• Journal of Technology Innovation
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• v.6 no.2
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• pp.159-177
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• 1998

Although each company is trying to develop an economic analysis model with its own particular style or format, the appropriate method is not yet developed because there are many problems to be solved such as uncertainity of outcomes and intangible benefits of technology. The purpose of tris paper therefore is to suggest an economic analysis methodology, which reflects the complexity and the risk of R&D investment, through a case study on the development of a superconductor fault current limiter. A self-developed Monte Carlo simulation program utilized as a main tool in this paper was very useful for risk analysis of R&D investment which could not be solved in the previous DCF(Discounted Cash Flow) model. We also introduce learning effect to consider the intangible benefits such as Know-How obtained from R&D execution. The expected value and its probability distribution for R&D investment can be obtained by combining the Monte Carlo method with the decision tree approach. This result is helpful in judging the priority and the resource-allocation of R&D projects. It is however necessary to develop more precise model for quantifying the technology stock and the simulation program using the continuous probability distribution in expected values to improve the reliability of economic analysis on R&D projects.

• The Journal of Information Systems
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• v.32 no.1
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• pp.179-199
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• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.957-962
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• 2008

We propose a model for the line expansion problem in the AMOLED (Active Matrix Organic Light Emitting Diodes) industry, which now faces market uncertainty: for example, changing customer needs, technological development path, etc. We focus on the optimal investment time and size of the AMOLED production lines. In particular, employed here is the ROV (Real Options Valuation) model to show how to capture the value of line expansion and to determine the optimal investment time. The ROV framework provides a systematic procedure to quantify an expected outcome of a flexible decision which is not possible in the frame of the traditional NPV (Net Present Value) approach. Furthermore, we also use Monte Carlo simulation to measure the uncertainty associated with the line expansion decision; Monte Carlo simulation estimates the volatility of a decision alternative. Lastly, we present a scenario planning to be conducted for what-if analysis of the ROV model.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.374-381
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• 2009

To meet the soaring demand recently, South Korea big shipbuilders are examining two things. One is new investment in plant and equipment. The other is replacement of production resources. Considering plant & equipment investment and replacement of production resources, even if actual production ability would be enough, the real output could be affected by limitation of logistics with lack of analysis. As we set up big shipyard in virtual space, we could perform actual production by using confirm production plan in virtual space. We've analyzed the load of block stock, load of road and load of transporter for logistics effects are followed by production increase. This research is to determine the possible problems of those analyzed results and to present the resolution using the current layout. And then modified yard layout, we reanalyzed previous three logistics effects. This simulation model could help administrator to make rational decision for changing yard layout.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.19-26
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• 2021

A purpose of this study is to suggest distribution tariff regulation that encourages private investment on the energy efficiency industry of Ukraine. As the electricity market reform and the regulation introduction to encourage energy efficiency are ongoing in Ukraine, it is best time for Korean companies to enter to the market. Therefore, studies on the regulation and the market of Ukraine are required in advance. A simulation of private investment feasibility on AMI business is conducted on one of 32 DSOs in Ukraine. Through the simulation, the directions of RAB tariff regulation, which is the core of the distribution service tariff regulation, were derived. It is essential for DSOs to permit AMI lease assets, introduced by private investors, as regulated assets while other regulations are maintained as it is for investment. This study provides a practical basis by presenting objective data through simulation. It is expected to be helpful for overseas expansion of companies if the study is expanded to the various energy efficiency industries.

• Korean System Dynamics Review
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• v.6 no.2
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• pp.25-35
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• 2005

This paper describes the mechanism for new investment to appear in waves of boom and bust causing alternative periods of over and under supply of electricity in Korean market. A system dynamics model was developed to describe the dynamic behavior of new investment in Korean market. The simulation results show the boom and bust cycle in the new investments. When the market price is high, investors decide to build new power plants. However, it takes some delay time to complete new power plants. When the new power plants are being added into the grid, the supply increases and the wholesale price begins to decrease. This causes the cancellation of new power plant or delay the construction. This mechanism causes the boom and bust cycle in new investment.

• Journal of Construction Engineering and Project Management
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• v.3 no.3
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• pp.23-34
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• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.163-163
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• 2019

The rapidly growing global population increases the awareness of water, energy, and food security worldwide. The concept of Water, Energy, and Food nexus (hereafter, WEF nexus) has been widely introduced as a new resources management concept that integrate the water, energy, and food in a single management framework. Recently, WEF nexus analyzes not only the interconnections among the resources, but also considers the external factors (such as environment, climate change, policy, finance, etc) to enhance the resources sustainability by proper understanding of their relations. A nation-level resources management is quite complex task since multiple regions (e.g., watersheds, cities, and counties) with different characteristics are spatially interconnected and transfer the resources each other. This study proposes a multiple region WEF nexus simulation and transfer model. The model is equipped with three simulation modules, such as local nexus simulation module, regional resources transfer module, and optimal investment planning module. The model intends to determine an optimal capital investment plan (CIP), such as build-up of power plants, water/waste water treatment plants, farmland development and to determine W-E-F import/export decisions among areas. The objective is to maximize overall resources sustainability while minimize financial cost. For demonstration, the proposed model is applied to a semi-hypothetical study area with three different characterized cities. It is expected the model can be used as a decision support tool for a long-term resources management planning process.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.375-385
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• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.