• Title/Summary/Keyword: smart factory

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Effects of Smart Factory Quality Characteristics and Dynamic Capabilities on Business Performance: Mediating Effect of Recognition Response

  • CHO, Ik-Jun;KIM, Jin-Kwon;YANG, Hoe-Chang;AHN, Tony-DongHui
    • The Journal of Industrial Distribution & Business
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    • v.11 no.12
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    • pp.17-28
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    • 2020
  • Purpose: The purpose of this study is to confirm the strategic direction of the firm regarding the capabilities of the organization and its employees in order to increase the utilization and business performance of employees by that introduce smart factories in the domestic manufacturing industry. Research design, data, and methodology: This study derived a structured research model to confirm the mediating effect of recognition responses between the quality characteristics of smart factories and dynamic capabilities. For the analysis, a total of 143 valid questionnaires were used for 200 companies that introduced smart factories from domestic SME's. Results: Quality Characteristics of Smart Factory and Dynamic Capabilities had a statistically significant effect on Usefulness. Recognition Response had a statistically mediating on the relationship between quality characteristics of smart factory and business performance. Recognition Response had a statistically significant effect on business performance. Conclusions: It suggests that firms introducing smart factory reflect them in their empowerment strategic because the recognition responses of its employees differ according to the quality characteristics and dynamic capabilities of smart factories. It also means that the information derived from the smart factory system is useful and effective to business performance and employees.

Quality Strategy for Building a Smart Factory in the Fourth Industrial Revolution (4차 산업혁명시대의 스마트 팩토리 구축을 위한 품질전략)

  • Chong, Hye Ran;Bae, Kyoung Han;Lee, Min Koo;Kwon, Hyuck Moo;Hong, Sung Hoon
    • Journal of Korean Society for Quality Management
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    • v.48 no.1
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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.

Factory Workers' Perception for Applying Smart Factory in Developing Country - Focusing on the survey results of the Indonesian garment manufacturing factory - (개발도상국 공장 근무자의 스마트팩토리 적용에 대한 인식 - 인도네시아 의류생산 공장 설문조사 결과를 중심으로 -)

  • Jung, Woo-Kyun;Lee, Jae-Won;Park, Yong-Chul;Ahn, Sung-Hoon
    • Journal of Appropriate Technology
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    • v.6 no.1
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    • pp.56-64
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    • 2020
  • Recently, major manufacturers are focusing their efforts on securing global competitiveness through smart factory, but developing countries have many difficulties in applying smart factory due to financial and technical conditions. This study is a preliminary study on the development of an ICT-based power monitoring system applicable to developing countries. The questionnaire surveyed and analyzed workers' perceptions of smart factory in a garment manufacturing factory in developing countries, Indonesia. Before and after the installation of the power monitoring system, the survey was conducted for 126 local managers and workers, and the correlation was analyzed using SPSS. As a result of analysis, factory workers in developing countries such as Indonesia are also positively aware of the necessity of introducing smart factory technology, and it is expected that the introduction of these technologies will affect job satisfaction and improve the factory environment. In addition, the result of the survey conducted after the installation of the power monitoring system increased the job satisfaction score by 5.5% compared to before the installation, and the scores on the perception of the necessity of the power monitoring system and the positive effect of the application of the system on the factory environment were increased 13% and 5.9%, respectively. It was also confirmed that managers rather than workers and female rather than male showed positive perception for the introduction of smart factory technology. The result of this study is expected to be an important reference in the direction of development of appropriate smart factory technology applicable to developing countries and the introduction of smart factory by manufacturers operating factories in developing countries.

Plan for Risk Reduction of Smart Factory Process through Accident Analysis and Status Survey (재해분석과 실태조사를 통한 스마트 팩토리 공정의 위험성 감소 방안)

  • Byeon, Junghwan
    • Journal of the Korean Society of Safety
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    • v.37 no.5
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    • pp.22-32
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    • 2022
  • The domestic smart factory is being built and spread rapidly, mainly by mid-sized companies and large enterprises according to the government's active introduction and support policy. But these factories only promote production system and efficiency, so harmfulness and risk factors are not considered. Therefore, to derive harmful risk factors in terms of industrial safety for 12,983 government-supported smart factory workplaces from 2014 to 2019, industrial accident status analysis compared workplaces with automation facilities and government-supported workplaces with automation facilities. Also, to reduce risks associated with domestic smart factory processes, twenty government-supported workplaces with automation facilities underwent analysis, evaluating risks through a status survey using the process evaluation table. In addition, the status survey considered region, size, industry, construction level, and accident rate; the difference in risk according to the structure of the process was confirmed. Based on the smart factory process evaluation results, statistical analysis confirmed that serial, parallel, and hybrid structures pose different risk levels and that the risks of mixed structures are greater. Finally, safety control system application was presented for risk assessment and reduction in the smart factory process, reflecting the results of disaster analysis and actual condition investigation.

Development of Smart Factory-Based Technology Education Platform Linking CPPS and VR (CPPS 및 VR을 연계한 스마트팩토리 기반 기술 교육 플랫폼 개발)

  • Lee, Hyun
    • Journal of Practical Engineering Education
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    • v.13 no.3
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    • pp.483-490
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    • 2021
  • In this paper, we proposed the development of a smart factory intergrated technology education platform using smart factory based CPPS (Cyber Physical Production System) and VR (Vitrual Reality) technology and educational methods using the platform. A platform has been developed to learn how to integrate 3D digital twin and BOP (Bill of Process)-based manufacturing processes. In addition, Digital Twin established a smart factory-based integrated education platform by linking mechanical systems, digital twins, and virtual reality through the OPC-UA server. Based on this platform, the smart factory integration platform is proposed to have individual elements of the smart factory integration platform through BOP-based digital twin simulation, OPC-UA integration, MES system, SCADA system, and VR interworking.

A Quantitative Review on Deep Learning and Smart Factory from 2010 to 2023

  • Yong Sauk Hau
    • International Journal of Internet, Broadcasting and Communication
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    • v.16 no.2
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    • pp.203-208
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    • 2024
  • The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as 'deep', 'learning', 'smart', 'detection', factory', 'data', 'system', 'manufacturing', 'neural', and 'network' that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

Factors Affecting Technology Acceptance of Smart Factory (스마트팩토리 기술수용에 영향을 미치는 요인에 관한 연구)

  • Kim, Joung-Rae;Lee, Sang-Jik
    • Journal of Information Technology Applications and Management
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    • v.27 no.1
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    • pp.75-95
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    • 2020
  • Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.

Design and Implement of Smart Gateway Interface API for Real-time Monitoring in Smart Factory (스마트 팩토리에서 원격 실시간 모니터링을 위한 게이트웨이 인터페이스 연동 API 설계 및 구현)

  • Jeon, Dong-cheol;Lee, Byung Mun;Hwang, Heejoung
    • Journal of Korea Multimedia Society
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    • v.22 no.5
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    • pp.601-612
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    • 2019
  • As the $4^{th}$ industrial revolution is accelerating, IT convergence application technologies are attracting attention in various fields. In the manufacturing industry, Smart Factory technology, which is blended with IT technology, has been developed to solve the problem casued by the decrease of the labor force, and a monitoring server is required to remotely control the equipment or to inquire about the operation status of the factory. In this paper, we designed and implemented RESTful API for data sharing between factory equipment and monitoring server in Smart Factory. In order to verify the designed API, a testbed was operated for an actual plastics manufacturing plant. As a result, it was confirmed that the testbed can be operated normally in actual operating environment.

Study on Minimum Security Requirement Using Risk Priority Number(SFRPN) for Secure Smart Factory (안전한 스마트공장 구축을 위한 위험우선순위(SFRPN) 기반 최소보안요구사항에 관한 연구)

  • Yi, Byung-gueon;Kim, Dong-won;Noh, Bong-nam
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.5
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    • pp.1323-1333
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    • 2016
  • According to spreading of smart devices and development of communication technology, the security issues come to the fore in the modern factory. Especially, the smart facpry should be considered the risk management plan how to identify and evaluate, control the risks. In this paper, we suggest the minimum security requirements applying SFRPN(Smart Factory Risk Priority Number) model to domestic smart factory on the basis of the results inspecting factories.

Development of Domestic Standardization in Smart Factory and Manufacturing Data (국내 스마트공장 및 제조 데이터 표준 개발 동향)

  • Cho, Woong
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.5
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    • pp.783-788
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    • 2021
  • Smart manufacturing is defined as the fully ICT-based manufacturing process which digitized, optimized, and automized the of manufacturing system in smart factory which includes product planning, design, production, quality, stock, procure. In this paper, we introduce the development of domestic standardization of smart factory and manufacturing data which are generated in operation of smart factory. We focus on general standardization of smart factory/ICT-based manufacturing system and data transactions related issues since the range of standardization is too wide. Based on these standardization review, we discuss the several concerns for utilization of manufacturing data.