• 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

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.351-356
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• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.

• 산업기술연구
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• 제22권B호
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• pp.79-88
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• 2002

This paper deals with a case study on the process improvement and the layout design of a boiler factory with FactoryCAD. Firstly, two alternatives of job reallocation are suggested with the understanding of problems in current process. The layout planning for these new alternatives is carried out by simple procedure and the detailed analyses are performed by the aid of FactoryCAD program. The result shows that the new layout plan brings on about 10% reduction of travel distance. The utilization of FactoryCAD program can simplify the process of layout design.

• International Journal of CAD/CAM
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• 제2권1호
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• pp.39-44
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• 2002

This paper presents a structure and architecture of an integrated simulation method (ISM) to meet the requirements of virtual factory engineering (VFE). Combining CAD, VR and discrete event simulation techniques, the ISM provides static and dynamic simulation functions for implementation of VFE throughout the lifecycle. The static simulation can be used to evaluate the factory layout. The dynamic simulation enables us to evaluate ergonomics of factory, process performance of production system, feasibility of production plan and operation of factory, and to train operators safely, which cover the whole VFE lifecycle. The principles of the key techniques of VFE, including virtual factory data management system (VFDMS), static and dynamic simulation, are also discussed. To demonstrate and validate the ISM, a case study has been carried out in an assembly factory.

• 산업공학
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• 제16권1호
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• pp.63-69
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• 2003

Virtual Manufacturing is a technology facilitating effective development and agile production of products via computer models representing physical and logical schema and the behavior of the real manufacturing systems including manufacturing resources, environments and products. For the successful application of this technology, a virtual factory as a well-designed and integrated environment is essential. To construct a virtual factory in effective and concurrent manners, a supporting information infrastructure for managing diverse models of virtual factory is very important. In this paper, we constructed the web-based information management system for many engineering activities related with a virtual factory. Using this system, users can handle all information and diverse digital files including attributes, parameters, 3-D CAD files, simulation models, and etc. of a cell, line and whole factory. We expect that this information management system for a virtual factory helps us achieve great time savings and advances in accuracy for construction and maintenance activities of virtual factories.

• 건축사
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• 7호통권459호
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• pp.48-53
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• 2007

Amisak is a rechargeable tool company and is aiming to be a leading company in Korea by 2010. Ochang factory will be a base for this aim. Client's bad experience in construction of their first Suwon factory and my regretable memory of 1996 design & construction of BTC factory in Whasung lead us to desire for an outstandingly good factory. Total floor area is about 5,000 square. meter (1,500 py) and it has been designed with 3 different facilities, Factory (steel structure, 2 levels), Office (RC structure, 3 levels), and Welfare (RC structure, 3 levels including basement). Factory area is expected to expand its facility at the rear end within 3 to 5 years. Aimsak Ochang factory is expected to be completed in December 2007.

• 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

• 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.

• 디지털융복합연구
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• 제15권5호
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• pp.107-115
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• 2017

최근 스마트 팩토리에 대한 관심이 크게 증가하고 있다. 그러나, 이러한 높은 관심에도 불구하고 정작 어떻게 스마트 팩토리를 구축해야 하는지에 대한 구체적인 가이드라인이 없는 실정이다. 또한 기존의 사례들은 주로 독일을 중심으로한 해외 사례로서 국내에 그대로 적용하기에는 다소의 괴리감이 존재한다. 또한 스마트 팩토리는 기업의 규모와 성격에 따라 구축방식이 상이할 수 있다. 이러한 상황에서 본 연구는 국내 대 중 소기업들을 대상으로 스마트 팩토리 성공구축 사례들을 분석 하였다. 사례분석 결과, 대기업의 경우 일부공장을 대상으로 시범적으로 스마트팩토리를 구축한 후 성공적으로 평가될 시 전체공장으로 확대시키는 전략이 효과적이다. 중소기업의 경우, 낮은 수준의 스마트팩토리 구축레벨에서 높은 수준의 구축레벨로 업그레이드하는 것이 효과적이다. 말하자면 본 연구에서 실시한 스마트 팩토리 성공사례 분석을 통해, 기업들에게 성공적 구축을 위한 실제적인 가이드라인을 제공하고자 한다. 아울러, 본 연구를 통해 제시된 구축전략들은 향후 스마트 팩토리 구축을 고려하고 있는 기업과 정부에게 중요한 함의를 제시하고자 한다.

• 한국연초학회지
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• 제22권1호
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• pp.76-83
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• 2000

This study was carried out to predict blending ratio of cut tobacco(CT), expanded stem(ES), and expanded cut tobacco(ECT) in cigarettes. CT, ES, and ECT samples from A brand were, ground and blended with reference to A blending ratio, and scanned by near infrared spectroscopy(NIRSystem Co., Model 6500). Calibration equations were developed and then determined blending ratio by NIRS. The standard error of calibration(SEC) and performance(SEP) of C factory samples between NIRS and known blending ratio were 0.97%, 1.93% for CT, 0.50%, 1.12 % for ES and 0.68%, 1.10% for ECT, respectively. The SEP of CT, ES and ECT of Band D factory samples determined by C factory calibration equation were more inaccurate than those of C factory samples determined by C factory calibration equations. These results were caused by the difference of CT, ES and ECT spectra followed by each factory. The SEP of CT, ES and ECT of Band D factories determined by calibration equations derived from each factory samples were more accurate than those of determined by calibration equation derived from C factory samples. Each factory SEP of CT, ES and ECT determined by calibration equation derived from all calibration samples(B+C+D factory) was similar to that determined by calibration equation derived from each factory samples. To improve the analytical inaccuracy caused by spectra difference, we need to apply a specific calibration equation for each factory sample. Data in development of specific calibrations between sample and NIRS spectra might supply a method for rapid determination of blending ratio of CT, ES, and ECT.