Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

Recently domestic manufacturing companies have been experiencing worsening profitability and stunted growth due to the long-term economic recession and the rapid rise of developing countries such as China and Southeast Asia. These difficulties force many companies to concentrate their core competencies on new value creation and innovation in order to gain momentum for new growth. Enterprise Resource Planning (ERP) has been considered as one of viable solutions. Among the various modules in ERP, shop floor control function in the production management module is rather limited. In order to overcome this problem, Manufacturing Execution System (MES) has been used as a subsystem which has a strong information gathering power and flexibility. Both systems interact closely with each other. In particular, ERP requires fast, accurate shop floor information at MES. This paper describes how to synchronize relevant information between ERP and MES with theory of constraints (TOC). The processing time information transmitted from the MES workplace is received at the ERP workplace. In the process, the received processing time is causing information distortion in ERP, when the information gathering standard of MES is different from the ERP information interpretation standard. The Drum-Buffer-Rope theory of TOC was applied to resolve this problem, therefore, information synchronization between both systems was made. As a precondition, the standard time of the upper ERP system was rearranged according to the capacity constraints resource. As a result, standard time restructuring has affected changes in labor costs. Standard labor costs have come close to actual ones, and information synchronization of MES transmission data has improved the reliability of standard product costs, such that it enabled various company-wide restructuring actions to be much more effective.

The Korean Ministry of Education (MOE) required cash flow statements, instead of fund statements, as a component of the financial statements of the Industry-Academic Cooperation Foundations (IACF) when it revised IACF accounting rules in 2012. While the fund statements were aligned its accounts to the operating statements’ accounts level, the newly adopted cash flow statements’ accounts retreated to two levels higher than those of the operating statements. It may be resulted from the consideration that IACFs’ burden of preparing cash flow statements especially in direct method. To help IACFs prepare the cash flow statements, MOE's guidance on IACF accounting rules introduced cash flow statement preparation method and presented a practical example, but it did not check whether the accounts of the cash flow statement were reconciled to the accounts of corresponding balance sheet and operating statement. That means that the guidance still lacks how to assure it was accurately prepared. Our study proposed cash flow statement preparation using reconciliation method which reconciles cash flow statement accounts’ amounts to balance sheet and operating statement accounts’ amounts with integrity checking and also provided a practical example by using the same case in MOE’s guidance on IACF accounting rules to help IACF accounting personnel to prepare a cash flow statement efficiently.

The objective of this study was to identify the primary source of radon in Seoul subway stations, and to investigate a relationship between geology and radon. Especially, we expected that the granite areas would have substantially high levels of radon in subway stations. The indoor radon concentrations in subway stations were lognormally distributed. The geometric mean and geometric standard deviation of indoor radon concentration were 48.11 Bq/㎥ and 2.15, respectively. Indoor radon concentrations of eight measuring sites exceeded U.S. EPA criteria (148 Bq/㎥). The geological structure of the subway station regions under this study is characterized by biotite granite, alluvium, banded biotite gneiss and diluvium. Results indicate that bedrock geology can account for a significant portion of the indoor radon in subway stations. Indoor radon concentrations of one subway station were higher than those of other stations. The bed rock in this particular subway station was that of alluvium. We assumed that the unusual increase in measured radon concentration should be related mainly to the existence of the near inferred fault zone (p<0.0001). We selected ten subway stations with homogeneous bedrock type in order to compare radon concentrations of each basement level. There was a significant difference in radon concentration, depending on the basement levels in subway stations (p<0.05).