In this study, we analyzed the factors affecting the introduction of Smart Factory by domestic SMEs through AHP analysis and tried to provide implications for the introduction of Smart Factory. It was confirmed that the manufacturing and introduction group, the non-manufacturing introduction group, and the already introduced group had the highest weight in the cost reduction in the first hierarchy standard. At this time, it can be seen that the weight for cost reduction is relatively high in the manufacturing introduction group and the introduction group, and the weight for the productivity improvement is relatively high in the non-manufacturing introduction group. It can also be seen that the portion of marketing enhancement does not have a significant impact on smart factory choices. It was confirmed that image enhancement is the highest in the manufacturing introduction group and the non-manufacturing introduction group in the first hierarchy standard, and the marketing has the highest weight in the introduction group. In the two - tiered standard, customer - friendly and proper inventory maintenance weights were relatively high in all the introduced groups, except for the high rankings.

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.

Today, the management environment of the manufacturing industry is faced with drastic changes. The manufacturing industry is planning to innovate for itself via upgrading manufacturing technology and securing manufacturing competitiveness through 'Industry 4.0' and 'Manufacturing 3.0' strategies combined with ICT (information and communications technologies) for smart factory construction. In addition, as the era of the fourth industrial revolution began, the smart factory is emerging as a new paradigm that can lead to new changes in the manufacturing industry and achieve sustainable development. However, most of SMEs (small and medium-sized manufacturing enterprises) in Korea have very low technology, investment capital and expertise for smartization, and the level of informatization is so low that they cannot build basic systems such as a management module of production records. Therefore, this study proposes a framework that integrates cloud-based production data management and production scheduling with intuitive rules for smart production site management of SMEs. The main features of the proposed framework for SMEs are as follows: 1) the collection and management of production data using the cloud system; 2) operation management using intuitive heuristic algorithm; 3) production scheduling through timing constraints-based simulation.

Semiconductor processes are mainly divided into FAB process, package and test. The FAB process is promoting smart factories over a long period of time. The situation of the package and test process is manual, but there is a wafer testing process that prepares the latest topic "SmartFactory". The purpose of this study is to study the key variables for the completion of the material allocation scheduling system which will be the base environment of the smart factory in the wafer test process and to build the system based on the designed research model.

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.