• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.17-33
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• 2022

An experimental study is carried out to investigate the performance of the cutting tool regarding the insert wear, surface roughness, cutting forces, cutting power and material removal rate of three coated carbides GC2015 (TiCN-Al₂O₃-TiN), GC4215 (Al₂O₃-Ti(C,N)) and GC1015 (TiN) during the dry turning of AISI4140 steel. For this purpose, a Taguchi design (L₉) was adopted for the planning of the experiments, the effects of cutting parameters on the surface roughness (R_a), tangential cutting force (F_z), the cutting power (P_c) and the material removal rate (MRR) were studied using analysis of variance (ANOVA), the response surface methodology (RSM) was used for mathematical modeling, with which linear mathematical models were developed for forecasting of R_a, F_z, P_c and MRR as a function of cutting parameters (V_c, f, and a_p). Then, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented for multi-objective optimization which allows manufacturers to enhance the production performances of the machined parts. Furthermore, in order to characterize and quantify the flank wear of the tested tools, some machining experiments were performed for 5 minutes of turning under a depth of 0.5 mm, a feed rate of 0.08 mm/rev, and a cutting speed of 350 m/min. The wear results led to a ratio (V_B-GC4215/V_B-GC2015) of 2.03 and (V_B-GC1015/V_B-GC2015) of 4.43, thus demonstrating the efficiency of the cutting insert GC2015. Moreover, SEM analysis shows the main wear mechanisms represented by abrasion, adhesion and chipping.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.1-23
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• 2008

As managing customer relationship gets more important, companies are strengthening information service using multi-channels to their customers as a part of their customer relationship management (CRM) initiatives. It means companies are now accepting such information services not as simple information -delivering tools, but as strategic initiatives for acquiring and maintaining customer loyalty. In this paper, we attempt to validate whether or not such various information services would impact on organizational performance in terms of CRM strategy. More specifically, our research objective is to answer the next three questions: first, how to construct the instruments to measure not information quality but information service quality?; second, which attributes of information service quality can influence corporate image and customer loyalty?; finally, does each information service type have unique characteristics compared with others in terms of influencing corporate image and customer loyalty? With respect to providing answers to those questions, the previous studies had been limited in that those studies failed to consider the variety of types of information service or restricted the quality of information service to information quality. An appropriate research model answering the above questions should consider the fact that most companies are utilizing multi channels for their information services, and include the recent strategic information service such as customer online community. Moreover, since corporate information service could be regarded as a type of products or services delivered to customer, it is necessary to adopt the criteria for assessing customer's perceived value when to measure the quality of information service. Therefore, considering both multi-channels and multi-traits may enable us to tell the detailed causal routes showing which quality attributes of which information service would affect corporate image and customer loyalty. As information service channels, we include not only homepage and DM (direct mail), which are the most frequently applied information service channels, but also online community, which is getting more strategic importance in recent years. With respect to information service quality, we abstract information quality, convenience of information service, and timeliness of information service through a wide range of relevant literature reviews. As our dependant variables, we consider corporate image and customer loyalty that both of them are the critical determinants of organizational performance, and also attempt to grasp the relationship between the two constructs. We conducted a huge online survey at the homepage of one of representative dairy companies in Korea, and gathered 367 valid samples from 407 customers. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The results from data analysis demonstrated that timeliness and convenience of homepage have positive effects on both corporate image and customer loyalty. In terms of DM, its' information quality was represented to influence both corporate image and customer loyalty, but we found its' convenience have a positive effect only on corporate image. With respect to online community, we found its timeliness contribute significantly both to corporate image and customer loyalty. Finally, as we expected, corporate image was revealed to provide a great influence to customer loyalty. This paper provides several academic and practical implications. Firstly, we think our research reinforces CRM literatures by developing the instruments for measuring information service quality. The previous relevant studies have mainly depended on the measurements of information quality or service quality which were developed independently. Secondly, the fact that we conducted our research in a real situation may enable academics and practitioners to understand the effects of information services more clearly. Finally, since our study involved three different types of information service which are most frequently applied in recent years, the results from our study might provide operational guidelines to the companies that are delivering their customers information by multi-channel. In other words, since we found that, in terms of customer loyalty, the key areas would be different from each other according to the types of information services, our analysis would help to make decisions such as selecting strengthening points or allocating resources by information service channels.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.112-115
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• 2008

CRM is one of fields that come into the spotlight in academic circles. A Marked changes of business environment makes it get a various information about competitive products unlike in the past and makes it understand the customer needs. Also, Market boundary become to be uncleared. Insurance industry is lied in the age of limitless competition due to uncleared market boundary. Channels for getting customer information and understandings become to be various. In this study, we collect the customer information using various channel and we analyze out a primary factor. Using this results, we present the method that cluster target customers. It is the object of this paper that analyze out the effects when we execute the One-to-One-Marketing using clustered target customer based customer pattern.

• Smart Media Journal
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• v.12 no.5
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• pp.9-16
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• 2023

In the recommendation system of the credit card company, it is necessary to understand the customer patterns to predict a customer's next merchant based on their histories. The data we want to model is much more complex and there are various patterns that customers choose. In such a situation, it is necessary to use an effective model that not only shows the relevance of the merchants, but also the relevance of the customers relative to these merchants. The proposed model aims to predict the next merchant for the customer. To improve prediction performance, we propose a novel model, called Customer-based Recommendation Model (CRM), to produce a more efficient representation of customers. For the next merchant recommendation system, we use a synthetic credit card usage dataset, BC'17. To demonstrate the applicability of the proposed model, we also apply it to the next item recommendation with another real-world transaction dataset, IJCAI'16.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.139-153
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• 2014

The purpose of this study is to examine the impact of relationship quality on customer value, customer satisfaction and relationship retention intention in the B2B transaction relationship. For the empirical study, we conducted a survey of the PCB corporation's client companies and used 110 surveys of them for analysis. The results of this study are summarized as follows : First, trust, relationship satisfaction, unity and performance as factors of relationship quality had significantly positive effect on relationship retention intention by the medium of customer satisfaction. Second, utilitarian value which is a parameter did not have significantly effect on customer satisfaction and relationship retention intention. Whereas, hedonic value which is influenced by relationship satisfaction, unity and performance had significantly positive effect on customer satisfaction and relationship retention intention. These results are not treated weightily in the preceding studies and managing the hedonic value in B2B transaction relationship is considered important to improve the customer satisfaction and relationship retention intention.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.163-167
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• 2002

Recently, owing to performance elevation of telecommunication technology, increase of wireless internet's subscriber and diffusion of wireless device, Interest about LBS (Location Based Service) which take advantage of user's location information and can receive information in concerning with user's location is increasing rapidly. So, MOMS (Moving Object Management System) that manage user's location information is required compulsorily to provide location base service. LBS of childhood such as service to find a friend need only current location, but to provide high-quality service in connection with Data Mining, CRM, We must be able to manage location information of past. In this paper, we design distributed manage system to insert and search Moving Object in a large amount. It has been consisted of CLIM (Current Location Information Manager), PLIM (Past-Location Information Manager) and BLIM (Distributed Location Information Manager). CLIM and PLIM prove performance of searching data by using spatiotemporal-index. DLIM distribute an enormous amount of location data to various database. Thus it keeps load-balance, regulates overload and manage a huge number of location information efficiently.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Analytical Science and Technology
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• v.26 no.5
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• pp.307-314
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• 2013

Selenium exists in various forms of chemical species. The activity and bioavailability is strongly dependent on its chemical form and concentration. Consequently the information on each selenium species and its concentration must be exactly determined for the food we take in. In this study, selenium species in seafood were separated and quantified by RP (reversed phase) HPLC (high performance liquid chromatography) coupled with ICP-MS (inductively coupled plasma mass spectrometry) using post-column isotope dilution. $^{79}Br$, which interferes on $^{80}Se$, has mostly been removed by solid phase extraction and then mathematical correction has been applied for the more accurate correction. The experimental result for CRM (certified reference material) DOLT-4 agreed well with the certified value but each selenium species could not be compared. SeCys (selenocysteine) and SeMet (selenomethionine) were the major species detected in seafood such as belt fish, spanish mackerel, and squid that have been serving as Korean diet. The concentrations found in Korean sea food for SeCys and SeMet were in the range of 0-661.6 mg/kg and 137.3-462.7 mg/kg, respectively.

• Analytical Science and Technology
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• v.28 no.6
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• pp.409-416
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• 2015

ICP-AES has been used in many laboratories due to the advantages of wide calibration range and multi-element analysis, but it may give erroneous results and suffer from spectral interference due to the large number of emission lines associated with each element. In this study, certified reference materials (CRMs) and field samples were analyzed by ICP-AES and HG-AAS according to the official Korean testing method for soil pollution to investigate analytical problems. The applicability of HG-ICP-AES was also tested as an alternative method. HG-AAS showed good accuracies (90.8~106.3%) in all CRMs, while ICP-AES deviated from the desired range in CRMs with low arsenic and high Fe/Al. The accuracy in CRM030 was estimated as below 39% at the wavelength of 193.696 nm by ICP-AES. Significant partial overlaps and sloping background interferences were observed near to 193.696 nm with the presence of 50 mg/L Fe and Al. Most CRMs were quantified with few or no interferences of Fe and Al at 188.980 nm. ICP-AES properly assessed low and high level arsenic for field samples, at 188.980 nm and 193.696 nm, respectively. The importance of the choice of measurement wavelengths corresponding to relative arsenic level should be noted. Because interferences were affected by the sample matrix, operation conditions and instrument figures, the analysts were required to consider spectral interferences and compare the analytical performance of the recommended wavelengths. HG-ICP-AES was evaluated as a suitable alternative method for ICP-AES due to improvement of the detection limit, wide calibration ranges, and reduced spectral interferences by HG.