• Journal of Information Technology Applications and Management
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• v.26 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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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.

• Korean Journal of Computational Design and Engineering
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• v.6 no.1
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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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• v.26 no.4
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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.

• Journal of Information Technology Services
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• v.11 no.sup
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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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• v.13 no.2
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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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.471-477
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• 2021

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

• Proceedings of the KSME Conference
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• 2001.06c
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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.