• Journal of Korean Society of Transportation
• /
• v.28 no.5
• /
• pp.65-75
• /
• 2010

The objective of this paper is to quantify economic values of parcel service attributes (safety, reliability, quickness, and kindness and customer service) using the contingent choice method and to investigate impact factors (such as sex, age, and education), which influence choice of desirable parcel services. As empirical results, the marginal willingness-to-pay for multiple attributes of parcel service is calculated as about 2,349.6 KRW for the safety attribute, about 829.3 KRW for the reliability attribute, about 588.5 KRW for the quickness attribute, and about 358.8 KRW for the kindness and customer service attribute, according to the estimation model without covariates. The overall results indicate that the safety attribute ranks highest among parcel service attributes, followed by the reliability attribute, quickness attribute, and kindness and customer service attribute. These results can be useful in the decision-making process for establishing desirable pricing policies for parcel service.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.6
• /
• pp.213-220
• /
• 2018

This paper deals with survival facility location problem(SFLP) that the store with less of demand threshold level is closed result from another new establishment of store in the same kind of comparative firms have a monopoly market. We will be faced with a difficult problem when a new establishment stores in market saturation that the closed stores more than opening stores. Serra et al. proposes recursive heuristic concentration algorithm, and Han et al. suggests maximum insurance of customer location. But the drawback of these algorithms is a recursively computation for many locations. This paper get the solution from only neighborhood search of comparative firm's stores that can be maximum customers and closed comparative firm's store, and the location with minimum customer exchange to the location that can be closed the comparative firm's store with maximum customer. The advantage of this algorithm is to get the solution using a MS-Excel.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.4
• /
• pp.50-58
• /
• 2018

Process Capability ($C_{pk}$) is a representative measure of how well the producer manages dispersion and bias for the specifications needed by the consumer. This is expressed as a ratio of 6 times the natural tolerance to the specification. As the producer manages the dispersion small, the capacity index becomes higher. And it is classified into 5 grades according to the degree of management. It is a measure of the quality of processes used in most industrial fields. However, $C_{pk}$ is calculated by only reflecting the mean and dispersion of the process, there is a disadvantage that it can not give information about the economic loss caused by the inconsistency of the process with the target value. Overcoming these drawbacks, process capability indexes reflecting various types of loss functions such as $C_{pm}$, $C^+_{pm}$ and $C_{pl}$ have been developed. However, all of these previous studies have applied the limit to the consumer specification, which is based on the traditional and passive quality perception that the quality characteristic should exist within the limits of the consumer specification. In this study, we will develop 'Customer Satisfaction Quality Indicator (CSQI)' which is a quantitative indicator that can be fully evaluated when the manufacturer's specification limit, which is an aggressive quality strategy, is applied. This is expected to be useful decision information for both producers and consumers.

• International Journal of Computer Science & Network Security
• /
• v.23 no.2
• /
• pp.111-125
• /
• 2023

Due to the car's central role in modern life, the industry has become more fiercely competitive, with each manufacturer doing everything it can to attract buyers with features like plush interiors, comprehensive warranties, and helpful customer service departments. Customers may not have the luxury of buying a new car, so they will have to buy a used car. Nevertheless, in most cases, the customer (car driver) may be deceived about the vehicle information and history and thus will be confused in making his/her decision to purchase. In addition, after all attempts to obtain vehicle information (plate number, model, year of manufacture, number of maintenance times, accidents, etc.), the customer's many attempts may fail. In general, the government records and verifies the information of all cars, even those that pass through their borders. However, there might still be some trouble in obtaining this information. From this standpoint, we will design a website that makes it easier for car drivers, car companies and governments to carry out all the above-mentioned processes. It will also allow users, whether a driver or a car company, to inquire about all vehicle information through detailed and integrated reports on its condition since its entry into the Kingdom of Saudi Arabia until the present time, in addition to information supported by numbers and statistics to ensure the integrity and reliability of the information. This platform will save the trouble of searching for car information for drivers and car companies. It will also help governments keep track of the information of all cars entering and leaving the Kingdom of Saudi Arabia, which will contribute to facilitating the process of viewing the history of any car that has previously entered the Kingdom's borders.

• Journal of Smart Tourism
• /
• v.4 no.2
• /
• pp.5-14
• /
• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1999.04a
• /
• pp.426-426
• /
• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.97-117
• /
• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Asia pacific journal of information systems
• /
• v.18 no.3
• /
• pp.91-121
• /
• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.3
• /
• pp.479-486
• /
• 2013

This study aims to survey the influence of the internet-based real estate transaction systems used by the common people on their decision-making for buying the real estate. That is, the purpose of this study is to review how the system for selection of the real estate has influence on the decision-making for buying the real estate, by exerting influence on the customer-satisfaction. Also, I want to present a scheme for the more effective internet-based real estate transaction system so that the real estate agents may grasp the on-line real estate transaction system and understand the intention of the customers for buying, and thereby establish the active marketing strategy.

• IE interfaces
• /
• v.15 no.2
• /
• pp.159-171
• /
• 2002

Data Warehouses integrate data from multiple heterogeneous information sources and transform them into a multidimensional representation for decision support applications. Data warehousing has emerged as one of the most powerful tools in delivering information to users. Most previous researches have focused on marketing, customer service, financing, and insurance industry. Further, relatively less research has been done on data warehouse systems in the complex manufacturing industry such as ship production, which is characterized complex product structures and production processes. In the ship production, data warehouse systems is a requisite for effective cost analysis because collecting and analysis of diverse and large of cost-related(material/production cost, productivity) data in its operational systems, was becoming increasingly cumbersome and time consuming. This paper proposes architecture of the data warehouse systems to support cost analysis in the ship production. Also, in order to illustrate the usefulness of the proposed architecture, the prototype system is designed and implemented with the object of the enterprise of producing a large-scale ship.