중소기업 경영환경이 스마트공장 수준에 미치는 영향

A smart factory is emerging as the most obvious countermeasure against the manpower shortage and the increase of customer demand which the manufacturing industry is experiencing all over the world. In the case of Korea, it is inferior in terms of technology and quality competitiveness to the US, Germany and Japan, which are traditional manufacturing powers and price competitiveness to China, which has been growing fast due to its labor intensiveness. In addition, There is also a negative outlook that Korea will drop from the 5th to 6th place in the ranking of the global manufacturing countries by 2020. Manufacturing proportion is close to 30%, SMEs account for more than 99% of all industries in Korea. For SMEs, Korea has established national standards(KS : Korean Industrial Standards) for the spread of smart factory in 2016 and is actively supporting the development of 30,000 smart factories by 2022 based on the best ICT competitiveness(World No.1 in 2017-2018) This study was conducted to provide practical assistance to SMEs, which have difficulty in developing their own smart factories due to poor available resources and insufficient systems· processes compared with large enterprises. For this study, 67 SMEs were assessed at the smart factory level by applying diagnostic model developed in accordance with manufacturing characteristics. We conducted statistical and qualitative analysis by classifying the data into various business conditions (industry type, production method, delivery to large company, sales amounts, etc.) of the company. In addition, we have presented priority tasks to improve the smart factory level by supplementing the weakness shown in the diagnosis result. As a result of the diagnosis, the overall smart factory level was 19%, the operational division 29% and the hardware/software division 8%. Diagnostic scores to classify into main five industries are 17∼21%, which is “Beginner” of the 5 steps of smart factory. While the levels of operational division were at 27∼31%, H/W·S/W were at only 5∼13%. The level of H/W·S/W in comparison with operational division is so low that ungent and sufficient support seem to be necessary. In operational division, Factory Field Basic, R&D Business, Production Control, Quality Control and Reference Information Standardization were excellent, Strategy, Facility Automation and Facility Management were weak. The correlations of R&D Business-Quality Control(r(correlation coefficient)= 0.639) and Production Control-Quality Control(r= 0.683) between diagnostic categories were the highest. This shows that when the R&D quality is high, the level of manufacturing quality control increases, the repetitive production control and quality monitoring activity have the same tendency. There were almost no significant differences in the results of diagnosis by the type of industry, production method and delivery to large company but significant differences regarding Pre-built IT system, number of employees, sales amounts and number of smart factory support. We found that the existing infrastructure and size of the company are important for building of smart factory. The smaller the size of a company, the more likely it is that H/W·S/W needs to be built after PI(Process Innovation) activities are prioritized to increase operation efficiency. The same is true of large company. “Facility Automation” was the worst of all categories in the operational division, so all companies seemed to be more interested in H/W and facility ICT applications. 65% of the priority tasks were the PI project for the improvement of the company structure. Through this study, we proposed benchmarking opportunities to the industry by presenting smart factory level assessment and analysis results according to SMEs business conditions. Finally, the limitations of this study and the direction of future research were discussed.