• Journal of Service Research and Studies
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• v.10 no.4
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• pp.89-100
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• 2020

The smart factory is an important axis of the 4th industrial revolution. Smart factory is a system that induces the maximum efficiency and effectiveness of production using the IoT and intelligent sensing systems. The product lifecycle management technique is a method that can actively reflect the consumer's requirements in the smart factory and manage the entire process from the consumer to the post management. There have been many studies on product lifecycle management, but studies on how to organize product lifecycle management knowledge domains in preparation for the era of the 4th industrial revolution were insufficient. This study analyzed the opinions of a group of experts preparing for the 4th industrial revolution in terms of product lifecycle management. The impact of the 4th industrial revolution on the detailed knowledge areas of product lifecycle management was investigated. The changes in product lifecycle management were summarized using a qualitative data analysis technique for a group of experts. Based on the opinions of experts, the product lifecycle management, which consists of a total of 30 detailed knowledge areas, was prepared to supplement or prepare for the 4th industrial revolution. This study investigates changes in product lifecycle management in preparation for the 4th industrial revolution in the knowledge domain of the existing defined product life cycle management. In future research, it is necessary to redefine the knowledge domain of product life cycle management suitable for the era of the 4th industrial revolution and investigate the perception of experts. Considering the social culture and technological change factors of the 4th industrial revolution, the scope and scope of product life cycle management can be newly defined.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.105-126
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• 2023

The domestic used car market continues to grow along with the used car online platform service. The used car online platform service discloses vehicle specifications, accident history, inspection history, and detailed options to service consumers. Most of the preceding studies were predictions of used car prices using vehicle specifications and some options for vehicles. As a result of the study, it was confirmed that there was a nonlinear relationship between used car prices and some specification variables. Accordingly, the researchers tried to solve the nonlinear problem by executing a Machine Learning model. In common, the Regression based Machine Learning model had the advantage of knowing the actual influence and direction of variables, but there was a disadvantage of low Cost Function figures compared to the Decision Tree based Machine Learning model. This study attempted to predict used car prices of six domestic brands by utilizing both vehicle specifications and vehicle options. Through this, we tried to collect the advantages of the two types of Machine Learning models. To this end, we sequentially conducted a regression based Machine Learning model and a decision tree based Machine Learning model. As a result of the analysis, the practical influence and direction of each brand variable, and the best tree based Machine Learning model were selected. The implications of this study are as follows. It will help buyers and sellers who use used car online platform services to predict approximate used car prices. And it is hoped that it will help solve the problem caused by information inequality among users of the used car online platform service.

• Information Systems Review
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• v.25 no.4
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• pp.113-130
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• 2023

Due to economic development and increasing gross national income, the number of automobiles continues to rise, leading to a serious issue of illegal parking due to limited road conditions and insufficient parking facilities. Illegal parking causes significant inconvenience and displeasure to people and can even result in accidents and loss of lives. The severity of accidents and their consequences, related to the growing number of vehicles and illegal parking, is escalating, particularly in the metropolitan areas. Consequently, efforts are being made to address this problem as a cause of social issues and come up with measures to reduce illegal parking. In particular, half of the public complaints in the metropolitan area are related to illegal parking, and the highest physical and human damage occurs in Gyeonggi. Thus, this study aims to use machine learning techniques based on data related to illegal parking in Suwon city, Gyeonggi, to categorize regional characteristics and propose effective measures to crack down on illegal parking. Additionally, practical, social, policy, and legal measures to decrease illegal parking in the metropolitan area are suggested. This study has academic significance in that it solved the problem of illegal parking, which is mentioned as one of the social problems that cause traffic congestion, by classifying regional characteristics using K-prototype, a machine learning algorithm. Furthermore, the results of this study contribute to practical and social aspects by providing measures to decrease illegal parking in the metropolitan area.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.27-43
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• 2024

The market share of online platform services in the used car market continues to expand. And The used car online platform service provides service users with specifications of vehicles, accident history, inspection details, detailed options, and prices of used cars. SUV vehicle type's share in the domestic automobile market will be more than 50% in 2023, Sales of Hybrid vehicle type are doubled compared to last year. And these vehicle types are also gaining popularity in the used car market. Prior research has proposed a used car price prediction model by executing a Machine Learning model for all vehicles or vehicles by brand. On the other hand, the popularity of SUV and Hybrid vehicles in the domestic market continues to rise, but It was difficult to find a study that proposed a used car price prediction model for these vehicle type. This study selects a used car price prediction model by vehicle type using vehicle specifications and options for Sedans, SUV, and Hybrid vehicles produced by domestic brands. Accordingly, after selecting feature through the Lasso regression model, which is a feature selection, the ensemble model was sequentially executed with the same sampling, and the best model by vehicle type was selected. As a result, the best model for all models was selected as the CBR model, and the contribution and direction of the features were confirmed by visualizing Tree SHAP Value for the best model for each model. The implications of this study are expected to propose a used car price prediction model by vehicle type to sales officials using online platform services, confirm the attribution and direction of features, and help solve problems caused by asymmetry fo information between them.

• 전기의세계
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• v.67 no.1
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• pp.26-29
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• 2018

• 한국관개배수회지
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• no.2
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• pp.6-13
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• 1993

• 간의등불
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• s.33
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• pp.21-23
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• 2003

• TTA Journal
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• s.48
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• pp.155-159
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• 1996

• Water for future
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• v.27 no.4
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• pp.23-28
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• 1994

• 방재와보험
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• s.75
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• pp.30-31
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• 1997