capabilities for the smart factory solutions on company performances, and the mediating effects of manufacturing capabilities have been analyzed using SPSS and AMOS. The data for this survey-based study were collected from the SMEs which implemented the smart factory solutions since 2015. The results show that the level of smart factory solutions adopted and operation capabilities for the smart factories do not have direct effects on the company performances, but their mediating effects on the manufacturing capabilities matter and the manufacturing capabilities effect directly on the company performances. In addition significant factors boosting the operation capability for the smart factory and the levels of the smart factory solutions are identified. Finally, the policy direction for enhancing the smart factory effects is presented, and the future research directions along with the limitations are suggested.

• 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.

• 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.

• 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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• 제13권2호
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• pp.229-234
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• 2024

A smart factory is defined as a cutting-edge, intelligent factory that integrates all production processes from product planning to sales with information and communication technology. Through these factories, each company produces customized products with minimal cost and time. The smart factory promotion project in Korea has produced positive results even in difficult environments such as the COVID-19 situation. Through the transition to a smart manufacturing production system, the competitiveness of small and medium-sized businesses has been greatly strengthened, including increased productivity and reduced costs. This study was based on surveyed data conducted by organizations related to smart factory promotion in 2020. Significant contents and major characteristics that emerged from the surveyed data were inferred and described. Since the meaningful contents reflect the reality of the company, more efficient promotion of smart factories will be possible in the future.

• 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.

• 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.

• 품질경영학회지
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• 제48권1호
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• pp.87-105
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• 2020

Purpose: This paper aims to propose a practical strategy for smart factories and a step-by-step quality strategy according to the maturity of smart factory construction. Methods: The characteristics, compositional requirements, and diagnosis system are examined for smart factories through theoretical considerations. Several cases of implementing smart factory are studied considering the company maturity level from the aspect of the smartness concept. And specific quality techniques and innovation activities are carefully reviewed. Results: The maturity level of smart factory was classified into five phases: 1) ICT non-application, 2) basic, 3) intermediate 1, 4) intermediate 2, 5) advanced level. A five-step quality strategy was established on the basis of case studies; identify, measure, analyze, optimize, and customize. Some quality techniques are introduced for step-by-step implementation of quality strategies. Conclusion: To build a successful smart factory, it is necessary to establish a quality strategy that suits the culture and size of the company. The quality management strategy proposed in this paper is expected to contribute to the establishment of appropriate strategies for the size and purpose of the company.

• 품질경영학회지
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• 제48권1호
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• pp.1-11
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• 2020

Purpose: This paper treats the survey result from the suppliers of smart factories. Based on the survey results, it is provided suggestions about government policies of the smart factory. Methods: For providing political suggestions, the survey of smart factory is conducted. The survey results are analyzed by the correlation and association methods based on the stratification. Results: The survey results are analyzed for extracting policy-level suggestions. Multiple policy-level suggestions are identified and presented in the conclusion. Conclusion: Six policy-level suggestions are presented for enhancing the management efficiency of suppliers of smart factory.