• Journal of Energy Engineering
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• v.29 no.2
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• pp.68-78
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• 2020

According to the new climate change agreement, technology development to reduce greenhouse gases is actively conducted worldwide, and research on energy efficiency improvement in the field of power generation and transmission and distribution is underway [1,2]. Economic analysis of the operation method of storing and supplying surplus electricity using energy storage devices, and using energy storage devices as a frequency adjustment reserve power in regional cogeneration plants has been reported as the most profitable operation method [3-7]. Therefore, this study conducted an economic analysis for the installation of energy storage devices in the combined heat and power plant in the Czech Republic. The most important factor in evaluating the economics of battery energy storage devices is the lifespan, and the warranty life is generally 10 to 15 years, based on charging and discharging once a day. For the simulation, the ratio of battery and PCS was designed as 1: 1 and 1: 2. In general, the primary frequency control is designed as 1: 4, but considering the characteristics of the cogeneration plant, it is set at a ratio of up to 1: 2, and the capacity is simulated at 1MW to 10MW and 2MWh to 20MWh according to each ratio. Therefore, life was evaluated based on the number of cycles per year. In the case of installing a battery energy storage system in a combined heat and power plant in the Czech Republic, the payback period of 3MW / 3MWh is more favorable than 5MW / 5MWh, considering the local infrastructure and power market. It is estimated to be about 3 years or 5 years from the simple payback period considering the estimated purchase price without subsidies. If you lower the purchase price by 50%, the purchase cost is an important part of the cost for the entire lifetime, so the payback period is about half as short. It can be, but it is impossible to secure profitability through the economy at the scale of 3MWh and 5MWh. If the price of the electricity market falls by 50%, the payback period will be three years longer in P1 mode and two years longer in P2 and P3 modes.

• Management & Information Systems Review
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• v.31 no.3
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• pp.223-248
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• 2012

This study analyses support policy and structural change of textile industry in Daegu Kyungbuk region, and suggests major issues for textile industry's innovation. In Daegu Kyungbuk, it was 1999 that a policy, so called Milano Project, in order to promote a textile industry was devised. In 2004, the Regional Industrial Promotion Plan was devised. The plan was born from a view point of establishing a regional innovation system and of promoting the innovative clusters under a knowledge based economy. After then, the Regional Industry Promotion Project or Regional Strategic Industry Promotion Project became a core of regional textile industrial policy. Research results indicated that the first stage Milano project (1999-2003) showed both positive and negative effects. There were no long-term development plan, clear vision and strategy. But, core industrial infrastructure for differentiated product development, such as New product Development Support Center and Dyeing Design Practical Application Center, was constructed. The second stage Daegu Textile Industry Promotion Plan (2004-2008) displayed a significant technological performance and new product sales with the assistance of Kyungbuk province. Also, textile industry revealed positive fruits such as financial structure, productivity, and profitability as a result of strong restructuring. In industrial structure, there was a important change from clothe textile material to industry textile material. Most of textile companies did not showed high capability in CEO's technology innovation intention, entrepreneurship, R&D and human resource competency in compare with other industry. We suggested that Daegu Kyungbuk has to select and concentrate on the high-tech textile material and living textile for sustainable development and competitiveness. We also proposed a confidence and cooperation based innovation network and company oriented innovation cluster.

• Korean Security Journal
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• no.28
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• pp.103-130
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• 2011

This study is the research of enterprise security for raising the profitability and stability of Korean companies in global business environment and strategic cooperation of business. As the scientific technology gets complicated as day goes by and new competitors appear regardless the border in the modern business environment, the situation happens frequently which the huge company hands over their market to the new one armed with the innovative thinking overnight. To survive such new environment, the answer is the change of paradigm regarding business management method at the new point of view. With the low level of security risk management of Korean companies which stick to old habit, the security management which helps the companies secure profits is not affordable. The global village where the population of 7 billions live in 21st century is facing up to the rapid ecological adaptation. The rapid change of climatic environment creates the hundreds of thousands of sufferers in a moment, and we have been watching the millions of livestock are buried alive due to new contagious disease everyday. Such change encourages the humans in global village to change the basic way of living. The business ecosystem which is the basic root for economic life cannot be an exception. To survive the business environment of 21st century, the security risk management at management level is required and the reporting line of companies should be established newly for raising business competing power through security risk management. The companies should bear in mind that they can be disappeared into our old memories overnight if they are not sensitive to the changing environment. Out of new risks for the modern companies, the field especially Korean companies are dealing easily is the security risk. Not like past, the security risk which's size is much more massive and its propagation velocity is very fast is the one of important business risks which the management should take care. Out of security risks which influence on the modern companies significantly, the brand of companies, protection of their reputation, continuity of production and operation and keeping customer's trust are prior to the others. This study offered the suggestion regarding enterprise security and the strategic cooperation of business to deal with such security risk effectively.

• 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.