The objective of this study is to identify the priority of elements for effective implementation of MES in small and medium-sized manufacturing enterprises trying to develop into smart factories. For this purpose, the Delphi method and the Analytic Hierarchy Process(AHP) mothod are applied. As a result of the study, the cooperation of the members in the supply chain is the most important factor for small and medium-sized enterprises in order to survive in the global competitive environment. Therefore, the enterprises need to make various efforts to create synergies through the technical strength of suppliers and the cooperation in the process of introducing and operating MES.

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

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.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

본 연구는 4차 산업혁명의 핵심 산업으로 주목받고 있는 스마트홈 관련 산업의 국민경제적 파급효과를 정량적으로 분석하여 그 잠재성을 평가하였다. 이를 위해 선행연구들에 대한 문헌검토(literature review)를 통해 스마트홈 관련 산업을 제조업과 서비스업으로 분류하고 외생화하였다. 그리고 2018년 산업연관표를 이용하여 이들 산업이 유발하는 생산, 부가가치, 고용 및 취업효과와 산업간 연쇄효과를 분석하였다. 분석결과 스마트홈 제조업과 서비스업은 각 산업 영역에서 타 산업에 비해 높은 수준의 부가가치유발효과를 보였으며 스마트홈 산업 내부적으로는 제조업의 서비스업 생산 견인 기능이 상대적으로 더 크게 나타났 다. 또한 4차 산업혁명기술을 활용하는 산업답게 기술집약적인 산업과의 연관성이 높을 뿐만 아니라 스마트시티, 스마트카, 핀테크 등을 구현하기 위한 서비스 부문과도 깊이 연관되어 있음을 확인하였다. 한편 스마트홈 제조업은 후방파급효과가 전방파급효과에 비해 상대적으로 높은 최종 수요적 산업인 반면 스마트홈 서비스업은 전방파급효과가 후방파급효과에 비해 상대적으로 높게 나타나 중간재 산업으로서 타 산업으로의 공급 기능이 높은 산업임을 알 수 있었다.

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.

This paper presents a log-transformed model-based performance analysis system for analyzing and improving manufacturing performance of the smart factory in the display business. Two years of data related to traditional manufacturing performance such as Cycle-time, WIP(Work-In-Process), and Throughput were investigated from the smart factory that producing the display for this research. We assessed manufacturing competitiveness based on how the operational level of automation affects improvements in manufacturing performances. We analyzed functional relationships between the indicators were derived using logtransformed regression analysis how the manufacturing performance indicators change according to the operational level of smart factory automation. As a result, we knew that the 170K production, which was planned capacity in the line design phase, achieved by running an automation level of only 59%. Based on this research, we suggest building an autopoietic optimize performance model to improving manufacturing competitiveness of smart manufacturing.

Smart Manufacturing Factory is a paradigm of the future lead to the fourth industrial revolution that led Germany and the United States. Now the automation of the production facility and won a certain degree, and through the process of integrating the entire process, including planning, design, distribution of information and communication technology products in emerging as a core competitiveness of the national economy. In particular, the company accelerated the smart factory building in order to improve the manufacturing industry, cost savings and productivity simply to incorporate internet of things(IoT),Robot, artificial intelligence, big data technology as a factory automation level of sophistication of the system and out to progress to the level that replaces human labor have. In this we should look at the trend of promoting domestic and foreign factories want to present these smart strategies for Korea.