• 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.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• 산업경영시스템학회지
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• 제24권64호
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• pp.77-83
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• 2001

In this paper, we investigated the relationship between the variables related to manufacturing environment and industrial accident. Also we wish to analyze how much these variables influence in production result of company: the manufacturing performances such as production quantity, quality, cost and delivery. For this investigation, we collected the real data from 16 small/medium-sized manufacturing companies by performing a questionnaire survey and one-site interview with the workers. Sixteen companies were made up of the following four industries: metal processing, machinery manufacturing, chemical products manufacturing and electronic products manufacturing, The data analysis was made using SPSS PC+. Based on the result of the analysis, we came to the conclusion that most of variables related to manufacturing environment and industrial safety were connecting with industrial accident occurrence and also influenced in manufacturing performance.

• 한국CDE학회논문집
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• 제6권1호
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• pp.17-23
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• 2001

Lasting machines for shoe manufacturing are continuously developed with the aid of automation and CAM(Computer Aided Manufacturing). Although automation and CAM techniques have tremendously reduced the labor in shoe manufacturing, there still remain some parts manufactured by experts. In order to enhance the capability and efficiency of machines for labor-free shoe manufacturing, CAD data of a shoe last is essential. While CAD datarization takes the fundamental role in the shoe design and manufacturing, there has been little research for the CAD datarization of a shoe last. In this paper, a new procedure for CAD datarization of a shoe last using finite element patches and a tension sl)line method is proposed for application to shoe manufacturing machines. The outer line of a shoe-last sole is interpolated by a tension spline method and bonding lines are extracted from the shoe CAD data. Data set for a control algorithm of the tasting machine can be produced from the CAD data.

• East Asian Economic Review
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• 제26권4호
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• pp.257-277
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• 2022

Does servitization improve manufacturing firms' performance, and in what condition? Following the seminal work of Crozet and Milet (2017), this study analyzes disaggregated firm-level data that covers 40,000 South Korean manufacturing firms surveyed by the Survey of Business Activities of Korea. We compute firm-level servitization intensity with available sales data of each firm by two-digit SIC sub-sectors. We find two novel empirical regularities: Korean servitization intensity distribution shows a very different shape from the French benchmark; Servitized firms tend to perform higher profitability and higher productivity than non-servitized firms.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.153-164
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• 2012

There is a belief that timely and precise data are important to decisions and the better decisions are related to better firm performance. However, empirical research investigating the effect of data quality on firm financial performance is still scarce up to recently. Current study empirically explores such an effect of data quality on firm accounting performance in the Korean manufacturing industry during 2008~2010 with secondary data. The results show that better data quality does not impact on sales and operating profit, but positively and significantly impacts on EVA(Economic Value Added). Raising the level of data quality management maturity by one level can increase EVA by about 34% in manufacturing firms.

• International Journal of Contents
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• 제13권2호
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• pp.21-28
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• 2017

Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• 한국정보통신학회논문지
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• 제25권3호
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• pp.471-477
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• 2021

디스플레이 분야에 스마트 팩토리란 작업 자동화 뿐만 아니라 기존의 공정관리, 이동설비, 공정이상, 결함 분류 등에 AI/BIG DATA 기술을 이용한 보다 효율적인 디스플레이 제조를 의미한다. 과거 디스플레이 제조 과정에서 불량이 나오면 결함 분류, 공정 이상에 대한 대처가 시시각각 달랐기 때문에 이에 대한 많은 시간 소모가 발생했었다. 하지만 디스플레이 제조 분야는 고도화된 공정 장비를 이용해야 하고 불량 원인을 신속하게 파악해 수율을 올리는 것이 디스플레이 제조 산업의 경쟁력이다. 본 논문에는 스마트 팩토리 AI/BIG DATA 기술을 디스플레이 제조에 접목한 사례들에 대해 정리해 보고 기존 방법 대비 어떤 장점이 도출 되어질 수 있는지에 대해 처음으로 분석해 보고자 한다. 이를 통해 향후 AI/BIG DATA를 이용한 디스플레이 제조 분야에 보다 향상된 스마트 팩토리 개발을 위한 사전지식으로 활용하고자 한다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.122-127
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• 2001

Lasting machines for shoe manufacturing are continuously developed with the aid of automation and Computer Aided Manufacturing (CAM). Although automation and CAM techniques have tremendously reduced the labor in shoe manufacturing field, there still remain some parts manufactured by experts. In order to enhance the capability and efficiency of machines for labor-free shoe manufacturing, CAD data of a shoe last is indispensable. While CAD datarization takes the fundamental role in the shoe design as well as the shoe manufacturing, there has been little research for the CAD datarization of a shoe last. In this paper, a new procedure for CAD datarization of a shoe last using finite element patches is proposed and some data for the control part of the shoe lasting machine are extracted and interpolated from the CAD data. The outer line of a shoe-last sole is interpolated by a tension spline method and bonding lines are extracted from the shoe CAD data. Finally, initial setting data for the lasting machine are extracted from the last CAD data and initial setup parts of the lasting machine.