In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.

The measurement of sewing work in the labor-intensive garment industry depends considerably on the person performing the measurements, making it difficult to quantitatively define the level of skill (L_S) of the sewing machine operator and the level of difficulty (L_D) of the unit process. In this study, a power monitoring system attached to the sewing machine was used to remotely collect power consumption data, which were then analyzed to extract the working times for a series of sewing tasks. L_S of each operator was then classified and L_D of each process was analyzed in terms of working time and quality. Finally, the resulting L_S and L_D weight factors considered to optimize the subject garment production line were compared against those proposed by experts. The L_S weight factor proposed by the experts was ~15% less than that indicated by the experimental results, whereas the L_D weight factor proposed by the experts was ~15%–40% greater than that indicated by the experimental results. The results of this study suggest that the proposed method could be applied in real time to inform the arrangement of line workers to increase the productivity of a garment production line.

Global climate change is causing various disasters and exacerbating health threats. In particular, the incidence of climate-sensitive diseases is increasing, and early warning of these phenomena is expected through integrated analysis of meteorological observations and disease outbreak trends. Meanwhile, isolated areas in developing countries, where healthcare infrastructure is lacking and access to the national power grid is limited, face significant challenges in predicting and responding to climate-sensitive diseases. North Korea, in particular, is known to have a high proportion of mountainous terrain, an underdeveloped national power grid, and poor health conditions at public health centers essential for the health of its residents. This study proposes a smart monitoring system that can secure the power infrastructure necessary for operating public health centers in isolated areas of developing countries, such as North Korea, and collect meteorological data that can be used to predict climate-sensitive diseases. Designed with the communication characteristics of remote areas, the smart monitoring system was installed at a public health center in Pyeongchang County, Gangwon Province, which has a similar environment to North Korea, and its feasibility was verified. The smart monitoring system developed through this study was utilized to obtain climate-sensitive disease prediction data and to estimate the amount of solar power generation required to operate remote protection centers. This system is expected to be useful in predicting climate-sensitive diseases and securing the power infrastructure necessary for medical activities in various isolated areas.