본 연구는 한국노동연구원의 사업체패널에 새롭게 포함된 스마트공장 관련 정 보들을 활용하여 스마트공장이 노동수요에 미칠 영향을 추정했다. 스마트공장의 도입은 전 반적으로 생산직의 업무량을 감소시키며, 스마트공장 수준이 고도화 될 수록 업무량 감소 정 도는 더욱 커진다. 특히 동일제품을 반복 생산하는 공정에서 두드러진다. 반대로 스마트공장 이 표방하는 지능화 및 연결성과 관련된 관리직, 기술전문직 등의 직종이나, 다양한 제품의 혼류 생산을 구현하는 과정의 생산직의 경우 업무량이 늘어나는 것이 관찰된다. 이는 기존 연구에서 제시한 결과와 전반적으로 부합한다. 본 연구의 의의는 최종 노동수요가 아닌 스마 트공장이 표준적인 업무량에 직접적으로 미치는 영향을 추정했다는 데 있다.

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.

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.

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.

This study examines the trends of domestic and foreign smart industries and discusses safety and security issues. Based on the actual situation survey and interview of the smart factory, we would like to examine the perspectives on risks and threats. We will examine safety and health issues related to new harmful and risk factors that may occur in smart factories and suggest institutional development directions for future safety and health. First, a safety and health-related work environment for smart factory workers is investigated and interviews are conducted. Second, we investigate new risk factors and threats to prevent industrial accidents for workers in smart factories. The purpose of this study is to examine what are the new risk factors in the smart factory. In addition, we will try to find reasonable improvement measures by finding out the risks and threats of smart factories through case studies in advanced countries, on-site interviews and surveys.

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.