Small and medium-sized manufacturing enterprises(SMEs) have traditionally relied on skilled labor to support multi-variety, small-batch production. However, demographic changes such as low birth rates and aging populations have led to severe labor shortages, prompting increased interest in collaborative robots(cobots) as a viable alternative. Despite this necessity, many SMEs continue to face significant challenges in implementing such technologies due to technical, organizational, and environmental(TOE) constraints. While prior research has mainly focused on technology adoption from the perspective of user organizations, this study adopts a differentiated approach by analyzing adoption factors from the perspective of smart factory experts—specifically, evaluators/mentors and solution providers—who play a critical role in Korea’s policy-driven smart manufacturing environment. Using the Analytic Hierarchy Process(AHP), the study evaluates the relative importance and prioritization of adoption factors across three dimensions: technology, organization, and environment. Survey data collected from 20 smart factory experts indicate that top management support, relative advantage, and safety are key determinants in cobot adoption. Furthermore, the findings reveal that organizational readiness and technical effectiveness have greater influence on implementation decisions than external pressures such as partner pressure. This study provides new insights by incorporating expert perspectives into the adoption framework and offers practical policy and managerial implications to support cobots implementation in the SMEs.

스마트계약은 자동화된 이행을 특징으로 한다. 이는 스마트계약을 계약의 일 종으로 이해하든, 컴퓨터 프로그램이라고 하는 정의하든 마찬가지이다. 스마트 계약에 의한 계약의 집행에서는 “코드가 법”이어서 “실행을 통제하거나 실행 에 영향을 주는 중재자 또는 제3자”가 필요없다. 이러한 스마트 계약을 부동산 거래에 적용할 수 있는지에 관해서 다양한 검토가 행해지고 있다. 외국에서는 스마트계약을 통한 부동산 거래가 시도되기도 했으며, 우리 정부도 이를 부동 산 거래에 도입하는 것을 추진하고 있다. 이 논문에서는 스마트계약을 통한 부 동산 거래의 가능성과 그 한계에 관하여 검토하였다. 스마트계약을 통해 부동 산 매매계약이 집행된다면 동시이행의 확보가 분명하고 효율적으로 달성될 것 이다. 거래의 효율화와 집행용이화는 거래계에서 흔히 볼 수 있는 일반적 흐름 이므로, 스마트계약이 이에 활용될 수 있다면 거래계에서 자연스럽게 채용될 것이다. 그러나 블록체인 상의 스마트계약을 통해서 부동산 거래를 할 필요성 이 있는지, 그러한 방식이 효율적이고 바람직한지에 대하여는 의문이 있다. 우 선 블록체인에서 거래하려면 이를 대체불가능토큰(NFT)화 해야 할 것인데, 부대체의 특정 자산인 부동산을 디지털 자산화 할 필요는 없을 것으로 생각된다. 또한 부동산 거래의 전 과정을 코드화 하려면 그 효용보다 비용이 더 클 것이 므로, 코드화 할 수 있는 것보다 코드화 할 수 없는 부분이 더 많을 것이다. 더 나아가 코드에 의해 이행된 경우에도 법적 분쟁이 발생하지 않는 것은 아니 며, 스마트계약의 불변성은 오히려 법적 해결에 걸림돌로 작용할 수 있을 것이 다. 더 나아가 탈중앙화라는 블록체인의 이상 자체가 부동산 거래에는 적합하 지 않다. 또한 블록체인 기술의 장점으로 평가되는 기록의 정확성과 불변성은 국가가 관리하는 등기부를 통해서도 충분한 정도로 달성될 수 있다. 따라서 블 록체인 스마트계약을 통해서 부동산거래를 할 수 있도록 제도를 마련할 필요 성은 없다고 생각된다.

This study aims to present a methodology and the corresponding results of an economic analysis, incorporating both costs and benefits, to assess the feasibility of introducing a smart on-board truck scale.The cost estimation was conducted based on direct expenditures associated with the installation and operation of smart on-board truck scales. The benefit analysis was performed by evaluating the reduction in social costs resulting from the mitigation of overloading, including transportation infrastructure maintenance costs, traffic accident costs, and environmental costs. The economic analysis outlines the variables required for each phase of the smart on-board truck scale implementation, along with their reasonable value ranges. In consideration of the uncertainty regarding the effectiveness of the smart on-board truck scales, a quantitative assessment of the impact of individual variables on the economic indicators was carried out through scenario analysis, focusing on key variables. The influence of the vehicle service life, the service life of the smart on-board truck scale, and personnel expenses—each related to installation and operation—on the benefit-cost ratio (B/C) and net present value (NPV) was determined to be limited. In contrast, the overload crackdown rate exhibited the most significant impact on the B/C and NPV, as it directly increased the number of vehicles contributing to measurable benefits. Notably, an increase in the discount rate led to a decrease in the values of both economic indicators. This outcome is expected, as the discount rate reduces the present value of future costs and benefits by increasing the denominator in the calculation. The introduction of smart on-board truck scales enables the achievement of economic feasibility in preemptive overload enforcement. Therefore, progressively expanding the number of vehicles equipped with smart on-board truck scales is essential to maximize their effectiveness in the near term.

In this study, we target demand-responsive smart mobility, i.e., a bus-type rural transportation model, that has recently been activated to target public transportation-vulnerable areas in urban-rural integrated cities, and empirically analyze the effects of travel time and service factors on user satisfaction with the transportation mode. An ordered logit model was used for the empirical analysis of a field survey of 449 passengers regarding their usage status and satisfaction with demand-responsive smart mobility in rural areas across the country. As access and travel times increased, bus user satisfaction decreased. Particularly, access time was approximately 1.6 times more important than travel time. Meanwhile, satisfaction with demand-responsive smart mobility was found to increase as drivers were kind and drove safely, vehicles were convenient and ran on time, lines and stops were appropriate, fares were satisfactory, and information on schedules and how to use them was available. Among these service elements, the kindness of the driver was analyzed as the most important variable. This suggests that to activate the use of demand-responsive smart mobility, considering the selection of pick-up and drop-off locations to reduce access time and to make efforts to increase the kindness of drivers is important. The essential flexible schedule of demand-responsive smart mobility, i.e., the use of demand-responsive smart mobility, can be activated only when an operating environment is created that reduces access time and in-vehicle travel time. In other words, it is difficult to revitalize the use of demand-responsive smart mobility if it operates on a fixed route and schedules similar to those of existing buses.

온실 내부 환경은 지역에 따라 외부 환경의 영향을 지속적으로 받는다. 본 연구는 몽골, UAE(아부다비), 호 주(퀸슬란드) 등 지역별로 구축된 한국형 스마트 온실의 환경 특성을 비교하고자 수행하였다. 몽골과 아부다비의 온실 모두 내외부 엔탈피 차이가 감소함에 따라 환기율이 증가하였다. 아부다비의 반밀폐형 온실에서는 10시부터 14시까지 평균 내부 기온이 외부 기온보다 약 7－10°C 낮았고 내부 VPD(12mbar)는 외부 VPD(56mbar)보다 4.6 배 낮았는데 이 결과는 포그 시스템 운영과 관련이 있는 것으로 보인다. 퀸즐랜드 온실의 경우, 내부 온도가 외부 온 도보다 11시 기준 약 3.81°C 높았고, 내부 엔탈피와 VPD가 외부 온도보다 높았으며, 내부와 외부의 엔탈피 차이가 증가함에 따라 환기율이 증가하였다. 이 결과로 엔탈피를 낮추는 것은 환기와, VPD를 낮추는 것은 포그 시스템 작 동과 관련이 있는 것을 알 수 있다. 또한, 작물 생육에 적합한 환경 조건을 효과적으로 관리하기 위해 엔탈피와 VPD 기반의 포그, 환기 또는 난방 시스템이 필요하다는 것을 알 수 있다.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

This study explores the utilization level of smart manufacturing systems in the value chain processes of manufacturing and empirically examines the effect of the utilization level of these systems on manufacturing competitiveness in SMEs. Smart manufacturing systems in the value chain processes are categorized into Sales, Purchasing, Production & Logistics, and Support systems. By analyzing the research model using structural equation modeling, this study identifies that Sales systems, Purchasing systems, Production & Logistics systems, and Support systems have a significant impact on manufacturing process efficiency. Additionally, Production & Logistics systems and manufacturing process efficiency positively and significantly influence manufacturing competitiveness. The findings suggest that the utilization of information is directly and positively related to manufacturing process efficiency, including reducing lead-time, decreasing work performance man-hours (M/H), and improving work accuracy. These improvements ultimately have a significant impact on manufacturing competitiveness. In conclusion, the use of smart manufacturing systems is becoming an integral part of the manufacturing industry. To gain a competitive edge, it will be necessary to introduce and utilize optimal smart manufacturing systems, taking into account the size of manufacturing firms.

해상 운송 시스템에 사이버 위협이 증가함에 따라, 안전한 운항을 보장하기 위한 사이버 복원력의 필요성이 부각되고 있다. 특 히, 자율운항선박과 같은 고도의 기술 융합이 요구되는 스마트선박은 기존보다 더 광범위한 사이버 공격 표면을 가지게 되어 이에 대한 리스크 관리가 필수적이다. 본 연구에서는 스마트선박의 사이버 복원력을 평가하기 위해 국제 표준인 IACS UR E26, E27, IEC 62443, NIST SP 800-160을 분석하고, 이를 통해 스마트선박의 선종과 자율화 수준에 따른 사이버 리스크 평가 및 각각의 리스크에 맞는 복원력 모델 개념을 설계하였다. 특히, 선박의 자율화 수준이 높아질수록 사이버 리스크가 커지므로 이를 반영한 맞춤형 대응 전략을 도출하고 스마트 선박의 사이버 복원력 향상을 위한 성숙도 모델을 제안했다.