• Journal of the Korea Institute of Building Construction
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• v.10 no.1
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• pp.199-212
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• 2010

To establish an infrastructure for technology and information in the domestic construction industry, several construction regulations pertaining to construction information have been institutionalized. However, there are major problems with the domestic information classification system, earned value management (EVM) and project management information system (PMIS). In particular, the functions of the current PMIS have consisted of a builder-oriented system, and as EVM is not applied to PMIS, the functions of reporting, analysis and forecast for owners are lacking. Moreover, owners cannot confirm information on construction schedule and cost in real time due to the differences between the EVM and PMIS operation systems. The purpose of this study is to provide a framework that is capable of operating PMIS efficiently under an e-business environment, by providing a proposal on how to establish a work breakdown structure (WBS) and an EVM - PMIS integration model, so that PMIS may provide the function of EVM, and stakeholders may have all information in common. At the core of EVM - PMIS integration is the idea that EVM and PMIS have the same operation system, in order to be an activity-based system. The principle of the integration is data integration, in which the information field of an activity is connected with the field of a relational database table consisting of sub-modules for the schedule and cost management function of PMIS using a relational database management system. Therefore, the planned value (PV), cost value (CV), actual cost (AC), schedule variance (SV), schedule performance index (SPI), cost variance (CV) and cost performance index (CPI) of an activity are connected with the field of the relational database table for the schedule and cost sub-modules of PMIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.151-157
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• 2019

This study is about the process capability index (PCI). In this study, we introduce several indices including the index $C_{PR}$ and present the characteristics of the $C_{PR}$ as well as its validity. The difference between the other indices and the $C_{PR}$ is the way we use to estimate the standard deviation. Calculating the index, most indices use sample standard deviation while the index $C_{PR}$ uses range R. The sample standard deviation is generally a better estimator than the range R. But in the case of the panel process, the $C_{PR}$ has more consistency than the other indices at the point of non-conforming ratio which is an important term in quality control. The reason why the $C_{PR}$ using the range has better consistency is explained by introducing the concept of 'flatness ratio'. At least one million cells are present in one panel, so we can't inspect all of them. In estimating the PCI, it is necessary to consider the inspection cost together with the consistency. Even though we want smaller sample size at the point of inspection cost, the small sample size makes the PCI unreliable. There is 'trade off' between the inspection cost and the accuracy of the PCI. Therefore, we should obtain as large a sample size as possible under the allowed inspection cost. In order for $C_{PR}$ to be used throughout the industry, it is necessary to analyze the characteristics of the $C_{PR}$. Because the $C_{PR}$ is a kind of index including subgroup concept, the analysis should be done at the point of sample size of the subgroup. We present numerical analysis results of $C_{PR}$ by the data from the random number generating method. In this study, we also show the difference between the $C_{PR}$ using the range and the $C_P$ which is a representative index using the sample standard deviation. Regression analysis was used for the numerical analysis of the sample data. In addition, residual analysis and equal variance analysis was also conducted.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of Nutrition and Health
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• v.27 no.8
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• pp.864-871
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• 1994

The productivity and 13 influencing variables in 14 conventional hospital foodservice systems the total direct and non-direct labor hours required to produce and serve the total number of patient meals plus the number of cafeteria meals. Human resource variable significantly influencing the productivity level was the labor cost. As this index decreased, the meals served per human hour worked increased. System resource variables correlating significantly with productivity were the length of cycle menu, the ratio of staff meals, and modified patient meal ratio. As the length of cycle menu and the ratio of modified patient meal decreased, more meals were produced per human hour. However, as staff meal ratio increased, the meals served per human hour worked increased. The stepwise regression analysis suggests that around 53% of the variance in productivity is explained by labor cost.

• Korean Journal of Computational Design and Engineering
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• v.18 no.5
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• pp.359-366
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• 2013

In the lower arm design process, a tolerance optimization of the variance of design variables should be preceded before manufacturing process, since it is very cost-effective compared to a strict management of tolerance of products. In this study, a design of experiment (DOE) based on response surface model (RSM) was carried out to find optimized design variables of the lower arm, which can meet a given requirement of probability constraint for the process capability index (Cpk) of the weight and maximum stress. Then, the design space was explored by using the central composite design method, in which the 2nd order Taylor expansion was applied to predict a standard deviation of the responses. The optimal solutions satisfying the probability constraint of the Cpk were found by considering both of the mean value and the standard deviation of the design variables.

• Journal of Korean Academy of Nursing
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• v.45 no.3
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• pp.429-438
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• 2015

Purpose: This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). Methods: This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. Results: We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. Conclusion: KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.9
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• pp.828-836
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• 2015

In this paper, the assessment technique for track accuracy in the phased array radar is proposed. It is assumed that ${\alpha}-{\beta}$ tracking filter to track the target is established in the phased array radar. In order to assess the track accuracy strictly, we should use the real target position data acquired from the special instrument, ACMI(Air Combat Maneuvering Instrument) pod or DGPS(Differential Global Positioning System). However, this method leads to increase the experiment cost and test time. We derive the relationship between the residuals of tracking filter and the standard deviations of range and angle tracking errors which are assigned as track assessment index. The theory of sample variance is introduced in this assessment because track accuracy has to be calculated with many residual samples.

• Journal of Families and Better Life
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• v.33 no.5
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• pp.1-24
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• 2015

The purpose of this study was to investigate the effects of Engel coefficient, Angel coefficient and Schwabe index influencing Household head's life satisfaction. For this study, the data from the 8th analysis of the 2013 Korea Welfare Panel Survey conducted by Korea Institute for Health and Social Affairs were used. For the sample, 903 male Household heads with children under the age of 18, were selected. For statistical analysis, SPSS program (Ver. 21.0) was used. And for statistical methods, frequency and percentile, mean and standard deviation, Pearson's correlation, one way analysis of variance, Duncan's multiple range tests, multiple regression analysis were used. The findings are as follows. First, as a results of analyzing the food costs, education costs and housing costs depending on Income Quintile, the food costs and education costs in the 5th Income Quintile compared with other Income Quintile, were highest. Also, the highest housing cost was in the 2nd Income Quintile, while the least housing cost was in the 1st Income Quintile. Second, by analyzing the differences of Engel coefficient, Angel coefficient and Schwabe index according to Income Quintile, the results show that Engel coefficient and Schwabe index decreases as Income Quintile increases, and Angel coefficient increases as Income Quintile becomes higher. Third, the level of HH's life satisfaction according to Income Quintile, 1st Income Quintile, 2nd Income Quintile, 4th Income Quintile, 3rd Income Quintile, 5th Income Quintile in order, increased. Fourth, as the result of analyzing the influence of Variables related to household and demographics about Engel coefficient, Angel coefficient and Schwabe index, it was shown that the variables effecting Engel coefficient, Angel coefficient, and Schwabe index are age, occupations, Number of workers, House ownership, Income Quintile. Fifth, As a result of analyzing the Variables effecting life satisfaction, especially while Schwabe index is not that significant, Engel coefficient and Angel coefficient are shown to have a significant influence. Therefore, the influence of Food costs and education costs can be confirmed.

• Nutrition Research and Practice
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• v.7 no.4
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• pp.326-329
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• 2013

Height and weight are important indicators to calculate Body Mass Index (BMI); measuring height and weight directly is the most exact method to get this information. However, it is ineffective in terms of cost and time on large population samples. The aim of our study was to investigate the validity of self-reported height and weight data compared to our measured data in Korean children to predict obese status. Four hundred twenty-two fifth-grade (mean age $10.5{\pm}0.5$ years) children who had self-reported and measured height and weight data were final subjects for this study. Overweight/obese was defined as a BMI of or above the 85th percentile of the gender-specific BMI for age in the 2007 Korean National Growth Charts or a BMI of 25 or higher (underweight : < 5th, normal : ${\geq}5th$ to < 85th, overweight : ${\geq}85th$ to < 95th). The differences between self-reported and measured data were tested using paired t-test. Differences based on overweight/obese status were tested using analysis of variance (ANOVA) and linear trends. Pearson's correlation and Cohen's kappa were tested to examine agreements between the self-reported and measured data. Although measured and self-reported height, weight and BMI were significantly different and children tended to overreport their height and underreport their weight, the correlation between the two methods of height, weight and BMI were high (r = 0.956, 0.969, 0.932, respectively; all P < 0.001), and both genders reported their overweight/non-overweight status accurately (Cohen's kappa = 0.792, P < 0.001). Although there were differences between the self-reported and our measured methods, the self-reported weight and height was valid enough to classify overweight/obesity status correctly, especially in non-overweight/obese children. Due to bigger underestimation of weight and overestimation of height in obese children, however, we need to be aware that the self-reported anthropometric data were less accurate in overweight/obese children than in non-overweight/obese children.

• Computers and Concrete
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• v.30 no.6
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• pp.393-407
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• 2022

Due to the enormous cement content, pozzolanic materials, and the use of different aggregates, sustainable controlled low-strength material (CLSM) has a higher material cost than conventional concrete and sustainable construction issues. However, by selecting appropriate materials and formulations, as well as cement and aggregate content, whitethorn costs can be reduced while having a positive environmental impact. This research explores the desire to optimize plastic properties and 28-day unconfined compressive strength (UCS) of CLSM containing powder content from unprocessed-fly ash (u-FA) and recycled fine aggregate (RFA). The mixtures' input parameters consist of water-to-cementitious material ratio (W/CM), fly ash-to-cementitious materials (FA/CM), and paste volume percentage (PV%), while flowability, bleeding, segregation index, and 28-day UCS were the desired responses. The central composite design (CCD) notion was used to produce twenty CLSM mixes and was experimentally validated using MATLAB by an Artificial Neural Network (ANN). Variance analysis (ANOVA) was used for the determination of statistical models. Results revealed that the plastic properties of CLSM improve with the FA/CM rise when the strength declines for 28 days-with an increase in FA/CM, the diameter of the flowability and bleeding decreased. Meanwhile, the u-FA's rise strengthens the CLSM's segregation resistance and raises its strength over 28 days. Using calcareous powder as a substitute for cement has a detrimental effect on bleeding, and 28-day UCS increases segregation resistance. The response surface method (RSM) can establish high correlations between responses and the constituent materials of sustainable CLSM, and the optimal values of variables can be measured to achieve the desired response properties.