• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.487-490
• /
• 2018

As the fourth industrial revolution 4.0 era arrives, the role of smart factory is emerging, which establishes a communication system between production devices and products through the Internet of Things and optimizes the entire production process. Germany wants to use smart factory technologies and data to upgrade and standardize the industry as a whole to create factories around the world, and the United States is aiming to create new business models and revenue streams by analyzing big data and improving productivity based on the technological prowess and innovation across ICT. In addition, Japan and China are also working to change and upgrade their manufacturing industries through smart factories. Accordingly, Korea is attempting to introduce smart factory based on the production industry 3.0. Therefore, this study describes the industrial trends of the fourth industrial revolution and smart factory and compares the major underlying technologies and introduction cases of smart factory.

• Journal of Convergence for Information Technology
• /
• v.8 no.2
• /
• pp.233-238
• /
• 2018

SMEs and small enterprises are making various attempts to manage SMEs in terms of equipment, safety and energy management as well as production management. However, SMEs do not have the investment capacity and it is not easy to build a smart factory to improve management and productivity of SMEs. In this paper, we propose a smart factory construction algorithm that partially integrates the factory equipment currently operated by SMEs. The proposed algorithm supports collection, storage, management and processing of product information and release information through IoT device during the whole manufacturing process so that SMEs' smart factory environment can be constructed and operated in stages. In addition, the proposed algorithm is characterized in that central server manages authentication information between devices to automate the linkage between IoT devices regardless of the number of IoT devices. As a result of the performance evaluation, the proposed algorithm obtained 13.7% improvement in the factory process and efficiency before building the Smart Factory environment, and 19.8% improvement in the processing time in the factory. Also, the cost of input of manpower into process process was reduced by 37.1%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.135-141
• /
• 2019

South Korea's smart factory drive is at a very important point. While large-scale funds and manpower are invested to secure international competitiveness and revitalize manufacturing, software investments that are only approached by IT suppliers may end up creating systems that do not meet the actual conditions of the field. As a result, there are problems in the manufacturing sector that can cause consumers to feel the fatigue of innovation in the manufacturing sector. SMEs should check from scratch and establish a gradual integration system so that they can reduce failures in IT investments and implement OT-oriented smart factories that are well utilized in the field. To this end, a process visualization solution was proposed and a step-by-step innovation was proposed at the basic level and the ICT unapplied level.

• Journal of the Korea Convergence Society
• /
• v.9 no.5
• /
• pp.179-189
• /
• 2018

Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.224-226
• /
• 2021

Recently, in order to increase the efficiency of the product production process, the automation of facilities and devices in the factory is in progress, and a smart factory is being built using ICT and IoT technologies. In order to organically solve many problems occurring in the smart factory, a system for monitoring the wireless communication function between facilities and devices and the manufacturing process environment of the smart factory is required. In this paper, we propose a monitoring system using a Bluetooth module, a temperature/humidity sensor and a fine dust sensor to remotely monitor the process environment of a smart factory. The proposed monitoring system collect Arduino sensor values wirelessly through Bluetooth communication.

• Journal of Venture Innovation
• /
• v.3 no.2
• /
• pp.41-76
• /
• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.8
• /
• pp.343-352
• /
• 2017

IEC62541, known as OPC-UA, is a standard communication protocol for Smart Grid (SG) and Smart Factory application platform. It was accepted as an IEC standard (IEC62541) in 2011 by IEC TC57, and is extending range of application as collaborating with other standrads. The government's policies to popularize EVs ("Workplace Charging Challenge"), the number of Electric vehicle which try to be charging in the factory is expected to increase. In this situation, indiscreet and uncontrolled EV charging can lead to some problems, such as excess of the peak demand capacity. Therefore, EVs, which is charging in SFs, must be monitoring and controlling to avoid and reduce peak demand. However, the standards for EVs charging differ from the standards for SFs. In other words, to increase the ease of use for drivers, and reduce risk for enterprise, we have needs of study to develop the protocols or to provide interoperability, for EVs charging in SFs. This paper deals with a EV charging management platform installing in a smart factory. And this platform can be easily integrated as part of SF management software. The main goal of this paper is to implement EV management system based on IEC61851 and IEC62541.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.12
• /
• pp.297-304
• /
• 2019

The technological advances such as communication, sensor, and artificial intelligence lead smart factory construction. Smart factory aims at efficient process control by utilizing data from the existing automation process and intelligence technology such as machine learning. As a result of constructing smart factory, productivity increases, but costs increase. Therefore, small companies try to make a step-by-step transition from existing process to smart factory. In this paper, we have proposed a remote control system that support data collection, monitoring, and control for manufacturing equipment to support the construction of CNC cutting machine based small-scale smart factory. We have proposed the structure and design of the proposed system and efficient sensing data transmission scheme. To check the feasibility, the system was implemented for CNC cutting machine and functionality verification was performed. For performance evaluation, the web page access time was measured. The results means that the implemented system is available level.

• Journal of Appropriate Technology
• /
• v.6 no.1
• /
• pp.56-64
• /
• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.262-264
• /
• 2019

최근 사물인터넷 분야에서 스마트 팩토리 부분은 많은 주목을 받고 있으며 관련된 연구도 많이 진행되고 있다. 기존의 스마트 팩토리 플랫폼은 제품 생산 효율성을 높여 비용을 줄이는 것을 목표로 센서로부터의 데이터 수집과 클라우드로의 전송, 대규모 데이터 분석을 통한 의사결정 등 자동화 부분에 집중되어 있다. 본 논문에서는 웹 GUI를 활용하여 응용 개발의 편의성을 높여 개발자가 아닌 공정관리자가 직접 공정을 제어할 수 있도록 했으며 유사 환경이 구축된 다른 공장에서 기존에 만들어진 공정 프로그램을 공유하고 재수정할 수 있는 시스템을 설계하였다. 또한, 프로토타입을 구현하여 제안된 웹 GUI기반 스마트 팩토리 공정 관리 및 공유 시스템의 운용 가능성을 검증하였다.