• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.196-206
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• 2009

The profitability of a hospital refers to business administration results achieved through its medical care and other management activities during applicable fiscal year. This study focused on operating margin as a measurement index of hospital profitability, which is a genuine medical return obtained by subtracting medical expenses from medical profits achieved during business administration of hospital. Based on the index, this study could deduce certain factors on hospital profitability in terms of various indices affecting profitability. And based on those factors, this study sought to provide more useful reference materials which allow us to devise possible ways to improve hospital profitability. As a result, it was found that public hospitals attained lower profitability than private ones. To analyze profitability depending on each index, this study divided hospitals broadly into deficit group and surplus group. As a result, it was found that there were significant differences in hospital profitability between two groups depending upon relevant indices such as labor cost ratio, maintenance expense ratio, number of operations per medical specialist and medical instrument turnover. According to analysis on potential effects of relevant indices upon profitability, it was found that each index had its explanatory power ranging from 25% to 74.5% depending on given model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.189-204
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• 2018

The purpose of this study is to examine an organizational performance difference by individual utilization level of the smart work. The Smart work help minimizing business process and improving organization productivity based on information technology. This new technology provides a flexible way of the task through smart-work center, videoconferencing, telecommuting, mobile(remote control) and business-only messenger. This investment changes organizational culture, institutions and behavior by new technology applying. The organization system change due to smart work has trouble between alteration preferences and existing maintains a group. In response, the organization should make investment justification of smart work for institutional and culture stabilized by a new system in organization. I set up the analytical process of four stages for empirical research. It will analyze an operation difference of the smart work between pre and post investment in the first-step analysis. The two-step analysis will conduct a text mining analysis of smart work operations. The three-step analysis will identify organization performance differences among individual levels in smart work. The four-step analysis will identify a factor difference in organizational performance by individual utilization level on smart work. According to the study, It has been revealed a difference between the pre and post investment performance on smart work. The text mining analyses many appeared an improvement opinion of organizational culture. Next, there is a difference in organization performance among utilize groups of smart work. Furthermore, the factors of organizational performance among groups appeared differently. The theoretical contribution of this study provided to expand the organizational theory of organization change and resistance. The practical implications provided to require a strong guideline an organizational culture and institution for smart work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.515-522
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• 2018

Current signs of a downturn in South Korea's economy include the sharp drop in consumption. Considering internal and external complications such as the raise in minimum wage and fierce global competition, people's taste and purpose of consumption is shifting as well. Under such circumstances, crowdfunding provides a new investment and distribution channel for businesses in their initial stages. Crowdfunding can serve as a key driver of growth in early stages of business operations and lead to growth in consumption as a new distribution channel. This study explores how customers perceive the process of reward crowdfunding, especially when it comes to fairness in procedure, interaction, and distribution. In addition, we further seek how perceived fairness affects customer satisfaction and loyalty in the industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.15-22
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• 2020

Economic indicators are a factor that affects air cargo volume. This study analyzes the different factors affecting air cargo volume by each Chinese cities according to the main characteristics. The purpose of this study is to help companies related to China, airlines, and other stakeholders predict and prepare for the fluctuations in air cargo volume and make optimal decisions. To this end, 20 economic data were used, and the entire data was reduced to 5 dimensions through factor analysis to build a dataset necessary and evaluated the influencing factors by multi regression. The result shows that Macro-Economic Indicators, Production/Service indicators are significant for every cities and Chinese manufacture/Customer indicators, Korean manufacture/Oil Price indicators, Trade/Current indicators are significant for each other city. All adjusted R2 values are high enough to explain our model and the result showed excellent performance in terms of analyzing the different factors which affects air cargo volume. If companies that are currently doing business with China can identify factors affecting China's cargo volume, they can be flexible in response to changes in plans such as plans to enter China, production plans and inventory management, and marketing strategies, which can be of great help in terms of corporate operations.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.473-488
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• 2021

The enterprise resource planning (ERP) system is a standardized and advanced business process that many companies are implementing now-a-days through customization. However, it affects the efficiency of operations as these customizations are based on uniqueness. In this study, we analyzed the impact of customized modules and processing time on customer service request (CSR), by utilizing the stacked CSR data during the construction and operation of ERP, focusing on small and medium-sized enterprises (SMEs). As a result, a positive correlation was found between unit companies and the length of ERP implementation; ERP modules and the length of ERP implementation; ERP modules and unit companies; and the type of ERP implementation and ERP module. In terms of CSR, a comparison of CSR processing time of CBO (customized business object) module and STD (standard) module revealed that while the five modules did not display statistically significant differences, one module demonstrated a statistically very significant difference. In sum, the analysis indicates that the CBO-type CSR and its processing cost are higher than those of STD-type CSR. These results indicate that companies planning to implement an ERP system should consider the ERP module and their customization ratio and level. It not only gives the theoretical validity that should be considered as an indicator for decision making when ERP is constructed, but also its implications on the impact of processing time suggesting that the maintenance costs and project scheduling of ERP software must also be considered. This study is the first to present the degree of impact on the operation and maintenance of customized modules based on actual data and can provide a theoretical basis for applying SW change ratio in the cost estimation of ERP system maintenance.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.211-220
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• 2022

This paper proposes a green closed-loop supply chain network (GCSN) for optimizing closed-loop supply chains. The GCSN focuses on the application of the recycling express box in logistics circulation, accelerates the standardization of logistics operations and the use of express packaging in e-commerce companies, and promotes the reduction and greening of recycling express box in the e-commerce industry. The GCSN is represented as a mathematical formulation and implemented using LINGO. Greening, environmental protection, and wisdom are the general trends for promoting the growth of the e-commerce industry. Meanwhile, the price of raw materials has increased owing to a shortage of resources, which emphasizes the need for e-commerce enterprises to develop green packaging. Therefore, this study considers the shared circular packaging launched by e-commerce enterprises as the research object, and integrates the problem of facility positioning and path planning in the logistics system. The conclusion summarizes the significance of this study.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• The Korean Journal of Franchise Management
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• v.2 no.2
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• pp.24-44
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• 2011

Many companies are increasingly using social network service(SNS) as an online marketing tool, and its marketing activation has been in the limelight as a differentiation strategy most recently. The purpose of this study is to analyze online marketing cases utilizing SNS and to apply it in Franchise Enterprise in order to activate its marketing activities. This study is more concerned with the cases of facebook, twitter, and blog among social network services and suggests some ways of utilizing them in Franchise Enterprise as follows: Based on the examples of facebook, firstly, we set up the role as a homepage in individul, Franchise Enterprise, and other organizations. Secondly, we also set up the role as an organizing tool in communities, and thirdly, setting up the role as a location map tool. Regarding some applications in marketing tool of Franchise Enterprise, we suggest the role as a public relation tool of the company and brand, and also propose the role of brand planning and development. Finally, we suggest a way of overcoming the limitation in offline operations.

• Journal of Fashion Business
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• v.26 no.2
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• pp.129-142
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• 2022

The purpose of this study is to develop socks to check the range of ankle movement during squats for men in their late 20s. Sensors of 6, 8, and 12 mm were selected, and each sample was impregnated 1 to 3 times. It was prepared using a CNT dispersion, and the GF value was measured using UTM. Among them, the sample with 2 impregnation showed the best GF value. As a result of applying each sample to the socks, the 12 mm sensor was wider than the area of the Achilles tendon, resulting in noise, and the 8 mm sensor was higher than the tensile strength of the socks, resulting in a decrease in the graph. Therefore, testing was performed using a 6 mm sensor. In order to determine the effectiveness of the sensor, the normal operating range was checked through squats, and significant changes were confirmed when the operating range was checked again through squats by performing operations that can increase the operating range through Gastrocnemius, Soleus stretching, and low lunge. Using the results of this study, it is expected that the average value of the ankle movement range of the user is checked prevent injury, to be provided as basic data for the production of shoe products and the promotion of physical health.