• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of Distribution Science
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• v.9 no.3
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• pp.83-94
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• 2011

Most domestic companies, with the exclusion of major firms, are reluctant to implement a supply chain management (SCM) network into their operations. Most small- and medium-sized enterprises are not even aware of SCM. Due to the inherent total-systems efficiency of SCM, it coordinates domestic manufacturers, subcontractors, distributors, and physical distributors and cuts down on cost of inventory control, as well as demand management. Furthermore, a lack of SCM causes a decrease in competitiveness for domestic companies. The reason lies in the fundamentality of SCM, which is the characteristic of information sharing, process innovation throughout SCM, and the vast range of problems the SCM management tool is able to address. This study suggests the contemplation and reformation of the current SCM situation by analyzing the SCM strategic plan, discourses and logical discussions on the topic, and a successful case for adapting SCM; hence, the study plans to productively "process" SCM. First, it is necessary to contemplate the theoretical background of SCM before discussing how to successfully process SCM. I will describe the concept and background of SCM in Chapter 2, with a definition of SCM, types of SCM promotional activities, fields of SCM, necessity of applying SCM, and the effects of SCM. All of the defects in currently processing SCM will be introduced in Chapter 3. Discussion items include the following: the Bullwhip Effect; the breakdown in supply chain and sales networks due to e-business; the issue that even though the key to a successful SCM is cooperation between the production and distribution company, during the process of SCM, the companies, many times, put their profits first, resulting in a possible defect in demands estimation. Furthermore, the problems of processing SCM in a domestic distribution-production company concern Information Technology; for example, the new system introduced to the company is not compatible with the pre-existing document architecture. Second, for effective management, distribution and production companies should cooperate and enhance their partnership in the aspect of the corporation; however, in reality, this seldom occurs. Third, in the aspect of the work process, introducing SCM could provoke corporations during the integration of the distribution-production process. Fourth, to increase the achievement of the SCM strategy process, they need to set up a cross-functional team; however, many times, business partners lack the cooperation and business-information sharing tools necessary to effect the transition to SCM. Chapter 4 will address an SCM strategic plan and a case study of LG Electronics. The purpose of the strategic plan, strategic plans for types of business, adopting SCM in a distribution company, and the global supply chain process of LG Electronics will be introduced. The conclusion of the study is located in Chapter 5, which addresses the issue of the fierce competition that companies currently face in the global market environment and their increased investment in SCM, in order to better cope with short product life cycle and high customer expectations. The SCM management system has evolved through the adaptation of improved information, communication, and transportation technologies; now, it demands the utilization of various strategic resources. The introduction of SCM provides benefits to the management of a network of interconnected businesses by securing customer loyalty with cost and time savings, derived through the consolidation of many distribution systems; additionally, SCM helps enterprises form a wide range of marketing strategies. Thus, we could conclude that not only the distributors but all types of businesses should adopt the systems approach to supply chain strategies. SCM deals with the basic stream of distribution and increases the value of a company by replacing physical distribution with information. By the company obtaining and sharing ready information, it is able to create customer satisfaction at the end point of delivery to the consumer.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.277-278
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• 2001

The successful operation of a product In service depends upon the effective provision of logistic support in order to achieve and maintain the required levels of performance and customer satisfaction. Logistic support encompasses the activities and facilities required to maintain a product (hardware and software) in service. Logistic support covers maintenance, manpower and personnel, training, spares, technical documentation and packaging handling, storage and transportation and support facilities.The cost of logistic support is often a major contributor to the Life Cycle Cost (LCC) of a product and increasingly customers are making purchase decisions based on lifecycle cost rather than initial purchase price alone. Logistic support considerations can therefore have a major impact on product sales by ensuring that the product can be easily maintained at a reasonable cost and that all the necessary facilities have been provided to fully support the product in the field so that it meets the required availability. Quantification of support costs allows the manufacturer to estimate the support cost elements and evaluate possible warranty costs. This reduces risk and allows support costs to be set at competitive rates.Integrated Logistic Support (ILS) is a management method by which all the logistic support services required by a customer can be brought together in a structured way and In harmony with a product. In essence the application of ILS:- causes logistic support considerations to be integrated into product design;- develops logistic support arrangements that are consistently related to the design and to each other;- provides the necessary logistic support at the beginning and during customer use at optimum cost.The method by which ILS achieves much of the above is through the application of Logistic Support Analysis (LSA). This is a series of support analysis tasks that are performed throughout the design process in order to ensure that the product can be supported efficiently In accordance with the requirements of the customer.The successful application of ILS will result in a number of customer and supplier benefits. These should include some or all of the following:- greater product uptime;- fewer product modifications due to supportability deficiencies and hence less supplier rework;- better adherence to production schedules in process plants through reduced maintenance, better support;- lower supplier product costs;- Bower customer support costs;- better visibility of support costs;- reduced product LCC;- a better and more saleable product;- Improved safety;- increased overall customer satisfaction;- increased product purchases;- potential for purchase or upgrade of the product sooner through customer savings on support of current product.ILS should be an integral part of the total management process with an on-going improvement activity using monitoring of achieved performance to tailor existing support and influence future design activities. For many years, ILS was predominantly applied to military procurement, primarily using standards generated by the US Government Department of Defense (DoD). The military standards refer to specialized government infrastructures and are too complex for commercial application. The methods and benefits of ILS, however, have potential for much wider application in commercial and civilian use. The concept of ILS is simple and depends on a structured procedure that assures that logistic aspects are fully considered throughout the design and development phases of a product, in close cooperation with the designers. The ability to effectively support the product is given equal weight to performance and is fully considered in relation to its cost.The application of ILS provides improvements in availability, maintenance support and longterm 3ogistic cost savings. Logistic costs are significant through the life of a system and can often amount to many times the initial purchase cost of the system.This study provides guidance on the minimum activities necessary to Implement effective ILS for a wide range of commercial suppliers. The guide supplements IEC60106-4, Guide on maintainability of equipment Part 4: Section Eight maintenance and maintenance support planning, which emphasizes the maintenance aspects of the support requirements and refers to other existing standards where appropriate. The use of Reliability and Maintainability studies is also mentioned in this study, as R&M is an important interface area to ILS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.262-268
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• 2017

The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.