A Smart factories are systems that enable quick response to customer demands, reduce defect rates, and maximize productivity. They have evolved from manual labor-intensive processes to automation and now to cyber-physical systems with the help of information and communication technology. However, many small and medium-sized enterprises (SMEs) are still unable to implement even the initial stages of smart factories due to various environmental and economic constraints. Additionally, there is a lack of awareness and understanding of the concept of smart factories. To address this issue, the Cooperationbased Smart Factory Construction Support Project was launched. This project is a differentiated support project that provides customized programs based on the size and level of the company. Research has been conducted to analyze the impact of this project on participating and non-participating companies. The study aims to determine the effectiveness of the support policy and suggest efficient measures for improvement. Furthermore, the research aims to provide direction for future support projects to enhance the manufacturing competitiveness of SMEs. Ultimately, the goal is to improve the overall manufacturing industry and drive innovation.

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

Recently, small and medium-sized manufacturing companies have shown increased interest in and participation in smart factories in order to survive in market competition. However, many SMEs build smart factories without a systematic review or preparation, which leads to them not being able to use them properly. This study considers the main reason for the low utilization rate of smart factories to be a lack of sufficient reflection of user requirements. Therefore, a method and procedure for deriving the priority of smart factory requirement functions and operating performance indicators based on QFD is proposed as a solution to this issue.

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using ‘technology readiness’ and ‘integrated technology acceptance model’. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium- sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

The smart factory is an important system that can reduce defects, maximize productivity, and respond to customer needs, from the labor-intensive era of traditional small and medium-sized manufacturing companies through the automation era to CPS using ICT. However, small and medium-sized manufacturers often fall short of the basic stage due to economic and environmental constraints, and there are many companies that do not even recognize the concept of a smart factory. In this situation, to expand the smart factory of small and medium-sized enterprises, the project to support the establishment of a smart factory for the win-win between large and small enterprises. The win-win smart factory construction support project provides a customized differentiation program support project according to the size and level of the company for all domestic manufacturing SMEs regardless of whether or not they are dealing with Samsung. In this study, we analyze the construction status and introduction performance of companies participating in the win-win smart factory support project to find out whether they have been helpful in management and to find efficient ways to improve support policies, and to suggest the direction of continuous support projects to improve the manufacturing competitiveness of SMEs in the future.

This study examines the effects of participation purpose, corporate readiness, and acceptance of changes that may occur in the course of expert guidance on the performance of smart factory. For this study, 129 questionnaires obtained from SMEs participating in the Smart Meister support project were used, and SPSS 18.0 and the AMOS 18.0 program were used for statistical processing for empirical analysis of the hypotheses test. It was found that the company's business participation motivation and readiness status had a significant effect on the acceptance and cooperation of changes that occurred during the consulting process. In addition, the acceptance and cooperation of changes within the company had a significant effect on the satisfaction with the Meister support project and the financial performance. Companies participating in the Meister support project need to clarify their motives for participating in the project and make stable corporate readiness in advance. In addition, based on the CEO’s support, it is necessary to have a motivational program and to build an organizational culture that can actively accept innovation.

Ppuri or Root technology primarily includes technologies such as casting, mold, plastic working, welding, heat treatment and surface treatment. It is regarded as an essential element for improving the competitiveness of the quality of final products. This study investigates the current status of smart factory implementation for Ppuri companies and analyzes the influencing relationships among various company factors. The factors affecting smart factory implementation for Ppuri companies are sales, exports, number of technical employees, and holding corporate research institutes. In addition, this research shows that even if smart factory implementation is pursued for data collection, data utilization is not implemented properly. Thus, it is suggested that the implementation of smart factories requires not only the availability of facilities and systems but also proper data utilization.

The construction of smart factories for government SMEs is not easy due to the lack of professional manpower. The use of retired professionals is a way to solve the problem to some extent and to solve the job problem of seniors by effectively utilizing social assets. This study examines the effectiveness of using Meister based on a survey of 195 companies participating in the Smart Meister Support Program. As a result, the better pre-participation readiness and the better management and coordination of change during the participation, the more significant influence was on Meister’s ability development and corporate performance. In particular, it was confirmed that Meister’s competence plays a role in both ‘pre-participation readiness and business performance’ and ‘between change management during participation and business performance’. In order to improve the performance of the smart meister business in the future, it is necessary to proactively promote the purpose and purpose of the business targeting companies that wish to participate in the business. In addition, it was found that it is necessary to support the development of change management in order to minimize the resistance to innovation during the project. It will be possible to enhance social competitiveness by resolving senior jobs and strengthening the competitiveness of SMEs by discovering and utilizing Meister, who is an expert among retirees.

Developed countries that have experienced decline in productivity due to the economic crisis in the past have come to recognize the smart factory as an important means to strengthen the competitiveness of the manufacturing industry due to the increase in labor costs, the avoidance of the manufacturing industry, and the resolution of the shortage of skilled manpower. The necessity of nurturing manpower for self-maintenance was felt through identifying factors for successful smart factory introduction by companies and providing smart factory education. Therefore, the effects of educational satisfaction and operational competency on self-efficacy as a parameter and self-efficacy as a parameter were analyzed using research models and hypotheses to determine whether there was an effect between job satisfaction as a dependent variable. As a result of the analysis, it was found that the mediating effect of self-efficacy and self-efficacy on job satisfaction was found to have significant effects on operational competency and self-efficacy as parameters, as well as educational satisfaction and operational competency. The implication of this study is that continuous education and innovation activities are important in order to increase the business performance of companies, and through this, the manufacturing competitiveness of SMEs can be improved.

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.

This study studied a system that can redesign the production site layout and respond with dynamic simulation through fabric production process innovation for smart factory promotion and digital-oriented decision making of the production process. We propose to reflect the required throughput and throughput per unit facility of fabric production process as probability distribution, and to construct data-driven metabolism such as data collection, data conversion processing, data rake generation, production site monitoring and simulation utilization. In this study, we demonstrate digital-centric field decision smartization through architectural design for the smartization of fabric production plants and dynamic simulations that reflect it.

In order to provide priorities of the factors affecting the introduction of Smart Factory, This study reconstructed the factors and calculated the priorities through AHP (Analytic Hierarchy Process). The first layer of the hierarchy have 4 factors; productivity increase, brand image improve, marketing improve, cost reduction. The second layer of the hierarchy have 3 factors belong to the first layer, so the total number of second layer is 12. We divided the characteristics of enterprises into type of manager and age. The C.R. (consistency ratio) values of the respondents were found to be less than 0.1 and were judged to be a 'reasonable test'. As a result, the weights of the higher layer and the lower layer were obtained respectively, and then the weights of the higher layer and the weights of the lower layer were multiplied to obtain the total weights. Unlike previous studies that only surveyed factors that companies consider when introducing smart factory, (1) weighing and prioritizing factors were achieved. There are differences in priorities, (2) smart factory can be studied with the type of manager and firm age. When establishing policies, it is a practical implication (3) to assess its strategy not only for government officials but also for executives.

The South Korean government is actively assisting the supply of the smart factory solutions to SMEs (Small & Medium-sized Enterprises) according to its manufacturing innovation 3.0 policy for the smart manufacturing as the 4th industrial revolution era unfolds. This study analyzed the impacts of the smart factory solutions, which have been supplied by the government, on the companies performances. The effects of the level of smart factory and the operation capabilities for the smart factory solutions on company performances, and the mediating effects of manufacturing capabilities have been analyzed using SPSS and AMOS. The data for this survey-based study were collected from the SMEs which implemented the smart factory solutions since 2015. The results show that the level of smart factory solutions adopted and operation capabilities for the smart factories do not have direct effects on the company performances, but their mediating effects on the manufacturing capabilities matter and the manufacturing capabilities effect directly on the company performances. In addition significant factors boosting the operation capability for the smart factory and the levels of the smart factory solutions are identified. Finally, the policy direction for enhancing the smart factory effects is presented, and the future research directions along with the limitations are suggested.