• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.135-164
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• 2003

The operating room is the major facility that costs the highest investment per unit area in a hospital. It requires commitment of hospital resources such as manpower, equipments and material. The quantity of these resources committed actually differs from one type of operation to another. Because of this, it is not an easy task to allocate the operating cost to individual clinical departments that share the operating room. A practical way to do so may be to collect and add the operating costs incurred by each clinical department and charge the net cost to the account of the corresponding clinical department. It has been customary to allocate the cost of the operating room to the account of each individual department on the basis of the ratio of the number of operations of the department or the total revenue by each operating room. In an attempt to set up more rational cost allocation method than the customary method, this study proposes a new cost allocation method that calls for itemizing the operation cost into its constituent expenses in detail and adding them up for the operating cost incurred by each individual department. For comparison of the new method with the conventional method, the operating room in the main building of hospital A near Seoul is chosen as a study object. It is selected because it is the biggest operating room in hospital A and most of operations in this hospital are conducted in this room. For this study the one-month operation record performed in January 2001 in this operating room is analyzed to allocate the per-month operation cost to six clinical departments that used this operating room; the departments of general surgery, orthopedic surgery, neuro-surgery, dental surgery, urology, and obstetrics & gynecology. In the new method(or method 1), each operation cost is categorized into three major expenses; personnel expense, material expense, and overhead expense and is allocated into the account of the clinical department that used the operating room. The method 1 shows that, among the total one-month operating cost of 814,054 thousand wons in this hospital, 163,714 thousand won is allocated to GS, 335,084 thousand won to as, 202,772 thousand won to NS, 42,265 thousand won to uno, 33,423 thousand won to OB/GY, and 36.796 thousand won to DS. The allocation of the operating cost to six departments by the new method is quite different from that by the conventional method. According to one conventional allocation method based on the ratio of the number of operations of a department to the total number of operations in the operating room(method 2 hereafter), 329,692 thousand won are allocated to GS, 262,125 thousand won to as, 87,104 thousand won to NS, 59,426 thousand won to URO, 51.285 thousand won to OB/GY, and 24,422 thousand won to DS. According to the other conventional allocation method based on the ratio of the revenue of a department(method 3 hereafter), 148,158 thousand won are allocated to GS, 272,708 thousand won to as, 268.638 thousand won to NS, 45,587 thousand won to uno, 51.285 thousand won to OB/GY, and 27.678 thousand won to DS. As can be noted from these results, the cost allocation to six departments by method 1 is strikingly different from those by method 2 and method 3. The operating cost allocated to GS by method 2 is about twice by method 1. Method 3 makes allocations of the operating cost to individual departments very similarly as method 1. However, there are still discrepancies between the two methods. In particular the cost allocations to OB/GY by the two methods have roughly 53.4% discrepancy. The conventional methods 2 and 3 fail to take into account properly the fact that the average time spent for the operation is different and dependent on the clinical department, whether or not to use expensive clinical material dictate the operating cost, and there is difference between the official operating cost and the actual operating cost. This is why the conventional methods turn out to be inappropriate as the operating cost allocation methods. In conclusion, the new method here may be laborious and cause a complexity in bookkeeping because it requires detailed bookkeeping of the operation cost by its constituent expenses and also by individual clinical department, treating each department as an independent accounting unit. But the method is worth adopting because it will allow the concerned hospital to estimate the operating cost as accurately as practicable. The cost data used in this study such as personnel expense, material cost, overhead cost may not be correct ones. Therefore, the operating cost estimated in the main text may not be the same as the actual cost. Also, the study is focused on the case of only hospital A, which is hardly claimed to represent the hospitals across the nation. In spite of these deficiencies, this study is noteworthy from the standpoint that it proposes a practical allocation method of the operating cost to each individual clinical department.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• Fire Science and Engineering
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• v.17 no.4
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• pp.76-85
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• 2003

In this study, we write down the definitions, their causes and the techniques of analysis as a theoretical consideration of common cause failures, and investigate the limitation and the importance of the common cause failures by applying to the analysis on the fire protection as a representative safety facility. As you can know in the reliability analysis, most impressive cause is the malfunctions of pumping operations; especially the common cause failure of two pumps is dominant. In other words, it is possible to assess system-reliability as twice as actual without CCF From these, CCF is extraordinarily important and the results are highly dependent on the CCF factor. And although it would increase with multiple installations, the reliability are not defined as linear with those multiplications. In addition, the differences in results due to the models for analysis are not significant, whereas the various sources of data produce highly different results. Therefore, we conclude that the reliabilities are dependent on the quality of the usable data much better than the variety of models. As a result, the basic and engineering device for the preventions of CCF of the multiple facilities is to design it as reliably as to design the fire-water pump. That is to say, we must assess those reliabilities using PFD whether they are appropriate to SIL (Safety Integrity Level) which is required for the reliability in SIS (Safety Instrumented System). The result of the analysis on the reliability of the fire-water supply with CCF shows that PFD is 3.80E-3, so that it cannot be said to be designed as safely as in the level of SIL5. However, without CCF, PFD is 1.82E-3 which means that they are designed as unsafely as before.

• Journal of Navigation and Port Research
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• v.40 no.6
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• pp.485-494
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• 2016

The primary purpose of this study is to analyze the impact and determinants of bilateral Free Trade Agreements on international total bulk shipment trade volume at the port of Korea using the Panel Gravity Model. The model estimates the aggregated panel data of exports and imports (excluding transshipment) as a dependent variable during the period from 1996 to 2015. GDP, GDP per capita, distances between bilateral countries, and FTA dummies are included as independent variables. And the economic integration of FTAs including ASEAN+3 and NAFTA3 countries were used as dummy variables. Study results show that GDP and GDP per capita have positive impacts on bulk shipment trade volume at the port of Korea. In addition, Korea's bilateral FTAs with Singapore, India and the United States have positive effects on total bulk trade volume in Korea. This is the so called trade creation effect. On the other hand, ASEAN+3 and NAFTA have negative effects on the total bulk trade. This is the so called trade diversion effect. Also, the distance between Korea and its trade partners has a negative impact. These findings provide insights for: further academic research, site operators who work in related trade and maritime sectors, and policy makers engaged in port and maritime operations. The results can be used to develop strategies for maximizing bulk port throughput.

• Journal of the Korean Geographical Society
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• v.36 no.5
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• pp.578-592
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• 2001

Based on a survey sample of farm households in three provinces of Korea, this article reports the perspectives of fuel-time farmers regarding trade liberalization, its effects on their lives, and the future of small-scale farming. While the agricultural sector is being transformed under neoliberal policies, farmers, with life or no preparation for a global market order, are forced to modernize their farming operations. The findings from the survey indicate that farmers accede to terms of global integration in principle while disapproving state rural policies in practice. The survey data also confirm that intra-regional differences in farmers'perceived satisfaction with living conditions, government farm policies, and socio-economic/labor issues. Disparities in the degree of discontent with government policies and socio-economic well-being are explicit between the relatively diversified region of Kyonggi Province and the farming-dependent regions of Chunbuk and Kyongbuk Provinces. The overall findings uphold that most farmers who have not been fully exposed to free market mechanisms are confronted by increased uncertainties and economic hardships. The findings propound that agricultural/rural policies need to reflect long-term, macroeconomic changes, and regionally/locally-based agricultural structure.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.99-124
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• 2012

Our society has long been talking about necessity for innovation. Since companies in particular need to carry out business innovation in their overall processes, they have attempted to apply many innovation factors on sites and become to pay more attention to their innovation. In order to achieve this goal, companies has applied various information technologies (IT) on sites as a means of innovation, and consequently IT have been greatly developed. It is natural for the field of IT to have faced another revolution which is called cloud computing, which is expected to result in innovative changes in software application via the Internet, data storing, the use of devices, and their operations. As a vehicle of innovation, cloud computing is expected to lead the changes and advancement of our society and the business world. Although many scholars have researched on a variety of topics regarding the innovation via IT, few studies have dealt with the issue of could computing as IT. Thus, the purpose of this paper is to set the variables of innovation attributes based on the previous articles as the characteristic variables and clarify how these variables affect "Performance Expectancy" of companies and the intention of using cloud computing. The result from the analysis of data collected in this study is as follows. The study utilized a research model developed on the innovation diffusion theory to identify influences on the adaptation and spreading IT for cloud computing services. Second, this study summarized the characteristics of cloud computing services as a new concept that introduces innovation at its early stage of adaptation for companies. Third, a theoretical model is provided that relates to the future innovation by suggesting variables for innovation characteristics to adopt cloud computing services. Finally, this study identified the factors affecting expectation and the intention to use the cloud computing service for the companies that consider adopting the cloud computing service. As the parameter and dependent variable respectively, the study deploys the independent variables that are aligned with the characteristics of the cloud computing services based on the innovation diffusion model, and utilizes the expectation for performance and Intention to Use based on the UTAUT theory. Independent variables for the research model include Relative Advantage, Complexity, Compatibility, Cost Saving, Trialability, and Observability. In addition, 'Acceptance for Adaptation' is applied as an adjustment variable to verify the influences on the expected performances from the cloud computing service. The validity of the research model was secured by performing factor analysis and reliability analysis. After confirmatory factor analysis is conducted using AMOS 7.0, the 20 hypotheses are verified through the analysis of the structural equation model, accepting 12 hypotheses among 20. For example, Relative Advantage turned out to have the positive effect both on Individual Performance and on Strategic Performance from the verification of hypothesis, while it showed meaningful correlation to affect Intention to Use directly. This indicates that many articles on the diffusion related Relative Advantage as the most important factor to predict the rate to accept innovation. From the viewpoint of the influence on Performance Expectancy among Compatibility and Cost Saving, Compatibility has the positive effect on both Individual Performance and on Strategic Performance, while it showed meaningful correlation with Intention to Use. However, the topic of the cloud computing service has become a strategic issue for adoption in companies, Cost Saving turns out to affect Individual Performance without a significant influence on Intention to Use. This indicates that companies expect practical performances such as time and cost saving and financial improvements through the adoption of the cloud computing service in the environment of the budget squeezing from the global economic crisis from 2008. Likewise, this positively affects the strategic performance in companies. In terms of effects, Trialability is proved to give no effects on Performance Expectancy. This indicates that the participants of the survey are willing to afford the risk from the high uncertainty caused by innovation, because they positively pursue information about new ideas as innovators and early adopter. In addition, they believe it is unnecessary to test the cloud computing service before the adoption, because there are various types of the cloud computing service. However, Observability positively affected both Individual Performance and Strategic Performance. It also showed meaningful correlation with Intention to Use. From the analysis of the direct effects on Intention to Use by innovative characteristics for the cloud computing service except the parameters, the innovative characteristics for the cloud computing service showed the positive influence on Relative Advantage, Compatibility and Observability while Complexity, Cost saving and the likelihood for the attempt did not affect Intention to Use. While the practical verification that was believed to be the most important factor on Performance Expectancy by characteristics for cloud computing service, Relative Advantage, Compatibility and Observability showed significant correlation with the various causes and effect analysis. Cost Saving showed a significant relation with Strategic Performance in companies, which indicates that the cost to build and operate IT is the burden of the management. Thus, the cloud computing service reflected the expectation as an alternative to reduce the investment and operational cost for IT infrastructure due to the recent economic crisis. The cloud computing service is not pervasive in the business world, but it is rapidly spreading all over the world, because of its inherited merits and benefits. Moreover, results of this research regarding the diffusion innovation are more or less different from those of the existing articles. This seems to be caused by the fact that the cloud computing service has a strong innovative factor that results in a new paradigm shift while most IT that are based on the theory of innovation diffusion are limited to companies and organizations. In addition, the participants in this study are believed to play an important role as innovators and early adapters to introduce the cloud computing service and to have competency to afford higher uncertainty for innovation. In conclusion, the introduction of the cloud computing service is a critical issue in the business world.