• 품질경영학회지
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• 제31권4호
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• pp.219-225
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• 2003

Two­dimensional warranty data can be modelled using two different approaches： two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.

• 품질경영학회지
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• 제32권1호
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• pp.113-129
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• 2004

The reliability of the product can be improved by making the product less sensitive to noises. Especially, it Is important to make products robust against various noise factors encountered in production and field environments. In this paper, the phenomenon of degradation assumes a simple random coefficient degradation model to present analysis procedures of degradation data for robust experimental design. To alleviate weak points of previous studies, such as Taguchi's, Wasserman's, and pseudo failure time methods, novel techniques for analysis of degradation data using the cross array that regards amount of degradation as a dynamic characteristic for time are proposed. Analysis approach for degradation data using robust experimental design are classified by assumptions on parametric or nonparametric degradation rate(or slope). Also, a simulation study demonstrates the superiority of proposed methods over some previous works.

• 품질경영학회지
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• 제25권1호
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• pp.44-55
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• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• 품질경영학회지
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• 제26권3호
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• pp.100-107
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• 1998

To evaluate the total quality activities is the most fundamental and critical process as one of the PDCA cycle. The quality award criteria such as Malcolm Baldrige National Quality Award and Deming Award can be a, pp.ied to the guideline for evaluating quality activities. We can identify several important factors for TQM by referring quality award criteria, but they don't suggest how efficiently implement TQM. In this paper, two methodologies are a, pp.ied to evaluate the TQM activities comparatively. One of them is the traditional scoring system (TSS) by analytic hierarchy process (AHP). TSS is the system which evaluates the performance of TQM by the weighted sum of critical success factors. Several quality award system are typical examples of TSS. The other is the efficiency measuring system (EMS) by data envelop analysis (DEA). DEA outperformed other alternative methods to measure the efficiency and it can be a, pp.ied to evaluate the TQM activities. The evaluation system by DEA can be named as EMS. The objective of this paper is to suggest a model called TAE (Total quality management Activities Evaluation), for evaluating TQM activities. In this model TQM organizations are classified into four types by considering TSS and EMS. Those types are high weighted sum and high efficiency type, high weighted sum and low efficiency type, low weighted sum and high efficiency type, and low weighted sum and low efficiency type. Therefore TQM organizations must not only make efforts to get the higher scores in terms of TSS, but also take necessary steps to enhance their efficiencies.

• 한국의료질향상학회지
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• 제9권1호
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• pp.18-32
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• 2002

Background : We developed a model for predicting premature discharge and identifying related factors. Methods : Prediction model was developed by data mining techniques. Basic data were collected from the total discharge data base of a university hospital in Chungnam Province during the period from July 1, 1999 to June 30, 2000. Results : 1. Among 22,873 patients, the number of patients discharged with usual discharge orders were 21,695 or 94.8%. The number of the prematurely discharged patients were 1,178 or 5.2%. 2. The primary reason for unusual discharge was transfer to other hospital. Move to a local hospital closer to their home and burdensome medical expenses were main reasons. 3. Predictability of each model was tested using the top 10 percent of patients with the highest probabilities of premature discharge. The neural network model was chosen as the most appropriate model for predicting prematurely discharged patients. 4. Ten percent of the total number of patients had been selected randomly to test the effectiveness of the neural network model. We have chosen the threshold of the neural network model as 0.7. The number of patients who were expected to discharge prematurely was 312. Among them, 241 had been discharged prematurely (77.2%). Conclusion : Of the several data mining techniques used, the neural network model was the most effective, It can be used to identify and manage the patients who are expected to discharge prematurely.

• 대한안전경영과학회지
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• 제19권4호
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• pp.221-230
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• 2017

Recently, service quality must reflect several demands of customers who show rapid and various changes so as to be compared with the past. So, objective and rapid methods for them are necessary more. For them, first of all, service company must calculate their standard of service quality accurately by measuring service quality exactly. To measure service quality accurately, this researcher collected and analyzed data by survey for customers who are customers of logistics services, grasped potential satisfaction standard(P) by 5 point Likert scale and one survey for accurate classification of quality attributes through weighted customer satisfaction coefficient changing quality attributes by developing the study on Kano model and Timko's customer satisfaction coefficient, and suggested Potential Customer Satisfaction Improvement index(PCSI) for examining the improvement of customer satisfaction so as to utilize them as an index of differentiated and concrete measurement of service quality.

• 품질경영학회지
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• 제25권3호
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• pp.1-10
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• 1997

In the context of accelerated life tests, procedures are given for estimating the parameters in the modified Arrhenius model and for estimating mean life at a given future stress level. The conditional confidence intervals are obtained by conditioning on ancillary statistics and pivotal quantity. Using the data of Tobias and Trindada(1986), we illustrate conditional confidence interval for parameters under use condition in the modified Arrhenius model.

• 융합경영연구
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• 제11권2호
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• pp.23-32
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• 2023

Purpose: This study examines the effect of full information disclosure on seller profit when there exists information asymmetry between sellers and buyers, focusing on the risk averseness of buyers. By investigating the interaction between product quality and perceived risk through online sales data, we attempt to figure out the incentive structure of full information disclosure specifically when buyers are risk-averse, so that we can suggest more feasible information disclosure strategy to sellers. Research design, data and methodology: Our empirical model analyzes the sales data of collectible goods from a major online seller using Poisson regression. In our model, we have specifically considered risk-averseness of buyers by estimating the interaction effect between the product quality and perceived risk on seller profit, aiming for a more precise empirical analysis on sellers' incentive structure of full disclosure. Results: Our empirical analysis strongly supports the effect of interaction between product quality and perceived risk, showing that the incentive for full disclosure is much stronger when product quality is higher, and vice versa. Therefore, sellers are strongly encouraged to voluntarily reveal product weaknesses when their product quality is higher than average, while it is more profitable to hide any product defects when quality claim is lower than average. Conclusions: This study supports the related literature by confirming economic incentives for full disclosure, and also supplements and strengthens previous studies by presenting that the effect of interaction between product quality and perceived risk strongly affects seller profit. Our unique finding supports both mandatory disclosure and voluntary disclosure arguments and presents practical implications to marketing managers by suggesting that seller's incentive for revealing weaknesses depends on the level of seller's product quality.

• 한국해양공학회지
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• 제22권6호
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• pp.13-19
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• 2008

Abnormally large swells that appeared on the coast of the East Sea in October in 2005 and 2006 were simulated using SWAN model to examine the accuracy of the model for future forecasting Seawind data calculated based on the weather chart ant bottom topography were used for input data, and the model was operated more than 20 days before the observed swells to avoid the problems from the cold start of the model. The comparisons with observed wind and wave data were unsatisfactory and neededmore improvement in terms of swell component in the wave model as well as the quality of seawind data. The satellite wind and wave data can be good candidates for future comparison of the wave model results in the East Sea.

• 한국지하수토양환경학회지:지하수토양환경
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• 제21권6호
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.