• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.181-201
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• 2002

Best practices are the best ways to perform a business process. Benchmarking, the search for those best practices that will lead to superior performance of a company, is indispensible to gain and maintain a competitive edge. Best practices of quality management in the world-class companies are examined. Customer-centered strategy, employee selection and training, employee satisfaction, customer satisfaction, performance measurement are considered in this paper.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.133-138
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• 2002

In this paper, we model a two-server queueing system with priority, to which we put a restriction of the number of servers for each customer class. A group of customers is divided into two different classes. The class 1 customers has non -preemptive priority over class 2 customers. We use the method of PGF depending on the state of server We find the PGF of the number of customers in queue, server utilization, mean queue length and mean waiting time for each class of customers.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.65-77
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• 2006

This study try to find the service quality factors of internet-based lecture and in-class lecture on the university level. The service quality factors of internet-based lecture are information quality and system quality factors which affect education service value, and customer satisfaction. This affects the service performance. The service quality factors of in-class lecture are tangibility, reliability, responsibility, assurance and empathy factors also affect education service value and customer satisfaction. This leads to service performance. This result will be helpful to enhance the service quality of university level lectures.

• Culinary science and hospitality research
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• v.12 no.1 s.28
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• pp.1-21
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• 2006

The purpose of this paper was to revise menu management in Korean restaurants of first class hotels in Cheju. This study was conducted to examine and analyze menu mixes using menu engineering and surveys of customer willingness to pay for price adjusting. The results were as follows. Those restaurants in this study need menu re-engineering and price adjustment for sales enhancement. And this paper suggested some recommendations. They have to develop new menu focusing on customer value based menu management. Furthermore, customer behavior analysis must be applied to menu engineering and new menu development.

• Korean Management Science Review
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• v.30 no.1
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• pp.1-14
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• 2013

We conduct the future studies suggested by Park and Seo [3]. They considered a seat inventory control problem in which flights depart sequentially during a similar time-interval and passengers purchase available seats depending on individual customer choice behavior. Customer choice behavior can lead to one among a horizontal shift, a diversion-up, and a booking loss when a desired fare class is unavailable. We investigate how seat availability calculation method, booking limit control mechanism, seat inventory capacity, number of booking class, type of seat demand influence on revenues in an airline industry through thorough computer simulation experiments.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.319-329
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• 2007

This paper presents a price-based quality-of-service (QoS) control framework for two-class network services, in which circuit-switched and packet-switched services are defined as "premium service class" and "best-effort service class," respectively. Given the service model, a customer may decide to use the other class as a perfect or an imperfect substitute when he or she perceives the higher utility of the class. Given the framework, fixed-point problems are solved numerically to investigate how static pricing can be used to control the demand and the QoS of each class. The rationale behind this is as follows: For a network service provider to determine the optimal prices that maximize its total revenue, the interactions between the QoS-dependent demand and the demand-dependent QoS should be thoroughly analyzed. To test the robustness of the proposed model, simulations were performed with gradually increasing customer demands or network workloads. The simulation results show that even with substantial demands or workloads, self-adjustment mechanism of the model works and it is feasible to obtain fixed points in equilibrium. This paper also presents a numerical example of guaranteeing the QoS statistically in the short term-that is, through the implementation of pricing strategies.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.535-538
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• 2006

We consider a two-period airline revenue management problem where customers may act strategically. Specifically, we study a two-fare-class airline seat inventory allocation problem which allow for the possibility that a customer may decide to defer to purchase in the hope that a cheaper ticket than those currently on offer (expensive tickets) become available. We also allow for the possibility that some customer will buy a more expensive ticket if the cheaper tickets are not available. We show how to find the optimal booking limits in the presence of such strategic customer behavior and investigate the impact of such strategic customer behavior on the expected revenue. The results are compared with those by the expected marginal seat revenue (EMSR) heuristic approach (Belobaba, 1987, 1989) with strategic customer behavior.

• Photonics industry news
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• s.60
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• pp.68-70
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• 2010