• International journal of advanced smart convergence
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• v.11 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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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.

• The Journal of Information Systems
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• v.32 no.3
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• pp.205-224
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• 2023

Purpose This study investigates the impact of organizational characteristics on organizational performance through case studies of smart factory implementation in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study adopts the smart factory index of KOSMO (Korea Smart Manufacturing Office) established by Korean Ministry of SMEs and Startups. We visited 3 firms implemented smart factory projects. This study presents the results of field study in detail with evaluation criteria on how organizational competences like AI technology adoption and facility automation can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach There are not so many results of empirical studies related to smart factories in Korea. This is because organizational support and user involvement are required for facility AI platform service beyond factory automation after the start of the 4th Industrial Revolution. Korean government's KOSMO (Korean Smart Manufacturing Office) has developed and proposed a level measurement index for smart factory implementation. This study conducts case studies based on the level measurement method proposed by KOSMO in the process of conducting case studies of three companies belonging to the root and mechanic industries in Korea. Findings The findings indicate that organizational competences, such as facility AI platform adoption and user involvement, are antecedents to influence smart factory implementation, while smart factory implementation has significant relationship with organizational performance. This study provides a better understanding of the connection between organizational competences and organizational performance through smart factory case studies. This study suggests that SMEs should focus on enhancing their organizational competences for improving organizational performance through implementing smart factory projects.

• Korean small business review
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• v.41 no.4
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• pp.1-36
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• 2019

The purpose of this study is to identify the relationships among purposes and contents of smart factory building and continuous utilization of smart factory. Specifically, this study identifies two types of purposes of smart factory building as follows: (1) improving productivity, (2) increasing flexibility. In this study, three aspects of smart factory building contents were suggested like this: (1) automation area (facility automation vs. work automation), (2) big data system focus (radical transformation vs. incremental improvement), and (3) value chain integration area (internal value chain integration vs. external value chain integration). In addition, we looked at how firm size moderates the purposes - contents - continuous utilization of smart factory relationship. A questionnaire survey was conducted on 151 manufacturing companies. More specifically, out of 151 companies, 100 are small-and-medium-sized enterprises and 51 large-sized enterprises. All questionnaires were targeted at companies with Smart Factory level above level 2. The analysis results of this study using Smart PLS statistical programs are as follows. First, the purposes of smart factory building including increasing productivity and flexibility had positive impacts on all of the contents of smart factory building. Second, all of smart factory building contents had positive impacts on the continuous use of smart factory except big data system for incremental improvement of manufacturing process. Third, the impacts of smart factory building purposes implementation on smart factory building contents varied depending on whether the purpose is productivity improvement or flexibility. Fourth, it was founded that firm size moderated the relationships of purposes - contents - continuous utilization of smart factory in such a way that large-sized firms tend to empathize the link between flexibility and smart factory building contents for continuous use of smart factory, while small-and-medium-sized-firms emphasizing the link between productivity and smart factory building contents. Most of the previous studies have focused on presenting current smart factory deployment cases. However, it is believed that this research has made a theoretical contribution in this field in that it established and verified a research model for the smart factory building strategy. Based on the findings from a working-level perspective, corporate practitioners also need to have a different approach to smart factory building, which should be emphasized depending on whether their purpose of building smart factory is to increase productivity or flexibility. In particular, since the results of this study identify the moderating effect of firm size, it is deemed necessary for firms to implement a smart factory building strategy suitable for their firm size.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1071-1080
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• 2017

Recently, the terms of the 4th Industrial Revolution and the Smart Factory are often heard through news and media. But most of the companies that are parties are not interested. Because there is no specific guidance on how to build Smart Factory and information about Smart Factory. The built of the Smart Factory should be carried out in accordance with the size of the company considering the purpose of the introduction. In the existing study, they analyzed successful cases of building Smart Factory in Korea As a result, in the case of large-size firms, it is an effective strategy that expanding from a model factory to whole factory for successful Smart Factory building. In addition, in the case of medium and small-size firms, it is an effective strategy that upgrading from low-level step to high-level step for successful Smart Factory building. In this study, selecting medium and small-size firms, and bottleneck section and processes requiring improvement are identified through 3D virtual process design, and then install sensors. Finally, after analyzing the data collected through the sensor, we will improve the process and build Smart Factory with improved productivity.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.545-571
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• 2016

The development of ICT brings a big change in manufacturing industries, and new information technology such as IoT, AR, and big data was applied on manufacturing process. As a result, the concept of smart factory has been introduced as a new manufacturing paradigm. In fact advanced countries like USA, Germany, and Japan have actively introduced smart factory in their manufacturing industries such as electronic, automobile, machinery, to improve production efficiency and quality. The manufacturing environment has been changed into flexible system, so that smart factory will be leading future manufacturing industries. Thes changes have more severe influence on Korean manufacturing industries. Mny industrial companies, have a strong interest in smart factory and they, particularly big enterprises, have been adopting smart factory to increase their manufacturing efficiencies. However, Korean small and medium-sized enterprises (SMEs) have many financial and technological difficulties so that the diffusion of smart factory in Korean SMEs has not been satisfiable up to present. However, smart factory is very important for enhancing their competitiveness in global market. Therefore, this study aims at identifying the standardization strategy of smart factory in so-called Korean 'roots industry' by presuming that the standardization will activate the diffusion of smart factory among Korean SMEs. For this purpose, first, this study examines the competitiveness of SMEs, especially in 'roots industry' and identifies the necessity of diffusion of smart factory among those SMEs. Second, based on the active review on the existing literature, this study identifies four factor groups that would influence the adoption or diffusion of standardized smart factory. They are technological, organizational, industrial and policy factors. Third, using those four factors, this study made two comprehensive case analyses on the adoption and diffusion of smart factory. These two companies belong to molding sector which is one of the important six sectors in 'root industry'. Finally, based on the theoretical and empirical analyse, this study suggests four strategies for activating the standardization of smart factory; international standardization, government-leading standardization, firm-leading standardization, and non-standardization.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.273-278
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• 2022

Uncertainty is increasing around the world due to COVID-19 and Ukraine crisis. In this situation, each company is making countless efforts to survive. In Korea, smart factory projects targeting small and medium-sized businesses with difficulties have been continuously promoted. As for the smart factory business that has been promoted so far, the base expansion of the smart factory is also steadily increasing as the number of companies carrying out the project is increasing. It was also found that it contributed to productivity improvement and quality improvement. Despite these positive aspects, difficulties and operational problems are also appearing in the process of promoting smart factories. In this study, we investigated and analyzed operational problems and difficulties in the process of promoting smart factories. In addition, improvement plans for problems were presented according to the contents of this analysis, and improvement plans were presented by classifying them into introduction and supply companies, considering that the smart factory business is formed in the form of a consortium between introduction and supply companies.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.281-294
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• 2023

Purpose - The purpose of this study is to explore the determinants of the organizational level for the advancement of smart factory. We suggested three determinants of the organizational level such as CEO's entrepreneurship, high-involvement human resource management, and cooperative industrial relations. Design/methodology/approach - The population of our survey was manufacturing SMEs, and we took a sample and conducted a survey of 232 companies. Since the level of smart factory advancement, which is a dependent variable, was measured on an ordinal scale, ordinal logistic regression analysis was used to test the hypothesis. Findings - The higher the level of high-involvement human resource management, the higher the level of smart factory advancement. As the level of high-involvement human resource management increases by one unit, the probability of smart factory advancement increases by 22.8%. On the other hand, the CEO's entrepreneurship did not significantly affect the level of smart factory advancement. Interestingly, the cooperative industrial relations negatively affected to the level of smart factory advancement, contrary to the hypothesis prediction. Research implications or Originality - This study explored determinants at the organizational level that affect the advancement of smart factories. Through this, various implications are presented for related research and policy fields.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.107-115
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• 2017

Recently, much attention in building smart factory have been dramatically increased. Despite the growing interest in smart factory, few guidelines exist how to successfully build smart factory. The purpose of this study is to investigate successful cases in building smart factory in Korea. Drawing on the analysis of successful cases, we suggest the valuable guidelines and directions toward success of smart factory. As a result, in the case of large-size firms, it is an effective strategy that expanding from a model factory to whole factory for successful smart factory building. In addition, in the case of medium and small-size firms, it is an effective strategy that upgrading from low-level step to high-level step for successful smart factory building. Therefore, this study provides companies and government with specific and practical success strategies as well as industrial policy improvements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.