• 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.

• 건축사
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• 통권551호
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• pp.42-47
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• 2015

• 건축사
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• 통권542호
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• pp.50-53
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• 2014

• 건축사
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• 통권456호
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• pp.54-58
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• 2007

• 산업공학
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• 제10권1호
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• pp.209-222
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• 1997

The paper provides a methodology to obtain the automaticity indicator of a factory and the sequence of enabling technologies of factory automation. The automaticity indicator is the measure of the current automation status of a factory and can be used as a crucial criteria for the future automation schedule and investment. Although most industries have their own computation methods which usually consider the number of workers in the shop floor, this research covers five evaluation items of automation, such as, production facility, material transfer system, inspection and test system, information system, and flexibility. The detailed evaluation models are developed for each item. Automation sequencing prioritizes the enabling technologies of factory automation on the basis of several criteria which consist of two phases. The first phase includes the automation indicator and the second phase includes six sub-criteria such as production rate, quality, number of workers, capital investment, development duration, development difficulty. For this evaluation, AHP(Analytical Hierarchy Process) is introduced to prevent the decision maker's subject intention. As results of the automaticity indicator and automation sequence, the manager can save time and cost in building constructive and transparent automation plans.

• 융합경영연구
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• 제12권4호
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• pp.65-76
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• 2024

Purpose: This study aims to identify the relationship between SMEs' digital transformation capabilities, smart factory utilization, and management performance. It also aims to suggest how companies strategically utilize smart factories to achieve a competitive advantage and sustainable growth through empirical analysis of differences in innovation resistance and organizational characteristics. Research design, data, and methodology: This study Implement for SME's building smart factories did. The survey was conducted for 90days from October 1st, 2023 to December 31th, 2023. Total of 210 surveys were collected, and 186 surveys, excluding ones with missing value and outliers (64 surveys), were used. Results: The results of the empirical analysis based on previous research are as follows. First, digital transformation capabilities such as digital technology, digital leadership, and digital strategy affect smart factory utilization. Second, smart factory use affects operational performance. Third, innovation resistance has a moderating effect in the relationship with digital transformation capabilities, smart factory utilization, and management performance. Fourth, organizational characteristics have a moderating effect in the relationship with digital transformation capabilities, smart factory utilization, and management performance. Conclusions: Explore strategic ways to improve your organization's digital transformation capabilities. It is necessary to establish a strategy to make organizational members aware of the necessity and importance of introducing a new system through centralization of the organization.

• 융합정보논문지
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• 제8권2호
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• pp.233-238
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• 2018

중소기업 및 영세기업들은 생산관리 뿐만 아니라 설비, 안전, 에너지 관리 측면에서 중소기업의 운영 관리를 위해서 다양한 시도를 하고 있다. 그러나, 중소기업은 투자 여력이 없어 중소기업의 경영 개선과 생산성 향상을 위한 스마트팩토리 구축이 쉽지 않은 상황이다. 본 논문에서는 중소기업에서 현재 운영 중인 공장 장비를 부분적으로 연동하는 스마트팩토리를 구축 알고리즘을 제안한다. 제안 알고리즘은 중소기업의 스마트팩토리 환경을 단계적으로 구축하여 운영할 수 있도록 전체 제조 공정 중 제품 정보와 출시 정보를 IoT 장치에 이용하여 수집 보관 관리 처리 하도록 하고 있다. 또한, 제안 알고리즘은 장치간 인증 정보를 중앙의 서버가 중앙집중식으로 관리함으로써 IoT 장치 수에 상관없이 IoT간 연계를 자동화하는 특징이 있다. 성능평가 결과, 제안 알고리즘은 스마트팩토리 환경을 구축하기 전의 공장 프로세스와 효율성을 평가한 결과 13.7% 향상된 결과를 얻었고, 공장 내 제품 처리 시간도 19.8% 향상된 결과를 얻었다. 또한, 공정 프로세스에 투입된 인력 투입 비용도 37.1% 감소된 결과를 얻었다.

• International Journal of Safety
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• 제7권1호
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• pp.1-4
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• 2008

Application of the Microsoft robotics studio (MSRS) to the design of a factory safety monitoring robot is presented. Basic structures of the MSRS and the service are introduced. The service is the key building block of the MSRS. Control of the safety monitoring robot is performed using four basic services: 1) the robot service which communicates with the embedded micro-processor and other services, 2) the sensor service that notifies the subscribing services of the change of the sensor value, 3) the motor service which controls the power levels to the motors, 4) the drive service which maneuvers the robot. With built-in capabilities of the MSRS, control of factory safety monitoring robot can be more easily performed.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.817-819
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• 2019

다양한 학습 모델이 발전하고 있는 지금, 학습을 통한 다양한 시도가 진행되고 있다. 이중 에너지 분야에서 많은 연구가 진행 중에 있으며, 대표적으로 BEMS(Building energy Management System)를 볼 수 있다. BEMS의 경우 건물을 기준으로 건물에서 생성되는 다양한 DATA를 이용하여, 에너지 예측 및 제어하는 다양한 기술이 발전해가고 있다. 하지만 FEMS(Factory Energy Management System)에 관련된 연구는 많이 발전하지 못했으며, 이는 BEMS와 FEAMS의 차이에서 비롯된다. 본 연구에서는 실제 공장에서 수집한 DATA를 기반으로 하여, 전력량 예측을 하였으며 예측을 위한 기술로 시계열 DATA 분석 방법인 LSTM 알고리즘을 이용하여 진행하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제26권4호
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• pp.63-85
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• 2017

Purpose Recently, much attention in building smart factory has dramatically increased with an emergence of the Industry 4.0. As we noted a connectivity gap between main concerns of MIS and the automated manufacturing systems such as POP and MES, it is recommended that CPS (Cyber-Physical System) can be an important building block for the smart factory and enrich the depth of MIS knowledge. Therefore, first, this study attempted to identify the connectivity gap between the traditional field of MIS (ERP, SCM, CRM, etc.) and the automated manufacturing systems, and then recommended CPS as a technical bridge to fill the gap. Secondly, we studied concepts and research trend of CPS that is believed to be a virtual mechanism to manage manufacturing systems in an integrated manner. Finally, we suggested research and educational opportunities in MIS based on the CPS perspectives. Design/methodology/approach Since this paper introduced relatively new idea of CPS originally discussed in the field of engineering, traditional MIS research method such as survey and experiment may not fit well. Therefore this research collected technical cases through literature survey in engineering fields, video clips from Youtube, and field references from various ICT Exhibitions and Conventions. Then we analyzed and reorganized them to highlight the necessity of CPS and draw some insight to share with MIS academia. Findings This paper introduced CPS to bridge the connectivity gap between the traditional MIS and automated manufacturing system (smart factory), a concern far away from the MIS academia. Further, this paper suggested future research subjects of MIS such as developing software to share big production data and systems to support manufacturing decisions, and innovating MIS curricula including smart and intelligent manufacturing technology within the context of traditional enterprise systems.