• 지식경영연구
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• 제15권2호
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• pp.147-164
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• 2014

This paper investigated the efficiency of automobile firms by using several non-parametric approaches. First, using Data Envelopment Analysis (DEA), the paper has investigated the critical factors that determine the relative efficiency of management performance in automobile companies. Second, we examined how the firm size impact on the difference of this efficiency by using Kruskl-Wallis Test. Third, by using Mann-whitney test, we also investigated the difference of the efficiency accoss existence of technological innovation activity. Finally, the paper explored the relationship between technological innovation and management efficiency by using logistic regression model. The findings of this study provided practical information for inefficient automobile firms to find benchmarking firms and strategic position to improve their efficiency. The result also provided theoretical and methodological implications for those who explore factors affecting management efficiencies. Future research directions with the limitation of the study are discussed.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.47-61
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• 2017

Purpose Although automobile production has increased since the development of the Korean automobile industry, the number of customers who can purchase automobiles decreases relatively. Therefore, automobile companies need to develop strategies to attract customers and promote their repurchase behaviors. To this end, this paper analyzed customer data from a Korean automobile company using data mining techniques to derive repurchase strategies. Design/methodology/approach We conducted under-sampling to balance the collected data and generated 10 datasets. We then implemented prediction models by applying a decision tree, naive Bayesian, and artificial neural network algorithms to each of the datasets. As a result, we derived 10 patterns consisting of 11 variables affecting customers' decisions about repurchases from the decision tree algorithm, which yielded the best accuracy. Using the derived patterns, we proposed helpful strategies for improving repurchase rates. Findings From the top 10 repurchase patterns, we found that 1) repurchases in January are associated with a specific residential region, 2) repurchases in spring or autumn are associated with whether it is a weekend or not, 3) repurchases in summer are associated with whether the automobile is equipped with a sunroof or not, and 4) a customized promotion for a specific occupation increases the number of repurchases.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.208-222
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• 2022

Through the analysis of the coordination mechanism of the supply chain system of China's automobile manufacturing industry, the factors affecting the supply subsystem, the manufacturing subsystem, the sales subsystem, and the consumption subsystem are sorted out, the supply chain coordination index system based on the influence factor of four subsystems is established. The evaluation models of the coordination degree in the subsystem of the supply chain, the coordination degree among the subsystems, and the comprehensive coordination degree are established by using the efficiency coefficient method and the collaborative entropy method. Experimental results verify the accuracy of the evaluation model using the empirical analysis of the collaborative evaluation index data of China's automobile manufacturing industry from 2000 to 2019. The supply chain synergy of automobile manufacturing industry was low from 2001 to 2005, and it increased to a certain extent from 2006 to 2008 with a small growth rate from 0.10 to 0.15. From 2009 to 2013, the supply chain synergy of automobile manufacturing industry increased rapidly from 0.24 to 0.49, and it also increased rapidly but fluctuated from 2014 to 2019, first rising from 0.68 to 0.84 then dropping to 0.71. These results provide reference for the development of China's automobile manufacturing supply chain system and scientific decision-making basis for the formulation of relevant policies of the automobile manufacturing industry.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제11권4호
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• pp.275-285
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• 2004

In this paper, the time series models for the number of reported death claims of compulsory automobile liability insurance in Korea are studied. We found that IMA${(0, 1, 1)}\;{\times}\;{(0, 1, 1)}_{12}$ would the most appropriate model for the number of reported claims by the Box-Jenkins method.

• 한국시스템다이내믹스연구
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• 제3권1호
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• pp.79-104
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• 2002

A system dynamics project is going on for forecasting automobile market in Korea. The project is made up of three stages, and the first stage has been wrapped up. As the first attempt, most efforts have been focused on the sound foundation rather than the exact forecast. The model consists of three sectors; the supply sector, the demand sector, and the population sector. The supply sector is a simple stock and flow diagrams representing the supply capacities of all automobile types. The major effort is made on the demand sector and the population sector. The demands are divided into three categories; replacement demands, new demands, and additional demands. The model applies “one car per person" concept, and assumes there will be no additional demands for a while. The replacement demands are calculated based on a simple stock and flow diagram. The new demands are calculated via Bass models; each bass model represents a diffusion for each age group. The population is divided into 101 age groups (age 0 to age 100). The model has been calibrated with past 10 year data (1990 - 1999), and tested for the next two years (2000-2001). The results ware acceptable, although a fine tuning is required. Now the second stage is going on, and most of efforts are made how to incorporate the economic and cultural factors.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1147-1157
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• 2005

Bonus-Malus system is generally constructed based on claim frequency and Bayesian credibility model is used to represent claim frequency distribution. However, there is a problem with traditionally used credibility model for the purpose of constructing bonus-malus system. In traditional Bonus-Malus system adopted credibility model, individual estimates of premium rates for insureds are determined based solely on the total number of claim frequency without considering when those claims occurred. In this paper, a new model which is a modification of structural time series model applicable to counting time series data are suggested. Based on the suggested model relatively higher premium rates are charged to insured with more claim records.

• 한국산업보건학회지
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• 제10권2호
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• pp.40-52
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• 2000

The aim of this study was to evaluate the prevailing ergonomic conditions regarding low back injury in an assembly factory, In this study, analytic biomechanical model and NIOSH guidelines were applied to evaluate risk levels of low back injury for automobile assembly jobs. Total of 246 workers were analysed. There were 10 jobs with greater back compressive forces than 350kg at L5/S1. Also there were 44 jobs over Action Limit in terms of 1981 NIOSH guidelines. This could in part be explained by the ergonomic conditions of the companys analysed as not hazardous, with a relatively low duration of 'combined' extreme work posture. However, more ergonomic intervention could be done based on those results.

• 한국자동차공학회논문집
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• 제13권1호
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• pp.184-193
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• 2005

Digital representations of products and parts have largely replaced physical drawings as the form in which product data are stored, analyzed, and communicated among the people contributing to the design of an automobile. Many individuals and companies participate in the design of an increasingly complex automobile; hence, the design process depends critically on team members' ability to share information about essential design elements. These trends have elevated the importance of the quality of product data and its efficient exchange. In this paper, we show state-of-the-art on Product Data Quality(PDQ), and activities of PDQ assurance. And we propose a novel design history-based approach for diagnosis and healing of a CAD model.

• 대한설비관리학회지
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• 제23권4호
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• pp.29-38
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• 2018

Through process quality information, the time required for process quality analysis has been drastically shortened, the process defect rate has been reduced, and the manufacturing lead time has been shortened and the on-time delivery rate has been improved. Therefore, The purpose of this study is to develop a quality information analysis system model that effectively shortens the time required for process quality analysis in automobile safety belt parts manufacturing process. As a result of experiments on communication operation between manufacturing execution system (MES) quality server, injection machine control computer, injection machine programmable logic controller (PLC) and terminal, in analyzing quality information, the conventional handwriting input method took an average of 20 minutes, but the new multi-network method took about 2 minutes on average. In addition, the process defect rate was reduced by 13% and the manufacturing lead time was shortened from 28 hours to 20 hours. The delivery compliance rate improved from 96 to 99%.